From e7fa62ebf7671640469761003492b6c59079a999 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 9 Jan 2024 10:12:13 +0100 Subject: [PATCH 01/16] Bump aquasecurity/trivy-action from 0.16.0 to 0.16.1 (#2244) Bumps [aquasecurity/trivy-action](https://github.com/aquasecurity/trivy-action) from 0.16.0 to 0.16.1. - [Release notes](https://github.com/aquasecurity/trivy-action/releases) - [Commits](https://github.com/aquasecurity/trivy-action/compare/0.16.0...0.16.1) --- updated-dependencies: - dependency-name: aquasecurity/trivy-action dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/trivy-zenml-core.yml | 2 +- .github/workflows/trivy-zenserver.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/trivy-zenml-core.yml b/.github/workflows/trivy-zenml-core.yml index db25b9b4b66..3f67011ef3f 100644 --- a/.github/workflows/trivy-zenml-core.yml +++ b/.github/workflows/trivy-zenml-core.yml @@ -16,7 +16,7 @@ jobs: uses: actions/checkout@v4.1.1 - name: zenml-Trivy vulnerability scanner - uses: aquasecurity/trivy-action@0.16.0 + uses: aquasecurity/trivy-action@0.16.1 with: image-ref: "docker.io/zenmldocker/zenml:latest" format: "sarif" diff --git a/.github/workflows/trivy-zenserver.yml b/.github/workflows/trivy-zenserver.yml index e3e8f8a6100..655571619a6 100644 --- a/.github/workflows/trivy-zenserver.yml +++ b/.github/workflows/trivy-zenserver.yml @@ -15,7 +15,7 @@ jobs: uses: actions/checkout@v4.1.1 - name: zenserver-Trivy vulnerability scanner - uses: aquasecurity/trivy-action@0.16.0 + uses: aquasecurity/trivy-action@0.16.1 with: image-ref: "docker.io/zenmldocker/zenml-server:latest" format: "sarif" From 60aef386a0a666de6565f8cd094fb2760ff82b35 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 9 Jan 2024 10:16:43 +0100 Subject: [PATCH 02/16] Bump crate-ci/typos from 1.16.26 to 1.17.0 (#2245) Bumps [crate-ci/typos](https://github.com/crate-ci/typos) from 1.16.26 to 1.17.0. - [Release notes](https://github.com/crate-ci/typos/releases) - [Changelog](https://github.com/crate-ci/typos/blob/master/CHANGELOG.md) - [Commits](https://github.com/crate-ci/typos/compare/v1.16.26...v1.17.0) --- updated-dependencies: - dependency-name: crate-ci/typos dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/setup-python-environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/setup-python-environment.yml b/.github/workflows/setup-python-environment.yml index 15c6190b338..3d95564838c 100644 --- a/.github/workflows/setup-python-environment.yml +++ b/.github/workflows/setup-python-environment.yml @@ -108,7 +108,7 @@ jobs: bash scripts/lint.sh - name: Spelling checker - uses: crate-ci/typos@v1.16.26 + uses: crate-ci/typos@v1.17.0 with: files: "." config: ./.typos.toml From 32477fc5938e2078d92a85e9f901ece34be899b7 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 9 Jan 2024 13:28:32 +0100 Subject: [PATCH 03/16] Add YAML formatting standardisation to formatting & linting scripts (#2224) * add yamlfix dev dependency * Fix formatting and linting issues This commit fixes formatting issues in the `format.sh` script and adds a step to standardize/format CI yaml files. It also adds a check for yaml formatting errors in the `lint.sh` script. * apply yamlfix formatting * apply yamlfix formatting to tests folder * update scripts to include test files * handle failing template tests * Auto-update of Starter template * Auto-update of E2E template * Auto-update of NLP template * formatting for examples * Auto-update of Starter template * Auto-update of E2E template * formatting for template push * Auto-update of Starter template * Auto-update of E2E template * Auto-update of NLP template * update templates script to format for yaml as well * format examples * install yamlfix in workflow directly * fix scarf-check script --------- Co-authored-by: GitHub Actions --- .github/ISSUE_TEMPLATE/bug_report.yml | 21 ++-- .github/ISSUE_TEMPLATE/config.yml | 1 + .github/actions/setup_environment/action.yml | 64 ++++++------- .github/branch-labels.yml | 9 +- .github/codecov.yml | 12 +-- .github/dependabot.yml | 24 +++-- .github/teams.yml | 3 +- .github/workflows/ci.yml | 78 +++++---------- .github/workflows/codeql.yml | 19 ++-- .github/workflows/image-optimiser.yml | 11 +-- .github/workflows/integration-test.yml | 95 ++++++------------- .github/workflows/mixpanel-test-data.yml | 8 +- .github/workflows/nightly_build.yml | 56 +++++------ .github/workflows/pr_labeler.yml | 16 ++-- .github/workflows/publish_api_docs.yml | 19 ++-- .github/workflows/publish_docker_image.yml | 11 +-- .github/workflows/publish_helm_chart.yml | 23 ++--- .github/workflows/publish_to_pypi.yml | 14 +-- .github/workflows/publish_to_pypi_nightly.yml | 41 ++++---- .github/workflows/release.yml | 67 +++++-------- .github/workflows/scarf-check.yml | 8 +- .../workflows/setup-python-environment.yml | 68 +++++-------- .github/workflows/templates-test.yml | 53 ++++------- .github/workflows/trivy-zenml-core.yml | 19 ++-- .github/workflows/trivy-zenserver.yml | 17 ++-- .../update-templates-to-examples.yml | 85 ++++++----------- pyproject.toml | 4 +- scripts/format.sh | 16 ++++ scripts/lint.sh | 16 ++++ tests/harness/cfg/deployments.yaml | 7 +- tests/harness/cfg/environments.yaml | 28 ++---- tests/harness/cfg/requirements.yaml | 14 +-- tests/harness/cfg/tests.yaml | 91 +++++------------- 33 files changed, 387 insertions(+), 631 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index f09ad3581f7..105845476b2 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -1,13 +1,13 @@ +--- name: Bug Report description: File a bug report -title: "[BUG]: " -labels: ["bug"] +title: '[BUG]: ' +labels: [bug] body: - type: markdown attributes: value: | Thanks for taking the time to fill out this bug report! - - type: input id: contact attributes: @@ -16,16 +16,16 @@ body: placeholder: your@awesome.com validations: required: false - - type: textarea id: system-information attributes: label: System Information description: zenml info -a -s - placeholder: "Copy paste the output of the above command here. If using ZenML < 0.36.0, then remove the `-s` flag above. If using ZenML version < 0.8.0, then paste in Python version, OS, ZenML version, and installed integrations." + placeholder: Copy paste the output of the above command here. If using ZenML + < 0.36.0, then remove the `-s` flag above. If using ZenML version < 0.8.0, + then paste in Python version, OS, ZenML version, and installed integrations. validations: required: true - - type: textarea id: what-happened attributes: @@ -34,12 +34,12 @@ body: placeholder: A clear and concise description of what the bug is. validations: required: true - - type: textarea id: reproduce attributes: label: Reproduction steps - description: "How do you trigger this bug? Please walk us through it step by step." + description: How do you trigger this bug? Please walk us through it step by + step. value: | 1. 2. @@ -47,14 +47,13 @@ body: ... validations: required: false - - type: textarea id: logs attributes: label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + description: Please copy and paste any relevant log output. This will be automatically + formatted into code, so no need for backticks. render: shell - - type: checkboxes id: terms attributes: diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index a4c62f33489..2248e22aaaa 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1,3 +1,4 @@ +--- blank_issues_enabled: true contact_links: - name: Feature Request diff --git a/.github/actions/setup_environment/action.yml b/.github/actions/setup_environment/action.yml index f8c7f98e86a..375c28d7782 100644 --- a/.github/actions/setup_environment/action.yml +++ b/.github/actions/setup_environment/action.yml @@ -1,81 +1,75 @@ -name: "Install ZenML" -description: "Install ZenML, most integrations, loads/uploads cached venv and pip download cache if applicable" +--- +name: Install ZenML +description: Install ZenML, most integrations, loads/uploads cached venv and pip download + cache if applicable inputs: cache_version: - description: 'Value gets appended to the cache key and will therefore invalidate the cache if it changes' + description: Value gets appended to the cache key and will therefore invalidate + the cache if it changes required: true python-version: - description: 'Python version' + description: Python version type: string required: true os: - description: 'OS' + description: OS type: string required: true install_integrations: - description: 'Install ZenML integrations' + description: Install ZenML integrations type: string required: false default: 'yes' runners_cache_access_key_id: - description: 'Runner cache AWS access key ID' + description: Runner cache AWS access key ID required: true runners_cache_secret_access_key: - description: 'Runner cache AWS secret access key' + description: Runner cache AWS secret access key required: true - - runs: - using: "composite" + using: composite steps: - name: Set up Python uses: actions/setup-python@v5.0.0 with: python-version: ${{ inputs.python-version }} - - name: Delete error-causing bash shell: bash if: ${{ inputs.os == 'windows-latest' }} run: rm.exe "C:/WINDOWS/system32/bash.EXE" - - name: Set path to bash for example runner shell: bash if: ${{ inputs.os == 'windows-latest' }} run: | echo "SHELL_EXECUTABLE=C:\Program Files\Git\bin\bash.exe" >> $GITHUB_ENV - - name: Configure git (non-Windows) if: ${{ inputs.os != 'windows-latest' }} shell: bash run: | git config --global user.email "info@zenml.io" git config --global user.name "ZenML GmbH" - - name: Configure git (Windows) if: ${{ inputs.os == 'windows-latest' }} shell: bash run: | "C:\Program Files\Git\bin\git.exe" config --global user.email "info@zenml.io" "C:\Program Files\Git\bin\git.exe" config --global user.name "ZenML GmbH" - - name: Get current week shell: bash id: date run: echo "::set-output name=week::$(date +'calendar-week-%W')" - - - uses: syphar/restore-virtualenv@v1 - if : ${{ inputs.os != 'ubuntu-dind-runners' }} + if: ${{ inputs.os != 'ubuntu-dind-runners' }} id: cache-virtualenv with: - requirement_files: 'pyproject.toml' + requirement_files: pyproject.toml # The virtualenv cache is invalidated when: # - manually triggered by means of a custom cache version token # - on a weekly basis # - any of the integration requirements change (a hash of the # __init__.py files is included in the cache key) - custom_cache_key_element: ${{ inputs.cache_version }}-${{steps.date.outputs.week}}-${{inputs.install_integrations}}-${{ hashFiles('src/zenml/integrations/*/__init__.py') }} - + custom_cache_key_element: ${{ inputs.cache_version }}-${{steps.date.outputs.week}}-${{inputs.install_integrations}}-${{ + hashFiles('src/zenml/integrations/*/__init__.py') }} - uses: tespkg/actions-cache@v1 if: ${{ inputs.os == 'ubuntu-dind-runners' }} id: custom-cache-pip @@ -84,10 +78,12 @@ runs: AWS_SECRET_ACCESS_KEY: ${{ inputs.runners_cache_secret_access_key }} with: endpoint: minio-service.minio.svc.cluster.local # optional - insecure: true # optional, use http instead of https. default false - bucket: "caching" # required - use-fallback: false # optional, use github actions cache fallback, default false - key: ${{ inputs.os }}-${{ inputs.cache_version }}-${{ inputs.python-version }}-${{steps.date.outputs.week}}-${{inputs.install_integrations}}-${{ hashFiles('src/zenml/integrations/*/__init__.py') }} + insecure: true # optional, use http instead of https. default false + bucket: caching # required + use-fallback: false # optional, use github actions cache fallback, default false + key: ${{ inputs.os }}-${{ inputs.cache_version }}-${{ inputs.python-version + }}-${{steps.date.outputs.week}}-${{inputs.install_integrations}}-${{ hashFiles('src/zenml/integrations/*/__init__.py') + }} path: | ~/.cache/pip restore-keys: | @@ -97,38 +93,36 @@ runs: # - uses: syphar/restore-pip-download-cache@v1 # with: # requirement_files: 'pyproject.toml' - # # The pip download cache can be updated on a weekly basis as new packages - # # don't appear that often + # # The pip download cache can be updated on a weekly basis as new packages + # # don't appear that often # custom_cache_key_element: ${{ inputs.cache_version }}-${{ inputs.python-version }}-${{steps.date.outputs.week}} - - name: Install Terraform (Windows) if: ${{ inputs.os == 'windows-latest' }} shell: bash run: choco install terraform -y - - name: Install Terraform (Mac) if: ${{ inputs.os == 'macos-latest' }} shell: bash run: | brew tap hashicorp/tap brew install hashicorp/tap/terraform - - name: Install ZenML and dependencies - if: steps.cache-virtualenv.outputs.cache-hit != 'true' && steps.custom-cache-pip.outputs.cache-hit != 'true' + if: steps.cache-virtualenv.outputs.cache-hit != 'true' && steps.custom-cache-pip.outputs.cache-hit + != 'true' shell: bash run: | scripts/install-zenml-dev.sh --integrations ${{ inputs.install_integrations }} # if using a cached virtualenv, just refresh the ZenML installation - name: Refresh ZenML installation - if: steps.cache-virtualenv.outputs.cache-hit == 'true' || steps.custom-cache-pip.outputs.cache-hit == 'true' + if: steps.cache-virtualenv.outputs.cache-hit == 'true' || steps.custom-cache-pip.outputs.cache-hit + == 'true' shell: bash run: | scripts/install-zenml-dev.sh --integrations ${{ inputs.install_integrations }} - - name: Check Python environment shell: bash - run: | + run: |- zenml integration list pip list pip check || true diff --git a/.github/branch-labels.yml b/.github/branch-labels.yml index 355519e29a7..438488a4717 100644 --- a/.github/branch-labels.yml +++ b/.github/branch-labels.yml @@ -1,4 +1,5 @@ -enhancement: 'feature/*' -bug: ['bugfix/*', 'bug/*'] -backport: 'backport/*' -documentation: ['documentation/*', 'docs/*'] +--- +enhancement: feature/* +bug: [bugfix/*, bug/*] +backport: backport/* +documentation: [documentation/*, docs/*] diff --git a/.github/codecov.yml b/.github/codecov.yml index fc3e2ca19e6..94c4758913d 100644 --- a/.github/codecov.yml +++ b/.github/codecov.yml @@ -1,16 +1,14 @@ +--- codecov: branch: main - -comment: false # don't comment on PRs +comment: false # don't comment on PRs github_checks: false - coverage: status: project: default: - threshold: 1% # allow coverage to drop by 1% before failing the PR - - patch: off + threshold: 1% # allow coverage to drop by 1% before failing the PR + patch: false # do not run coverage on changes - changes: off \ No newline at end of file + changes: false diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 0fe158987f2..0ac1e67cc46 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -1,15 +1,13 @@ +--- version: 2 updates: -- package-ecosystem: "github-actions" - directory: "/" - schedule: - interval: "weekly" - day: "tuesday" - time: "09:00" - timezone: "Europe/Amsterdam" - reviewers: - - "strickvl" - labels: - - "dependencies" - - "internal" - target-branch: "develop" + - package-ecosystem: github-actions + directory: / + schedule: + interval: weekly + day: tuesday + time: 09:00 + timezone: Europe/Amsterdam + reviewers: [strickvl] + labels: [dependencies, internal] + target-branch: develop diff --git a/.github/teams.yml b/.github/teams.yml index 8cf241f9c2d..223702517da 100644 --- a/.github/teams.yml +++ b/.github/teams.yml @@ -1,3 +1,4 @@ +--- internal: - '@htahir1' - '@hamza-zenml' @@ -11,7 +12,7 @@ internal: - '@AlexejPenner' - '@stefannica' - '@jwwwb' - - '@wjayesh' + - '@wjayesh' - '@ayush714' - '@safoinme' - '@fa9r' diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a6f2c5f1c13..59f7f4a42dc 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,20 +1,18 @@ +--- name: Build, Lint, Unit, Integration and Templates Tests - on: workflow_dispatch: workflow_call: push: - branches: ["main"] - paths-ignore: ["docs/**", "docker/**", "*", "!pyproject.toml", "**.md"] + branches: [main] + paths-ignore: [docs/**, docker/**, '*', '!pyproject.toml', '**.md'] pull_request: types: [opened, synchronize, ready_for_review] - paths-ignore: ["docs/**", "docker/**", "*", "!pyproject.toml", "**.md"] - + paths-ignore: [docs/**, docker/**, '*', '!pyproject.toml', '**.md'] concurrency: # New commit on branch cancels running workflows of the same branch group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true - jobs: mlstacks-compatibility-check: if: github.event.pull_request.draft == false @@ -22,142 +20,118 @@ jobs: steps: - name: Checkout code uses: actions/checkout@v4.1.1 - - name: Set up Python uses: actions/setup-python@v5.0.0 with: - python-version: "3.8" - + python-version: '3.8' - name: Install current package as editable run: | pip install -U pip pip install -e . - - name: Install mlstacks package run: pip install mlstacks - - name: Check for broken dependencies run: pip check - - custom-ubuntu-runners-setup-and-test-python: if: github.event.pull_request.draft == false strategy: matrix: os: [ubuntu-dind-runners] - python-version: ["3.8", "3.9", "3.10", "3.11"] + python-version: ['3.8', '3.9', '3.10', '3.11'] fail-fast: false uses: ./.github/workflows/setup-python-environment.yml with: python-version: ${{ matrix.python-version }} os: ${{ matrix.os }} secrets: inherit - windows-setup-and-test-python: if: github.event.pull_request.draft == false strategy: matrix: os: [windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] + python-version: ['3.8', '3.9', '3.10', '3.11'] fail-fast: false uses: ./.github/workflows/setup-python-environment.yml with: python-version: ${{ matrix.python-version }} os: ${{ matrix.os }} secrets: inherit - macos-setup-and-test-python: if: github.event.pull_request.draft == false strategy: matrix: os: [macos-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] + python-version: ['3.8', '3.9', '3.10', '3.11'] fail-fast: false uses: ./.github/workflows/setup-python-environment.yml with: python-version: ${{ matrix.python-version }} os: ${{ matrix.os }} secrets: inherit - update-templates-to-examples: # this doesn't work on forked repositories (i.e. outside contributors) # so we disable template updates for those PRs / branches if: github.event.pull_request.head.repo.full_name == 'zenml-io/zenml' uses: ./.github/workflows/update-templates-to-examples.yml with: - python-version: "3.8" + python-version: '3.8' os: ubuntu-latest secrets: inherit - - windows-integration-test: - needs: - - windows-setup-and-test-python + needs: [windows-setup-and-test-python] strategy: matrix: os: [windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] - test_environment: ["default"] - + python-version: ['3.8', '3.9', '3.10', '3.11'] + test_environment: [default] fail-fast: false - uses: ./.github/workflows/integration-test.yml with: os: ${{ matrix.os }} python-version: ${{ matrix.python-version }} test_environment: ${{ matrix.test_environment }} secrets: inherit - macos-integration-test: - needs: - - macos-setup-and-test-python + needs: [macos-setup-and-test-python] strategy: matrix: os: [macos-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] - test_environment: ["default"] - + python-version: ['3.8', '3.9', '3.10', '3.11'] + test_environment: [default] fail-fast: false - uses: ./.github/workflows/integration-test.yml with: os: ${{ matrix.os }} python-version: ${{ matrix.python-version }} test_environment: ${{ matrix.test_environment }} secrets: inherit - custom-ubuntu-runners-integration-test: - needs: - - custom-ubuntu-runners-setup-and-test-python + needs: [custom-ubuntu-runners-setup-and-test-python] strategy: matrix: os: [ubuntu-dind-runners] # Disabled temporarily while CI is overhauled - python-version: ["3.8", "3.9", "3.10"] + python-version: ['3.8', '3.9', '3.10'] # python-version: ["3.8", "3.9", "3.10", "3.11"] test_environment: - [ - "default", - "docker-server-docker-orchestrator-mysql", - "docker-server-docker-orchestrator-mariadb", - ] + - default + - docker-server-docker-orchestrator-mysql + - docker-server-docker-orchestrator-mariadb exclude: # docker is time-consuming to run, so we only run it on 3.8 - test_environment: docker-server-docker-orchestrator-mysql - python-version: "3.9" + python-version: '3.9' - test_environment: docker-server-docker-orchestrator-mysql - python-version: "3.10" + python-version: '3.10' - test_environment: docker-server-docker-orchestrator-mysql - python-version: "3.11" + python-version: '3.11' - test_environment: docker-server-docker-orchestrator-mariadb - python-version: "3.9" + python-version: '3.9' - test_environment: docker-server-docker-orchestrator-mariadb - python-version: "3.10" + python-version: '3.10' - test_environment: docker-server-docker-orchestrator-mariadb - python-version: "3.11" - + python-version: '3.11' fail-fast: false - uses: ./.github/workflows/integration-test.yml with: os: ${{ matrix.os }} diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 388c67bcd6e..1c6fbf9d60b 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -1,33 +1,29 @@ -name: "CodeQL vulnerability check" - +--- +name: CodeQL vulnerability check on: push: - branches: ["main", "develop"] + branches: [main, develop] pull_request: # The branches below must be a subset of the branches above - branches: ["develop"] + branches: [develop] types: [opened] - jobs: analyze: if: github.event.pull_request.draft == false name: Analyze - runs-on: 'ubuntu-latest' + runs-on: ubuntu-latest timeout-minutes: 360 permissions: actions: read contents: read security-events: write - strategy: fail-fast: false matrix: - language: ["python"] - + language: [python] steps: - name: Checkout repository uses: actions/checkout@v4.1.1 - - name: Initialize CodeQL uses: github/codeql-action/init@v3 with: @@ -38,8 +34,7 @@ jobs: # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs # queries: security-extended,security-and-quality - - name: Perform CodeQL Analysis uses: github/codeql-action/analyze@v3 with: - category: "/language:${{matrix.language}}" + category: /language:${{matrix.language}} diff --git a/.github/workflows/image-optimiser.yml b/.github/workflows/image-optimiser.yml index b793fd39f1b..c64ca19ddc3 100644 --- a/.github/workflows/image-optimiser.yml +++ b/.github/workflows/image-optimiser.yml @@ -1,23 +1,20 @@ +--- name: Compress Images on: pull_request: # Run Image Actions when JPG, JPEG, PNG or WebP files are added or changed. # See https://help.github.com/en/actions/automating-your-workflow-with-github-actions/workflow-syntax-for-github-actions#onpushpull_requestpaths for reference. - paths: - - '**.jpg' - - '**.jpeg' - - '**.png' - - '**.webp' + paths: ['**.jpg', '**.jpeg', '**.png', '**.webp'] jobs: build: # Only run on non-draft PRs within the same repository. - if: github.event.pull_request.head.repo.full_name == github.repository && github.event.pull_request.draft == false + if: github.event.pull_request.head.repo.full_name == github.repository && github.event.pull_request.draft + == false name: calibreapp/image-actions runs-on: ubuntu-latest steps: - name: Checkout Repo uses: actions/checkout@v4.1.1 - - name: Compress Images uses: calibreapp/image-actions@1.1.0 with: diff --git a/.github/workflows/integration-test.yml b/.github/workflows/integration-test.yml index 83f44c12c64..cc72b30b24a 100644 --- a/.github/workflows/integration-test.yml +++ b/.github/workflows/integration-test.yml @@ -1,55 +1,50 @@ +--- name: Integration Tests - on: workflow_call: inputs: os: - description: 'OS' + description: OS type: string required: true python-version: - description: 'Python version' + description: Python version type: string required: true test_environment: - description: 'The test environment' + description: The test environment type: string required: true enable_tmate: - description: 'Enable tmate session for debugging' + description: Enable tmate session for debugging type: string required: false - default: "never" + default: never tmate_timeout: - description: 'Timeout for tmate session (minutes)' + description: Timeout for tmate session (minutes) type: number required: false default: 30 - workflow_dispatch: inputs: os: - description: 'OS' + description: OS type: choice - options: + options: - ubuntu-latest - macos-latest - windows-latest - ubuntu-dind-runners required: false - default: 'ubuntu-latest' + default: ubuntu-latest python-version: - description: 'Python version' + description: Python version type: choice - options: - - '3.8' - - '3.9' - - '3.10' - - '3.11' + options: ['3.8', '3.9', '3.10', '3.11'] required: false default: '3.8' test_environment: - description: 'The test environment' + description: The test environment type: choice options: # Default ZenML deployments @@ -68,25 +63,19 @@ on: - docker-server-airflow-orchestrator-mysql - docker-server-airflow-orchestrator-mariadb required: false - default: 'default' + default: default enable_tmate: - description: 'Enable tmate session for debugging' + description: Enable tmate session for debugging type: choice - options: - - 'no' - - 'on-failure' - - 'always' - - 'before-tests' + options: [no, on-failure, always, before-tests] required: false default: 'no' tmate_timeout: - description: 'Timeout for tmate session (minutes)' + description: Timeout for tmate session (minutes) type: number required: false default: 30 - jobs: - integration-and-template-tests: name: integration-and-template-tests runs-on: ${{ inputs.os }} @@ -95,12 +84,11 @@ jobs: env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: 'utf-8' + PYTHONIOENCODING: utf-8 # on MAC OS, we need to set this environment variable # to fix problems with the fork() calls (see this thread # for more information: http://sealiesoftware.com/blog/archive/2017/6/5/Objective-C_and_fork_in_macOS_1013.html) OBJC_DISABLE_INITIALIZE_FORK_SAFETY: 'YES' - AWS_ACCESS_KEY_ID: ${{ secrets.AWS_US_EAST_1_ENV_ACCESS_KEY_ID }} AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_US_EAST_1_ENV_SECRET_ACCESS_KEY }} AWS_US_EAST_1_SERVER_URL: ${{ secrets.AWS_US_EAST_1_SERVER_URL }} @@ -110,11 +98,9 @@ jobs: GCP_US_EAST4_SERVER_USERNAME: ${{ secrets.GCP_US_EAST4_SERVER_USERNAME }} GCP_US_EAST4_SERVER_PASSWORD: ${{ secrets.GCP_US_EAST4_SERVER_PASSWORD }} if: ${{ ! startsWith(github.event.head_commit.message, 'GitBook:') }} - defaults: run: shell: bash - steps: - name: Maximize space for Docker uses: easimon/maximize-build-space@v10 @@ -125,34 +111,31 @@ jobs: remove-android: 'true' remove-haskell: 'true' build-mount-path: /var/lib/docker/ - if: inputs.os=='ubuntu-latest' && (contains(inputs.test_environment, 'docker') || contains(inputs.test_environment, 'kubeflow') || contains(inputs.test_environment, 'airflow') || contains(inputs.test_environment, 'kubernetes')) - + if: inputs.os=='ubuntu-latest' && (contains(inputs.test_environment, 'docker') + || contains(inputs.test_environment, 'kubeflow') || contains(inputs.test_environment, + 'airflow') || contains(inputs.test_environment, 'kubernetes')) - name: Reload Docker - run: - sudo systemctl restart docker - if: inputs.os=='ubuntu-latest' && (contains(inputs.test_environment, 'docker') || contains(inputs.test_environment, 'kubeflow') || contains(inputs.test_environment, 'airflow') || contains(inputs.test_environment, 'kubernetes')) - + run: sudo systemctl restart docker + if: inputs.os=='ubuntu-latest' && (contains(inputs.test_environment, 'docker') + || contains(inputs.test_environment, 'kubeflow') || contains(inputs.test_environment, + 'airflow') || contains(inputs.test_environment, 'kubernetes')) - uses: actions/checkout@v4.1.1 - - name: Configure AWS credentials uses: aws-actions/configure-aws-credentials@v1 with: role-to-assume: ${{ secrets.AWS_US_EAST_1_ENV_ROLE_ARN }} aws-region: us-east-1 if: contains(inputs.test_environment, 'aws') - - name: Configure GCP credentials - uses: 'google-github-actions/auth@v2' + uses: google-github-actions/auth@v2 with: - credentials_json: '${{ secrets.GCP_US_EAST4_ENV_CREDENTIALS }}' + credentials_json: ${{ secrets.GCP_US_EAST4_ENV_CREDENTIALS }} if: contains(inputs.test_environment, 'gcp') - - - name: 'Set up gcloud SDK' - uses: 'google-github-actions/setup-gcloud@v1' + - name: Set up gcloud SDK + uses: google-github-actions/setup-gcloud@v1 with: - install_components: 'gke-gcloud-auth-plugin' + install_components: gke-gcloud-auth-plugin if: contains(inputs.test_environment, 'gcp') - - name: Setup environment uses: ./.github/actions/setup_environment with: @@ -161,23 +144,18 @@ jobs: os: ${{ inputs.os }} runners_cache_access_key_id: ${{ secrets.RUNNERS_CACHE_ACCESS_KEY_ID }} runners_cache_secret_access_key: ${{ secrets.RUNNERS_CACHE_SECRET_ACCESS_KEY }} - - name: Install docker-compose for non-default environments if: inputs.test_environment != 'default' run: pip install docker-compose - - name: Install Linux System Dependencies if: inputs.os=='ubuntu-latest' run: sudo apt install graphviz - - name: Install MacOS System Dependencies if: runner.os=='macOS' run: brew install graphviz - - name: Install Windows System Dependencies if: runner.os=='Windows' run: choco install graphviz - - name: Install Docker and Colima on MacOS if: runner.os=='macOS' run: | @@ -193,41 +171,34 @@ jobs: # This is required for the Docker Python SDK to work sudo ln -sf $HOME/.colima/default/docker.sock /var/run/docker.sock - - name: Install kubectl on Linux run: | curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl" sudo install -o root -g 0 -m 0755 kubectl /usr/local/bin/kubectl if: inputs.os=='ubuntu-latest' - - name: Install kubectl on MacOS run: | curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/darwin/amd64/kubectl" sudo install -o root -g 0 -m 0755 kubectl /usr/local/bin/kubectl if: runner.os=='macOS' - - name: Install K3D run: | curl -s https://raw.githubusercontent.com/rancher/k3d/main/install.sh | bash if: runner.os!='Windows' && contains(inputs.test_environment, 'kubeflow') - - name: Login to Amazon ECR id: login-ecr run: | aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 715803424590.dkr.ecr.us-east-1.amazonaws.com if: contains(inputs.test_environment, 'aws') - - name: Login to Amazon EKS id: login-eks run: | aws eks --region us-east-1 update-kubeconfig --name zenml-ci-cluster --alias zenml-ci-aws-us-east-1 if: contains(inputs.test_environment, 'aws') - - name: Login to Google ECR run: | gcloud auth configure-docker --project zenml-ci if: contains(inputs.test_environment, 'gcp') - - name: Login to Google GKE uses: google-github-actions/get-gke-credentials@v2 with: @@ -235,25 +206,21 @@ jobs: location: us-east4 project_id: zenml-ci if: contains(inputs.test_environment, 'gcp') - - name: Setup tmate session before tests if: ${{ inputs.enable_tmate == 'before-tests' }} uses: mxschmitt/action-tmate@v3.17 with: timeout-minutes: ${{ inputs.tmate_timeout }} - - name: Test run: | bash scripts/test-coverage-xml.sh integration ${{ inputs.test_environment }} - - name: Setup tmate session after tests if: ${{ inputs.enable_tmate == 'always' || (inputs.enable_tmate == 'on-failure' && failure()) }} uses: mxschmitt/action-tmate@v3.17 with: timeout-minutes: ${{ inputs.tmate_timeout }} - - name: Verify Python Env unaffected - run: | + run: |- zenml integration list pip list pip check || true diff --git a/.github/workflows/mixpanel-test-data.yml b/.github/workflows/mixpanel-test-data.yml index f4d0a5401c5..dbab5c394f2 100644 --- a/.github/workflows/mixpanel-test-data.yml +++ b/.github/workflows/mixpanel-test-data.yml @@ -1,11 +1,10 @@ +--- name: Post mocked events to Segment - on: workflow_dispatch: schedule: # run this on a schedule at 5:30 in the morning every day - - cron: '30 5 * * *' - + - cron: 30 5 * * * jobs: post_mocked_data: runs-on: ubuntu-latest @@ -15,16 +14,13 @@ jobs: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} steps: - uses: actions/checkout@v4.1.1 - - name: Set up Python uses: actions/setup-python@v5.0.0 with: python-version: 3.8 - - name: Install dependencies run: | python -m pip install --upgrade pip python -m pip install analytics-python - - name: Post fake metadata to segment run: python3 scripts/mixpanel_filters_test.py diff --git a/.github/workflows/nightly_build.yml b/.github/workflows/nightly_build.yml index b82604bbfe4..2af33516990 100644 --- a/.github/workflows/nightly_build.yml +++ b/.github/workflows/nightly_build.yml @@ -1,36 +1,32 @@ +--- name: Nightly Build - on: workflow_dispatch: schedule: # Run this workflow every day at midnight - - cron: '30 0 * * *' - + - cron: 30 0 * * * jobs: - setup-and-test: - uses: ./.github/workflows/setup-python-environment.yml - with: - os: "ubuntu-latest" - python-version: "3.8" - git-ref: "develop" - secrets: inherit - - publish-python-package: - needs: setup-and-test - uses: ./.github/workflows/publish_to_pypi_nightly.yml - secrets: inherit - - wait-for-package-release: - runs-on: ubuntu-latest - needs: publish-python-package - steps: - - name: Sleep for 4 minutes - run: sleep 240 - shell: bash - - publish-docker-image: - needs: wait-for-package-release - uses: ./.github/workflows/publish_docker_image.yml - with: - config_file: release-cloudbuild-nightly.yaml - secrets: inherit + setup-and-test: + uses: ./.github/workflows/setup-python-environment.yml + with: + os: ubuntu-latest + python-version: '3.8' + git-ref: develop + secrets: inherit + publish-python-package: + needs: setup-and-test + uses: ./.github/workflows/publish_to_pypi_nightly.yml + secrets: inherit + wait-for-package-release: + runs-on: ubuntu-latest + needs: publish-python-package + steps: + - name: Sleep for 4 minutes + run: sleep 240 + shell: bash + publish-docker-image: + needs: wait-for-package-release + uses: ./.github/workflows/publish_docker_image.yml + with: + config_file: release-cloudbuild-nightly.yaml + secrets: inherit diff --git a/.github/workflows/pr_labeler.yml b/.github/workflows/pr_labeler.yml index 15c1f110f78..b142a27d238 100644 --- a/.github/workflows/pr_labeler.yml +++ b/.github/workflows/pr_labeler.yml @@ -1,9 +1,9 @@ +--- # https://github.com/marketplace/actions/team-labeler-action # https://github.com/TimonVS/pr-labeler-action on: pull_request: types: [opened] - name: pr-label jobs: pr-labeler: @@ -12,12 +12,10 @@ jobs: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - steps: - - uses: JulienKode/team-labeler-action@v1.1.0 - with: - repo-token: "${{ secrets.GITHUB_TOKEN }}" - - - uses: TimonVS/pr-labeler-action@v4.1.1 - with: - configuration-path: .github/branch-labels.yml + - uses: JulienKode/team-labeler-action@v1.1.0 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + - uses: TimonVS/pr-labeler-action@v4.1.1 + with: + configuration-path: .github/branch-labels.yml diff --git a/.github/workflows/publish_api_docs.yml b/.github/workflows/publish_api_docs.yml index f6aa5a4e03e..dd1f5538745 100644 --- a/.github/workflows/publish_api_docs.yml +++ b/.github/workflows/publish_api_docs.yml @@ -1,12 +1,11 @@ +--- # This is a basic workflow that publishes the website name: Publish API Docs - # Controls when the action will run. on: push: branches: - - "release/**" - + - release/** # A workflow run is made up of one or more jobs that can run sequentially or in parallel jobs: # This workflow contains a single job called "publish" @@ -16,25 +15,21 @@ jobs: env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: "utf-8" - + PYTHONIOENCODING: utf-8 steps: - uses: actions/checkout@v4.1.1 with: - fetch-depth: 0 # fetch all commits/branches including gh-pages - + fetch-depth: 0 # fetch all commits/branches including gh-pages - name: Get the version from the github branch name id: get_version run: | BRANCH='${{github.ref_name}}' echo ::set-output name=VERSION::${BRANCH#release/} - - uses: actions/setup-node@v4.0.1 with: - node-version: "14" + node-version: '14' - run: npm install - run: npm install html-minifier -g - - name: Setup environment uses: ./.github/actions/setup_environment with: @@ -43,12 +38,10 @@ jobs: os: ubuntu-latest # We need to install integrations in a different way to avoid conflicts install_integrations: 'no' - - name: Setup git user run: | git config --global user.name 'Code Monkey' git config --global user.email 'codemonkey@zenml.io' - - name: Runs docs generation - run: | + run: |- bash scripts/generate-docs.sh -s src/zenml/ --push --latest -v ${{ steps.get_version.outputs.VERSION }} diff --git a/.github/workflows/publish_docker_image.yml b/.github/workflows/publish_docker_image.yml index cd8ca3b5b7e..415be048eb9 100644 --- a/.github/workflows/publish_docker_image.yml +++ b/.github/workflows/publish_docker_image.yml @@ -1,14 +1,13 @@ +--- name: Publish Docker images - on: workflow_call: inputs: config_file: - description: 'YAML config for Google Cloud Build' + description: YAML config for Google Cloud Build required: false type: string - default: "release-cloudbuild.yaml" - + default: release-cloudbuild.yaml jobs: publish_to_docker: name: Publish Docker 🐋 image 🖼️ to Dockerhub @@ -16,11 +15,9 @@ jobs: env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: 'utf-8' - + PYTHONIOENCODING: utf-8 steps: - uses: actions/checkout@v4.1.1 - - name: Get the version from the github tag ref id: get_version run: echo ::set-output name=VERSION::${GITHUB_REF/refs\/tags\//} diff --git a/.github/workflows/publish_helm_chart.yml b/.github/workflows/publish_helm_chart.yml index 8c41e49cd43..5d7a17d2e6f 100644 --- a/.github/workflows/publish_helm_chart.yml +++ b/.github/workflows/publish_helm_chart.yml @@ -1,24 +1,20 @@ +--- # package and push the zenml helm chart to the ECR repository name: Publish Helm Chart - -on: +on: workflow_call: workflow_dispatch: - jobs: publish_to_ecr: name: Publish Helm Chart 🛞 to ECR ☁️ runs-on: ubuntu-latest - permissions: - contents: "read" - id-token: "write" - + contents: read + id-token: write env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: 'utf-8' - + PYTHONIOENCODING: utf-8 steps: - name: Checkout repo uses: actions/checkout@v4.1.1 @@ -28,13 +24,11 @@ jobs: - name: Add tag version to Chart.yaml run: | sed -i "s/version: .*/version: \"${{ github.ref_name }}\"/" src/zenml/zen_server/deploy/helm/Chart.yaml - - name: Configure AWS credentials uses: aws-actions/configure-aws-credentials@v2 with: role-to-assume: arn:aws:iam::715803424590:role/gh-action-role-zenml-helm aws-region: us-east-1 - - name: Login to Amazon ECR Public id: login-ecr-public uses: aws-actions/amazon-ecr-login@v2 @@ -42,18 +36,17 @@ jobs: mask-password: 'true' registry-type: public - #TODO: revisit the pinned helm version + find a higher version + # TODO: revisit the pinned helm version + find a higher version - name: install helm with version 3.9.2 uses: azure/setup-helm@v3.5 with: - version: "v3.9.2" # default is latest (stable) + version: v3.9.2 # default is latest (stable) id: install-helm - - name: Package and push helm chart to Amazon ECR Public env: REGISTRY: ${{ steps.login-ecr-public.outputs.registry }} REGISTRY_ALIAS: zenml REPOSITORY: zenml - run: | + run: |- helm package src/zenml/zen_server/deploy/helm helm push $REPOSITORY-${{ github.ref_name }}.tgz oci://$REGISTRY/$REGISTRY_ALIAS diff --git a/.github/workflows/publish_to_pypi.yml b/.github/workflows/publish_to_pypi.yml index e32fe5593f4..494ce77f750 100644 --- a/.github/workflows/publish_to_pypi.yml +++ b/.github/workflows/publish_to_pypi.yml @@ -1,8 +1,7 @@ +--- name: Publish Pypi package - on: workflow_call: - jobs: publish_to_pypi: name: Publish Python 🐍 package 📦 to PyPI @@ -10,36 +9,29 @@ jobs: env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: 'utf-8' - + PYTHONIOENCODING: utf-8 steps: - uses: actions/checkout@v4.1.1 - - name: Get the version from the github tag ref id: get_version run: echo ::set-output name=VERSION::${GITHUB_REF/refs\/tags\//} - - name: Set up Python uses: actions/setup-python@v5.0.0 with: python-version: '3.8' - - name: Install Poetry uses: snok/install-poetry@v1.3.4 with: virtualenvs-create: true virtualenvs-in-project: true - - name: Include latest dashboard run: bash scripts/install-dashboard.sh - - name: publish env: PYPI_USERNAME: ${{ secrets.PYPI_USERNAME }} PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} - run: | + run: |- if [ "$(cat src/zenml/VERSION)" = "$(echo ${GITHUB_REF} | sed 's|refs/tags/||g')" ]; then ./scripts/publish.sh; else echo "Version mismatch between src/zenml/VERSION and branch tag" && exit 1; fi - diff --git a/.github/workflows/publish_to_pypi_nightly.yml b/.github/workflows/publish_to_pypi_nightly.yml index 2e6e54293ec..c8ac84cac61 100644 --- a/.github/workflows/publish_to_pypi_nightly.yml +++ b/.github/workflows/publish_to_pypi_nightly.yml @@ -1,8 +1,7 @@ +--- name: Publish Nightly PyPI package - on: workflow_call: - jobs: publish_to_pypi: name: Publish Nightly Python 🐍 package 📦 to PyPI @@ -10,53 +9,45 @@ jobs: env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: 'utf-8' - + PYTHONIOENCODING: utf-8 steps: - uses: actions/checkout@v4.1.1 with: - ref: 'develop' - + ref: develop - name: Set up Python uses: actions/setup-python@v5.0.0 with: python-version: '3.8' - - name: Install Poetry uses: snok/install-poetry@v1.3.4 with: virtualenvs-create: true virtualenvs-in-project: true - - name: Set nightly version run: | - # Extract the current version - CURRENT_VERSION=$(poetry version -s) - - # Get the current date in the format of YYYY-MM-DD - DATE=$(date +"%Y%m%d") + # Extract the current version + CURRENT_VERSION=$(poetry version -s) - # Combine the current version with the date to form - # the new version string - NIGHTLY_VERSION="${CURRENT_VERSION}.dev${DATE}" + # Get the current date in the format of YYYY-MM-DD + DATE=$(date +"%Y%m%d") - # Set the nightly version - echo "NIGHTLY_VERSION=$NIGHTLY_VERSION" >> $GITHUB_ENV + # Combine the current version with the date to form + # the new version string + NIGHTLY_VERSION="${CURRENT_VERSION}.dev${DATE}" + # Set the nightly version + echo "NIGHTLY_VERSION=$NIGHTLY_VERSION" >> $GITHUB_ENV - name: Modify pyproject.toml for nightly release run: | - # Change the package name to `zenml-nightly` - sed -i 's/name = "zenml"/name = "zenml-nightly"/' pyproject.toml - - # Update the version to the nightly version - poetry version $NIGHTLY_VERSION + # Change the package name to `zenml-nightly` + sed -i 's/name = "zenml"/name = "zenml-nightly"/' pyproject.toml + # Update the version to the nightly version + poetry version $NIGHTLY_VERSION - name: Include latest dashboard run: bash scripts/install-dashboard.sh - - name: publish env: PYPI_USERNAME: ${{ secrets.PYPI_USERNAME }} PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} run: bash scripts/publish.sh - diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index e480a8d7211..3f653c71bef 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -1,76 +1,63 @@ +--- # This is a basic workflow to help you get started with Actions name: Release Package & Docker Image - # Controls when the action will run. Triggers the workflow on push of a tag on: push: - tags: - - '*' - + tags: ['*'] jobs: setup-and-test: uses: ./.github/workflows/setup-python-environment.yml with: - os: "ubuntu-latest" - python-version: "3.8" + os: ubuntu-latest + python-version: '3.8' secrets: inherit - mlstacks-compatibility-check: needs: setup-and-test runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v4.1.1 - - name: Set up Python uses: actions/setup-python@v5.0.0 with: - python-version: "3.8" - + python-version: '3.8' - name: Install current package as editable run: pip install -e . - - name: Install mlstacks package run: pip install mlstacks - - name: Check for broken dependencies run: pip check - mysql-db-migration-testing: runs-on: ubuntu-dind-runners - steps: - - name: Checkout code - uses: actions/checkout@v4.1.1 - - - name: Set up Python 3.9 - uses: actions/setup-python@v5.0.0 - with: - python-version: "3.9" - - - name: Test migrations across versions - run: bash scripts/test-migrations.sh mysql - + - name: Checkout code + uses: actions/checkout@v4.1.1 + - name: Set up Python 3.9 + uses: actions/setup-python@v5.0.0 + with: + python-version: '3.9' + - name: Test migrations across versions + run: bash scripts/test-migrations.sh mysql sqlite-db-migration-testing: runs-on: ubuntu-dind-runners - steps: - - name: Checkout code - uses: actions/checkout@v4.1.1 - - - name: Set up Python 3.9 - uses: actions/setup-python@v5.0.0 - with: - python-version: "3.9" - - - name: Test migrations across versions - run: bash scripts/test-migrations.sh sqlite - + - name: Checkout code + uses: actions/checkout@v4.1.1 + - name: Set up Python 3.9 + uses: actions/setup-python@v5.0.0 + with: + python-version: '3.9' + - name: Test migrations across versions + run: bash scripts/test-migrations.sh sqlite publish-python-package: - needs: [setup-and-test, mlstacks-compatibility-check, sqlite-db-migration-testing, mysql-db-migration-testing] + needs: + - setup-and-test + - mlstacks-compatibility-check + - sqlite-db-migration-testing + - mysql-db-migration-testing uses: ./.github/workflows/publish_to_pypi.yml secrets: inherit - wait-for-package-release: runs-on: ubuntu-latest needs: publish-python-package @@ -78,12 +65,10 @@ jobs: - name: Sleep for 4 minutes run: sleep 240 shell: bash - publish-docker-image: needs: wait-for-package-release uses: ./.github/workflows/publish_docker_image.yml secrets: inherit - publish-helm-chart: needs: publish-docker-image uses: ./.github/workflows/publish_helm_chart.yml diff --git a/.github/workflows/scarf-check.yml b/.github/workflows/scarf-check.yml index 3301e8c5316..ae0d3ba27bc 100644 --- a/.github/workflows/scarf-check.yml +++ b/.github/workflows/scarf-check.yml @@ -1,20 +1,20 @@ +--- name: Scarf checker on: workflow_dispatch: pull_request: types: [opened, synchronize, ready_for_review] # Run when md files are involved in the PR. - paths: - - '**.md' + paths: ['**.md'] jobs: check-for-scarf: # Only run on non-draft PRs within the same repository. - if: github.event.pull_request.head.repo.full_name == github.repository && github.event.pull_request.draft == false + if: github.event.pull_request.head.repo.full_name == github.repository && github.event.pull_request.draft + == false name: check-for-scarf runs-on: ubuntu-latest steps: - name: Checkout Repo uses: actions/checkout@v4.1.1 - - name: Check for scarf run: bash scripts/scarf-checker.sh diff --git a/.github/workflows/setup-python-environment.yml b/.github/workflows/setup-python-environment.yml index 3d95564838c..a290febbce5 100644 --- a/.github/workflows/setup-python-environment.yml +++ b/.github/workflows/setup-python-environment.yml @@ -1,70 +1,60 @@ +--- name: Setup Python Environment, Lint and Unit Test - on: workflow_call: inputs: os: - description: 'OS' + description: OS type: string required: true python-version: - description: 'Python version' + description: Python version type: string required: true enable_tmate: - description: 'Enable tmate session for debugging' + description: Enable tmate session for debugging type: string required: false - default: "never" + default: never install_integrations: - description: 'Install ZenML integrations' + description: Install ZenML integrations type: string required: false default: 'yes' git-ref: - description: 'Git branch or ref' + description: Git branch or ref type: string required: false default: '' - workflow_dispatch: inputs: os: - description: 'OS' + description: OS type: choice - options: + options: - ubuntu-latest - macos-latest - windows-latest - ubuntu-dind-runners required: false - default: 'ubuntu-latest' + default: ubuntu-latest python-version: - description: 'Python version' + description: Python version type: choice - options: - - '3.8' - - '3.9' - - '3.10' - - '3.11' + options: ['3.8', '3.9', '3.10', '3.11'] required: false default: '3.8' enable_tmate: - description: 'Enable tmate session for debugging' + description: Enable tmate session for debugging type: choice - options: - - 'no' - - 'on-failure' - - 'always' - - 'before-tests' + options: [no, on-failure, always, before-tests] required: false default: 'no' git-ref: - description: 'Git branch or ref' + description: Git branch or ref type: string required: false default: '' - jobs: setup-and-test: name: setup-and-test @@ -72,23 +62,20 @@ jobs: env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: 'utf-8' + PYTHONIOENCODING: utf-8 # on MAC OS, we need to set this environment variable # to fix problems with the fork() calls (see this thread # for more information: http://sealiesoftware.com/blog/archive/2017/6/5/Objective-C_and_fork_in_macOS_1013.html) - OBJC_DISABLE_INITIALIZE_FORK_SAFETY: "YES" + OBJC_DISABLE_INITIALIZE_FORK_SAFETY: 'YES' # Exit if it's a commit from Gitbook if: ${{ ! startsWith(github.event.head_commit.message, 'GitBook:') }} - defaults: run: shell: bash - steps: - uses: actions/checkout@v4.1.1 with: ref: ${{ github.event.inputs.git-ref || github.ref }} - - name: Setup environment uses: ./.github/actions/setup_environment with: @@ -98,64 +85,55 @@ jobs: install_integrations: ${{ inputs.install_integrations }} runners_cache_access_key_id: ${{ secrets.RUNNERS_CACHE_ACCESS_KEY_ID }} runners_cache_secret_access_key: ${{ secrets.RUNNERS_CACHE_SECRET_ACCESS_KEY }} - - name: Setup tmate session before tests if: ${{ inputs.enable_tmate == 'before-tests' }} uses: mxschmitt/action-tmate@v3.17 - - name: Lint check run: | bash scripts/lint.sh - - name: Spelling checker uses: crate-ci/typos@v1.17.0 with: - files: "." + files: . config: ./.typos.toml # Only run the spell check on ubuntu-latest and python 3.8 if: ${{ inputs.os == 'ubuntu-dind-runners' && inputs.python-version == '3.8' }} - - name: Docstring check run: bash scripts/docstring.sh # Only run the docstring check on ubuntu-latest and python 3.8 if: ${{ inputs.os == 'ubuntu-dind-runners' && inputs.python-version == '3.8' }} - - name: Security check run: bash scripts/check-security.sh - - name: Markdown link check uses: gaurav-nelson/github-action-markdown-link-check@1.0.15 with: use-quiet-mode: 'yes' use-verbose-mode: 'no' - folder-path: './examples, ./docs/book, ./src' - file-path: './README.md, ./LICENSE, ./RELEASE_NOTES.md, CODE-OF-CONDUCT.md, CONTRIBUTING.md, CLA.md, RELEASE_NOTES.md, ROADMAP.md' + folder-path: ./examples, ./docs/book, ./src + file-path: ./README.md, ./LICENSE, ./RELEASE_NOTES.md, CODE-OF-CONDUCT.md, + CONTRIBUTING.md, CLA.md, RELEASE_NOTES.md, ROADMAP.md config-file: .github/workflows/markdown_check_config.json continue-on-error: true # Only run the markdown link check on ubuntu-latest and python 3.8 if: ${{ inputs.os == 'ubuntu-dind-runners' && inputs.python-version == '3.8' }} - - name: Check for alembic branch divergence env: ZENML_DEBUG: 0 run: | bash scripts/check-alembic-branches.sh - - name: Run unit tests run: | bash scripts/test-coverage-xml.sh unit # - name: Upload coverage - # # only do it for python 3.8, we don't need to do it for every version + # # only do it for python 3.8, we don't need to do it for every version # if: ${{ inputs.os == 'ubuntu-dind-runners' && inputs.python-version == '3.8' }} # uses: codecov/codecov-action@v2 - - name: Setup tmate session after tests if: ${{ inputs.enable_tmate == 'always' || (inputs.enable_tmate == 'on-failure' && failure()) }} uses: mxschmitt/action-tmate@v3.17 - - name: Verify Python Env unaffected - run: | + run: |- zenml integration list pip list pip check || true diff --git a/.github/workflows/templates-test.yml b/.github/workflows/templates-test.yml index b0b0bf0d6f0..04ca12fe62e 100644 --- a/.github/workflows/templates-test.yml +++ b/.github/workflows/templates-test.yml @@ -1,75 +1,62 @@ +--- name: Run tests on templates - on: workflow_call: inputs: os: - description: 'OS' + description: OS type: string required: true python-version: - description: 'Python version' + description: Python version type: string required: true - workflow_dispatch: inputs: os: - description: 'OS' + description: OS type: choice - options: - - ubuntu-latest - - macos-latest - - windows-latest + options: [ubuntu-latest, macos-latest, windows-latest] required: false - default: 'ubuntu-latest' + default: ubuntu-latest python-version: - description: 'Python version' + description: Python version type: choice - options: - - '3.8' - - '3.9' - - '3.10' - - '3.11' + options: ['3.8', '3.9', '3.10', '3.11'] required: false default: '3.8' - jobs: all-template-tests: name: all-template-tests runs-on: ${{ inputs.os }} strategy: - matrix: - stack-name: [local] - template-repo: [ - {repo: zenml-io/zenml-project-templates, path: .github/actions/starter_template_test}, - {repo: zenml-io/template-e2e-batch, path: .github/actions/e2e_template_test}, - {repo: zenml-io/template-nlp, path: .github/actions/nlp_template_test} - ] - fail-fast: false + matrix: + stack-name: [local] + template-repo: + - {repo: zenml-io/zenml-project-templates, path: .github/actions/starter_template_test} + - {repo: zenml-io/template-e2e-batch, path: .github/actions/e2e_template_test} + - {repo: zenml-io/template-nlp, path: .github/actions/nlp_template_test} + fail-fast: false env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: "utf-8" - OBJC_DISABLE_INITIALIZE_FORK_SAFETY: "YES" + PYTHONIOENCODING: utf-8 + OBJC_DISABLE_INITIALIZE_FORK_SAFETY: 'YES' if: ${{ ! startsWith(github.event.head_commit.message, 'GitBook:') }} - defaults: run: shell: bash - steps: - name: Run template tests for ${{ matrix.template-repo.repo }} uses: jenseng/dynamic-uses@v1 with: uses: ${{ matrix.template-repo.repo }}/${{ matrix.template-repo.path }}@main - with: '{ "python-version": "${{ inputs.python-version }}", "stack-name": "${{ matrix.stack-name }}", "ref-zenml": "${{ github.ref }}" }' - + with: '{ "python-version": "${{ inputs.python-version }}", "stack-name": + "${{ matrix.stack-name }}", "ref-zenml": "${{ github.ref }}" }' - name: message-on-error if: failure() - run: | + run: |- echo "::error title=${{ matrix.template-repo.repo }} project template testing failed with new version of ZenML core!::\ Breaking changes affecting templates have been introduced. To mitigate this issue,\ please make the code in ${{ matrix.template-repo.repo }} compatible with new version of\ ZenML core, release it and update release tag in zenml.cli.base.ZENML_PROJECT_TEMPLATES" - diff --git a/.github/workflows/trivy-zenml-core.yml b/.github/workflows/trivy-zenml-core.yml index 3f67011ef3f..36c9fa8cc1f 100644 --- a/.github/workflows/trivy-zenml-core.yml +++ b/.github/workflows/trivy-zenml-core.yml @@ -1,12 +1,10 @@ +--- name: Trivy ZenML (core) Scan - on: push: - branches: ["main"] + branches: [main] schedule: - - cron: '0 4 * * 1' # At 04:00 on Monday - - + - cron: 0 4 * * 1 # At 04:00 on Monday jobs: trivy-scan: name: Trivy zenml scan & analysis @@ -14,16 +12,13 @@ jobs: steps: - name: Checkout code uses: actions/checkout@v4.1.1 - - name: zenml-Trivy vulnerability scanner uses: aquasecurity/trivy-action@0.16.1 with: - image-ref: "docker.io/zenmldocker/zenml:latest" - format: "sarif" - output: "trivy-results-zenml.sarif" - + image-ref: docker.io/zenmldocker/zenml:latest + format: sarif + output: trivy-results-zenml.sarif - name: Upload Trivy zenml scan results to GitHub Security tab uses: github/codeql-action/upload-sarif@v3 with: - sarif_file: "trivy-results-zenml.sarif" - + sarif_file: trivy-results-zenml.sarif diff --git a/.github/workflows/trivy-zenserver.yml b/.github/workflows/trivy-zenserver.yml index 655571619a6..0c26ed2a3dc 100644 --- a/.github/workflows/trivy-zenserver.yml +++ b/.github/workflows/trivy-zenserver.yml @@ -1,11 +1,10 @@ +--- name: Trivy ZenServer Scan - on: push: - branches: ["main"] + branches: [main] schedule: - - cron: '0 4 * * 1' # At 04:00 on Monday - + - cron: 0 4 * * 1 # At 04:00 on Monday jobs: trivy-scan: name: Trivy ZenServer scan & analysis @@ -13,15 +12,13 @@ jobs: steps: - name: Checkout code uses: actions/checkout@v4.1.1 - - name: zenserver-Trivy vulnerability scanner uses: aquasecurity/trivy-action@0.16.1 with: - image-ref: "docker.io/zenmldocker/zenml-server:latest" - format: "sarif" - output: "trivy-results-zenserver.sarif" - + image-ref: docker.io/zenmldocker/zenml-server:latest + format: sarif + output: trivy-results-zenserver.sarif - name: Upload Trivy zenserver scan results to GitHub Security tab uses: github/codeql-action/upload-sarif@v3 with: - sarif_file: "trivy-results-zenserver.sarif" + sarif_file: trivy-results-zenserver.sarif diff --git a/.github/workflows/update-templates-to-examples.yml b/.github/workflows/update-templates-to-examples.yml index 8f6a9f7cb5d..6e242a6c62c 100644 --- a/.github/workflows/update-templates-to-examples.yml +++ b/.github/workflows/update-templates-to-examples.yml @@ -1,40 +1,34 @@ +--- name: Auto-update all templates - on: workflow_call: inputs: os: - description: 'OS' + description: OS type: string required: true python-version: - description: 'Python version' + description: Python version type: string required: true - workflow_dispatch: inputs: os: - description: 'OS' + description: OS type: choice - options: + options: - ubuntu-latest - macos-latest - windows-latest - ubuntu-dind-runners required: false - default: 'ubuntu-latest' + default: ubuntu-latest python-version: - description: 'Python version' + description: Python version type: choice - options: - - '3.8' - - '3.9' - - '3.10' - - '3.11' + options: ['3.8', '3.9', '3.10', '3.11'] required: false default: '3.8' - jobs: update-e2e-batch-template-to-examples: name: update-e2e-batch-template-to-examples @@ -42,14 +36,13 @@ jobs: env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: "utf-8" - OBJC_DISABLE_INITIALIZE_FORK_SAFETY: "YES" - if: github.event_name == 'pull_request' && ! startsWith(github.event.head_commit.message, 'GitBook:') - + PYTHONIOENCODING: utf-8 + OBJC_DISABLE_INITIALIZE_FORK_SAFETY: 'YES' + if: github.event_name == 'pull_request' && ! startsWith(github.event.head_commit.message, + 'GitBook:') defaults: run: shell: bash - steps: - name: Run template tests for zenml-io/template-e2e-batch uses: zenml-io/template-e2e-batch/.github/actions/e2e_template_test@main @@ -57,12 +50,10 @@ jobs: python-version: ${{ inputs.python-version }} stack-name: local ref-zenml: ${{ github.ref }} - ref-template: '2023.12.12' # Make sure it is aligned with ZENML_PROJECT_TEMPLATES from src/zenml/cli/base.py - + ref-template: 2023.12.12 # Make sure it is aligned with ZENML_PROJECT_TEMPLATES from src/zenml/cli/base.py - name: Clean-up run: | rm -rf ./local_checkout - - name: message-on-error if: failure() run: | @@ -70,18 +61,16 @@ jobs: Breaking changes affecting templates have been introduced. To mitigate this issue,\ please make the code in zenml-io/template-e2e-batch compatible with new version of\ ZenML core, release it and update release tag in zenml.cli.base.ZENML_PROJECT_TEMPLATES" - - uses: actions/checkout@v4.1.1 with: ref: ${{ github.event.pull_request.head.ref }} - - name: Check-out fresh E2E template run: | rm -rf examples/e2e mkdir -p examples/e2e printf 'info@zenml.io' | zenml init --path examples/e2e --template e2e_batch --template-with-defaults + pip install yamlfix bash scripts/format.sh - - name: Check for changes id: check_changes run: | @@ -92,7 +81,6 @@ jobs: echo "Active Git changes found." echo "changes=true" >> $GITHUB_OUTPUT fi - - name: Commit and push template if: steps.check_changes.outputs.changes == 'true' run: | @@ -101,7 +89,6 @@ jobs: git add . git commit -am "Auto-update of E2E template" git push origin HEAD:${{ github.event.pull_request.head.ref }} - - name: Create PR comment if: steps.check_changes.outputs.changes == 'true' uses: actions/github-script@v7.0.1 @@ -114,22 +101,19 @@ jobs: repo: context.repo.repo, body: 'E2E template updates in `examples/e2e` have been pushed.' }) - - update-nlp-template-to-examples: name: update-nlp-template-to-examples runs-on: ${{ inputs.os }} env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: "utf-8" - OBJC_DISABLE_INITIALIZE_FORK_SAFETY: "YES" - if: github.event_name == 'pull_request' && ! startsWith(github.event.head_commit.message, 'GitBook:') - + PYTHONIOENCODING: utf-8 + OBJC_DISABLE_INITIALIZE_FORK_SAFETY: 'YES' + if: github.event_name == 'pull_request' && ! startsWith(github.event.head_commit.message, + 'GitBook:') defaults: run: shell: bash - steps: - name: Run template tests for zenml-io/template-nlp uses: zenml-io/template-nlp/.github/actions/nlp_template_test@main @@ -137,12 +121,10 @@ jobs: python-version: ${{ inputs.python-version }} stack-name: local ref-zenml: ${{ github.ref }} - ref-template: '0.45.0' # Make sure it is aligned with ZENML_PROJECT_TEMPLATES from src/zenml/cli/base.py - + ref-template: 0.45.0 # Make sure it is aligned with ZENML_PROJECT_TEMPLATES from src/zenml/cli/base.py - name: Clean-up run: | rm -rf ./local_checkout - - name: message-on-error if: failure() run: | @@ -150,17 +132,15 @@ jobs: Breaking changes affecting templates have been introduced. To mitigate this issue,\ please make the code in zenml-io/template-nlp compatible with new version of\ ZenML core, release it and update release tag in zenml.cli.base.ZENML_PROJECT_TEMPLATES" - - uses: actions/checkout@v4.1.1 with: ref: ${{ github.event.pull_request.head.ref }} - - name: Check-out fresh NLP template run: | mkdir -p examples/nlp-case printf 'info@zenml.io' | zenml init --path examples/nlp-case --template nlp --template-with-defaults + pip install yamlfix bash scripts/format.sh - - name: Check for changes id: check_changes run: | @@ -171,7 +151,6 @@ jobs: echo "Active Git changes found." echo "changes=true" >> $GITHUB_OUTPUT fi - - name: Commit and push template if: steps.check_changes.outputs.changes == 'true' run: | @@ -180,7 +159,6 @@ jobs: git add . git commit -am "Auto-update of NLP template" git push origin HEAD:${{ github.event.pull_request.head.ref }} - - name: Create PR comment if: steps.check_changes.outputs.changes == 'true' uses: actions/github-script@v7.0.1 @@ -193,22 +171,19 @@ jobs: repo: context.repo.repo, body: 'NLP template updates in `examples/nlp-case` have been pushed.' }) - - update-starter-template-to-examples: name: update-starter-template-to-examples runs-on: ${{ inputs.os }} env: ZENML_DEBUG: 1 ZENML_ANALYTICS_OPT_IN: false - PYTHONIOENCODING: "utf-8" - OBJC_DISABLE_INITIALIZE_FORK_SAFETY: "YES" - if: github.event_name == 'pull_request' && ! startsWith(github.event.head_commit.message, 'GitBook:') - + PYTHONIOENCODING: utf-8 + OBJC_DISABLE_INITIALIZE_FORK_SAFETY: 'YES' + if: github.event_name == 'pull_request' && ! startsWith(github.event.head_commit.message, + 'GitBook:') defaults: run: shell: bash - steps: - name: Run template tests for zenml-io/template-starter uses: zenml-io/template-starter/.github/actions/starter_template_test@main @@ -216,12 +191,10 @@ jobs: python-version: ${{ inputs.python-version }} stack-name: local ref-zenml: ${{ github.ref }} - ref-template: '2023.12.18' # Make sure it is aligned with ZENML_PROJECT_TEMPLATES from src/zenml/cli/base.py - + ref-template: 2023.12.18 # Make sure it is aligned with ZENML_PROJECT_TEMPLATES from src/zenml/cli/base.py - name: Clean-up run: | rm -rf ./local_checkout - - name: message-on-error if: failure() run: | @@ -230,18 +203,16 @@ jobs: Breaking changes affecting templates have been introduced. To mitigate this issue,\ please make the code in zenml-io/template-starter compatible with new version of\ ZenML core, release it and update release tag in zenml.cli.base.ZENML_PROJECT_TEMPLATES" - - uses: actions/checkout@v4.1.1 with: ref: ${{ github.event.pull_request.head.ref }} - - name: Check-out fresh Starter template run: | rm -rf examples/quickstart mkdir -p examples/quickstart printf 'info@zenml.io' | zenml init --path examples/quickstart --template starter --template-with-defaults + pip install yamlfix bash scripts/format.sh - - name: Check for changes id: check_changes run: | @@ -252,7 +223,6 @@ jobs: echo "Active Git changes found." echo "changes=true" >> $GITHUB_OUTPUT fi - - name: Commit and push template if: steps.check_changes.outputs.changes == 'true' run: | @@ -261,13 +231,12 @@ jobs: git add . git commit -am "Auto-update of Starter template" git push origin HEAD:${{ github.event.pull_request.head.ref }} - - name: Create PR comment if: steps.check_changes.outputs.changes == 'true' uses: actions/github-script@v7.0.1 with: github-token: ${{ secrets.GITHUB_TOKEN }} - script: | + script: |- github.issues.createComment({ issue_number: ${{ github.event.pull_request.number }}, owner: context.repo.owner, diff --git a/pyproject.toml b/pyproject.toml index 89b2e5cf1e2..b1cef290a0f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,7 +77,6 @@ ipinfo = { version = ">=4.4.3", optional = true } # Optional dependencies for project templates copier = { version = ">=8.1.0", optional = true } jinja2-time = { version = "^0.2.0", optional = true } -ruff = { version = "0.1.7", optional = true } # Optional terraform dependency for stack recipes and ZenServer deployments python-terraform = { version = "^0.10.1", optional = true } @@ -130,6 +129,8 @@ tox = { version = "^3.24.3", optional = true } hypothesis = { version = "^6.43.1", optional = true } typing-extensions = { version = ">=3.7.4", optional = true } darglint = { version = "^1.8.1", optional = true } +ruff = { version = "0.1.7", optional = true } +yamlfix = { version = "^1.16.0", optional = true } # pytest pytest = { version = "^7.4.0", optional = true } @@ -202,6 +203,7 @@ connectors-azure = [ dev = [ "bandit", "ruff", + "yamlfix", "coverage", "pytest", "mypy", diff --git a/scripts/format.sh b/scripts/format.sh index 189cfff85d4..be090370036 100755 --- a/scripts/format.sh +++ b/scripts/format.sh @@ -1,6 +1,7 @@ #!/bin/sh -e set -x +# Default source directories SRC=${1:-"src/zenml tests examples docs/mkdocstrings_helper.py scripts"} export ZENML_DEBUG=1 @@ -13,6 +14,21 @@ ruff $SRC --select F401,F841 --fix --exclude "__init__.py" --isolated ruff $SRC --select I --fix --ignore D ruff format $SRC +# Flag check for skipping yamlfix +SKIP_YAMLFIX=false +for arg in "$@" +do + if [ "$arg" = "--no-yamlfix" ]; then + SKIP_YAMLFIX=true + break + fi +done + +# standardises / formats CI yaml files +if [ "$SKIP_YAMLFIX" = false ]; then + yamlfix .github tests +fi + set +x # Adds scarf snippet to docs files where it is missing # Text to be searched in each file diff --git a/scripts/lint.sh b/scripts/lint.sh index 039c6970e10..21076880260 100755 --- a/scripts/lint.sh +++ b/scripts/lint.sh @@ -3,6 +3,7 @@ set -e set -x set -o pipefail +# Default source directories SRC=${1:-"src/zenml tests examples"} SRC_NO_TESTS=${1:-"src/zenml tests/harness"} TESTS_EXAMPLES=${1:-"tests examples"} @@ -13,6 +14,21 @@ ruff $SRC_NO_TESTS # TODO: Fix docstrings in tests and examples and remove the `--extend-ignore D` flag ruff $TESTS_EXAMPLES --extend-ignore D +# Flag check for skipping yamlfix +SKIP_YAMLFIX=false +for arg in "$@" +do + if [ "$arg" = "--no-yamlfix" ]; then + SKIP_YAMLFIX=true + break + fi +done + +# checks for yaml formatting errors +if [ "$SKIP_YAMLFIX" = false ]; then + yamlfix --check .github tests +fi + # autoflake replacement: checks for unused imports and variables ruff $SRC --select F401,F841 --exclude "__init__.py" --isolated diff --git a/tests/harness/cfg/deployments.yaml b/tests/harness/cfg/deployments.yaml index 6785bde6e9d..36379970289 100644 --- a/tests/harness/cfg/deployments.yaml +++ b/tests/harness/cfg/deployments.yaml @@ -1,3 +1,4 @@ +--- # A list of ZenML deployments, either local or remote (external), that # can be included in test environments to be used to run automated tests or # passed to pytest directly using the `--deployment` command line argument. @@ -61,19 +62,16 @@ # value indicates that it doesn't. # deployments: - - name: default description: >- Default deployment. server: none database: sqlite - - name: client-mysql description: >- Local client connected directly to MySQL running in container. server: none database: mysql - - name: client-mariadb description: >- Local client connected directly to MariaDB running in container. @@ -92,7 +90,6 @@ deployments: database: sqlite capabilities: server: true - - name: docker-server description: >- Local ZenML server running in docker using the default SQLite database. @@ -100,7 +97,6 @@ deployments: database: sqlite capabilities: server: true - - name: docker-server-mysql description: >- Local ZenML server and MySQL both running in docker with docker-compose. @@ -108,7 +104,6 @@ deployments: database: mysql capabilities: server: true - - name: docker-server-mariadb description: >- Local ZenML server and MariaDB both running in docker with docker-compose. diff --git a/tests/harness/cfg/environments.yaml b/tests/harness/cfg/environments.yaml index 4d5c64fa992..6451953c5f9 100644 --- a/tests/harness/cfg/environments.yaml +++ b/tests/harness/cfg/environments.yaml @@ -1,3 +1,4 @@ +--- # Environments couple together a deployment with an optional set of # requirements to be configured on the deployment. The deployment entry # can be a reference to a global deployment entry by name or an inline @@ -34,7 +35,6 @@ # inherited from environment requirements. # environments: - - name: default description: >- Default deployment with local orchestrator and all local @@ -47,7 +47,6 @@ environments: - mlflow-local-deployer capabilities: synchronized: true - - name: default-docker-orchestrator description: >- Default deployment with docker orchestrator and all local @@ -58,9 +57,7 @@ environments: - mlflow-local-tracker - mlflow-local-registry - mlflow-local-deployer - mandatory_requirements: - - docker-local - + mandatory_requirements: [docker-local] - name: default-airflow-orchestrator description: >- Default server deployment with airflow local orchestrator and all local @@ -122,9 +119,7 @@ environments: - mlflow-local-tracker - mlflow-local-registry - mlflow-local-deployer - mandatory_requirements: - - airflow-local - + mandatory_requirements: [airflow-local] - name: docker-server-mysql description: >- Server docker-compose deployment with local orchestrator and all local @@ -137,7 +132,6 @@ environments: - mlflow-local-deployer capabilities: synchronized: true - - name: docker-server-docker-orchestrator-mysql description: >- Server docker-compose deployment with docker orchestrator and all local @@ -148,9 +142,7 @@ environments: - mlflow-local-tracker - mlflow-local-registry - mlflow-local-deployer - mandatory_requirements: - - docker-local - + mandatory_requirements: [docker-local] - name: docker-server-airflow-orchestrator-mysql description: >- Server docker-compose deployment with local airflow orchestrator and all @@ -161,9 +153,7 @@ environments: - mlflow-local-tracker - mlflow-local-registry - mlflow-local-deployer - mandatory_requirements: - - airflow-local - + mandatory_requirements: [airflow-local] - name: docker-server-mariadb description: >- Server docker-compose deployment with local orchestrator and all local @@ -176,7 +166,6 @@ environments: - mlflow-local-deployer capabilities: synchronized: true - - name: docker-server-docker-orchestrator-mariadb description: >- Server docker-compose deployment with docker orchestrator and all local @@ -187,9 +176,7 @@ environments: - mlflow-local-tracker - mlflow-local-registry - mlflow-local-deployer - mandatory_requirements: - - docker-local - + mandatory_requirements: [docker-local] - name: docker-server-airflow-orchestrator-mariadb description: >- Server docker-compose deployment with local airflow orchestrator and all @@ -200,5 +187,4 @@ environments: - mlflow-local-tracker - mlflow-local-registry - mlflow-local-deployer - mandatory_requirements: - - airflow-local + mandatory_requirements: [airflow-local] diff --git a/tests/harness/cfg/requirements.yaml b/tests/harness/cfg/requirements.yaml index 69c8e193b91..d44bf724c7c 100644 --- a/tests/harness/cfg/requirements.yaml +++ b/tests/harness/cfg/requirements.yaml @@ -1,3 +1,4 @@ +--- # Software requirements and stack components that are needed # by tests can be defined globally here and referenced by name in # environments or per-test configurations or passed to pytest directly using the @@ -120,8 +121,7 @@ requirements: # Local docker orchestrator - name: docker-local - system_tools: - - docker + system_tools: [docker] stacks: - name: docker-local type: orchestrator @@ -132,10 +132,8 @@ requirements: # Local airflow orchestrator - name: airflow-local - system_tools: - - docker - packages: - - apache-airflow-providers-docker + system_tools: [docker] + packages: [apache-airflow-providers-docker] stacks: - name: airflow-local type: orchestrator @@ -166,9 +164,7 @@ requirements: # Local mlflow model deployer - name: mlflow-local-deployer - system_os: - - linux - - macos + system_os: [linux, macos] stacks: - name: mlflow-local type: model_deployer diff --git a/tests/harness/cfg/tests.yaml b/tests/harness/cfg/tests.yaml index f9a3014cd67..2db7ee1ba81 100644 --- a/tests/harness/cfg/tests.yaml +++ b/tests/harness/cfg/tests.yaml @@ -1,3 +1,4 @@ +--- # Test configurations specify which requirements are needed for the test to run. # These requirements refer to both software packages and ZenML stack components. # @@ -40,73 +41,56 @@ # flavor: seldon # ``` tests: - - module: tests.integration.functional.test_zen_server_api requirements: - capabilities: server: true - - module: tests.integration.examples.test_deepchecks requirements: - - integrations: - - sklearn + - integrations: [sklearn] stacks: - type: data_validator flavor: deepchecks - capabilities: synchronized: true - - module: tests.integration.examples.test_evidently requirements: - - integrations: - - sklearn - packages: - - pyarrow + - integrations: [sklearn] + packages: [pyarrow] stacks: - type: data_validator flavor: evidently - capabilities: synchronized: true - - module: tests.integration.examples.test_great_expectations requirements: - - integrations: - - sklearn + - integrations: [sklearn] stacks: - type: data_validator flavor: great_expectations - capabilities: synchronized: true - - module: tests.integration.examples.test_tensorflow requirements: - - integrations: - - tensorflow - - tensorboard + - integrations: [tensorflow, tensorboard] - capabilities: synchronized: true - - module: tests.integration.examples.test_sklearn requirements: - - integrations: - - sklearn + - integrations: [sklearn] - capabilities: synchronized: true - - module: tests.integration.examples.test_mlflow_tracking requirements: - - integrations: - - tensorflow + - integrations: [tensorflow] stacks: - type: experiment_tracker flavor: mlflow - capabilities: synchronized: true - - module: tests.integration.examples.test_mlflow_registry requirements: - - integrations: - - tensorflow + - integrations: [tensorflow] stacks: - type: experiment_tracker flavor: mlflow @@ -116,78 +100,54 @@ tests: flavor: mlflow - capabilities: synchronized: true - - module: tests.integration.examples.test_neural_prophet requirements: - - integrations: - - neural_prophet + - integrations: [neural_prophet] - capabilities: synchronized: true - - module: tests.integration.examples.test_pytorch requirements: - - integrations: - - pytorch - packages: - - torchvision + - integrations: [pytorch] + packages: [torchvision] - capabilities: synchronized: true - - module: tests.integration.examples.test_whylogs requirements: - - integrations: - - sklearn + - integrations: [sklearn] stacks: - type: data_validator flavor: whylogs - system_os: - - linux - - macos + system_os: [linux, macos] - capabilities: synchronized: true - - module: tests.integration.examples.test_xgboost requirements: - - integrations: - - xgboost + - integrations: [xgboost] - capabilities: synchronized: true - - module: tests.integration.examples.test_huggingface requirements: - - integrations: - - huggingface - - tensorflow + - integrations: [huggingface, tensorflow] - capabilities: synchronized: true - - module: tests.integration.examples.test_lightgbm requirements: - - integrations: - - lightgbm + - integrations: [lightgbm] - capabilities: synchronized: true - - module: tests.integration.examples.test_scipy requirements: - - integrations: - - sklearn + - integrations: [sklearn] - capabilities: synchronized: true - - module: tests.integration.examples.test_facets requirements: - - integrations: - - facets - - sklearn + - integrations: [facets, sklearn] - capabilities: synchronized: true - - module: tests.integration.examples.test_mlflow_deployment requirements: - - integrations: - - tensorflow - - mlflow + - integrations: [tensorflow, mlflow] stacks: - type: experiment_tracker flavor: mlflow @@ -195,13 +155,9 @@ tests: flavor: mlflow - capabilities: synchronized: true - - module: tests.integration.examples.test_slack requirements: - - integrations: - - sklearn - - mlflow - - slack + - integrations: [sklearn, mlflow, slack] stacks: - type: experiment_tracker flavor: mlflow @@ -211,12 +167,9 @@ tests: flavor: slack - capabilities: synchronized: true - - module: tests.integration.examples.test_seldon requirements: - - integrations: - - sklearn - - tensorflow + - integrations: [sklearn, tensorflow] stacks: - type: model_deployer flavor: seldon From 8ae5a0cd904f40b5802079a18d22d9bf60c6fb9f Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 9 Jan 2024 16:48:03 +0100 Subject: [PATCH 04/16] Remove text annotation (#2246) --- examples/README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/README.md b/examples/README.md index 1f19f0d13f5..82006bf5342 100644 --- a/examples/README.md +++ b/examples/README.md @@ -31,7 +31,6 @@ this. | e2e | Trains one or more scikit-learn classification models to make predictions on tabular classification datasets | scikit-learn | ✅ | | generative_chat | LEGACY: constructs a vector store to be used by a LLM-based chatbot based on documentation and data from a variety of sources | langchain, llama-index, slack | | | label_studio_annotation | LEGACY: shows how to use the Label Studio integration for annotation in a computer-vision use case and series of pipelines | label_studio, pillow | | -| label_studio_text_annotation | LEGACY: shows how to use the Label Studio integration in a NLP / text-based use case and series of pipelines | label_studio | | ## ❓ Questions / Further Assistance From 10305514a71de52a8c0d72deaf545ac45f5519dd Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 9 Jan 2024 19:41:58 +0100 Subject: [PATCH 05/16] Add MariaDB migration testing (#2170) * update script to handle mariadb migration testing * WIP * reformat temporary script * parameterize DB startup delay * add a check for mysql vs mariadb * try another way to isolate mariadb vs mysql * try a different mariadb connection path * Fix database connection URL in test-migrations.sh script * Adjust settings for MySQL and MariaDB engines in migration script * log values * temporarily disable full CI runs for testing * print values * trying a different connection approach * trying innodb * Add support for MariaDB in primary key modification * split mariadb from mysql db migration testing * split mariadb from mysql db migration testing (core) * Remove support for MariaDB and add version 0.53.1 * Split out mariadb from mysql in migration testing * add latest zenml version * Update migration testing scripts for different databases * restore the CI to working state * formatting * use zenml init * reformat yml * revert to copier instantiation * add conditional check for version >= 0.43.0 * compare set of vals instead of complicated semantic check * remove extra cd * update mariadb script * add test flag to init command * remove (extra) analytics opt-out * Add additional template versions for testing migrations * enable the CI again * Add comment explaining pre-template-versions --- .github/workflows/release.yml | 16 +++- scripts/test-migrations-mariadb.sh | 86 +++++++++++++++++++ ...migrations.sh => test-migrations-mysql.sh} | 26 ++++-- 3 files changed, 119 insertions(+), 9 deletions(-) create mode 100755 scripts/test-migrations-mariadb.sh rename scripts/{test-migrations.sh => test-migrations-mysql.sh} (77%) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 3f653c71bef..5489bf5bdad 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -38,7 +38,7 @@ jobs: with: python-version: '3.9' - name: Test migrations across versions - run: bash scripts/test-migrations.sh mysql + run: bash scripts/test-migrations-mysql.sh mysql sqlite-db-migration-testing: runs-on: ubuntu-dind-runners steps: @@ -49,13 +49,25 @@ jobs: with: python-version: '3.9' - name: Test migrations across versions - run: bash scripts/test-migrations.sh sqlite + run: bash scripts/test-migrations-mysql.sh sqlite + mariadb-db-migration-testing: + runs-on: ubuntu-dind-runners + steps: + - name: Checkout code + uses: actions/checkout@v4.1.1 + - name: Set up Python 3.9 + uses: actions/setup-python@v4.8.0 + with: + python-version: '3.9' + - name: Test migrations across versions + run: bash scripts/test-migrations-mariadb.sh publish-python-package: needs: - setup-and-test - mlstacks-compatibility-check - sqlite-db-migration-testing - mysql-db-migration-testing + - mariadb-db-migration-testing uses: ./.github/workflows/publish_to_pypi.yml secrets: inherit wait-for-package-release: diff --git a/scripts/test-migrations-mariadb.sh b/scripts/test-migrations-mariadb.sh new file mode 100755 index 00000000000..eb5234d7aa1 --- /dev/null +++ b/scripts/test-migrations-mariadb.sh @@ -0,0 +1,86 @@ +#!/bin/bash + +DB="mariadb" +DB_STARTUP_DELAY=30 # Time in seconds to wait for the database container to start + +function run_tests_for_version() { + set -e # Exit immediately if a command exits with a non-zero status + local VERSION=$1 + + echo "===== Testing version $VERSION =====" + + mkdir test_starter + zenml init --template starter --path test_starter --template-with-defaults --test + cd test_starter + + export ZENML_ANALYTICS_OPT_IN=false + export ZENML_DEBUG=true + + echo "===== Installing sklearn integration =====" + zenml integration install sklearn -y + + echo "===== Running starter template pipeline =====" + python3 run.py + # Add additional CLI tests here + zenml version + + # Confirm DB works and is accessible + zenml pipeline runs list + + cd .. + rm -rf test_starter + echo "===== Finished testing version $VERSION =====" +} + +echo "===== Testing MariaDB =====" +# run a mariadb instance in docker +docker run --name mariadb -d -p 3306:3306 -e MYSQL_ROOT_PASSWORD=password mariadb:10.6 +# mariadb takes a while to start up +sleep $DB_STARTUP_DELAY + +# List of versions to test +VERSIONS=("0.54.0") + +# Start completely fresh +rm -rf ~/.config/zenml + +for VERSION in "${VERSIONS[@]}" +do + set -e # Exit immediately if a command exits with a non-zero status + # Create a new virtual environment + python3 -m venv ".venv-$VERSION" + source ".venv-$VERSION/bin/activate" + + # Install the specific version + pip3 install -U pip setuptools wheel + pip3 install "zenml[templates,server]==$VERSION" + + zenml connect --url mysql://127.0.0.1/zenml --username root --password password + + # Run the tests for this version + run_tests_for_version $VERSION + + zenml disconnect + sleep 5 + + deactivate +done + +# Test the most recent migration with MariaDB +echo "===== TESTING CURRENT BRANCH =====" +set -e +python3 -m venv ".venv-current-branch" +source ".venv-current-branch/bin/activate" + +pip3 install -U pip setuptools wheel +pip3 install -e ".[templates,server]" +pip3 install importlib_metadata + +zenml connect --url mysql://127.0.0.1/zenml --username root --password password + +run_tests_for_version current_branch_mariadb + +zenml disconnect +docker rm -f mariadb + +deactivate diff --git a/scripts/test-migrations.sh b/scripts/test-migrations-mysql.sh similarity index 77% rename from scripts/test-migrations.sh rename to scripts/test-migrations-mysql.sh index 7c152eda94b..7c57261dc28 100755 --- a/scripts/test-migrations.sh +++ b/scripts/test-migrations-mysql.sh @@ -1,6 +1,8 @@ #!/bin/bash DB="sqlite" +DB_STARTUP_DELAY=30 # Time in seconds to wait for the database container to start + if [ -z "$1" ]; then echo "No argument passed, using default: $DB" else @@ -10,12 +12,20 @@ fi function run_tests_for_version() { set -e # Exit immediately if a command exits with a non-zero status local VERSION=$1 + # versions pre-templates and pre-init test flag + # (zenml init --test allows for a non-interactive init) + local PRE_TEMPLATE_VERSIONS=("0.40.0" "0.40.3" "0.41.0" "0.43.0" "0.44.1" "0.44.3" "0.45.2" "0.45.3" "0.45.4" "0.45.5" "0.45.6" "0.46.0" "0.47.0") echo "===== Testing version $VERSION =====" - # Initialize zenml with the appropriate template - # hardcoded to 0.43.0 since this is the latest template-starter repo - # release tag - copier copy -l --trust -r release/0.43.0 https://github.com/zenml-io/template-starter.git test_starter + + # Check if VERSION is in PRE_TEMPLATE_VERSIONS + if printf '%s\n' "${PRE_TEMPLATE_VERSIONS[@]}" | grep -q "^$VERSION$"; then + copier copy -l --trust -r release/0.43.0 https://github.com/zenml-io/template-starter.git test_starter + else + mkdir test_starter + zenml init --template starter --path test_starter --template-with-defaults --test + fi + cd test_starter export ZENML_ANALYTICS_OPT_IN=false @@ -37,17 +47,18 @@ function run_tests_for_version() { echo "===== Finished testing version $VERSION =====" } + + if [ "$1" == "mysql" ]; then echo "===== Testing MySQL =====" # run a mysql instance in docker docker run --name mysql -d -p 3306:3306 -e MYSQL_ROOT_PASSWORD=password mysql:latest - # mysql takes a while to start up - sleep 30 + sleep $DB_STARTUP_DELAY fi # List of versions to test -VERSIONS=("0.40.0" "0.40.3" "0.41.0" "0.43.0" "0.44.1" "0.44.3" "0.45.2" "0.45.3" "0.45.4" "0.45.5" "0.45.6" "0.46.0" "0.47.0" "0.50.0" "0.51.0" "0.52.0") +VERSIONS=("0.40.0" "0.40.3" "0.41.0" "0.43.0" "0.44.1" "0.44.3" "0.45.2" "0.45.3" "0.45.4" "0.45.5" "0.45.6" "0.46.0" "0.47.0" "0.50.0" "0.51.0" "0.52.0" "0.53.0" "0.53.1" "0.54.0") # Start completely fresh rm -rf ~/.config/zenml @@ -95,6 +106,7 @@ done # Test the most recent migration with MySQL +echo "===== TESTING CURRENT BRANCH =====" set -e python3 -m venv ".venv-current-branch" source ".venv-current-branch/bin/activate" From 5e47b81ffc9694ef514c1ef7bc7ca811761c9301 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Wed, 10 Jan 2024 09:07:22 +0100 Subject: [PATCH 06/16] Delete artifact links from model version via Client, ModelVersion and API (#2191) * typo * client and ModelVersion implementations * adding endpoints * split endpoints * lint * coderabbitai feedback * lint * Apply suggestions from code review Co-authored-by: Alex Strick van Linschoten * finish renaming * renaming leftover * move deletion to sql --------- Co-authored-by: Alex Strick van Linschoten --- src/zenml/client.py | 49 ++++++++++++- src/zenml/model/model_version.py | 63 +++++++++++++++++ src/zenml/models/v2/core/model_version.py | 2 +- .../routers/model_versions_endpoints.py | 26 ++++++- src/zenml/zen_stores/rest_zen_store.py | 16 +++++ src/zenml/zen_stores/sql_zen_store.py | 41 ++++++++++- src/zenml/zen_stores/zen_store_interface.py | 13 ++++ .../functional/model/test_model_version.py | 69 +++++++++++++++++++ .../functional/zen_stores/test_zen_store.py | 28 +++++++- 9 files changed, 299 insertions(+), 8 deletions(-) diff --git a/src/zenml/client.py b/src/zenml/client.py index eb6506bc5ed..c8bad22ddee 100644 --- a/src/zenml/client.py +++ b/src/zenml/client.py @@ -2948,7 +2948,7 @@ def delete_artifact_version( database, not the actual object stored in the artifact store. Args: - name_id_or_prefix: The ID or name or prefix of the artifact to + name_id_or_prefix: The ID of artifact version or name or prefix of the artifact to delete. version: The version of the artifact to delete. delete_metadata: If True, delete the metadata of the artifact @@ -5003,8 +5003,6 @@ def update_model_version( ################################################# # Model Versions Artifacts - # - # Only view capabilities are exposed via client. ################################################# def list_model_version_artifact_links( @@ -5074,6 +5072,51 @@ def list_model_version_artifact_links( hydrate=hydrate, ) + def delete_model_version_artifact_link( + self, model_version_id: UUID, artifact_version_id: UUID + ) -> None: + """Delete model version to artifact link in Model Control Plane. + + Args: + model_version_id: The id of the model version holding the link. + artifact_version_id: The id of the artifact version to be deleted. + + Raises: + RuntimeError: If more than one artifact link is found for given filters. + """ + artifact_links = self.list_model_version_artifact_links( + model_version_id=model_version_id, + artifact_version_id=artifact_version_id, + ) + if artifact_links.items: + if artifact_links.total > 1: + raise RuntimeError( + "More than one artifact link found for give model version " + f"`{model_version_id}` and artifact version " + f"`{artifact_version_id}`. This should not be happening and " + "might indicate a corrupted state of your ZenML database. " + "Please seek support via Community Slack." + ) + self.zen_store.delete_model_version_artifact_link( + model_version_id=model_version_id, + model_version_artifact_link_name_or_id=artifact_links.items[ + 0 + ].id, + ) + + def delete_all_model_version_artifact_links( + self, model_version_id: UUID, only_links: bool + ) -> None: + """Delete all model version to artifact links in Model Control Plane. + + Args: + model_version_id: The id of the model version holding the link. + only_links: If true, only delete the link to the artifact. + """ + self.zen_store.delete_all_model_version_artifact_links( + model_version_id, only_links + ) + ################################################# # Model Versions Pipeline Runs # diff --git a/src/zenml/model/model_version.py b/src/zenml/model/model_version.py index 5755d456499..b1afb31fa84 100644 --- a/src/zenml/model/model_version.py +++ b/src/zenml/model/model_version.py @@ -346,6 +346,69 @@ def metadata(self) -> Dict[str, "MetadataType"]: for name, response in response.run_metadata.items() } + def delete_artifact( + self, + name: str, + version: Optional[str] = None, + only_link: bool = True, + delete_metadata: bool = True, + delete_from_artifact_store: bool = False, + ) -> None: + """Delete the artifact linked to this model version. + + Args: + name: The name of the artifact to delete. + version: The version of the artifact to delete (None for latest/non-versioned) + only_link: Whether to only delete the link to the artifact. + delete_metadata: Whether to delete the metadata of the artifact. + delete_from_artifact_store: Whether to delete the artifact from the artifact store. + """ + from zenml.client import Client + from zenml.models import ArtifactVersionResponse + + artifact_version = self.get_artifact(name, version) + if isinstance(artifact_version, ArtifactVersionResponse): + client = Client() + client.delete_model_version_artifact_link( + model_version_id=self.id, + artifact_version_id=artifact_version.id, + ) + if not only_link: + client.delete_artifact_version( + name_id_or_prefix=artifact_version.id, + delete_metadata=delete_metadata, + delete_from_artifact_store=delete_from_artifact_store, + ) + + def delete_all_artifacts( + self, + only_link: bool = True, + delete_from_artifact_store: bool = False, + ) -> None: + """Delete all artifacts linked to this model version. + + Args: + only_link: Whether to only delete the link to the artifact. + delete_from_artifact_store: Whether to delete the artifact from the artifact store. + """ + from zenml.client import Client + + client = Client() + + if not only_link and delete_from_artifact_store: + mv = self._get_model_version() + artifact_responses = mv.data_artifacts + artifact_responses.update(mv.model_artifacts) + artifact_responses.update(mv.deployment_artifacts) + + for artifact_ in artifact_responses.values(): + for artifact_response_ in artifact_.values(): + client._delete_artifact_from_artifact_store( + artifact_version=artifact_response_ + ) + + client.delete_all_model_version_artifact_links(self.id, only_link) + ######################### # Internal methods # ######################### diff --git a/src/zenml/models/v2/core/model_version.py b/src/zenml/models/v2/core/model_version.py index 8bc76545f18..dc1377fcd74 100644 --- a/src/zenml/models/v2/core/model_version.py +++ b/src/zenml/models/v2/core/model_version.py @@ -388,7 +388,7 @@ def data_artifacts( } @property - def endpoint_artifacts( + def deployment_artifacts( self, ) -> Dict[str, Dict[str, "ArtifactVersionResponse"]]: """Get all deployment artifacts linked to this model version. diff --git a/src/zenml/zen_server/routers/model_versions_endpoints.py b/src/zenml/zen_server/routers/model_versions_endpoints.py index f6357b0da34..ddde60e35f9 100644 --- a/src/zenml/zen_server/routers/model_versions_endpoints.py +++ b/src/zenml/zen_server/routers/model_versions_endpoints.py @@ -240,7 +240,7 @@ def delete_model_version_artifact_link( model_version_artifact_link_name_or_id: Union[str, UUID], _: AuthContext = Security(authorize), ) -> None: - """Deletes a model version link. + """Deletes a model version to artifact link. Args: model_version_id: ID of the model version containing the link. @@ -256,6 +256,30 @@ def delete_model_version_artifact_link( ) +@router.delete( + "/{model_version_id}" + ARTIFACTS, + responses={401: error_response, 404: error_response, 422: error_response}, +) +@handle_exceptions +def delete_all_model_version_artifact_links( + model_version_id: UUID, + only_links: bool = True, + _: AuthContext = Security(authorize), +) -> None: + """Deletes all model version to artifact links. + + Args: + model_version_id: ID of the model version containing links. + only_links: Whether to only delete the link to the artifact. + """ + model_version = zen_store().get_model_version(model_version_id) + verify_permission_for_model(model_version, action=Action.UPDATE) + + zen_store().delete_all_model_version_artifact_links( + model_version_id, only_links + ) + + ############################## # Model Version Pipeline Runs ############################## diff --git a/src/zenml/zen_stores/rest_zen_store.py b/src/zenml/zen_stores/rest_zen_store.py index 866105675c3..3df596c58df 100644 --- a/src/zenml/zen_stores/rest_zen_store.py +++ b/src/zenml/zen_stores/rest_zen_store.py @@ -2836,6 +2836,22 @@ def delete_model_version_artifact_link( route=f"{MODEL_VERSIONS}/{model_version_id}{ARTIFACTS}", ) + def delete_all_model_version_artifact_links( + self, + model_version_id: UUID, + only_links: bool = True, + ) -> None: + """Deletes all links between model version and an artifact. + + Args: + model_version_id: ID of the model version containing the link. + only_links: Flag deciding whether to delete only links or all. + """ + self.delete( + f"{MODEL_VERSIONS}/{model_version_id}{ARTIFACTS}", + params={"only_links": only_links}, + ) + # ---------------------- Model Versions Pipeline Runs ---------------------- def create_model_version_pipeline_run_link( diff --git a/src/zenml/zen_stores/sql_zen_store.py b/src/zenml/zen_stores/sql_zen_store.py index c38dbb69c16..e26747576b7 100644 --- a/src/zenml/zen_stores/sql_zen_store.py +++ b/src/zenml/zen_stores/sql_zen_store.py @@ -50,7 +50,7 @@ OperationalError, ) from sqlalchemy.orm import noload -from sqlmodel import Session, SQLModel, create_engine, or_, select +from sqlmodel import Session, SQLModel, col, create_engine, delete, or_, select from sqlmodel.sql.expression import Select, SelectOfScalar from zenml.analytics.enums import AnalyticsEvent @@ -1624,7 +1624,7 @@ def get_artifact_version( if artifact_version is None: raise KeyError( f"Unable to get artifact version with ID " - f"{artifact_version_id}: No artifact versionwith this ID " + f"{artifact_version_id}: No artifact version with this ID " f"found." ) return artifact_version.to_model(hydrate=hydrate) @@ -6827,6 +6827,43 @@ def delete_model_version_artifact_link( session.delete(model_version_artifact_link) session.commit() + def delete_all_model_version_artifact_links( + self, + model_version_id: UUID, + only_links: bool = True, + ) -> None: + """Deletes all model version to artifact links. + + Args: + model_version_id: ID of the model version containing the link. + only_links: Whether to only delete the link to the artifact. + """ + with Session(self.engine) as session: + if not only_links: + artifact_version_ids = session.execute( + select( + ModelVersionArtifactSchema.artifact_version_id + ).where( + ModelVersionArtifactSchema.model_version_id + == model_version_id + ) + ).fetchall() + session.execute( + delete(ArtifactVersionSchema).where( + col(ArtifactVersionSchema.id).in_( + [a[0] for a in artifact_version_ids] + ) + ), + ) + session.execute( + delete(ModelVersionArtifactSchema).where( + ModelVersionArtifactSchema.model_version_id + == model_version_id + ) + ) + + session.commit() + # ---------------------- Model Versions Pipeline Runs ---------------------- def create_model_version_pipeline_run_link( diff --git a/src/zenml/zen_stores/zen_store_interface.py b/src/zenml/zen_stores/zen_store_interface.py index b3d7a20f3aa..35d7d89083c 100644 --- a/src/zenml/zen_stores/zen_store_interface.py +++ b/src/zenml/zen_stores/zen_store_interface.py @@ -2199,6 +2199,19 @@ def delete_model_version_artifact_link( KeyError: specified ID or name not found. """ + @abstractmethod + def delete_all_model_version_artifact_links( + self, + model_version_id: UUID, + only_links: bool = True, + ) -> None: + """Deletes all model version to artifact links. + + Args: + model_version_id: ID of the model version containing the link. + only_links: Flag deciding whether to delete only links or all. + """ + # -------------------- Model Versions Pipeline Runs -------------------- @abstractmethod diff --git a/tests/integration/functional/model/test_model_version.py b/tests/integration/functional/model/test_model_version.py index 83150fe4991..f0eaae0c926 100644 --- a/tests/integration/functional/model/test_model_version.py +++ b/tests/integration/functional/model/test_model_version.py @@ -68,6 +68,12 @@ def step_metadata_logging_functional(): assert get_step_context().model_version.metadata["foo"] == "bar" +@step +def simple_producer() -> str: + """Simple producer step.""" + return "foo" + + @step def consume_from_model_version( is_consume: bool, @@ -393,6 +399,69 @@ def my_pipeline(): assert len(mv.metadata) == 1 assert mv.metadata["foo"] == "bar" + @pytest.mark.parametrize("delete_artifacts", [False, True]) + def test_deletion_of_links( + self, clean_client: "Client", delete_artifacts: bool + ): + """Test that user can delete artifact links (with artifacts) from ModelVersion.""" + + @pipeline( + model_version=ModelVersion( + name=MODEL_NAME, + ), + enable_cache=False, + ) + def _inner_pipeline(): + simple_producer() + simple_producer(id="other_named_producer") + + _inner_pipeline() + + mv = ModelVersion(name=MODEL_NAME, version="latest") + artifact_ids = mv._get_model_version().data_artifact_ids + assert len(artifact_ids) == 2 + + # delete run to enable artifacts deletion + run = clean_client.get_pipeline( + name_id_or_prefix="_inner_pipeline" + ).last_run + clean_client.delete_pipeline_run(run.id) + + mv.delete_artifact( + only_link=not delete_artifacts, + name="_inner_pipeline::other_named_producer::output", + ) + assert len(mv._get_model_version().data_artifact_ids) == 1 + versions_ = artifact_ids[ + "_inner_pipeline::other_named_producer::output" + ]["1"] + if delete_artifacts: + with pytest.raises(KeyError): + clean_client.get_artifact_version(versions_) + else: + assert clean_client.get_artifact_version(versions_).id == versions_ + + _inner_pipeline() + mv = ModelVersion(name=MODEL_NAME, version="latest") + artifact_ids = mv._get_model_version().data_artifact_ids + assert len(artifact_ids) == 2 + + # delete run to enable artifacts deletion + run = clean_client.get_pipeline( + name_id_or_prefix="_inner_pipeline" + ).last_run + clean_client.delete_pipeline_run(run.id) + + mv.delete_all_artifacts(only_link=not delete_artifacts) + assert len(mv._get_model_version().data_artifact_ids) == 0 + for versions_ in artifact_ids.values(): + for id_ in versions_.values(): + if delete_artifacts: + with pytest.raises(KeyError): + clean_client.get_artifact_version(id_) + else: + assert clean_client.get_artifact_version(id_).id == id_ + def test_that_artifacts_are_not_linked_to_models_outside_of_the_context( self, clean_client: "Client" ): diff --git a/tests/integration/functional/zen_stores/test_zen_store.py b/tests/integration/functional/zen_stores/test_zen_store.py index cbc1613cee5..6d7e7813d18 100644 --- a/tests/integration/functional/zen_stores/test_zen_store.py +++ b/tests/integration/functional/zen_stores/test_zen_store.py @@ -4210,6 +4210,32 @@ def test_link_delete_found(self): ) assert len(mvls) == 0 + def test_link_delete_all(self): + with ModelVersionContext(True, create_artifacts=2) as ( + model_version, + artifacts, + ): + zs = Client().zen_store + for artifact in artifacts: + zs.create_model_version_artifact_link( + ModelVersionArtifactRequest( + user=model_version.user.id, + workspace=model_version.workspace.id, + model=model_version.model.id, + model_version=model_version.id, + artifact_version=artifact.id, + ) + ) + zs.delete_all_model_version_artifact_links( + model_version_id=model_version.id, + ) + mvls = zs.list_model_version_artifact_links( + model_version_artifact_link_filter_model=ModelVersionArtifactFilter( + model_version_id=model_version.id + ), + ) + assert len(mvls) == 0 + def test_link_delete_not_found(self): with ModelVersionContext(True) as model_version: zs = Client().zen_store @@ -4318,7 +4344,7 @@ def test_link_list_populated(self): ) assert ( mv.get_deployment_artifact(artifacts[2].name, "1") - == mv.endpoint_artifacts[artifacts[2].name]["1"] + == mv.deployment_artifacts[artifacts[2].name]["1"] ) From ad05fb5c00ec10002c11badc7293a71b2df09be2 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 11 Jan 2024 08:50:58 +0100 Subject: [PATCH 07/16] Default/Non-Default step params produce conflict with yaml ones as defaults are set in code (#2247) * fix for default/non-default params in step * extend test case * Update tests/integration/functional/pipelines/test_pipeline_config.py Co-authored-by: Alex Strick van Linschoten * Auto-update of Starter template * wandb lint * skip yamlfix on windows --------- Co-authored-by: Alex Strick van Linschoten Co-authored-by: GitHub Actions --- .../workflows/setup-python-environment.yml | 2 + scripts/lint.sh | 20 ++++++---- .../wandb_experiment_tracker.py | 2 +- .../wandb_experiment_tracker_flavor.py | 2 +- src/zenml/new/pipelines/pipeline.py | 3 ++ src/zenml/steps/base_step.py | 8 +++- src/zenml/steps/step_invocation.py | 11 ++++++ .../pipelines/test_pipeline_config.py | 39 +++++++++++++++++++ tests/unit/orchestrators/test_cache_utils.py | 1 + 9 files changed, 76 insertions(+), 12 deletions(-) diff --git a/.github/workflows/setup-python-environment.yml b/.github/workflows/setup-python-environment.yml index a290febbce5..bad2f33d979 100644 --- a/.github/workflows/setup-python-environment.yml +++ b/.github/workflows/setup-python-environment.yml @@ -89,6 +89,8 @@ jobs: if: ${{ inputs.enable_tmate == 'before-tests' }} uses: mxschmitt/action-tmate@v3.17 - name: Lint check + env: + OS: ${{ inputs.os }} run: | bash scripts/lint.sh - name: Spelling checker diff --git a/scripts/lint.sh b/scripts/lint.sh index 21076880260..b6576a3532d 100755 --- a/scripts/lint.sh +++ b/scripts/lint.sh @@ -15,14 +15,18 @@ ruff $SRC_NO_TESTS ruff $TESTS_EXAMPLES --extend-ignore D # Flag check for skipping yamlfix -SKIP_YAMLFIX=false -for arg in "$@" -do - if [ "$arg" = "--no-yamlfix" ]; then - SKIP_YAMLFIX=true - break - fi -done +if [ "$OS" = "windows-latest" ]; then + SKIP_YAMLFIX=true +else + SKIP_YAMLFIX=false + for arg in "$@" + do + if [ "$arg" = "--no-yamlfix" ]; then + SKIP_YAMLFIX=true + break + fi + done +fi # checks for yaml formatting errors if [ "$SKIP_YAMLFIX" = false ]; then diff --git a/src/zenml/integrations/wandb/experiment_trackers/wandb_experiment_tracker.py b/src/zenml/integrations/wandb/experiment_trackers/wandb_experiment_tracker.py index 4689d1117dd..e52e1e88ae6 100644 --- a/src/zenml/integrations/wandb/experiment_trackers/wandb_experiment_tracker.py +++ b/src/zenml/integrations/wandb/experiment_trackers/wandb_experiment_tracker.py @@ -133,7 +133,7 @@ def _initialize_wandb( self, run_name: str, tags: List[str], - settings: Union["Settings", Dict[str, Any], None] = None, # type: ignore + settings: Union["Settings", Dict[str, Any], None] = None, ) -> None: """Initializes a wandb run. diff --git a/src/zenml/integrations/wandb/flavors/wandb_experiment_tracker_flavor.py b/src/zenml/integrations/wandb/flavors/wandb_experiment_tracker_flavor.py index 626a9e1c8d9..f3e2157f205 100644 --- a/src/zenml/integrations/wandb/flavors/wandb_experiment_tracker_flavor.py +++ b/src/zenml/integrations/wandb/flavors/wandb_experiment_tracker_flavor.py @@ -58,7 +58,7 @@ class WandbExperimentTrackerSettings(BaseSettings): @validator("settings", pre=True) def _convert_settings( cls, - value: Union[Dict[str, Any], "Settings"], # type: ignore + value: Union[Dict[str, Any], "Settings"], ) -> Dict[str, Any]: """Converts settings to a dictionary. diff --git a/src/zenml/new/pipelines/pipeline.py b/src/zenml/new/pipelines/pipeline.py index 2cc7e9e4987..b5006ad1ae8 100644 --- a/src/zenml/new/pipelines/pipeline.py +++ b/src/zenml/new/pipelines/pipeline.py @@ -1260,6 +1260,7 @@ def add_step_invocation( input_artifacts: Dict[str, StepArtifact], external_artifacts: Dict[str, "ExternalArtifact"], parameters: Dict[str, Any], + default_parameters: Dict[str, Any], upstream_steps: Set[str], custom_id: Optional[str] = None, allow_id_suffix: bool = True, @@ -1271,6 +1272,7 @@ def add_step_invocation( input_artifacts: The input artifacts for the invocation. external_artifacts: The external artifacts for the invocation. parameters: The parameters for the invocation. + default_parameters: The default parameters for the invocation. upstream_steps: The upstream steps for the invocation. custom_id: Custom ID to use for the invocation. allow_id_suffix: Whether a suffix can be appended to the invocation @@ -1306,6 +1308,7 @@ def add_step_invocation( input_artifacts=input_artifacts, external_artifacts=external_artifacts, parameters=parameters, + default_parameters=default_parameters, upstream_steps=upstream_steps, pipeline=self, ) diff --git a/src/zenml/steps/base_step.py b/src/zenml/steps/base_step.py index e93add1dc0a..23eac3367c6 100644 --- a/src/zenml/steps/base_step.py +++ b/src/zenml/steps/base_step.py @@ -443,6 +443,7 @@ def _parse_call_args( Dict[str, "StepArtifact"], Dict[str, "ExternalArtifact"], Dict[str, Any], + Dict[str, Any], ]: """Parses the call args for the step entrypoint. @@ -470,6 +471,7 @@ def _parse_call_args( artifacts = {} external_artifacts = {} parameters = {} + default_parameters = {} for key, value in bound_args.arguments.items(): self.entrypoint_definition.validate_input(key=key, value=value) @@ -510,9 +512,9 @@ def _parse_call_args( and key not in external_artifacts and key not in self.configuration.parameters ): - parameters[key] = value + default_parameters[key] = value - return artifacts, external_artifacts, parameters + return artifacts, external_artifacts, parameters, default_parameters def __call__( self, @@ -548,6 +550,7 @@ def __call__( input_artifacts, external_artifacts, parameters, + default_parameters, ) = self._parse_call_args(*args, **kwargs) upstream_steps = { @@ -563,6 +566,7 @@ def __call__( input_artifacts=input_artifacts, external_artifacts=external_artifacts, parameters=parameters, + default_parameters=default_parameters, upstream_steps=upstream_steps, custom_id=id, allow_id_suffix=not id, diff --git a/src/zenml/steps/step_invocation.py b/src/zenml/steps/step_invocation.py index f6f74034723..4e3583514a8 100644 --- a/src/zenml/steps/step_invocation.py +++ b/src/zenml/steps/step_invocation.py @@ -32,6 +32,7 @@ def __init__( input_artifacts: Dict[str, "StepArtifact"], external_artifacts: Dict[str, "ExternalArtifact"], parameters: Dict[str, Any], + default_parameters: Dict[str, Any], upstream_steps: Set[str], pipeline: "Pipeline", ) -> None: @@ -43,6 +44,7 @@ def __init__( input_artifacts: The input artifacts for the invocation. external_artifacts: The external artifacts for the invocation. parameters: The parameters for the invocation. + default_parameters: The default parameters for the invocation. upstream_steps: The upstream steps for the invocation. pipeline: The parent pipeline of the invocation. """ @@ -51,6 +53,7 @@ def __init__( self.input_artifacts = input_artifacts self.external_artifacts = external_artifacts self.parameters = parameters + self.default_parameters = default_parameters self.invocation_upstream_steps = upstream_steps self.pipeline = pipeline @@ -129,6 +132,14 @@ def finalize(self, parameters_to_ignore: Set[str]) -> "StepConfiguration": for key, value in self.parameters.items() if key not in parameters_to_ignore } + parameters_to_apply.update( + { + key: value + for key, value in self.default_parameters.items() + if key not in parameters_to_ignore + and key not in parameters_to_apply + } + ) self.step.configure(parameters=parameters_to_apply) external_artifacts: Dict[str, ExternalArtifactConfiguration] = {} diff --git a/tests/integration/functional/pipelines/test_pipeline_config.py b/tests/integration/functional/pipelines/test_pipeline_config.py index cf9b060cdf5..1562d947884 100644 --- a/tests/integration/functional/pipelines/test_pipeline_config.py +++ b/tests/integration/functional/pipelines/test_pipeline_config.py @@ -196,6 +196,17 @@ def assert_input_params(bar: str): assert bar == "bar" +@step +def assert_input_params_with_defaults( + bar: str, + foo: str = "some default value", + this_will_be_default: str = "bar", +): + assert bar == "bar" + assert foo == "bar" + assert this_will_be_default == "bar" + + def test_pipeline_config_from_file_works_with_pipeline_parameters( clean_workspace, tmp_path ): @@ -283,3 +294,31 @@ def assert_input_params_pipe(foo: str): "`assert_input_params_pipe` conflict with parameter passed in runtime", ): p(foo="foo") + + +def test_pipeline_config_from_file_works_with_pipeline_parameters_on_conflict_with_default_parameters( + clean_workspace, tmp_path +): + """Test that the pipeline will not fail with error. + + If configured with parameters from a yaml file for the steps + and same parameters are set with some defaults in the code. + """ + config_path = tmp_path / "config.yaml" + file_config = { + "steps": { + "assert_input_params_with_defaults": { + "parameters": {"foo": "bar", "bar": "bar"} + } + }, + "enable_cache": False, + } + config_path.write_text(yaml.dump(file_config)) + + @pipeline(enable_cache=False) + def assert_input_params_pipe(): + assert_input_params_with_defaults() + + p = assert_input_params_pipe.with_options(config_path=str(config_path)) + + p() diff --git a/tests/unit/orchestrators/test_cache_utils.py b/tests/unit/orchestrators/test_cache_utils.py index 7585fe865e3..1d879d73cf2 100644 --- a/tests/unit/orchestrators/test_cache_utils.py +++ b/tests/unit/orchestrators/test_cache_utils.py @@ -38,6 +38,7 @@ def _compile_step(step: BaseStep) -> Step: input_artifacts={}, external_artifacts={}, parameters={}, + default_parameters={}, upstream_steps=set(), pipeline=pipeline, ) From f3a9838b1cb560a0b173577f8a7d498428c7ca2d Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 11 Jan 2024 08:51:14 +0100 Subject: [PATCH 08/16] Prune of unused artifacts links via client (#2192) * prune artifacts in client * more tests * remove branching * Update src/zenml/client.py Co-authored-by: Alex Strick van Linschoten * Update tests/integration/functional/test_client.py Co-authored-by: Alex Strick van Linschoten * Update tests/integration/functional/test_client.py Co-authored-by: Alex Strick van Linschoten * nits * resolve branching * `prune_artifact_versions` in SQLStore * lint * wandb lint * skip yamlfix on windows --------- Co-authored-by: Alex Strick van Linschoten --- src/zenml/client.py | 23 +++++++ src/zenml/zen_server/rbac/endpoint_utils.py | 16 +++++ src/zenml/zen_server/rbac/models.py | 1 + .../routers/artifact_version_endpoints.py | 22 +++++++ src/zenml/zen_stores/rest_zen_store.py | 13 ++++ src/zenml/zen_stores/sql_zen_store.py | 61 ++++++++++++++++++- src/zenml/zen_stores/zen_store_interface.py | 11 ++++ tests/integration/functional/test_client.py | 54 ++++++++++++++++ 8 files changed, 200 insertions(+), 1 deletion(-) diff --git a/src/zenml/client.py b/src/zenml/client.py index c8bad22ddee..c06dff4b476 100644 --- a/src/zenml/client.py +++ b/src/zenml/client.py @@ -2789,6 +2789,29 @@ def delete_artifact( self.zen_store.delete_artifact(artifact_id=artifact.id) logger.info(f"Deleted artifact '{artifact.name}'.") + def prune_artifacts( + self, + only_versions: bool = True, + delete_from_artifact_store: bool = False, + ) -> None: + """Delete all unused artifacts and artifact versions. + + Args: + only_versions: Only delete artifact versions, keeping artifacts + delete_from_artifact_store: Delete data from artifact metadata + """ + if delete_from_artifact_store: + unused_artifact_versions = depaginate( + partial(self.list_artifact_versions, only_unused=True) + ) + for unused_artifact_version in unused_artifact_versions: + self._delete_artifact_from_artifact_store( + unused_artifact_version + ) + + self.zen_store.prune_artifact_versions(only_versions) + logger.info("All unused artifacts and artifact versions deleted.") + # --------------------------- Artifact Versions --------------------------- def get_artifact_version( diff --git a/src/zenml/zen_server/rbac/endpoint_utils.py b/src/zenml/zen_server/rbac/endpoint_utils.py index 1f7b37bd10d..a0fc037342c 100644 --- a/src/zenml/zen_server/rbac/endpoint_utils.py +++ b/src/zenml/zen_server/rbac/endpoint_utils.py @@ -148,3 +148,19 @@ def verify_permissions_and_delete_entity( model = get_method(id) verify_permission_for_model(model, action=Action.DELETE) delete_method(model.id) + + +def verify_permissions_and_prune_entities( + resource_type: ResourceType, + prune_method: Callable[..., None], + **kwargs: Any, +) -> None: + """Verify permissions and prune entities of certain type. + + Args: + resource_type: The resource type of the entities to prune. + prune_method: The method to prune the entities. + kwargs: Keyword arguments to pass to the prune method. + """ + verify_permission(resource_type=resource_type, action=Action.PRUNE) + prune_method(**kwargs) diff --git a/src/zenml/zen_server/rbac/models.py b/src/zenml/zen_server/rbac/models.py index 96deda7bc47..b9ea5f4eb07 100644 --- a/src/zenml/zen_server/rbac/models.py +++ b/src/zenml/zen_server/rbac/models.py @@ -29,6 +29,7 @@ class Action(StrEnum): UPDATE = "update" DELETE = "delete" READ_SECRET_VALUE = "read_secret_value" + PRUNE = "prune" # Service connectors CLIENT = "client" diff --git a/src/zenml/zen_server/routers/artifact_version_endpoints.py b/src/zenml/zen_server/routers/artifact_version_endpoints.py index 9319002b2c7..c79330554ba 100644 --- a/src/zenml/zen_server/routers/artifact_version_endpoints.py +++ b/src/zenml/zen_server/routers/artifact_version_endpoints.py @@ -33,6 +33,7 @@ verify_permissions_and_create_entity, verify_permissions_and_delete_entity, verify_permissions_and_get_entity, + verify_permissions_and_prune_entities, verify_permissions_and_update_entity, ) from zenml.zen_server.rbac.models import ResourceType @@ -194,6 +195,27 @@ def delete_artifact_version( ) +@artifact_version_router.delete( + "/", + responses={401: error_response, 404: error_response, 422: error_response}, +) +@handle_exceptions +def prune_artifact_versions( + only_versions: bool = True, + _: AuthContext = Security(authorize), +) -> None: + """Prunes unused artifact versions and their artifacts. + + Args: + only_versions: Only delete artifact versions, keeping artifacts + """ + verify_permissions_and_prune_entities( + resource_type=ResourceType.ARTIFACT_VERSION, + prune_method=zen_store().prune_artifact_versions, + only_versions=only_versions, + ) + + @artifact_version_router.get( "/{artifact_version_id}" + VISUALIZE, response_model=LoadedVisualization, diff --git a/src/zenml/zen_stores/rest_zen_store.py b/src/zenml/zen_stores/rest_zen_store.py index 3df596c58df..d2cb81cad98 100644 --- a/src/zenml/zen_stores/rest_zen_store.py +++ b/src/zenml/zen_stores/rest_zen_store.py @@ -817,6 +817,19 @@ def delete_artifact_version(self, artifact_version_id: UUID) -> None: resource_id=artifact_version_id, route=ARTIFACT_VERSIONS ) + def prune_artifact_versions( + self, + only_versions: bool = True, + ) -> None: + """Prunes unused artifact versions and their artifacts. + + Args: + only_versions: Only delete artifact versions, keeping artifacts + """ + self.delete( + path=ARTIFACT_VERSIONS, params={"only_versions": only_versions} + ) + # ------------------------ Artifact Visualizations ------------------------ def get_artifact_visualization( diff --git a/src/zenml/zen_stores/sql_zen_store.py b/src/zenml/zen_stores/sql_zen_store.py index e26747576b7..10accb8d1fc 100644 --- a/src/zenml/zen_stores/sql_zen_store.py +++ b/src/zenml/zen_stores/sql_zen_store.py @@ -50,7 +50,16 @@ OperationalError, ) from sqlalchemy.orm import noload -from sqlmodel import Session, SQLModel, col, create_engine, delete, or_, select +from sqlmodel import ( + Session, + SQLModel, + and_, + col, + create_engine, + delete, + or_, + select, +) from sqlmodel.sql.expression import Select, SelectOfScalar from zenml.analytics.enums import AnalyticsEvent @@ -1731,6 +1740,56 @@ def delete_artifact_version(self, artifact_version_id: UUID) -> None: session.delete(artifact_version) session.commit() + def prune_artifact_versions( + self, + only_versions: bool = True, + ) -> None: + """Prunes unused artifact versions and their artifacts. + + Args: + only_versions: Only delete artifact versions, keeping artifacts + """ + with Session(self.engine) as session: + unused_artifact_versions = [ + a[0] + for a in session.execute( + select(ArtifactVersionSchema.id).where( + and_( + col(ArtifactVersionSchema.id).notin_( + select(StepRunOutputArtifactSchema.artifact_id) + ), + col(ArtifactVersionSchema.id).notin_( + select(StepRunInputArtifactSchema.artifact_id) + ), + ) + ) + ).fetchall() + ] + session.execute( + delete(ArtifactVersionSchema).where( + col(ArtifactVersionSchema.id).in_( + unused_artifact_versions + ), + ) + ) + if not only_versions: + unused_artifacts = [ + a[0] + for a in session.execute( + select(ArtifactSchema.id).where( + col(ArtifactSchema.id).notin_( + select(ArtifactVersionSchema.artifact_id) + ) + ) + ).fetchall() + ] + session.execute( + delete(ArtifactSchema).where( + col(ArtifactSchema.id).in_(unused_artifacts) + ) + ) + session.commit() + # ------------------------ Artifact Visualizations ------------------------ def get_artifact_visualization( diff --git a/src/zenml/zen_stores/zen_store_interface.py b/src/zenml/zen_stores/zen_store_interface.py index 35d7d89083c..4a8bbb4e3c4 100644 --- a/src/zenml/zen_stores/zen_store_interface.py +++ b/src/zenml/zen_stores/zen_store_interface.py @@ -504,6 +504,17 @@ def delete_artifact_version(self, artifact_version_id: UUID) -> None: KeyError: if the artifact version doesn't exist. """ + @abstractmethod + def prune_artifact_versions( + self, + only_versions: bool = True, + ) -> None: + """Prunes unused artifact versions and their artifacts. + + Args: + only_versions: Only delete artifact versions, keeping artifacts + """ + # -------------------- Artifact Visualization -------------------- @abstractmethod diff --git a/tests/integration/functional/test_client.py b/tests/integration/functional/test_client.py index 543a5f35bca..5398b6fd3df 100644 --- a/tests/integration/functional/test_client.py +++ b/tests/integration/functional/test_client.py @@ -27,6 +27,7 @@ int_plus_one_test_step, ) from tests.integration.functional.utils import sample_name +from zenml import ExternalArtifact from zenml.client import Client from zenml.config.pipeline_spec import PipelineSpec from zenml.config.source import Source @@ -1128,6 +1129,59 @@ def test_basic_crud_for_entity( pass +class TestArtifact: + def test_prune_full(self, clean_client: "Client"): + """Test that artifact pruning works.""" + artifact_id = ExternalArtifact(value="foo").upload_by_value() + artifact = clean_client.get_artifact_version(artifact_id) + assert artifact is not None + clean_client.prune_artifacts( + only_versions=False, delete_from_artifact_store=True + ) + # artifact version, artifact and data are deleted + with pytest.raises(KeyError): + clean_client.get_artifact_version(artifact_id) + with pytest.raises(KeyError): + assert ( + clean_client.get_artifact(artifact.artifact.id).id + == artifact.artifact.id + ) + assert not os.path.exists(artifact.uri) + + def test_prune_data_and_version(self, clean_client: "Client"): + """Test that artifact pruning works with delete_from_artifact_store flag.""" + artifact_id = ExternalArtifact(value="foo").upload_by_value() + artifact = clean_client.get_artifact_version(artifact_id) + assert artifact is not None + clean_client.prune_artifacts( + only_versions=False, delete_from_artifact_store=False + ) + # artifact version and artifact are deleted, data is kept + with pytest.raises(KeyError): + clean_client.get_artifact_version(artifact_id) + with pytest.raises(KeyError): + assert ( + clean_client.get_artifact(artifact.artifact.id).id + == artifact.artifact.id + ) + assert os.path.exists(artifact.uri) + + def test_prune_only_artifact_version(self, clean_client: "Client"): + """Test that artifact pruning works with only versions flag.""" + artifact_id = ExternalArtifact(value="foo").upload_by_value() + artifact = clean_client.get_artifact_version(artifact_id) + assert artifact is not None + clean_client.prune_artifacts(only_versions=True) + # artifact version is deleted, rest kept + with pytest.raises(KeyError): + clean_client.get_artifact_version(artifact_id) + assert ( + clean_client.get_artifact(artifact.artifact.id).id + == artifact.artifact.id + ) + assert os.path.exists(artifact.uri) + + class TestModel: MODEL_NAME = "foo" From 250e65cb306163d4ebc379ea950919826c33a620 Mon Sep 17 00:00:00 2001 From: Safoine El Khabich <34200873+safoinme@users.noreply.github.com> Date: Thu, 11 Jan 2024 15:47:06 +0100 Subject: [PATCH 09/16] Rename nlp example (#2221) * rename nlp case * rename to e2e_nlp * Add HuggingFace and Skypilot deployment steps --- .../update-templates-to-examples.yml | 2 +- .../.assets/00_pipelines_composition.png | Bin .../{nlp-case => e2e_nlp}/.copier-answers.yml | 8 +-- examples/{nlp-case => e2e_nlp}/.dockerignore | 0 examples/{nlp-case => e2e_nlp}/LICENSE | 0 examples/{nlp-case => e2e_nlp}/Makefile | 0 examples/{nlp-case => e2e_nlp}/README.md | 30 +++------ examples/{nlp-case => e2e_nlp}/config.yaml | 0 .../{nlp-case => e2e_nlp}/gradio/Dockerfile | 0 .../{nlp-case => e2e_nlp}/gradio/__init__.py | 0 examples/{nlp-case => e2e_nlp}/gradio/app.py | 0 .../gradio/requirements.txt | 0 .../{nlp-case => e2e_nlp}/gradio/serve.yaml | 0 .../pipelines/__init__.py | 0 .../pipelines/deploying.py | 13 +++- .../pipelines/promoting.py | 0 .../pipelines/training.py | 0 .../{nlp-case => e2e_nlp}/requirements.txt | 0 examples/{nlp-case => e2e_nlp}/run.py | 0 .../{nlp-case => e2e_nlp}/steps/__init__.py | 2 + .../steps/alerts/__init__.py | 0 .../steps/alerts/notify_on.py | 0 .../steps/dataset_loader/__init__.py | 0 .../steps/dataset_loader/data_loader.py | 0 .../steps/deploying/__init__.py | 6 ++ .../steps/deploying/huggingface_deployment.py | 63 ++++++++++++++++++ .../steps/deploying/local_deployment.py | 0 .../steps/deploying/save_model.py | 0 .../steps/deploying/skypilot_deployment.py | 53 +++++++++++++++ .../steps/promotion/__init__.py | 0 .../steps/promotion/promote_get_metrics.py | 0 .../promote_metric_compare_promoter.py | 0 .../steps/register/__init__.py | 0 .../steps/register/model_log_register.py | 0 .../steps/tokenizer_loader/__init__.py | 0 .../tokenizer_loader/tokenizer_loader.py | 0 .../steps/tokenzation/__init__.py | 0 .../steps/tokenzation/tokenization.py | 0 .../steps/training/__init__.py | 0 .../steps/training/model_trainer.py | 0 examples/{nlp-case => e2e_nlp}/utils/misc.py | 0 41 files changed, 150 insertions(+), 27 deletions(-) rename examples/{nlp-case => e2e_nlp}/.assets/00_pipelines_composition.png (100%) rename examples/{nlp-case => e2e_nlp}/.copier-answers.yml (80%) rename examples/{nlp-case => e2e_nlp}/.dockerignore (100%) rename examples/{nlp-case => e2e_nlp}/LICENSE (100%) rename examples/{nlp-case => e2e_nlp}/Makefile (100%) rename examples/{nlp-case => e2e_nlp}/README.md (94%) rename examples/{nlp-case => e2e_nlp}/config.yaml (100%) rename examples/{nlp-case => e2e_nlp}/gradio/Dockerfile (100%) rename examples/{nlp-case => e2e_nlp}/gradio/__init__.py (100%) rename examples/{nlp-case => e2e_nlp}/gradio/app.py (100%) rename examples/{nlp-case => e2e_nlp}/gradio/requirements.txt (100%) rename examples/{nlp-case => e2e_nlp}/gradio/serve.yaml (100%) rename examples/{nlp-case => e2e_nlp}/pipelines/__init__.py (100%) rename examples/{nlp-case => e2e_nlp}/pipelines/deploying.py (89%) rename examples/{nlp-case => e2e_nlp}/pipelines/promoting.py (100%) rename examples/{nlp-case => e2e_nlp}/pipelines/training.py (100%) rename examples/{nlp-case => e2e_nlp}/requirements.txt (100%) rename examples/{nlp-case => e2e_nlp}/run.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/__init__.py (95%) rename examples/{nlp-case => e2e_nlp}/steps/alerts/__init__.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/alerts/notify_on.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/dataset_loader/__init__.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/dataset_loader/data_loader.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/deploying/__init__.py (86%) create mode 100644 examples/e2e_nlp/steps/deploying/huggingface_deployment.py rename examples/{nlp-case => e2e_nlp}/steps/deploying/local_deployment.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/deploying/save_model.py (100%) create mode 100644 examples/e2e_nlp/steps/deploying/skypilot_deployment.py rename examples/{nlp-case => e2e_nlp}/steps/promotion/__init__.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/promotion/promote_get_metrics.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/promotion/promote_metric_compare_promoter.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/register/__init__.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/register/model_log_register.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/tokenizer_loader/__init__.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/tokenizer_loader/tokenizer_loader.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/tokenzation/__init__.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/tokenzation/tokenization.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/training/__init__.py (100%) rename examples/{nlp-case => e2e_nlp}/steps/training/model_trainer.py (100%) rename examples/{nlp-case => e2e_nlp}/utils/misc.py (100%) diff --git a/.github/workflows/update-templates-to-examples.yml b/.github/workflows/update-templates-to-examples.yml index 6e242a6c62c..02974f1ae7f 100644 --- a/.github/workflows/update-templates-to-examples.yml +++ b/.github/workflows/update-templates-to-examples.yml @@ -169,7 +169,7 @@ jobs: issue_number: ${{ github.event.pull_request.number }}, owner: context.repo.owner, repo: context.repo.repo, - body: 'NLP template updates in `examples/nlp-case` have been pushed.' + body: 'NLP template updates in `examples/e2e_nlp` have been pushed.' }) update-starter-template-to-examples: name: update-starter-template-to-examples diff --git a/examples/nlp-case/.assets/00_pipelines_composition.png b/examples/e2e_nlp/.assets/00_pipelines_composition.png similarity index 100% rename from examples/nlp-case/.assets/00_pipelines_composition.png rename to examples/e2e_nlp/.assets/00_pipelines_composition.png diff --git a/examples/nlp-case/.copier-answers.yml b/examples/e2e_nlp/.copier-answers.yml similarity index 80% rename from examples/nlp-case/.copier-answers.yml rename to examples/e2e_nlp/.copier-answers.yml index f62d7fafb31..2fd034ee4c8 100644 --- a/examples/nlp-case/.copier-answers.yml +++ b/examples/e2e_nlp/.copier-answers.yml @@ -1,12 +1,12 @@ # Changes here will be overwritten by Copier -_commit: 0.45.0 -_src_path: gh:zenml-io/template-nlp +_commit: 0.45.0-17-gbd996ed +_src_path: template-nlp accelerator: cpu cloud_of_choice: aws dataset: airline_reviews deploy_locally: true -deploy_to_huggingface: false -deploy_to_skypilot: false +deploy_to_huggingface: true +deploy_to_skypilot: true email: '' full_name: ZenML GmbH metric_compare_promotion: true diff --git a/examples/nlp-case/.dockerignore b/examples/e2e_nlp/.dockerignore similarity index 100% rename from examples/nlp-case/.dockerignore rename to examples/e2e_nlp/.dockerignore diff --git a/examples/nlp-case/LICENSE b/examples/e2e_nlp/LICENSE similarity index 100% rename from examples/nlp-case/LICENSE rename to examples/e2e_nlp/LICENSE diff --git a/examples/nlp-case/Makefile b/examples/e2e_nlp/Makefile similarity index 100% rename from examples/nlp-case/Makefile rename to examples/e2e_nlp/Makefile diff --git a/examples/nlp-case/README.md b/examples/e2e_nlp/README.md similarity index 94% rename from examples/nlp-case/README.md rename to examples/e2e_nlp/README.md index 948b6ef908e..812691d327b 100644 --- a/examples/nlp-case/README.md +++ b/examples/e2e_nlp/README.md @@ -1,11 +1,14 @@ # ZenML NLP project This is a comprehensive supervised ML project built with the -ZenML framework and its integration. The project trains one or more -scikit-learn classification models to make predictions on the tabular -classification datasets provided by the scikit-learn library. The project was -generated from the [NLP ZenML project template](https://github.com/zenml-io/template-nlp). +ZenML framework and its integration. The project will be +a comprehensive starting point for anyone looking to build +and deploy NLP models using the ZenML framework by streamlining +the process of training, promoting, and deploying NLP models +with a focus on reproducibility, scalability, and ease of use. +The project was generated from the [NLP ZenML project template](https://github.com/zenml-io/template-nlp). with the following properties: + - Project name: ZenML NLP project - Technical Name: nlp_use_case - Version: `0.0.1` @@ -16,6 +19,8 @@ Settings of your project are: - Accelerator: `cpu` - Trained model promotion to `staging` based on accuracy metric vs currently deployed model - Local deployment enabled +- Deployment to HuggingFace Hub enabled +- Deployment to SkyPilot enabled - Dataset: `airline_reviews` - Model: `distilbert-base-uncased` - Notifications about failures enabled @@ -181,20 +186,3 @@ The project loosely follows [the recommended ZenML project structure](https://do ├── config.yaml # ZenML configuration file └── run.py # CLI tool to run pipelines on ZenML Stack ``` - - - - - - - - - - - - -integrations -├── README.md # this file -├── requirements.txt # extra Python dependencies -└── run.py # CLI tool to run pipelines on ZenML Stack -``` \ No newline at end of file diff --git a/examples/nlp-case/config.yaml b/examples/e2e_nlp/config.yaml similarity index 100% rename from examples/nlp-case/config.yaml rename to examples/e2e_nlp/config.yaml diff --git a/examples/nlp-case/gradio/Dockerfile b/examples/e2e_nlp/gradio/Dockerfile similarity index 100% rename from examples/nlp-case/gradio/Dockerfile rename to examples/e2e_nlp/gradio/Dockerfile diff --git a/examples/nlp-case/gradio/__init__.py b/examples/e2e_nlp/gradio/__init__.py similarity index 100% rename from examples/nlp-case/gradio/__init__.py rename to examples/e2e_nlp/gradio/__init__.py diff --git a/examples/nlp-case/gradio/app.py b/examples/e2e_nlp/gradio/app.py similarity index 100% rename from examples/nlp-case/gradio/app.py rename to examples/e2e_nlp/gradio/app.py diff --git a/examples/nlp-case/gradio/requirements.txt b/examples/e2e_nlp/gradio/requirements.txt similarity index 100% rename from examples/nlp-case/gradio/requirements.txt rename to examples/e2e_nlp/gradio/requirements.txt diff --git a/examples/nlp-case/gradio/serve.yaml b/examples/e2e_nlp/gradio/serve.yaml similarity index 100% rename from examples/nlp-case/gradio/serve.yaml rename to examples/e2e_nlp/gradio/serve.yaml diff --git a/examples/nlp-case/pipelines/__init__.py b/examples/e2e_nlp/pipelines/__init__.py similarity index 100% rename from examples/nlp-case/pipelines/__init__.py rename to examples/e2e_nlp/pipelines/__init__.py diff --git a/examples/nlp-case/pipelines/deploying.py b/examples/e2e_nlp/pipelines/deploying.py similarity index 89% rename from examples/nlp-case/pipelines/deploying.py rename to examples/e2e_nlp/pipelines/deploying.py index 932c771bba4..cc4fa2abd77 100644 --- a/examples/nlp-case/pipelines/deploying.py +++ b/examples/e2e_nlp/pipelines/deploying.py @@ -19,6 +19,8 @@ from steps import ( deploy_locally, + deploy_to_huggingface, + deploy_to_skypilot, notify_on_failure, notify_on_success, save_model_to_deploy, @@ -87,7 +89,16 @@ def nlp_use_case_deploy_pipeline( tokenizer_name_or_path=tokenizer_name_or_path, after=["save_model_to_deploy"], ) - last_step_name = "deploy_locally" + ########## Deploy to HuggingFace ########## + deploy_to_huggingface( + repo_name=repo_name, + after=["save_model_to_deploy"], + ) + ########## Deploy to Skypilot ########## + deploy_to_skypilot( + after=["save_model_to_deploy"], + ) + last_step_name = "deploy_to_skypilot" notify_on_success(after=[last_step_name]) ### YOUR CODE ENDS HERE ### diff --git a/examples/nlp-case/pipelines/promoting.py b/examples/e2e_nlp/pipelines/promoting.py similarity index 100% rename from examples/nlp-case/pipelines/promoting.py rename to examples/e2e_nlp/pipelines/promoting.py diff --git a/examples/nlp-case/pipelines/training.py b/examples/e2e_nlp/pipelines/training.py similarity index 100% rename from examples/nlp-case/pipelines/training.py rename to examples/e2e_nlp/pipelines/training.py diff --git a/examples/nlp-case/requirements.txt b/examples/e2e_nlp/requirements.txt similarity index 100% rename from examples/nlp-case/requirements.txt rename to examples/e2e_nlp/requirements.txt diff --git a/examples/nlp-case/run.py b/examples/e2e_nlp/run.py similarity index 100% rename from examples/nlp-case/run.py rename to examples/e2e_nlp/run.py diff --git a/examples/nlp-case/steps/__init__.py b/examples/e2e_nlp/steps/__init__.py similarity index 95% rename from examples/nlp-case/steps/__init__.py rename to examples/e2e_nlp/steps/__init__.py index 68a8d4287c8..7188c33fe86 100644 --- a/examples/nlp-case/steps/__init__.py +++ b/examples/e2e_nlp/steps/__init__.py @@ -36,4 +36,6 @@ from .deploying import ( save_model_to_deploy, deploy_locally, + deploy_to_huggingface, + deploy_to_skypilot, ) diff --git a/examples/nlp-case/steps/alerts/__init__.py b/examples/e2e_nlp/steps/alerts/__init__.py similarity index 100% rename from examples/nlp-case/steps/alerts/__init__.py rename to examples/e2e_nlp/steps/alerts/__init__.py diff --git a/examples/nlp-case/steps/alerts/notify_on.py b/examples/e2e_nlp/steps/alerts/notify_on.py similarity index 100% rename from examples/nlp-case/steps/alerts/notify_on.py rename to examples/e2e_nlp/steps/alerts/notify_on.py diff --git a/examples/nlp-case/steps/dataset_loader/__init__.py b/examples/e2e_nlp/steps/dataset_loader/__init__.py similarity index 100% rename from examples/nlp-case/steps/dataset_loader/__init__.py rename to examples/e2e_nlp/steps/dataset_loader/__init__.py diff --git a/examples/nlp-case/steps/dataset_loader/data_loader.py b/examples/e2e_nlp/steps/dataset_loader/data_loader.py similarity index 100% rename from examples/nlp-case/steps/dataset_loader/data_loader.py rename to examples/e2e_nlp/steps/dataset_loader/data_loader.py diff --git a/examples/nlp-case/steps/deploying/__init__.py b/examples/e2e_nlp/steps/deploying/__init__.py similarity index 86% rename from examples/nlp-case/steps/deploying/__init__.py rename to examples/e2e_nlp/steps/deploying/__init__.py index faeba00221b..eee0e0e0590 100644 --- a/examples/nlp-case/steps/deploying/__init__.py +++ b/examples/e2e_nlp/steps/deploying/__init__.py @@ -19,3 +19,9 @@ from .save_model import save_model_to_deploy from .local_deployment import deploy_locally + + +from .huggingface_deployment import deploy_to_huggingface + + +from .skypilot_deployment import deploy_to_skypilot diff --git a/examples/e2e_nlp/steps/deploying/huggingface_deployment.py b/examples/e2e_nlp/steps/deploying/huggingface_deployment.py new file mode 100644 index 00000000000..2e9786cd0c8 --- /dev/null +++ b/examples/e2e_nlp/steps/deploying/huggingface_deployment.py @@ -0,0 +1,63 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os + +from huggingface_hub import HfApi + +from zenml import step +from zenml.client import Client +from zenml.logger import get_logger + +# Initialize logger +logger = get_logger(__name__) + + +@step() +def deploy_to_huggingface( + repo_name: str, +): + """ + This step deploy the model to huggingface. + + Args: + repo_name: The name of the repo to create/use on huggingface. + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + secret = Client().get_secret("huggingface_creds") + assert secret, "No secret found with name 'huggingface_creds'. Please create one that includes your `username` and `token`." + token = secret.secret_values["token"] + api = HfApi(token=token) + hf_repo = api.create_repo( + repo_id=repo_name, repo_type="space", space_sdk="gradio", exist_ok=True + ) + zenml_repo_root = Client().root + if not zenml_repo_root: + logger.warning( + "You're running the `deploy_to_huggingface` step outside of a ZenML repo. " + "Since the deployment step to huggingface is all about pushing the repo to huggingface, " + "this step will not work outside of a ZenML repo where the gradio folder is present." + ) + raise + gradio_folder_path = os.path.join(zenml_repo_root, "gradio") + space = api.upload_folder( + folder_path=gradio_folder_path, + repo_id=hf_repo.repo_id, + repo_type="space", + ) + logger.info(f"Space created: {space}") + ### YOUR CODE ENDS HERE ### diff --git a/examples/nlp-case/steps/deploying/local_deployment.py b/examples/e2e_nlp/steps/deploying/local_deployment.py similarity index 100% rename from examples/nlp-case/steps/deploying/local_deployment.py rename to examples/e2e_nlp/steps/deploying/local_deployment.py diff --git a/examples/nlp-case/steps/deploying/save_model.py b/examples/e2e_nlp/steps/deploying/save_model.py similarity index 100% rename from examples/nlp-case/steps/deploying/save_model.py rename to examples/e2e_nlp/steps/deploying/save_model.py diff --git a/examples/e2e_nlp/steps/deploying/skypilot_deployment.py b/examples/e2e_nlp/steps/deploying/skypilot_deployment.py new file mode 100644 index 00000000000..099dc582d82 --- /dev/null +++ b/examples/e2e_nlp/steps/deploying/skypilot_deployment.py @@ -0,0 +1,53 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +import os +import re + +import sky + +from zenml import step +from zenml.client import Client +from zenml.logger import get_logger + +# Initialize logger +logger = get_logger(__name__) + + +@step() +def deploy_to_skypilot(): + """ + This step deploy the model to a VM using SkyPilot. + + This step requires `skypilot` to be installed. + aswell as a configured cloud account locally (e.g. AWS, GCP, Azure). + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + zenml_repo_root = Client().root + if not zenml_repo_root: + logger.warning( + "You're running the `deploy_to_huggingface` step outside of a ZenML repo. " + "Since the deployment step to huggingface is all about pushing the repo to huggingface, " + "this step will only work within a ZenML repo where the gradio folder is present." + ) + raise + gradio_task_yaml = os.path.join(zenml_repo_root, "gradio", "serve.yaml") + task = sky.Task.from_yaml(gradio_task_yaml) + cluster_name = re.sub(r"[^a-zA-Z0-9]+", "-", "nlp_use_case-cluster") + sky.launch(task, cluster_name=cluster_name, detach_setup=True) + ### YOUR CODE ENDS HERE ### diff --git a/examples/nlp-case/steps/promotion/__init__.py b/examples/e2e_nlp/steps/promotion/__init__.py similarity index 100% rename from examples/nlp-case/steps/promotion/__init__.py rename to examples/e2e_nlp/steps/promotion/__init__.py diff --git a/examples/nlp-case/steps/promotion/promote_get_metrics.py b/examples/e2e_nlp/steps/promotion/promote_get_metrics.py similarity index 100% rename from examples/nlp-case/steps/promotion/promote_get_metrics.py rename to examples/e2e_nlp/steps/promotion/promote_get_metrics.py diff --git a/examples/nlp-case/steps/promotion/promote_metric_compare_promoter.py b/examples/e2e_nlp/steps/promotion/promote_metric_compare_promoter.py similarity index 100% rename from examples/nlp-case/steps/promotion/promote_metric_compare_promoter.py rename to examples/e2e_nlp/steps/promotion/promote_metric_compare_promoter.py diff --git a/examples/nlp-case/steps/register/__init__.py b/examples/e2e_nlp/steps/register/__init__.py similarity index 100% rename from examples/nlp-case/steps/register/__init__.py rename to examples/e2e_nlp/steps/register/__init__.py diff --git a/examples/nlp-case/steps/register/model_log_register.py b/examples/e2e_nlp/steps/register/model_log_register.py similarity index 100% rename from examples/nlp-case/steps/register/model_log_register.py rename to examples/e2e_nlp/steps/register/model_log_register.py diff --git a/examples/nlp-case/steps/tokenizer_loader/__init__.py b/examples/e2e_nlp/steps/tokenizer_loader/__init__.py similarity index 100% rename from examples/nlp-case/steps/tokenizer_loader/__init__.py rename to examples/e2e_nlp/steps/tokenizer_loader/__init__.py diff --git a/examples/nlp-case/steps/tokenizer_loader/tokenizer_loader.py b/examples/e2e_nlp/steps/tokenizer_loader/tokenizer_loader.py similarity index 100% rename from examples/nlp-case/steps/tokenizer_loader/tokenizer_loader.py rename to examples/e2e_nlp/steps/tokenizer_loader/tokenizer_loader.py diff --git a/examples/nlp-case/steps/tokenzation/__init__.py b/examples/e2e_nlp/steps/tokenzation/__init__.py similarity index 100% rename from examples/nlp-case/steps/tokenzation/__init__.py rename to examples/e2e_nlp/steps/tokenzation/__init__.py diff --git a/examples/nlp-case/steps/tokenzation/tokenization.py b/examples/e2e_nlp/steps/tokenzation/tokenization.py similarity index 100% rename from examples/nlp-case/steps/tokenzation/tokenization.py rename to examples/e2e_nlp/steps/tokenzation/tokenization.py diff --git a/examples/nlp-case/steps/training/__init__.py b/examples/e2e_nlp/steps/training/__init__.py similarity index 100% rename from examples/nlp-case/steps/training/__init__.py rename to examples/e2e_nlp/steps/training/__init__.py diff --git a/examples/nlp-case/steps/training/model_trainer.py b/examples/e2e_nlp/steps/training/model_trainer.py similarity index 100% rename from examples/nlp-case/steps/training/model_trainer.py rename to examples/e2e_nlp/steps/training/model_trainer.py diff --git a/examples/nlp-case/utils/misc.py b/examples/e2e_nlp/utils/misc.py similarity index 100% rename from examples/nlp-case/utils/misc.py rename to examples/e2e_nlp/utils/misc.py From a1824a1f3aaea0fd509500c31bb6869c3840a760 Mon Sep 17 00:00:00 2001 From: Stefan Nica Date: Thu, 11 Jan 2024 20:39:39 +0100 Subject: [PATCH 10/16] Support refreshing service connector credentials in the Vertex step operator to support long-running jobs (#2198) * Refresh creds on Vertex step operator job polling * Fix docstring error --- .../step_operators/vertex_step_operator.py | 25 +++++++++++-------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/src/zenml/integrations/gcp/step_operators/vertex_step_operator.py b/src/zenml/integrations/gcp/step_operators/vertex_step_operator.py index 4428de7478e..18ca23edce3 100644 --- a/src/zenml/integrations/gcp/step_operators/vertex_step_operator.py +++ b/src/zenml/integrations/gcp/step_operators/vertex_step_operator.py @@ -21,6 +21,7 @@ import time from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, cast +from google.api_core.exceptions import ServerError from google.cloud import aiplatform from zenml import __version__ @@ -186,7 +187,6 @@ def launch( Raises: RuntimeError: If the run fails. - ConnectionError: If the run fails due to a connection error. """ resource_settings = info.config.resource_settings if resource_settings.cpu_count or resource_settings.memory: @@ -300,27 +300,30 @@ def launch( try: response = client.get_custom_job(name=job_id) retry_count = 0 - # Handle transient connection error. - except ConnectionError as err: + # Handle transient connection errors and credential expiration by + # recreating the Python API client. + except (ConnectionError, ServerError) as err: if retry_count < CONNECTION_ERROR_RETRY_LIMIT: retry_count += 1 logger.warning( - "ConnectionError (%s) encountered when polling job: " - "%s. Trying to recreate the API client.", - err, - job_id, + f"Error encountered when polling job " + f"{job_id}: {err}\nRetrying...", ) + # This call will refresh the credentials if they expired. + credentials, project_id = self._get_authentication() # Recreate the Python API client. client = aiplatform.gapic.JobServiceClient( - client_options=client_options + credentials=credentials, client_options=client_options ) else: - logger.error( + logger.exception( "Request failed after %s retries.", CONNECTION_ERROR_RETRY_LIMIT, ) - raise - + raise RuntimeError( + f"Request failed after {CONNECTION_ERROR_RETRY_LIMIT} " + f"retries: {err}" + ) if response.state in VERTEX_JOB_STATES_FAILED: err_msg = ( "Job '{}' did not succeed. Detailed response {}.".format( From 1edad9275209bb1cf5f65db1b8d22207fcbaa28a Mon Sep 17 00:00:00 2001 From: Stefan Nica Date: Fri, 12 Jan 2024 09:07:39 +0100 Subject: [PATCH 11/16] Refactor secrets stores to store all secret metadata in the DB (#2193) * Refactored secrets stores to store all secret metadata in the DB * Fixed linter errors * Ensure backwards-compatibility with existing REST secrets store configurations * Fix linter errors * Add migration script * Fix migration script * Fix docstrings * Add missing DB revision * Remove secrets from DB before migration * Fix bugs found by unit tests and manual tests * Fix remaining unit test failures * Fix db revision head split * Updated secrets store docs with required permissions * Rebased DB migrations on top of latest * Fixed docstrings * Move all eprecated code to the DB migration script * Fix docstrings --- docker/zenml-server-hf-spaces.Dockerfile | 1 - .../zenml-self-hosted/deploy-with-docker.md | 62 +- .../zenml-self-hosted/deploy-with-helm.md | 61 ++ .../component-guide/artifact-stores/s3.md | 2 +- src/zenml/config/store_config.py | 27 +- src/zenml/enums.py | 11 +- .../gcp_service_connector.py | 38 +- src/zenml/models/v2/core/secret.py | 19 + src/zenml/zen_server/deploy/deployer.py | 4 - .../deploy/helm/templates/server-db-job.yaml | 2 - .../helm/templates/server-deployment.yaml | 2 - src/zenml/zen_server/deploy/helm/values.yaml | 10 - src/zenml/zen_stores/base_zen_store.py | 84 +- src/zenml/zen_stores/enums.py | 27 - src/zenml/zen_stores/migrations/alembic.py | 10 +- src/zenml/zen_stores/migrations/env.py | 2 +- ...c1b9c04da1_make_secrets_values_optional.py | 35 + .../7b651bf6822e_track_secrets_in_db.py | 715 ++++++++++++++++++ src/zenml/zen_stores/rest_zen_store.py | 160 +++- .../zen_stores/schemas/secret_schemas.py | 97 ++- .../secrets_stores/aws_secrets_store.py | 585 ++------------ .../secrets_stores/azure_secrets_store.py | 425 ++--------- .../secrets_stores/base_secrets_store.py | 325 +------- .../secrets_stores/gcp_secrets_store.py | 457 ++--------- .../secrets_stores/hashicorp_secrets_store.py | 437 ++--------- .../secrets_stores/rest_secrets_store.py | 266 ------- .../secrets_stores/secrets_store_interface.py | 115 +-- .../service_connector_secrets_store.py | 12 +- .../secrets_stores/sql_secrets_store.py | 277 +------ src/zenml/zen_stores/sql_zen_store.py | 455 ++++++++++- src/zenml/zen_stores/zen_store_interface.py | 119 +++ .../zen_stores/test_secrets_store.py | 63 +- .../functional/zen_stores/test_zen_store.py | 26 +- .../functional/zen_stores/utils.py | 11 +- 34 files changed, 2096 insertions(+), 2846 deletions(-) delete mode 100644 src/zenml/zen_stores/enums.py create mode 100644 src/zenml/zen_stores/migrations/versions/1ac1b9c04da1_make_secrets_values_optional.py create mode 100644 src/zenml/zen_stores/migrations/versions/7b651bf6822e_track_secrets_in_db.py delete mode 100644 src/zenml/zen_stores/secrets_stores/rest_secrets_store.py diff --git a/docker/zenml-server-hf-spaces.Dockerfile b/docker/zenml-server-hf-spaces.Dockerfile index 8f211ca96c4..e56c2b7a5c0 100644 --- a/docker/zenml-server-hf-spaces.Dockerfile +++ b/docker/zenml-server-hf-spaces.Dockerfile @@ -42,7 +42,6 @@ ENV ZENML_SERVER_DEPLOYMENT_TYPE="hf_spaces" # ENV ZENML_SECRETS_STORE_AWS_ACCESS_KEY_ID="" # ENV ZENML_SECRETS_STORE_AWS_SECRET_ACCESS_KEY="" # ENV ZENML_SECRETS_STORE_AWS_SESSION_TOKEN="" -# ENV ZENML_SECRETS_STORE_SECRET_LIST_REFRESH_TIMEOUT="" # # GCP Secrets Store Configuration # ENV ZENML_SECRETS_STORE_PROJECT_ID="" diff --git a/docs/book/deploying-zenml/zenml-self-hosted/deploy-with-docker.md b/docs/book/deploying-zenml/zenml-self-hosted/deploy-with-docker.md index cd0f5385620..77b7a30e2d7 100644 --- a/docs/book/deploying-zenml/zenml-self-hosted/deploy-with-docker.md +++ b/docs/book/deploying-zenml/zenml-self-hosted/deploy-with-docker.md @@ -76,11 +76,35 @@ These configuration options are only relevant if you're using the AWS Secrets Ma * **ZENML\_SECRETS\_STORE\_TYPE:** Set this to `aws` in order to set this type of secret store. -The AWS Secrets Store uses the ZenML AWS Service Connector under the hood to authenticate with the AWS Secrets Manager API. This means that you can use any of the [authentication methods supported by the AWS Service Connector](https://docs.zenml.io/stacks-and-components/auth-management/aws-service-connector#authentication-methods) to authenticate with the AWS Secrets Manager API. The following configuration options are supported: +The AWS Secrets Store uses the ZenML AWS Service Connector under the hood to authenticate with the AWS Secrets Manager API. This means that you can use any of the [authentication methods supported by the AWS Service Connector](https://docs.zenml.io/stacks-and-components/auth-management/aws-service-connector#authentication-methods) to authenticate with the AWS Secrets Manager API. + +The minimum set of permissions that must be attached to the implicit or configured AWS credentials are: `secretsmanager:CreateSecret`, `secretsmanager:GetSecretValue`, `secretsmanager:DescribeSecret`, `secretsmanager:PutSecretValue`, `secretsmanager:TagResource` and `secretsmanager:DeleteSecret` and they must be associated with secrets that have a name starting with `zenml/` in the target region and account. The following IAM policy example can be used as a starting point: + +``` +{ + "Version": "2012-10-17", + "Statement": [ + { + "Sid": "ZenMLSecretsStore", + "Effect": "Allow", + "Action": [ + "secretsmanager:CreateSecret", + "secretsmanager:GetSecretValue", + "secretsmanager:DescribeSecret", + "secretsmanager:PutSecretValue", + "secretsmanager:TagResource", + "secretsmanager:DeleteSecret" + ], + "Resource": "arn:aws:secretsmanager:::secret:zenml/*" + } + ] +} +``` + +The following configuration options are supported: * **ZENML\_SECRETS\_STORE\_AUTH\_METHOD**: The AWS Service Connector authentication method to use (e.g. `secret-key` or `iam-role`). * **ZENML\_SECRETS\_STORE\_AUTH\_CONFIG**: The AWS Service Connector configuration, in JSON format (e.g. `{"role_arn": "arn:aws:iam::123456789012:role/MyRole"}`). -* **ZENML\_SECRETS\_STORE\_SECRET\_LIST\_REFRESH\_TIMEOUT**: AWS' [Secrets Manager](https://aws.amazon.com/secrets-manager) has a known issue where it does not immediately reflect new and updated secrets in the `list_secrets` results. To work around this issue, you can set this refresh timeout value to a non-zero value to get the ZenML server to wait after creating or updating an AWS secret until the changes are reflected in the secrets returned by `list_secrets` or the number of seconds specified by this value has elapsed. Defaults to `0` (disabled). Should not be set to a high value as it may cause thread starvation in the ZenML server on high load. > **Note:** The remaining configuration options are deprecated and may be removed in a future release. Instead, you should set the `ZENML_SECRETS_STORE_AUTH_METHOD` and `ZENML_SECRETS_STORE_AUTH_CONFIG` variables to use the AWS Service Connector authentication method. @@ -94,6 +118,40 @@ These configuration options are only relevant if you're using the GCP Secrets Ma * **ZENML\_SECRETS\_STORE\_TYPE:** Set this to `gcp` in order to set this type of secret store. +The minimum set of permissions that must be attached to the implicit or configured GCP credentials are as follows: + +* `secretmanager.secrets.create` for the target GCP project (i.e. no condition on the name prefix) +* `secretmanager.secrets.get`, `secretmanager.secrets.update`, `secretmanager.versions.access`, `secretmanager.versions.add` and `secretmanager.secrets.delete` for the target GCP project and for secrets that have a name starting with `zenml-` + +This can be achieved by creating two custom IAM roles and attaching them to the principal (e.g. user or service account) that will be used to access the GCP Secrets Manager API with a condition configured when attaching the second role to limit access to secrets with a name prefix of `zenml-`. The following `gcloud` CLI command examples can be used as a starting point: + +```bash +gcloud iam roles create ZenMLServerSecretsStoreCreator \ + --project \ + --title "ZenML Server Secrets Store Creator" \ + --description "Allow the ZenML Server to create new secrets" \ + --stage GA \ + --permissions "secretmanager.secrets.create" + +gcloud iam roles create ZenMLServerSecretsStoreEditor \ + --project \ + --title "ZenML Server Secrets Store Editor" \ + --description "Allow the ZenML Server to manage its secrets" \ + --stage GA \ + --permissions "secretmanager.secrets.get,secretmanager.secrets.update,secretmanager.versions.access,secretmanager.versions.add,secretmanager.secrets.delete" + +gcloud projects add-iam-policy-binding \ + --member serviceAccount: \ + --role projects//roles/ZenMLServerSecretsStoreCreator \ + --condition None + +# NOTE: use the GCP project NUMBER, not the project ID in the condition +gcloud projects add-iam-policy-binding \ + --member serviceAccount: \ + --role projects//roles/ZenMLServerSecretsStoreEditor \ + --condition 'title=limit_access_zenml,description="Limit access to secrets with prefix zenml-",expression=resource.name.startsWith("projects//secrets/zenml-")' +``` + The GCP Secrets Store uses the ZenML GCP Service Connector under the hood to authenticate with the GCP Secrets Manager API. This means that you can use any of the [authentication methods supported by the GCP Service Connector](https://docs.zenml.io/stacks-and-components/auth-management/gcp-service-connector#authentication-methods) to authenticate with the GCP Secrets Manager API. The following configuration options are supported: * **ZENML\_SECRETS\_STORE\_AUTH\_METHOD**: The GCP Service Connector authentication method to use (e.g. `service-account`). diff --git a/docs/book/deploying-zenml/zenml-self-hosted/deploy-with-helm.md b/docs/book/deploying-zenml/zenml-self-hosted/deploy-with-helm.md index 852938a014d..29181f0ea51 100644 --- a/docs/book/deploying-zenml/zenml-self-hosted/deploy-with-helm.md +++ b/docs/book/deploying-zenml/zenml-self-hosted/deploy-with-helm.md @@ -314,6 +314,29 @@ This method requires you to configure a DNS service like AWS Route 53 or Google Unless explicitly disabled or configured otherwise, the ZenML server will use the SQL database as a secrets store backend. If you want to use the AWS Secrets Manager instead, you need to configure it in the Helm values. Depending on where you deploy your ZenML server and how your Kubernetes cluster is configured, you may also need to provide the AWS credentials needed to access the AWS Secrets Manager API. +The minimum set of permissions that must be attached to the implicit or configured AWS credentials are: `secretsmanager:CreateSecret`, `secretsmanager:GetSecretValue`, `secretsmanager:DescribeSecret`, `secretsmanager:PutSecretValue`, `secretsmanager:TagResource` and `secretsmanager:DeleteSecret` and they must be associated with secrets that have a name starting with `zenml/` in the target region and account. The following IAM policy example can be used as a starting point: + +``` +{ + "Version": "2012-10-17", + "Statement": [ + { + "Sid": "ZenMLSecretsStore", + "Effect": "Allow", + "Action": [ + "secretsmanager:CreateSecret", + "secretsmanager:GetSecretValue", + "secretsmanager:DescribeSecret", + "secretsmanager:PutSecretValue", + "secretsmanager:TagResource", + "secretsmanager:DeleteSecret" + ], + "Resource": "arn:aws:secretsmanager:::secret:zenml/*" + } + ] +} +``` + The AWS Secrets Store uses the ZenML AWS Service Connector under the hood to authenticate with the AWS Secrets Manager API. This means that you can use any of the [authentication methods supported by the AWS Service Connector](https://docs.zenml.io/stacks-and-components/auth-management/aws-service-connector#authentication-methods) to authenticate with the AWS Secrets Manager API: @@ -347,6 +370,8 @@ The AWS Secrets Store uses the ZenML AWS Service Connector under the hood to aut aws_access_key_id: aws_secret_access_key: ``` + + {% endtab %} {% tab title="GCP" %} @@ -354,6 +379,40 @@ The AWS Secrets Store uses the ZenML AWS Service Connector under the hood to aut Unless explicitly disabled or configured otherwise, the ZenML server will use the SQL database as a secrets store backend. If you want to use the GCP Secrets Manager instead, you need to configure it in the Helm values. Depending on where you deploy your ZenML server and how your Kubernetes cluster is configured, you may also need to provide the GCP credentials needed to access the GCP Secrets Manager API. +The minimum set of permissions that must be attached to the implicit or configured GCP credentials are as follows: + +* `secretmanager.secrets.create` for the target GCP project (i.e. no condition on the name prefix) +* `secretmanager.secrets.get`, `secretmanager.secrets.update`, `secretmanager.versions.access`, `secretmanager.versions.add` and `secretmanager.secrets.delete` for the target GCP project and for secrets that have a name starting with `zenml-` + +This can be achieved by creating two custom IAM roles and attaching them to the principal (e.g. user or service account) that will be used to access the GCP Secrets Manager API with a condition configured when attaching the second role to limit access to secrets with a name prefix of `zenml-`. The following `gcloud` CLI command examples can be used as a starting point: + +```bash +gcloud iam roles create ZenMLServerSecretsStoreCreator \ + --project \ + --title "ZenML Server Secrets Store Creator" \ + --description "Allow the ZenML Server to create new secrets" \ + --stage GA \ + --permissions "secretmanager.secrets.create" + +gcloud iam roles create ZenMLServerSecretsStoreEditor \ + --project \ + --title "ZenML Server Secrets Store Editor" \ + --description "Allow the ZenML Server to manage its secrets" \ + --stage GA \ + --permissions "secretmanager.secrets.get,secretmanager.secrets.update,secretmanager.versions.access,secretmanager.versions.add,secretmanager.secrets.delete" + +gcloud projects add-iam-policy-binding \ + --member serviceAccount: \ + --role projects//roles/ZenMLServerSecretsStoreCreator \ + --condition None + +# NOTE: use the GCP project NUMBER, not the project ID in the condition +gcloud projects add-iam-policy-binding \ + --member serviceAccount: \ + --role projects//roles/ZenMLServerSecretsStoreEditor \ + --condition 'title=limit_access_zenml,description="Limit access to secrets with prefix zenml-",expression=resource.name.startsWith("projects//secrets/zenml-")' +``` + The GCP Secrets Store uses the ZenML GCP Service Connector under the hood to authenticate with the GCP Secrets Manager API. This means that you can use any of the [authentication methods supported by the GCP Service Connector](https://docs.zenml.io/stacks-and-components/auth-management/gcp-service-connector#authentication-methods) to authenticate with the GCP Secrets Manager API: @@ -408,6 +467,8 @@ The GCP Secrets Store uses the ZenML GCP Service Connector under the hood to aut iam.gke.io/gcp-service-account: @.iam.gserviceaccount.com ``` + + {% endtab %} {% tab title="Azure" %} diff --git a/docs/book/stacks-and-components/component-guide/artifact-stores/s3.md b/docs/book/stacks-and-components/component-guide/artifact-stores/s3.md index 6897a62ad5d..45cfcd16617 100644 --- a/docs/book/stacks-and-components/component-guide/artifact-stores/s3.md +++ b/docs/book/stacks-and-components/component-guide/artifact-stores/s3.md @@ -223,7 +223,7 @@ This method has some advantages over the implicit authentication method: * you can combine the S3 artifact store with other stack components that are not running in AWS > **Note**: When you create the IAM user for your AWS access key, please remember to grant the created IAM user -> permissions to read and write to your S3 bucket (the permissions are "s3:PutObject", "s3:GetObject", "s3:ListBucket", "s3:DeleteObject") +> permissions to read and write to your S3 bucket (i.e. at a minimum: `s3:PutObject`, `s3:GetObject`, `s3:ListBucket`, `s3:DeleteObject`) After having set up the IAM user and generated the access key, as described in the [AWS documentation](https://aws.amazon.com/premiumsupport/knowledge-center/create-access-key/), you can register the diff --git a/src/zenml/config/store_config.py b/src/zenml/config/store_config.py index 91ede9e6577..a80d10c79e3 100644 --- a/src/zenml/config/store_config.py +++ b/src/zenml/config/store_config.py @@ -14,9 +14,9 @@ """Functionality to support ZenML store configurations.""" from pathlib import PurePath -from typing import Optional +from typing import Any, Dict, Optional -from pydantic import BaseModel +from pydantic import BaseModel, root_validator from zenml.config.secrets_store_config import SecretsStoreConfiguration from zenml.enums import StoreType @@ -92,6 +92,29 @@ def supports_url_scheme(cls, url: str) -> bool: """ return True + @root_validator(pre=True) + def validate_secrets_store(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Validate the secrets store configuration. + + Args: + values: The values of the store configuration. + + Returns: + The values of the store configuration. + """ + if values.get("secrets_store") is None: + return values + + # Remove the legacy REST secrets store configuration since it is no + # longer supported/needed + secrets_store = values["secrets_store"] + if isinstance(secrets_store, dict): + secrets_store_type = secrets_store.get("type") + if secrets_store_type == "rest": + del values["secrets_store"] + + return values + class Config: """Pydantic configuration class.""" diff --git a/src/zenml/enums.py b/src/zenml/enums.py index 2a3d43173f6..d2657a4104b 100644 --- a/src/zenml/enums.py +++ b/src/zenml/enums.py @@ -133,15 +133,10 @@ class StoreType(StrEnum): class SecretsStoreType(StrEnum): - """Secrets Store Backend Types. + """Secrets Store Backend Types.""" - NOTE: this is a superset of the StoreType values because the set of secrets - store backends includes all the backends supported for zen stores. - """ - - NONE = "none" # indicates that the secrets store is disabled - SQL = StoreType.SQL.value - REST = StoreType.REST.value + NONE = "none" # indicates that no secrets store is used + SQL = "sql" AWS = "aws" GCP = "gcp" AZURE = "azure" diff --git a/src/zenml/integrations/gcp/service_connectors/gcp_service_connector.py b/src/zenml/integrations/gcp/service_connectors/gcp_service_connector.py index 917d137d6a1..577f935dbf0 100644 --- a/src/zenml/integrations/gcp/service_connectors/gcp_service_connector.py +++ b/src/zenml/integrations/gcp/service_connectors/gcp_service_connector.py @@ -36,7 +36,7 @@ from google.cloud import container_v1, storage from google.oauth2 import credentials as gcp_credentials from google.oauth2 import service_account as gcp_service_account -from pydantic import Field, SecretStr, validator +from pydantic import Field, SecretStr, root_validator, validator from zenml.constants import ( DOCKER_REGISTRY_RESOURCE_TYPE, @@ -86,6 +86,24 @@ class GCPUserAccountCredentials(AuthenticationConfig): "distribute the user account credentials JSON to clients instead.", ) + @root_validator(pre=True) + def validate_user_account_dict( + cls, values: Dict[str, Any] + ) -> Dict[str, Any]: + """Convert the user account credentials to JSON if given in dict format. + + Args: + values: The configuration values. + + Returns: + The validated configuration values. + """ + if isinstance(values.get("user_account_json"), dict): + values["user_account_json"] = json.dumps( + values["user_account_json"] + ) + return values + @validator("user_account_json") def validate_user_account_json(cls, v: SecretStr) -> SecretStr: """Validate the user account credentials JSON. @@ -146,6 +164,24 @@ class GCPServiceAccountCredentials(AuthenticationConfig): "distribute the service account key JSON to clients instead.", ) + @root_validator(pre=True) + def validate_service_account_dict( + cls, values: Dict[str, Any] + ) -> Dict[str, Any]: + """Convert the service account credentials to JSON if given in dict format. + + Args: + values: The configuration values. + + Returns: + The validated configuration values. + """ + if isinstance(values.get("service_account_json"), dict): + values["service_account_json"] = json.dumps( + values["service_account_json"] + ) + return values + @validator("service_account_json") def validate_service_account_json(cls, v: SecretStr) -> SecretStr: """Validate the service account credentials JSON. diff --git a/src/zenml/models/v2/core/secret.py b/src/zenml/models/v2/core/secret.py index d8401ba26fc..f1922a9bb96 100644 --- a/src/zenml/models/v2/core/secret.py +++ b/src/zenml/models/v2/core/secret.py @@ -84,6 +84,17 @@ class SecretUpdate(SecretRequest): default=None, title="The scope of the secret." ) + def get_secret_values_update(self) -> Dict[str, Optional[str]]: + """Returns a dictionary with the secret values to update. + + Returns: + A dictionary with the secret values to update. + """ + return { + k: v.get_secret_value() if v is not None else None + for k, v in self.values.items() + } + # ------------------ Response Model ------------------ @@ -197,6 +208,14 @@ def remove_secrets(self) -> None: """Removes all secret values from the secret but keep the keys.""" self.get_body().values = {k: None for k in self.values.keys()} + def set_secrets(self, values: Dict[str, str]) -> None: + """Sets the secret values of the secret. + + Args: + values: The secret values to set. + """ + self.get_body().values = {k: SecretStr(v) for k, v in values.items()} + # ------------------ Filter Model ------------------ diff --git a/src/zenml/zen_server/deploy/deployer.py b/src/zenml/zen_server/deploy/deployer.py index 0ef53f27cd3..4a87d777727 100644 --- a/src/zenml/zen_server/deploy/deployer.py +++ b/src/zenml/zen_server/deploy/deployer.py @@ -31,9 +31,6 @@ ServerProviderNotFoundError, ) from zenml.zen_stores.rest_zen_store import RestZenStoreConfiguration -from zenml.zen_stores.secrets_stores.rest_secrets_store import ( - RestSecretsStoreConfiguration, -) logger = get_logger(__name__) @@ -256,7 +253,6 @@ def connect_to_server( username=username, password=password, verify_ssl=verify_ssl, - secrets_store=RestSecretsStoreConfiguration(), ) if gc.store == store_config: diff --git a/src/zenml/zen_server/deploy/helm/templates/server-db-job.yaml b/src/zenml/zen_server/deploy/helm/templates/server-db-job.yaml index d3fbff15fc6..55471731b4a 100644 --- a/src/zenml/zen_server/deploy/helm/templates/server-db-job.yaml +++ b/src/zenml/zen_server/deploy/helm/templates/server-db-job.yaml @@ -89,8 +89,6 @@ spec: - name: ZENML_SECRETS_STORE_REGION_NAME value: {{ .Values.zenml.secretsStore.aws.region_name | quote }} {{- end }} - - name: ZENML_SECRETS_STORE_SECRET_LIST_REFRESH_TIMEOUT - value: {{ .Values.zenml.secretsStore.aws.secret_list_refresh_timeout | quote }} {{- else if eq .Values.zenml.secretsStore.type "gcp" }} - name: ZENML_SECRETS_STORE_AUTH_METHOD value: {{ .Values.zenml.secretsStore.gcp.authMethod | quote }} diff --git a/src/zenml/zen_server/deploy/helm/templates/server-deployment.yaml b/src/zenml/zen_server/deploy/helm/templates/server-deployment.yaml index 1ff1a3ad563..1f6d9925790 100644 --- a/src/zenml/zen_server/deploy/helm/templates/server-deployment.yaml +++ b/src/zenml/zen_server/deploy/helm/templates/server-deployment.yaml @@ -167,8 +167,6 @@ spec: - name: ZENML_SECRETS_STORE_REGION_NAME value: {{ .Values.zenml.secretsStore.aws.region_name | quote }} {{- end }} - - name: ZENML_SECRETS_STORE_SECRET_LIST_REFRESH_TIMEOUT - value: {{ .Values.zenml.secretsStore.aws.secret_list_refresh_timeout | quote }} {{- else if eq .Values.zenml.secretsStore.type "gcp" }} - name: ZENML_SECRETS_STORE_AUTH_METHOD value: {{ .Values.zenml.secretsStore.gcp.authMethod | quote }} diff --git a/src/zenml/zen_server/deploy/helm/values.yaml b/src/zenml/zen_server/deploy/helm/values.yaml index 6eb2974fa55..be2388b01c3 100644 --- a/src/zenml/zen_server/deploy/helm/values.yaml +++ b/src/zenml/zen_server/deploy/helm/values.yaml @@ -319,16 +319,6 @@ zenml: aws_secret_access_key: aws_session_token: - # The AWS Secrets Manager has a known issue where it does not immediately - # reflect new and updated secrets in the `list_secrets` results. To work - # around this issue, you can set this value to a non-zero value to - # get the ZenML server to wait after creating or updating an AWS secret - # until the changes are reflected in the secrets returned by - # `list_secrets` or the number of seconds specified by this value has - # elapsed. Should not be set to a high value as it may cause thread - # starvation in the ZenML server on high load. - secret_list_refresh_timeout: 0 - # GCP secrets store configuration. Only relevant if the `gcp` secrets store # type is configured. diff --git a/src/zenml/zen_stores/base_zen_store.py b/src/zenml/zen_stores/base_zen_store.py index 710a6d46c43..311e64c446e 100644 --- a/src/zenml/zen_stores/base_zen_store.py +++ b/src/zenml/zen_stores/base_zen_store.py @@ -16,10 +16,7 @@ from abc import ABC from typing import ( Any, - Callable, ClassVar, - Dict, - List, Optional, Tuple, Type, @@ -56,12 +53,6 @@ UserResponse, WorkspaceResponse, ) -from zenml.utils.proxy_utils import make_proxy_class -from zenml.zen_stores.enums import StoreEvent -from zenml.zen_stores.secrets_stores.base_secrets_store import BaseSecretsStore -from zenml.zen_stores.secrets_stores.secrets_store_interface import ( - SecretsStoreInterface, -) from zenml.zen_stores.secrets_stores.sql_secrets_store import ( SqlSecretsStoreConfiguration, ) @@ -70,11 +61,9 @@ logger = get_logger(__name__) -@make_proxy_class(SecretsStoreInterface, "_secrets_store") class BaseZenStore( BaseModel, ZenStoreInterface, - SecretsStoreInterface, ABC, ): """Base class for accessing and persisting ZenML core objects. @@ -84,8 +73,6 @@ class BaseZenStore( """ config: StoreConfiguration - _secrets_store: Optional[BaseSecretsStore] = None - _event_handlers: Dict[StoreEvent, List[Callable[..., Any]]] = {} TYPE: ClassVar[StoreType] CONFIG_TYPE: ClassVar[Type[StoreConfiguration]] @@ -243,23 +230,6 @@ def create_store( **kwargs, ) - secrets_store_config = store.config.secrets_store - - # Initialize the secrets store - if ( - secrets_store_config - and secrets_store_config.type != SecretsStoreType.NONE - ): - secrets_store_class = BaseSecretsStore.get_store_class( - secrets_store_config - ) - store._secrets_store = secrets_store_class( - zen_store=store, - config=secrets_store_config, - ) - # Update the config with the actual secrets store config - # to reflect the default values in the saved configuration - store.config.secrets_store = store._secrets_store.config return store @staticmethod @@ -307,15 +277,6 @@ def type(self) -> StoreType: """ return self.TYPE - @property - def secrets_store(self) -> Optional["BaseSecretsStore"]: - """The secrets store associated with this store. - - Returns: - The secrets store associated with this store. - """ - return self._secrets_store - def validate_active_config( self, active_workspace_name_or_id: Optional[Union[str, UUID]] = None, @@ -408,18 +369,21 @@ def get_store_info(self) -> ServerModel: Returns: Information about the store. """ + from zenml.zen_stores.sql_zen_store import SqlZenStore + server_config = ServerConfiguration.get_server_config() deployment_type = server_config.deployment_type auth_scheme = server_config.auth_scheme + secrets_store_type = SecretsStoreType.NONE + if isinstance(self, SqlZenStore): + secrets_store_type = self.secrets_store.type return ServerModel( id=GlobalConfiguration().user_id, version=zenml.__version__, deployment_type=deployment_type, database_type=ServerDatabaseType.OTHER, debug=IS_DEBUG_ENV, - secrets_store_type=self.secrets_store.type - if self.secrets_store - else SecretsStoreType.NONE, + secrets_store_type=secrets_store_type, auth_scheme=auth_scheme, ) @@ -487,42 +451,6 @@ def _get_default_stack( ) return default_stacks.items[0] - # -------------- - # Event Handlers - # -------------- - - def register_event_handler( - self, - event: StoreEvent, - handler: Callable[..., Any], - ) -> None: - """Register an external event handler. - - The handler will be called when the store event is triggered. - - Args: - event: The event to register the handler for. - handler: The handler function to register. - """ - self._event_handlers.setdefault(event, []).append(handler) - - def _trigger_event(self, event: StoreEvent, **kwargs: Any) -> None: - """Trigger an event and call all registered handlers. - - Args: - event: The event to trigger. - **kwargs: The event arguments. - """ - for handler in self._event_handlers.get(event, []): - try: - handler(event, **kwargs) - except Exception as e: - logger.error( - f"Silently ignoring error caught while triggering event " - f"store handler for event {event.value}: {e}", - exc_info=True, - ) - def get_external_user(self, user_id: UUID) -> UserResponse: """Get a user by external ID. diff --git a/src/zenml/zen_stores/enums.py b/src/zenml/zen_stores/enums.py deleted file mode 100644 index f35cfde6e76..00000000000 --- a/src/zenml/zen_stores/enums.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. -"""Zen Store enums.""" - -from zenml.utils.enum_utils import StrEnum - - -class StoreEvent(StrEnum): - """Events that can be triggered by the store.""" - - # Triggered just before deleting a workspace. The workspace ID is passed as - # a `workspace_id` UUID argument. - WORKSPACE_DELETED = "workspace_deleted" - # Triggered just before deleting a user. The user ID is passed as - # a `user_id` UUID argument. - USER_DELETED = "user_deleted" diff --git a/src/zenml/zen_stores/migrations/alembic.py b/src/zenml/zen_stores/migrations/alembic.py index 731ccbd76b4..e2c2286a325 100644 --- a/src/zenml/zen_stores/migrations/alembic.py +++ b/src/zenml/zen_stores/migrations/alembic.py @@ -19,7 +19,15 @@ """ from pathlib import Path -from typing import Any, Callable, Dict, List, Optional, Sequence, Union +from typing import ( + Any, + Callable, + Dict, + List, + Optional, + Sequence, + Union, +) from alembic.config import Config from alembic.runtime.environment import EnvironmentContext diff --git a/src/zenml/zen_stores/migrations/env.py b/src/zenml/zen_stores/migrations/env.py index 7b60835a458..4923a3a4f66 100644 --- a/src/zenml/zen_stores/migrations/env.py +++ b/src/zenml/zen_stores/migrations/env.py @@ -37,7 +37,7 @@ def run_migrations() -> None: if store_cfg is None or store_cfg.type != StoreType.SQL: store_cfg = GlobalConfiguration().get_default_store() - store = SqlZenStore( # type: ignore + store = SqlZenStore( config=store_cfg, skip_default_registrations=True, skip_migrations=True, diff --git a/src/zenml/zen_stores/migrations/versions/1ac1b9c04da1_make_secrets_values_optional.py b/src/zenml/zen_stores/migrations/versions/1ac1b9c04da1_make_secrets_values_optional.py new file mode 100644 index 00000000000..9749e8f8e5c --- /dev/null +++ b/src/zenml/zen_stores/migrations/versions/1ac1b9c04da1_make_secrets_values_optional.py @@ -0,0 +1,35 @@ +"""make secrets values optional [1ac1b9c04da1]. + +Revision ID: 1ac1b9c04da1 +Revises: 0.54.0 +Create Date: 2023-12-22 23:06:32.466128 + +""" +import sqlalchemy as sa +from alembic import op + +# revision identifiers, used by Alembic. +revision = "1ac1b9c04da1" +down_revision = "0.54.0" +branch_labels = None +depends_on = None + + +def upgrade() -> None: + """Upgrade database schema and/or data, creating a new revision.""" + # ### commands auto generated by Alembic - please adjust! ### + with op.batch_alter_table("secret", schema=None) as batch_op: + batch_op.alter_column("values", existing_type=sa.TEXT(), nullable=True) + + # ### end Alembic commands ### + + +def downgrade() -> None: + """Downgrade database schema and/or data back to the previous revision.""" + # ### commands auto generated by Alembic - please adjust! ### + with op.batch_alter_table("secret", schema=None) as batch_op: + batch_op.alter_column( + "values", existing_type=sa.TEXT(), nullable=False + ) + + # ### end Alembic commands ### diff --git a/src/zenml/zen_stores/migrations/versions/7b651bf6822e_track_secrets_in_db.py b/src/zenml/zen_stores/migrations/versions/7b651bf6822e_track_secrets_in_db.py new file mode 100644 index 00000000000..d7732884d10 --- /dev/null +++ b/src/zenml/zen_stores/migrations/versions/7b651bf6822e_track_secrets_in_db.py @@ -0,0 +1,715 @@ +"""track secrets in db [7b651bf6822e]. + +Revision ID: 7b651bf6822e +Revises: 1ac1b9c04da1 +Create Date: 2023-12-22 20:56:18.131906 + +""" + +from abc import abstractmethod +from datetime import datetime +from typing import Any, Dict, List, Optional +from uuid import UUID + +import sqlalchemy as sa +from alembic import op +from pydantic import BaseModel + +from zenml.enums import StoreType +from zenml.logger import get_logger + +logger = get_logger(__name__) + +# revision identifiers, used by Alembic. + + +revision = "7b651bf6822e" +down_revision = "1ac1b9c04da1" +branch_labels = None +depends_on = None + + +ZENML_SECRET_LABEL = "zenml" +ZENML_SECRET_ID_LABEL = "zenml_secret_id" +ZENML_SECRET_NAME_LABEL = "zenml_secret_name" +ZENML_SECRET_SCOPE_LABEL = "zenml_secret_scope" +ZENML_SECRET_USER_LABEL = "zenml_secret_user" +ZENML_SECRET_WORKSPACE_LABEL = "zenml_secret_workspace" + + +class ZenMLSecretMetadata(BaseModel): + """ZenML secret metadata. + + This class reflects the structure of the ZenML secret metadata stored in the + backend secret (e.g. in secret tags/labels) at the time when the secret + metadata was migrated to the database. It is roughly aligned to the + structure of the ZenML SecretResponse model as it was at that time. + """ + + id: UUID + name: str + user_id: UUID + workspace_id: UUID + created: datetime + updated: datetime + scope: str + + +class BaseSecretsStoreBackend(BaseModel): + """Base class for accessing and listing ZenML secrets.""" + + server_id: UUID + client: Any + + def _get_secret_metadata( + self, + secret_id: Optional[UUID] = None, + ) -> Dict[str, str]: + """Get a dictionary with metadata that can be used as tags/labels. + + This utility method can be used with Secrets Managers that can + associate metadata (e.g. tags, labels) with a secret. The metadata can + be configured alongside each secret. + + NOTE: the ZENML_SECRET_LABEL is always included in the metadata to + distinguish ZenML secrets from other secrets that might be stored in + the same backend, as well as to distinguish between different ZenML + deployments using the same backend. Its value is set to the ZenML + deployment ID. + + Args: + secret_id: Optional secret ID to include in the metadata. + + Returns: + Dictionary with secret metadata information. + """ + # Always include the main ZenML label to distinguish ZenML secrets + # from other secrets that might be stored in the same backend and + # to distinguish between different ZenML deployments using the same + # backend. + metadata: Dict[str, str] = {ZENML_SECRET_LABEL: str(self.server_id)} + + # Include the secret ID if provided. + if secret_id is not None: + metadata[ZENML_SECRET_ID_LABEL] = str(secret_id) + + return metadata + + def _create_secret_from_metadata( + self, + metadata: Dict[str, str], + created: datetime, + updated: datetime, + ) -> ZenMLSecretMetadata: + """Create a ZenML secret metadata object from metadata stored in the secrets store backend. + + Args: + metadata: ZenML secret metadata collected from the backend secret + (e.g. from secret tags/labels). + created: The secret creation time. + updated: The secret last updated time. + + Returns: + The ZenML secret. + + Raises: + KeyError: If the secret does not have the required metadata, if it + is not managed by this ZenML instance or if it is linked to a + user or workspace that no longer exists. + """ + # Double-check that the secret is managed by this ZenML instance. + if metadata.get(ZENML_SECRET_LABEL) != str(self.server_id): + raise KeyError("Secret is not managed by this ZenML instance") + + # Recover the ZenML secret fields from the input secret metadata. + try: + secret_id = UUID(metadata[ZENML_SECRET_ID_LABEL]) + name = metadata[ZENML_SECRET_NAME_LABEL] + scope = metadata[ZENML_SECRET_SCOPE_LABEL] + workspace_id = UUID(metadata[ZENML_SECRET_WORKSPACE_LABEL]) + user_id = UUID(metadata[ZENML_SECRET_USER_LABEL]) + except KeyError as e: + raise KeyError( + f"Secret could not be retrieved: missing required metadata: {e}" + ) + + secret_model = ZenMLSecretMetadata( + id=secret_id, + name=name, + workspace_id=workspace_id, + user_id=user_id, + created=created, + updated=updated, + scope=scope, + ) + + return secret_model + + @abstractmethod + def list_secrets( + self, + ) -> List[ZenMLSecretMetadata]: + """List all ZenML secrets in the secrets store backend. + + Returns: + A list of all secrets. + """ + + +class AWSSecretsStoreBackend(BaseSecretsStoreBackend): + """AWS ZenML secrets store backend.""" + + @staticmethod + def _get_aws_secret_filters( + metadata: Dict[str, str], + ) -> List[Dict[str, str]]: + """Convert ZenML secret metadata to AWS secret filters. + + Args: + metadata: The ZenML secret metadata. + + Returns: + The AWS secret filters. + """ + aws_filters: List[Dict[str, Any]] = [] + for k, v in metadata.items(): + aws_filters.append( + { + "Key": "tag-key", + "Values": [ + k, + ], + } + ) + aws_filters.append( + { + "Key": "tag-value", + "Values": [ + str(v), + ], + } + ) + + return aws_filters + + def list_secrets( + self, + ) -> List[ZenMLSecretMetadata]: + """List all ZenML secrets in the AWS secrets store backend. + + Returns: + A list of all secrets. + + Raises: + RuntimeError: If the AWS Secrets Manager API returns an unexpected + error. + """ + from botocore.exceptions import ClientError + + # The metadata will always contain at least the filter criteria + # required to exclude everything but AWS secrets that belong to the + # current ZenML deployment. + metadata = self._get_secret_metadata() + aws_filters = self._get_aws_secret_filters(metadata) + + results: List[ZenMLSecretMetadata] = [] + + try: + # AWS Secrets Manager API pagination is wrapped around the + # `list_secrets` method call. We use it because we need to fetch all + # secrets matching the (partial) filter that we set up. Note that + # the pagination used here has nothing to do with the pagination + # that we do for the method caller. + paginator = self.client.get_paginator("list_secrets") + pages = paginator.paginate( + Filters=aws_filters, + PaginationConfig={ + "PageSize": 100, + }, + ) + + for page in pages: + for secret in page["SecretList"]: + try: + # Convert the AWS secret tags to a metadata dictionary. + unpacked_metadata: Dict[str, str] = { + tag["Key"]: tag["Value"] for tag in secret["Tags"] + } + + secret_model = self._create_secret_from_metadata( + metadata=unpacked_metadata, + created=secret["CreatedDate"], + updated=secret["LastChangedDate"], + ) + except KeyError: + # The _convert_aws_secret method raises a KeyError + # if the secret is not well formed. Here we pretend that + # the secret does not exist. + continue + + results.append(secret_model) + except ClientError as e: + raise RuntimeError(f"Error listing AWS secrets: {e}") + + return results + + +ZENML_GCP_DATE_FORMAT_STRING = "%Y-%m-%d-%H-%M-%S" +ZENML_GCP_SECRET_CREATED_KEY = "zenml-secret-created" +ZENML_GCP_SECRET_UPDATED_KEY = "zenml-secret-updated" + + +class GCPSecretsStoreBackend(BaseSecretsStoreBackend): + """GCP ZenML secrets store backend.""" + + project_id: str + + @property + def parent_name(self) -> str: + """Construct the GCP parent path to the secret manager. + + Returns: + The parent path to the secret manager + """ + return f"projects/{self.project_id}" + + def _convert_gcp_secret( + self, + labels: Dict[str, str], + ) -> ZenMLSecretMetadata: + """Create a ZenML secret model from data stored in an GCP secret. + + If the GCP secret cannot be converted, the method acts as if the + secret does not exist and raises a KeyError. + + Args: + labels: The GCP secret labels. + + Returns: + The ZenML secret model. + + Raises: + KeyError: if the GCP secret cannot be converted. + """ + # Recover the ZenML secret metadata from the AWS secret tags. + + # The GCP secret labels do not really behave like a dictionary: when + # a key is not found, it does not raise a KeyError, but instead + # returns an empty string. That's why we make this conversion. + label_dict = dict(labels) + + try: + created = datetime.strptime( + label_dict[ZENML_GCP_SECRET_CREATED_KEY], + ZENML_GCP_DATE_FORMAT_STRING, + ) + updated = datetime.strptime( + label_dict[ZENML_GCP_SECRET_UPDATED_KEY], + ZENML_GCP_DATE_FORMAT_STRING, + ) + except KeyError as e: + raise KeyError( + f"Invalid GCP secret: missing required tag '{e}'" + ) from e + + return self._create_secret_from_metadata( + metadata=label_dict, + created=created, + updated=updated, + ) + + def list_secrets(self) -> List[ZenMLSecretMetadata]: + """List all secrets. + + Returns: + A list of all secrets. + + Raises: + RuntimeError: if the GCP Secrets Manager API returns an unexpected + error. + """ + from google.cloud.secretmanager import SecretManagerServiceClient + + assert isinstance(self.client, SecretManagerServiceClient) + + try: + # get all the secrets and their labels from GCP + secrets = [] + for secret in self.client.list_secrets( + request={ + "parent": self.parent_name, + "filter": "", + } + ): + try: + secrets.append(self._convert_gcp_secret(secret.labels)) + except KeyError: + # keep going / ignore if this secret version doesn't exist + # or isn't a ZenML secret + continue + except Exception as e: + raise RuntimeError(f"Error listing GCP secrets: {e}") from e + + return secrets + + +ZENML_AZURE_SECRET_CREATED_KEY = "zenml_secret_created" +ZENML_AZURE_SECRET_UPDATED_KEY = "zenml_secret_updated" + + +class AzureSecretsStoreBackend(BaseSecretsStoreBackend): + """Azure ZenML secrets store backend.""" + + def _convert_azure_secret( + self, + tags: Dict[str, str], + ) -> ZenMLSecretMetadata: + """Create a ZenML secret model from data stored in an Azure secret. + + If the Azure secret cannot be converted, the method acts as if the + secret does not exist and raises a KeyError. + + Args: + tags: The Azure secret tags. + + Returns: + The ZenML secret. + + Raises: + KeyError: if the Azure secret cannot be converted. + """ + try: + created = datetime.fromisoformat( + tags[ZENML_AZURE_SECRET_CREATED_KEY], + ) + updated = datetime.fromisoformat( + tags[ZENML_AZURE_SECRET_UPDATED_KEY], + ) + except KeyError as e: + raise KeyError( + f"Secret could not be retrieved: missing required metadata: {e}" + ) + + return self._create_secret_from_metadata( + metadata=tags, + created=created, + updated=updated, + ) + + def list_secrets(self) -> List[ZenMLSecretMetadata]: + """List all secrets. + + Returns: + A list of all secrets. + + Raises: + RuntimeError: If the Azure Key Vault API returns an unexpected + error. + """ + from azure.core.exceptions import HttpResponseError + from azure.keyvault.secrets import SecretClient + + assert isinstance(self.client, SecretClient) + + results: List[ZenMLSecretMetadata] = [] + + try: + all_secrets = self.client.list_properties_of_secrets() + for secret_property in all_secrets: + try: + # NOTE: we do not include the secret values in the + # response. We would need a separate API call to fetch + # them for each secret, which would be very inefficient + # anyway. + assert secret_property.tags is not None + secret_model = self._convert_azure_secret( + tags=secret_property.tags, + ) + except KeyError: + # The _convert_azure_secret method raises a KeyError + # if the secret is tied to a workspace or user that no + # longer exists or if it is otherwise not valid. Here we + # pretend that the secret does not exist. + continue + + results.append(secret_model) + except HttpResponseError as e: + raise RuntimeError(f"Error listing Azure Key Vault secrets: {e}") + + return results + + +HVAC_ZENML_SECRET_NAME_PREFIX = "zenml" +ZENML_VAULT_SECRET_METADATA_KEY = "zenml_secret_metadata" +ZENML_VAULT_SECRET_CREATED_KEY = "zenml_secret_created" +ZENML_VAULT_SECRET_UPDATED_KEY = "zenml_secret_updated" + + +class HashiCorpVaultSecretsStoreBackend(BaseSecretsStoreBackend): + """HashiCorp Vault ZenML secrets store backend.""" + + def _convert_vault_secret( + self, + vault_secret: Dict[str, Any], + ) -> ZenMLSecretMetadata: + """Create a ZenML secret model from data stored in an HashiCorp Vault secret. + + If the HashiCorp Vault secret cannot be converted, the method acts as if + the secret does not exist and raises a KeyError. + + Args: + vault_secret: The HashiCorp Vault secret in JSON form. + + Returns: + The ZenML secret. + + Raises: + KeyError: if the HashiCorp Vault secret cannot be converted. + """ + try: + metadata = vault_secret[ZENML_VAULT_SECRET_METADATA_KEY] + created = datetime.fromisoformat( + vault_secret[ZENML_VAULT_SECRET_CREATED_KEY], + ) + updated = datetime.fromisoformat( + vault_secret[ZENML_VAULT_SECRET_UPDATED_KEY], + ) + except (KeyError, ValueError) as e: + raise KeyError( + f"Secret could not be retrieved: missing required metadata: {e}" + ) + + return self._create_secret_from_metadata( + metadata=metadata, + created=created, + updated=updated, + ) + + def list_secrets(self) -> List[ZenMLSecretMetadata]: + """List all secrets. + + Note that returned secrets do not include any secret values. To fetch + the secret values, use `get_secret`. + + Returns: + A list of all secrets. + + Raises: + RuntimeError: If the HashiCorp Vault API returns an unexpected + error. + """ + from hvac import Client # type: ignore[import-untyped] + from hvac.exceptions import ( # type: ignore[import-untyped] + InvalidPath, + VaultError, + ) + + assert isinstance(self.client, Client) + + results: List[ZenMLSecretMetadata] = [] + + try: + # List all ZenML secrets in the Vault + all_secrets = ( + self.client.secrets.kv.v2.list_secrets( + path=HVAC_ZENML_SECRET_NAME_PREFIX + ) + .get("data", {}) + .get("keys", []) + ) + except InvalidPath: + # no secrets created yet + pass + except VaultError as e: + raise RuntimeError(f"Error listing HashiCorp Vault secrets: {e}") + else: + # Convert the Vault secrets to ZenML secrets + for secret_uuid in all_secrets: + vault_secret_id = ( + f"{HVAC_ZENML_SECRET_NAME_PREFIX}/{secret_uuid}" + ) + try: + vault_secret = ( + self.client.secrets.kv.v2.read_secret( + path=vault_secret_id + ) + .get("data", {}) + .get("data", {}) + ) + except (InvalidPath, VaultError) as e: + logger.warning( + f"Error fetching secret with ID {vault_secret_id}: {e}", + ) + continue + + try: + secret_model = self._convert_vault_secret( + vault_secret, + ) + except KeyError as e: + # The _convert_vault_secret method raises a KeyError + # if the secret is not valid. Here we + # pretend that the secret does not exist. + logger.warning( + f"Error fetching secret with ID {vault_secret_id}: {e}", + ) + continue + + results.append(secret_model) + + return results + + +def upgrade() -> None: + """Upgrade database schema and/or data, creating a new revision. + + Raises: + NotImplementedError: If the secrets store type is not supported. + """ + from zenml.config.global_config import GlobalConfiguration + from zenml.enums import SecretsStoreType + from zenml.zen_stores.sql_zen_store import SqlZenStore + + store_cfg = GlobalConfiguration().store + + if store_cfg is None or store_cfg.type != StoreType.SQL: + return + + # We create a new SqlZenStore instance with the same configuration as the + # one used to run migrations, but we skip the default registrations and + # migrations. This is because we can't use the same store instance that is + # used to run migrations because it would create an infinite loop. + store = SqlZenStore( + config=store_cfg, + skip_default_registrations=True, + skip_migrations=True, + ) + + secrets_store = store.secrets_store + + if secrets_store.TYPE == SecretsStoreType.SQL: + # If the secrets store is already a SQL secrets store, we don't need + # to transfer secrets from the external secrets store to the db. + return + + # Transfer secrets from the external secrets store to the db + + conn = op.get_bind() + meta = sa.MetaData(bind=op.get_bind()) + meta.reflect(only=("secret", "user", "workspace", "identity")) + secrets = sa.Table("secret", meta) + users = sa.Table("user", meta) + workspaces = sa.Table("workspace", meta) + identity = sa.Table("identity", meta) + + # Extract the ZenML deployment ID from the identity table + server_id = UUID(conn.execute(identity.select()).first()["id"]) # type: ignore[index] + + # Initialize the secrets store backend based on the backend client extracted + # from the secrets store. + backend: BaseSecretsStoreBackend + if secrets_store.TYPE == SecretsStoreType.AWS: + from zenml.zen_stores.secrets_stores.aws_secrets_store import ( + AWSSecretsStore, + ) + + assert isinstance(secrets_store, AWSSecretsStore) + backend = AWSSecretsStoreBackend( + server_id=server_id, + client=secrets_store.client, + ) + elif secrets_store.TYPE == SecretsStoreType.GCP: + from zenml.zen_stores.secrets_stores.gcp_secrets_store import ( + GCPSecretsStore, + ) + + assert isinstance(secrets_store, GCPSecretsStore) + backend = GCPSecretsStoreBackend( + server_id=server_id, + client=secrets_store.client, + project_id=secrets_store.config.project_id, + ) + elif secrets_store.TYPE == SecretsStoreType.AZURE: + from zenml.zen_stores.secrets_stores.azure_secrets_store import ( + AzureSecretsStore, + ) + + assert isinstance(secrets_store, AzureSecretsStore) + backend = AzureSecretsStoreBackend( + server_id=server_id, + client=secrets_store.client, + ) + elif secrets_store.TYPE == SecretsStoreType.HASHICORP: + from zenml.zen_stores.secrets_stores.hashicorp_secrets_store import ( + HashiCorpVaultSecretsStore, + ) + + assert isinstance(secrets_store, HashiCorpVaultSecretsStore) + backend = HashiCorpVaultSecretsStoreBackend( + server_id=server_id, + client=secrets_store.client, + ) + else: + raise NotImplementedError( + f"Secrets store type {secrets_store.TYPE} not supported" + ) + + # List all secrets in the backend + external_secrets = backend.list_secrets() + + for secret in external_secrets: + logger.info( + f"Transferring secret '{secret.name}' from the " + f"{secrets_store.type} secrets store to the db." + ) + # Check if a user with the given ID exists in the db + user = conn.execute( + users.select().where( + users.c.id == str(secret.user_id).replace("-", "") + ) + ).first() + if user is None: + logger.warning( + f"User with ID {secret.user_id} not found in the db. " + f"Skipping secret '{secret.name}'." + ) + continue + # Check if a workspace with the given ID exists in the db + workspace = conn.execute( + workspaces.select().where( + workspaces.c.id == str(secret.workspace_id).replace("-", "") + ) + ).first() + if workspace is None: + logger.warning( + f"Workspace with ID {secret.workspace_id} not found in the db. " + f"Skipping secret '{secret.name}'." + ) + continue + + # Check if there is already a secret with the same ID in the db + # and delete it if there is. This can happen for example if the user has + # previously migrated the secrets from the db to an external secrets + # store and forgot to delete the secrets from the db. + conn.execute( + secrets.delete().where( + secrets.c.id == str(secret.id).replace("-", "") + ) + ) + conn.execute( + secrets.insert().values( + id=str(secret.id).replace("-", ""), + created=secret.created, + updated=secret.updated, + name=secret.name, + scope=secret.scope, + values=None, + workspace_id=str(secret.workspace_id).replace("-", ""), + user_id=str(secret.user_id).replace("-", ""), + ) + ) + + +def downgrade() -> None: + """Downgrade database schema and/or data back to the previous revision.""" + pass diff --git a/src/zenml/zen_stores/rest_zen_store.py b/src/zenml/zen_stores/rest_zen_store.py index d2cb81cad98..50e7c376060 100644 --- a/src/zenml/zen_stores/rest_zen_store.py +++ b/src/zenml/zen_stores/rest_zen_store.py @@ -37,7 +37,6 @@ import zenml from zenml.analytics import source_context from zenml.config.global_config import GlobalConfiguration -from zenml.config.secrets_store_config import SecretsStoreConfiguration from zenml.config.store_config import StoreConfiguration from zenml.constants import ( API, @@ -69,6 +68,7 @@ RUN_METADATA, RUNS, SCHEDULES, + SECRETS, SERVICE_ACCOUNTS, SERVICE_CONNECTOR_CLIENT, SERVICE_CONNECTOR_RESOURCES, @@ -85,7 +85,6 @@ ) from zenml.enums import ( OAuthGrantTypes, - SecretsStoreType, StoreType, ) from zenml.exceptions import ( @@ -164,6 +163,10 @@ ScheduleRequest, ScheduleResponse, ScheduleUpdate, + SecretFilter, + SecretRequest, + SecretResponse, + SecretUpdate, ServerModel, ServiceAccountFilter, ServiceAccountRequest, @@ -205,9 +208,6 @@ ) from zenml.zen_server.exceptions import exception_from_response from zenml.zen_stores.base_zen_store import BaseZenStore -from zenml.zen_stores.secrets_stores.rest_secrets_store import ( - RestSecretsStoreConfiguration, -) logger = get_logger(__name__) @@ -228,9 +228,6 @@ class RestZenStoreConfiguration(StoreConfiguration): Attributes: type: The type of the store. - secrets_store: The configuration of the secrets store to use. - This defaults to a REST secrets store that extends the REST ZenML - store. username: The username to use to connect to the Zen server. password: The password to use to connect to the Zen server. api_key: The service account API key to use to connect to the Zen @@ -247,8 +244,6 @@ class RestZenStoreConfiguration(StoreConfiguration): type: StoreType = StoreType.REST - secrets_store: Optional[SecretsStoreConfiguration] = None - username: Optional[str] = None password: Optional[str] = None api_key: Optional[str] = None @@ -256,31 +251,6 @@ class RestZenStoreConfiguration(StoreConfiguration): verify_ssl: Union[bool, str] = True http_timeout: int = DEFAULT_HTTP_TIMEOUT - @validator("secrets_store") - def validate_secrets_store( - cls, secrets_store: Optional[SecretsStoreConfiguration] - ) -> SecretsStoreConfiguration: - """Ensures that the secrets store uses an associated REST secrets store. - - Args: - secrets_store: The secrets store config to be validated. - - Returns: - The validated secrets store config. - - Raises: - ValueError: If the secrets store is not of type REST. - """ - if secrets_store is None: - secrets_store = RestSecretsStoreConfiguration() - elif secrets_store.type != SecretsStoreType.REST: - raise ValueError( - "The secrets store associated with a REST zen store must be " - f"of type REST, but is of type {secrets_store.type}." - ) - - return secrets_store - @root_validator def validate_credentials(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Validates the credentials provided in the values dictionary. @@ -1683,6 +1653,126 @@ def delete_schedule(self, schedule_id: UUID) -> None: route=SCHEDULES, ) + # --------------------------- Secrets --------------------------- + + def create_secret(self, secret: SecretRequest) -> SecretResponse: + """Creates a new secret. + + The new secret is also validated against the scoping rules enforced in + the secrets store: + + - only one workspace-scoped secret with the given name can exist + in the target workspace. + - only one user-scoped secret with the given name can exist in the + target workspace for the target user. + + Args: + secret: The secret to create. + + Returns: + The newly created secret. + """ + return self._create_workspace_scoped_resource( + resource=secret, + route=SECRETS, + response_model=SecretResponse, + ) + + def get_secret( + self, secret_id: UUID, hydrate: bool = True + ) -> SecretResponse: + """Get a secret by ID. + + Args: + secret_id: The ID of the secret to fetch. + hydrate: Flag deciding whether to hydrate the output model(s) + by including metadata fields in the response. + + Returns: + The secret. + """ + return self._get_resource( + resource_id=secret_id, + route=SECRETS, + response_model=SecretResponse, + params={"hydrate": hydrate}, + ) + + def list_secrets( + self, secret_filter_model: SecretFilter, hydrate: bool = False + ) -> Page[SecretResponse]: + """List all secrets matching the given filter criteria. + + Note that returned secrets do not include any secret values. To fetch + the secret values, use `get_secret`. + + Args: + secret_filter_model: All filter parameters including pagination + params. + hydrate: Flag deciding whether to hydrate the output model(s) + by including metadata fields in the response. + + Returns: + A list of all secrets matching the filter criteria, with pagination + information and sorted according to the filter criteria. The + returned secrets do not include any secret values, only metadata. To + fetch the secret values, use `get_secret` individually with each + secret. + """ + return self._list_paginated_resources( + route=SECRETS, + response_model=SecretResponse, + filter_model=secret_filter_model, + params={"hydrate": hydrate}, + ) + + def update_secret( + self, secret_id: UUID, secret_update: SecretUpdate + ) -> SecretResponse: + """Updates a secret. + + Secret values that are specified as `None` in the update that are + present in the existing secret are removed from the existing secret. + Values that are present in both secrets are overwritten. All other + values in both the existing secret and the update are kept (merged). + + If the update includes a change of name or scope, the scoping rules + enforced in the secrets store are used to validate the update: + + - only one workspace-scoped secret with the given name can exist + in the target workspace. + - only one user-scoped secret with the given name can exist in the + target workspace for the target user. + + Args: + secret_id: The ID of the secret to be updated. + secret_update: The update to be applied. + + Returns: + The updated secret. + """ + return self._update_resource( + resource_id=secret_id, + resource_update=secret_update, + route=SECRETS, + response_model=SecretResponse, + # The default endpoint behavior is to replace all secret values + # with the values in the update. We want to merge the values + # instead. + params=dict(patch_values=True), + ) + + def delete_secret(self, secret_id: UUID) -> None: + """Delete a secret. + + Args: + secret_id: The id of the secret to delete. + """ + self._delete_resource( + resource_id=secret_id, + route=SECRETS, + ) + # --------------------------- Service Accounts --------------------------- def create_service_account( diff --git a/src/zenml/zen_stores/schemas/secret_schemas.py b/src/zenml/zen_stores/schemas/secret_schemas.py index adfaa0d3750..ae9cf7b0452 100644 --- a/src/zenml/zen_stores/schemas/secret_schemas.py +++ b/src/zenml/zen_stores/schemas/secret_schemas.py @@ -49,7 +49,7 @@ class SecretSchema(NamedSchema, table=True): scope: SecretScope - values: bytes = Field(sa_column=Column(TEXT, nullable=False)) + values: Optional[bytes] = Field(sa_column=Column(TEXT, nullable=True)) workspace_id: UUID = build_foreign_key_field( source=__tablename__, @@ -136,14 +136,11 @@ def _load_secret_values( def from_request( cls, secret: SecretRequest, - encryption_engine: Optional[AesGcmEngine] = None, ) -> "SecretSchema": """Create a `SecretSchema` from a `SecretRequest`. Args: secret: The `SecretRequest` from which to create the schema. - encryption_engine: The encryption engine to use to encrypt the - secret values. If None, the values will be base64 encoded. Returns: The created `SecretSchema`. @@ -154,64 +151,42 @@ def from_request( scope=secret.scope, workspace_id=secret.workspace, user_id=secret.user, - values=cls._dump_secret_values( - secret.secret_values, encryption_engine - ), + # Don't store secret values implicitly in the secret. The + # SQL secret store will call `store_secret_values` to store the + # values separately if SQL is used as the secrets store. + values=None, ) def update( self, secret_update: SecretUpdate, - encryption_engine: Optional[AesGcmEngine] = None, ) -> "SecretSchema": """Update a `SecretSchema` from a `SecretUpdate`. - The method also knows how to handle the `values` field of the secret - update model: It will update the existing values with the new values - and drop `None` values. - Args: secret_update: The `SecretUpdate` from which to update the schema. - encryption_engine: The encryption engine to use to encrypt the - secret values. If None, the values will be base64 encoded. Returns: The updated `SecretSchema`. """ + # Don't update the secret values implicitly in the secret. The + # SQL secret store will call `set_secret_values` to update the + # values separately if SQL is used as the secrets store. for field, value in secret_update.dict( - exclude_unset=True, exclude={"workspace", "user"} + exclude_unset=True, exclude={"workspace", "user", "values"} ).items(): - if field == "values": - existing_values = self._load_secret_values( - self.values, encryption_engine - ) - existing_values.update(secret_update.secret_values) - # Drop values removed in the update - for k, v in secret_update.values.items(): - if v is None and k in existing_values: - del existing_values[k] - self.values = self._dump_secret_values( - existing_values, encryption_engine - ) - else: - setattr(self, field, value) + setattr(self, field, value) self.updated = datetime.utcnow() return self def to_model( self, - encryption_engine: Optional[AesGcmEngine] = None, - include_values: bool = True, hydrate: bool = False, ) -> SecretResponse: """Converts a secret schema to a secret model. Args: - encryption_engine: The encryption engine to use to decrypt the - secret values. If None, the values will be base64 decoded. - include_values: Whether to include the secret values in the - response model or not. hydrate: Flag deciding whether to hydrate the output model(s) by including metadata fields in the response. @@ -224,14 +199,14 @@ def to_model( workspace=self.workspace.to_model(), ) + # Don't load the secret values implicitly in the secret. The + # SQL secret store will call `get_secret_values` to load the + # values separately if SQL is used as the secrets store. body = SecretResponseBody( user=self.user.to_model() if self.user else None, created=self.created, updated=self.updated, scope=self.scope, - values=self._load_secret_values(self.values, encryption_engine) - if include_values - else {}, ) return SecretResponse( id=self.id, @@ -239,3 +214,49 @@ def to_model( body=body, metadata=metadata, ) + + def get_secret_values( + self, + encryption_engine: Optional[AesGcmEngine] = None, + ) -> Dict[str, str]: + """Get the secret values for this secret. + + This method is used by the SQL secrets store to load the secret values + from the database. + + Args: + encryption_engine: The encryption engine to use to decrypt the + secret values. If None, the values will be base64 decoded. + + Returns: + The secret values + + Raises: + KeyError: if no secret values for the given ID are stored in the + secrets store. + """ + if not self.values: + raise KeyError( + f"Secret values for secret {self.id} have not been stored in " + f"the SQL secrets store." + ) + return self._load_secret_values(self.values, encryption_engine) + + def set_secret_values( + self, + secret_values: Dict[str, str], + encryption_engine: Optional[AesGcmEngine] = None, + ) -> None: + """Create a `SecretSchema` from a `SecretRequest`. + + This method is used by the SQL secrets store to store the secret values + in the database. + + Args: + secret_values: The new secret values. + encryption_engine: The encryption engine to use to encrypt the + secret values. If None, the values will be base64 encoded. + """ + self.values = self._dump_secret_values( + secret_values, encryption_engine + ) diff --git a/src/zenml/zen_stores/secrets_stores/aws_secrets_store.py b/src/zenml/zen_stores/secrets_stores/aws_secrets_store.py index cba349cee16..e2c8fa884f7 100644 --- a/src/zenml/zen_stores/secrets_stores/aws_secrets_store.py +++ b/src/zenml/zen_stores/secrets_stores/aws_secrets_store.py @@ -14,17 +14,11 @@ """AWS Secrets Store implementation.""" import json -import math -import re -import time -import uuid -from datetime import datetime from typing import ( Any, ClassVar, Dict, List, - Optional, Type, ) from uuid import UUID @@ -33,15 +27,9 @@ from botocore.exceptions import ClientError from pydantic import root_validator -from zenml.analytics.enums import AnalyticsEvent -from zenml.analytics.utils import track_decorator from zenml.enums import ( - GenericFilterOps, - LogicalOperators, - SecretScope, SecretsStoreType, ) -from zenml.exceptions import EntityExistsError from zenml.integrations.aws import ( AWS_CONNECTOR_TYPE, AWS_RESOURCE_TYPE, @@ -50,15 +38,6 @@ AWSAuthenticationMethods, ) from zenml.logger import get_logger -from zenml.models import ( - Page, - SecretFilter, - SecretRequest, - SecretResponse, - SecretResponseBody, - SecretResponseMetadata, - SecretUpdate, -) from zenml.zen_stores.secrets_stores.service_connector_secrets_store import ( ServiceConnectorSecretsStore, ServiceConnectorSecretsStoreConfiguration, @@ -75,23 +54,9 @@ class AWSSecretsStoreConfiguration(ServiceConnectorSecretsStoreConfiguration): Attributes: type: The type of the store. - list_page_size: The number of secrets to fetch per page when - listing secrets. - secret_list_refresh_timeout: The number of seconds to wait after - creating or updating an AWS secret until the changes are reflected - in the secrets returned by `list_secrets`. Set this to zero to - disable the wait. This may be necessary because it can take some - time for new secrets and updated secrets to be reflected in the - result returned by `list_secrets` on the client side. This value - should not be set to a large value, because it blocks ZenML server - threads while waiting and can cause performance issues. - Disable this if you don't need changes to be reflected immediately - on the client side. """ type: SecretsStoreType = SecretsStoreType.AWS - list_page_size: int = 100 - secret_list_refresh_timeout: int = 0 @property def region(self) -> str: @@ -144,7 +109,7 @@ def populate_config(cls, values: Dict[str, Any]) -> Dict[str, Any]: class Config: """Pydantic configuration class.""" - # Forbid extra attributes set in the class. + # Allow extra attributes set in the class. extra = "allow" @@ -162,36 +127,13 @@ class AWSSecretsStore(ServiceConnectorSecretsStore): `zenml` prefix in the form `zenml/{zenml_secret_uuid}`. This clearly identifies a secret as being managed by ZenML in the AWS console. - * the Secrets Store also makes heavy use of AWS secret tags to store all the - metadata associated with a ZenML secret (e.g. the secret name, scope, user - and workspace) and to filter secrets by these metadata. The `zenml` tag in - particular is used to identify and group all secrets that belong to the same - ZenML deployment. + * the Secrets Store also uses AWS secret tags to store additional + metadata associated with a ZenML secret. The `zenml` tag in particular is + used to identify and group all secrets that belong to the same ZenML + deployment. * all secret key-values configured in a ZenML secret are stored as a single JSON string value in the AWS secret value. - - * when a user or workspace is deleted, the secrets associated with it are - deleted automatically via registered event handlers. - - - Known challenges and limitations: - - * there is a known problem with the AWS Secrets Manager API that can cause - the `list_secrets` method to return stale data for a long time (seconds) - after a secret is created or updated. The AWS secrets store tries to - mitigate this problem by waiting for a maximum configurable number of - seconds after creating or updating a secret until the changes are reflected - in the `list_secrets` AWS content. However, this is not a perfect solution, - because it blocks ZenML server API threads while waiting. This can be - disabled by setting the `secret_list_refresh_timeout` configuration - parameter to zero. - - * only updating the secret values is reflected in the secret's `updated` - timestamp. Updating the secret metadata (e.g. name, scope, user or - workspace) does not update the secret's `updated` timestamp. This is a - limitation of the AWS Secrets Manager API (updating AWS tags does not update - the secret's `updated` timestamp). """ config: AWSSecretsStoreConfiguration @@ -230,35 +172,6 @@ def _initialize_client_from_connector(self, client: Any) -> Any: # Secrets # ------ - @staticmethod - def _validate_aws_secret_name(name: str) -> None: - """Validate a secret name. - - AWS secret names must contain only alphanumeric characters and the - characters /_+=.@-. The `/` character is only used internally to - implement the global namespace sharing scheme. - - Given that the ZenML secret name is stored as an AWS secret tag, the - maximum value length is also restricted to 255 characters. - - Args: - name: the secret name - - Raises: - ValueError: if the secret name is invalid - """ - if not re.fullmatch(r"[a-zA-Z0-9_+=\.@\-]*", name): - raise ValueError( - f"Invalid secret name '{name}'. Must contain only alphanumeric " - f"characters and the characters _+=.@-." - ) - - if len(name) > 255: - raise ValueError( - f"Invalid secret name '{name}'. The maximum length is 255 " - f"characters." - ) - @staticmethod def _get_aws_secret_id( secret_id: UUID, @@ -277,41 +190,6 @@ def _get_aws_secret_id( """ return f"{AWS_ZENML_SECRET_NAME_PREFIX}/{str(secret_id)}" - def _convert_aws_secret( - self, - tags: List[Dict[str, str]], - created: datetime, - updated: datetime, - values: Optional[str] = None, - hydrate: bool = False, - ) -> SecretResponse: - """Create a ZenML secret model from data stored in an AWS secret. - - If the AWS secret cannot be converted, the method acts as if the - secret does not exist and raises a KeyError. - - Args: - tags: The AWS secret tags. - created: The AWS secret creation time. - updated: The AWS secret last updated time. - values: The AWS secret values encoded as a JSON string (optional). - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - The ZenML secret. - """ - # Convert the AWS secret tags to a metadata dictionary. - metadata: Dict[str, str] = {tag["Key"]: tag["Value"] for tag in tags} - - return self._create_secret_from_metadata( - metadata=metadata, - created=created, - updated=updated, - values=json.loads(values) if values else None, - hydrate=hydrate, - ) - @staticmethod def _get_aws_secret_tags( metadata: Dict[str, str], @@ -335,145 +213,26 @@ def _get_aws_secret_tags( return aws_tags - @staticmethod - def _get_aws_secret_filters( - metadata: Dict[str, str], - ) -> List[Dict[str, str]]: - """Convert ZenML secret metadata to AWS secret filters. - - Args: - metadata: The ZenML secret metadata. - - Returns: - The AWS secret filters. - """ - aws_filters: List[Dict[str, Any]] = [] - for k, v in metadata.items(): - aws_filters.append( - { - "Key": "tag-key", - "Values": [ - k, - ], - } - ) - aws_filters.append( - { - "Key": "tag-value", - "Values": [ - str(v), - ], - } - ) - - return aws_filters - - def _wait_for_secret_to_propagate( - self, aws_secret_id: str, tags: List[Dict[str, str]] + def store_secret_values( + self, + secret_id: UUID, + secret_values: Dict[str, str], ) -> None: - """Wait for an AWS secret to be refreshed in the list of secrets. - - The AWS Secrets Manager does not immediately reflect newly created - and updated secrets in the `list_secrets` API. It is important that we - wait for the secret to be refreshed in the `list_secrets` API - before returning from create_secret/update_secret, otherwise the secret - will not be available to the user. We also rely on `list_secrets` - to enforce the scope rules, but given that the ZenML server runs - requests in separate threads, it is not entirely possible to - guarantee them. + """Store secret values for a new secret. Args: - aws_secret_id: The AWS secret ID. - tags: The AWS secret tags that are expected to be present in the - `list_secrets` response. - """ - if self.config.secret_list_refresh_timeout <= 0: - return - - # We wait for the secret to be available in the `list_secrets` API. - for _ in range(self.config.secret_list_refresh_timeout): - logger.debug(f"Waiting for secret {aws_secret_id} to be listed...") - secret_exists = False - try: - secrets = self.client.list_secrets( - Filters=[{"Key": "name", "Values": [aws_secret_id]}] - ) - if len(secrets["SecretList"]) > 0: - listed_secret = secrets["SecretList"][0] - # The supplied tags must exactly match those reported in the - # `list_secrets` response. - secret_exists = all( - tag in listed_secret["Tags"] for tag in tags - ) - except ClientError as e: - logger.warning( - f"Error checking if secret {aws_secret_id} is listed: " - f"{e}. Retrying..." - ) - - if not secret_exists: - logger.debug( - f"Secret {aws_secret_id} not yet listed. Retrying..." - ) - time.sleep(1) - else: - logger.debug(f"Secret {aws_secret_id} listed.") - break - else: - logger.warning( - f"Secret {aws_secret_id} not updated in `list_secrets` " - f"after {self.config.secret_list_refresh_timeout} seconds. " - ) - - @track_decorator(AnalyticsEvent.CREATED_SECRET) - def create_secret(self, secret: SecretRequest) -> SecretResponse: - """Creates a new secret. - - The new secret is also validated against the scoping rules enforced in - the secrets store: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. - - Args: - secret: The secret to create. - - Returns: - The newly created secret. + secret_id: ID of the secret. + secret_values: Values for the secret. Raises: - EntityExistsError: If a secret with the same name already exists - in the same scope. RuntimeError: If the AWS Secrets Manager API returns an unexpected error. """ - self._validate_aws_secret_name(secret.name) - user, workspace = self._validate_user_and_workspace( - secret.user, secret.workspace - ) - - # Check if a secret with the same name already exists in the same - # scope. - secret_exists, msg = self._check_secret_scope( - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace, - user=secret.user, - ) - if secret_exists: - raise EntityExistsError(msg) - - # Generate a new UUID for the secret - secret_id = uuid.uuid4() aws_secret_id = self._get_aws_secret_id(secret_id) - secret_value = json.dumps(secret.secret_values) + secret_value = json.dumps(secret_values) # Convert the ZenML secret metadata to AWS tags - metadata = self._get_secret_metadata_for_secret( - secret, secret_id=secret_id - ) + metadata = self._get_secret_metadata(secret_id=secret_id) tags = self._get_aws_secret_tags(metadata) try: @@ -482,50 +241,23 @@ def create_secret(self, secret: SecretRequest) -> SecretResponse: SecretString=secret_value, Tags=tags, ) - # We need a separate AWS API call to get the secret creation - # date, since the create_secret API does not return it. - describe_secret_response = self.client.describe_secret( - SecretId=aws_secret_id - ) except ClientError as e: raise RuntimeError(f"Error creating secret: {e}") - logger.debug("Created AWS secret: %s", aws_secret_id) - - self._wait_for_secret_to_propagate(aws_secret_id, tags=tags) - - secret_model = SecretResponse( - id=secret_id, - name=secret.name, - body=SecretResponseBody( - user=user, - created=describe_secret_response["CreatedDate"], - updated=describe_secret_response["LastChangedDate"], - scope=secret.scope, - values=secret.secret_values, - ), - metadata=SecretResponseMetadata( - workspace=workspace, - ), - ) - - return secret_model + logger.debug(f"Created AWS secret: {aws_secret_id}") - def get_secret( - self, secret_id: UUID, hydrate: bool = True - ) -> SecretResponse: - """Get a secret by ID. + def get_secret_values(self, secret_id: UUID) -> Dict[str, str]: + """Get the secret values for an existing secret. Args: - secret_id: The ID of the secret to fetch. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. + secret_id: ID of the secret. Returns: - The secret. + The secret values. Raises: - KeyError: If the secret does not exist. + KeyError: if no secret values for the given ID are stored in the + secrets store. RuntimeError: If the AWS Secrets Manager API returns an unexpected error. """ @@ -555,227 +287,57 @@ def get_secret( f"Error fetching secret with ID {secret_id} {e}" ) - # The _convert_aws_secret method raises a KeyError if the - # secret is tied to a workspace or user that no longer exists. Here we + # Convert the AWS secret tags to a metadata dictionary. + metadata: Dict[str, str] = { + tag["Key"]: tag["Value"] + for tag in describe_secret_response["Tags"] + } + + # The _verify_secret_metadata method raises a KeyError if the + # secret is not valid or does not belong to this server. Here we # simply pass the exception up the stack, as if the secret was not found - # in the first place, knowing that it will be cascade-deleted soon. - return self._convert_aws_secret( - tags=describe_secret_response["Tags"], - created=describe_secret_response["CreatedDate"], - updated=describe_secret_response["LastChangedDate"], - values=get_secret_value_response["SecretString"], - hydrate=hydrate, + # in the first place. + self._verify_secret_metadata( + secret_id=secret_id, + metadata=metadata, ) - def list_secrets( - self, secret_filter_model: SecretFilter, hydrate: bool = False - ) -> Page[SecretResponse]: - """List all secrets matching the given filter criteria. - - Note that returned secrets do not include any secret values. To fetch - the secret values, use `get_secret`. + values = get_secret_value_response["SecretString"] - Args: - secret_filter_model: All filter parameters including pagination - params. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - A list of all secrets matching the filter criteria, with pagination - information and sorted according to the filter criteria. The - returned secrets do not include any secret values, only metadata. To - fetch the secret values, use `get_secret` individually with each - secret. - - Raises: - ValueError: If the filter contains an out-of-bounds page number. - RuntimeError: If the AWS Secrets Manager API returns an unexpected - error. - """ - # The AWS Secrets Manager API does not natively support the entire - # range of filtering, sorting and pagination options that ZenML - # supports. The implementation of this method is therefore a bit - # involved. We try to make use of the AWS filtering API as much as - # possible to reduce the number of secrets that we need to fetch, then - # we apply the rest of filtering, sorting and pagination on - # the client side. - - metadata_args: Dict[str, Any] = {} - if secret_filter_model.logical_operator == LogicalOperators.AND: - # We can only filter on the AWS server side if we have an AND - # logical operator. Otherwise, we need to filter on the client - # side. - - for filter in secret_filter_model.list_of_filters: - # The AWS Secrets Manager API only supports prefix matching. We - # take advantage of this to filter as much as possible on the - # AWS server side, and we leave the rest to the client. - if filter.operation not in [ - GenericFilterOps.EQUALS, - GenericFilterOps.STARTSWITH, - ]: - continue - - if filter.column == "id": - metadata_args["secret_id"] = UUID(filter.value) - elif filter.column == "name": - metadata_args["secret_name"] = filter.value - elif filter.column == "scope": - metadata_args["scope"] = SecretScope(filter.value) - elif filter.column == "workspace_id": - metadata_args["workspace"] = UUID(filter.value) - elif filter.column == "user_id": - metadata_args["user"] = UUID(filter.value) - else: - # AWS doesn't support filtering on the created/updated - # timestamps, so we'll have to do that on the client side. - continue - - # The metadata will always contain at least the filter criteria - # required to exclude everything but AWS secrets that belong to the - # current ZenML deployment. - metadata = self._get_secret_metadata(**metadata_args) - aws_filters = self._get_aws_secret_filters(metadata) - - results: List[SecretResponse] = [] + logger.debug(f"Fetched AWS secret: {aws_secret_id}") - try: - # AWS Secrets Manager API pagination is wrapped around the - # `list_secrets` method call. We use it because we need to fetch all - # secrets matching the (partial) filter that we set up. Note that - # the pagination used here has nothing to do with the pagination - # that we do for the method caller. - paginator = self.client.get_paginator("list_secrets") - pages = paginator.paginate( - Filters=aws_filters, - PaginationConfig={ - "PageSize": self.config.list_page_size, - }, - ) + secret_values = json.loads(values) - for page in pages: - for secret in page["SecretList"]: - try: - # NOTE: we do not include the secret values in the - # response. We would need a separate API call to fetch - # them for each secret, which would be very inefficient - # anyway. - secret_model = self._convert_aws_secret( - tags=secret["Tags"], - created=secret["CreatedDate"], - updated=secret["LastChangedDate"], - hydrate=hydrate, - ) - except KeyError: - # The _convert_aws_secret method raises a KeyError - # if the secret is tied to a workspace or user that no - # longer exists. Here we pretend that the secret does - # not exist. - continue - - # Filter again on the client side to cover all filter - # operations. - if not secret_filter_model.secret_matches(secret_model): - continue - results.append(secret_model) - except ClientError as e: - raise RuntimeError(f"Error listing AWS secrets: {e}") - - # Sort the results - sorted_results = secret_filter_model.sort_secrets(results) - - # Paginate the results - total = len(sorted_results) - if total == 0: - total_pages = 1 - else: - total_pages = math.ceil(total / secret_filter_model.size) - - if secret_filter_model.page > total_pages: - raise ValueError( - f"Invalid page {secret_filter_model.page}. The requested page " - f"size is {secret_filter_model.size} and there are a total of " - f"{total} items for this query. The maximum page value " - f"therefore is {total_pages}." + if not isinstance(secret_values, dict): + raise RuntimeError( + f"AWS secret values for secret ID {aws_secret_id} could not be " + "decoded: expected a dictionary." ) - return Page[SecretResponse]( - total=total, - total_pages=total_pages, - items=sorted_results[ - (secret_filter_model.page - 1) - * secret_filter_model.size : secret_filter_model.page - * secret_filter_model.size - ], - index=secret_filter_model.page, - max_size=secret_filter_model.size, - ) - - def update_secret( - self, secret_id: UUID, secret_update: SecretUpdate - ) -> SecretResponse: - """Updates a secret. + return secret_values - Secret values that are specified as `None` in the update that are - present in the existing secret are removed from the existing secret. - Values that are present in both secrets are overwritten. All other - values in both the existing secret and the update are kept (merged). - - If the update includes a change of name or scope, the scoping rules - enforced in the secrets store are used to validate the update: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + def update_secret_values( + self, + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Updates secret values for an existing secret. Args: secret_id: The ID of the secret to be updated. - secret_update: The update to be applied. - - Returns: - The updated secret. + secret_values: The new secret values. Raises: - EntityExistsError: If the update includes a change of name or - scope and a secret with the same name already exists in the - same scope. + KeyError: if no secret values for the given ID are stored in the + secrets store. RuntimeError: If the AWS Secrets Manager API returns an unexpected error. """ - secret = self.get_secret(secret_id) - - if secret_update.name is not None: - self._validate_aws_secret_name(secret_update.name) - secret.name = secret_update.name - if secret_update.scope is not None: - secret.get_body().scope = secret_update.scope - if secret_update.values is not None: - # Merge the existing values with the update values. - # The values that are set to `None` in the update are removed from - # the existing secret when we call `.secret_values` later. - secret.get_body().values.update(secret_update.values) - - if secret_update.name is not None or secret_update.scope is not None: - # Check if a secret with the same name already exists in the same - # scope. - assert secret.user is not None - secret_exists, msg = self._check_secret_scope( - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace.id, - user=secret.user.id, - exclude_secret_id=secret.id, - ) - if secret_exists: - raise EntityExistsError(msg) - aws_secret_id = self._get_aws_secret_id(secret_id) - secret_value = json.dumps(secret.secret_values) + secret_value = json.dumps(secret_values) # Convert the ZenML secret metadata to AWS tags - metadata = self._get_secret_metadata_for_secret(secret) + metadata = self._get_secret_metadata(secret_id) tags = self._get_aws_secret_tags(metadata) try: @@ -789,49 +351,30 @@ def update_secret( SecretId=aws_secret_id, Tags=tags, ) - # And another call to get the updated secret metadata which - # includes the created and updated timestamps. - describe_secret_response = self.client.describe_secret( - SecretId=aws_secret_id - ) except ClientError as e: + if e.response["Error"]["Code"] == "ResourceNotFoundException": + raise KeyError(f"Secret with ID {secret_id} not found") raise RuntimeError(f"Error updating secret: {e}") - logger.debug("Updated AWS secret: %s", aws_secret_id) - - self._wait_for_secret_to_propagate(aws_secret_id, tags=tags) - - secret_model = SecretResponse( - id=secret_id, - name=secret.name, - body=SecretResponseBody( - user=secret.user, - created=describe_secret_response["CreatedDate"], - updated=describe_secret_response["LastChangedDate"], - scope=secret.scope, - values=secret.secret_values, - ), - metadata=SecretResponseMetadata( - workspace=secret.workspace, - ), - ) - - return secret_model + logger.debug(f"Updated AWS secret: {aws_secret_id}") - def delete_secret(self, secret_id: UUID) -> None: - """Delete a secret. + def delete_secret_values(self, secret_id: UUID) -> None: + """Deletes secret values for an existing secret. Args: - secret_id: The id of the secret to delete. + secret_id: The ID of the secret. Raises: - KeyError: If the secret does not exist. + KeyError: if no secret values for the given ID are stored in the + secrets store. RuntimeError: If the AWS Secrets Manager API returns an unexpected error. """ + aws_secret_id = self._get_aws_secret_id(secret_id) + try: self.client.delete_secret( - SecretId=self._get_aws_secret_id(secret_id), + SecretId=aws_secret_id, # We set this to force immediate deletion of the AWS secret # instead of waiting for the recovery window to expire. ForceDeleteWithoutRecovery=True, @@ -849,3 +392,5 @@ def delete_secret(self, secret_id: UUID) -> None: raise RuntimeError( f"Error deleting secret with ID {secret_id}: {e}" ) + + logger.debug(f"Deleted AWS secret: {aws_secret_id}") diff --git a/src/zenml/zen_stores/secrets_stores/azure_secrets_store.py b/src/zenml/zen_stores/secrets_stores/azure_secrets_store.py index 6bebaf775ff..e63bd75a65a 100644 --- a/src/zenml/zen_stores/secrets_stores/azure_secrets_store.py +++ b/src/zenml/zen_stores/secrets_stores/azure_secrets_store.py @@ -15,16 +15,10 @@ import json import logging -import math -import re -import uuid -from datetime import datetime from typing import ( Any, ClassVar, Dict, - List, - Optional, Type, cast, ) @@ -35,12 +29,9 @@ from azure.keyvault.secrets import SecretClient from pydantic import root_validator -from zenml.analytics.enums import AnalyticsEvent -from zenml.analytics.utils import track_decorator from zenml.enums import ( SecretsStoreType, ) -from zenml.exceptions import EntityExistsError from zenml.integrations.azure import ( AZURE_CONNECTOR_TYPE, AZURE_RESOURCE_TYPE, @@ -49,15 +40,6 @@ AzureAuthenticationMethods, ) from zenml.logger import get_logger -from zenml.models import ( - Page, - SecretFilter, - SecretRequest, - SecretResponse, - SecretResponseBody, - SecretResponseMetadata, - SecretUpdate, -) from zenml.zen_stores.secrets_stores.service_connector_secrets_store import ( ServiceConnectorSecretsStore, ServiceConnectorSecretsStoreConfiguration, @@ -67,8 +49,6 @@ AZURE_ZENML_SECRET_NAME_PREFIX = "zenml" -ZENML_AZURE_SECRET_CREATED_KEY = "zenml_secret_created" -ZENML_AZURE_SECRET_UPDATED_KEY = "zenml_secret_updated" class AzureSecretsStoreConfiguration( @@ -139,29 +119,12 @@ class AzureSecretsStore(ServiceConnectorSecretsStore): `zenml` prefix in the form `zenml-{zenml_secret_uuid}`. This clearly identifies a secret as being managed by ZenML in the Azure console. - * the Secrets Store also makes heavy use of Azure Key Vault secret tags to - store all the metadata associated with a ZenML secret (e.g. the secret name, - scope, user and workspace) and to filter secrets by these metadata. The - `zenml` tag in particular is used to identify and group all secrets that - belong to the same ZenML deployment. + * the Secrets Store also uses Azure Key Vault secret tags to store metadata + associated with a ZenML secret. The `zenml` tag in particular is used to + identify and group all secrets that belong to the same ZenML deployment. * all secret key-values configured in a ZenML secret are stored as a single JSON string value in the Azure Key Vault secret value. - - * when a user or workspace is deleted, the secrets associated with it are - deleted automatically via registered event handlers. - - - Known challenges and limitations: - - * every Azure Key Vault secret has one or more versions. Every update to a - secret creates a new version. The created_on and updated_on timestamps - returned by the Secrets Store API are the timestamps of the latest version - of the secret. This means that we need to fetch the first version of the - secret to get the created_on timestamp. This is not ideal, as we'd need to - fetch all versions for every secret to get the created_on timestamp during - a list operation. So instead we manage the `created` and `updated` - timestamps ourselves and save them as tags in the Azure Key Vault secret. """ config: AzureSecretsStoreConfiguration @@ -216,35 +179,6 @@ def _initialize_client_from_connector(self, client: Any) -> Any: # Secrets # ------ - @staticmethod - def _validate_azure_secret_name(name: str) -> None: - """Validate a secret name. - - Azure secret names must contain only alphanumeric characters and the - character `-`. - - Given that the ZenML secret name is stored as an Azure Key Vault secret - label, we are also limited by the 256 maximum size limitation that Azure - imposes on label values. - - Args: - name: the secret name - - Raises: - ValueError: if the secret name is invalid - """ - if not re.fullmatch(r"[0-9a-zA-Z-]+", name): - raise ValueError( - f"Invalid secret name or namespace '{name}'. Must contain " - f"only alphanumeric characters and the character -." - ) - - if len(name) > 256: - raise ValueError( - f"Invalid secret name or namespace '{name}'. The length is " - f"limited to maximum 256 characters." - ) - @staticmethod def _get_azure_secret_id( secret_id: UUID, @@ -263,105 +197,26 @@ def _get_azure_secret_id( """ return f"{AZURE_ZENML_SECRET_NAME_PREFIX}-{str(secret_id)}" - def _convert_azure_secret( + def store_secret_values( self, - tags: Dict[str, str], - values: Optional[str] = None, - hydrate: bool = False, - ) -> SecretResponse: - """Create a ZenML secret model from data stored in an Azure secret. - - If the Azure secret cannot be converted, the method acts as if the - secret does not exist and raises a KeyError. - - Args: - tags: The Azure secret tags. - values: The Azure secret values encoded as a JSON string - (optional). - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - The ZenML secret. - - Raises: - KeyError: if the Azure secret cannot be converted. - """ - try: - created = datetime.fromisoformat( - tags[ZENML_AZURE_SECRET_CREATED_KEY], - ) - updated = datetime.fromisoformat( - tags[ZENML_AZURE_SECRET_UPDATED_KEY], - ) - except KeyError as e: - raise KeyError( - f"Secret could not be retrieved: missing required metadata: {e}" - ) - - return self._create_secret_from_metadata( - metadata=tags, - created=created, - updated=updated, - values=json.loads(values) if values else None, - hydrate=hydrate, - ) - - @track_decorator(AnalyticsEvent.CREATED_SECRET) - def create_secret(self, secret: SecretRequest) -> SecretResponse: - """Creates a new secret. - - The new secret is also validated against the scoping rules enforced in - the secrets store: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Store secret values for a new secret. Args: - secret: The secret to create. - - Returns: - The newly created secret. + secret_id: ID of the secret. + secret_values: Values for the secret. Raises: - EntityExistsError: If a secret with the same name already exists - in the same scope. - RuntimeError: If the Azure Key Vault API returns an unexpected + RuntimeError: if the Azure Key Vault API returns an unexpected error. """ - self._validate_azure_secret_name(secret.name) - user, workspace = self._validate_user_and_workspace( - secret.user, secret.workspace - ) - - # Check if a secret with the same name already exists in the same - # scope. - secret_exists, msg = self._check_secret_scope( - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace, - user=secret.user, - ) - if secret_exists: - raise EntityExistsError(msg) - - # Generate a new UUID for the secret - secret_id = uuid.uuid4() azure_secret_id = self._get_azure_secret_id(secret_id) - secret_value = json.dumps(secret.secret_values) + secret_value = json.dumps(secret_values) # Use the ZenML secret metadata as Azure tags - metadata = self._get_secret_metadata_for_secret( - secret, secret_id=secret_id - ) - - # We manage the created and updated times ourselves, so we don't need to - # rely on the Azure Key Vault API to set them. - created = datetime.utcnow() - metadata[ZENML_AZURE_SECRET_CREATED_KEY] = created.isoformat() - metadata[ZENML_AZURE_SECRET_UPDATED_KEY] = created.isoformat() + metadata = self._get_secret_metadata(secret_id=secret_id) try: self.client.set_secret( @@ -373,41 +228,21 @@ def create_secret(self, secret: SecretRequest) -> SecretResponse: except HttpResponseError as e: raise RuntimeError(f"Error creating secret: {e}") - logger.debug("Created Azure secret: %s", azure_secret_id) - - secret_model = SecretResponse( - id=secret_id, - name=secret.name, - body=SecretResponseBody( - user=user, - created=created, - updated=created, - scope=secret.scope, - values=secret.secret_values, - ), - metadata=SecretResponseMetadata( - workspace=workspace, - ), - ) - - return secret_model + logger.debug(f"Created Azure secret: {azure_secret_id}") - def get_secret( - self, secret_id: UUID, hydrate: bool = True - ) -> SecretResponse: - """Get a secret by ID. + def get_secret_values(self, secret_id: UUID) -> Dict[str, str]: + """Get the secret values for an existing secret. Args: - secret_id: The ID of the secret to fetch. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. + secret_id: ID of the secret. Returns: - The secret. + The secret values. Raises: - KeyError: If the secret does not exist. - RuntimeError: If the Azure Key Vault API returns an unexpected + KeyError: if no secret values for the given ID are stored in the + secrets store. + RuntimeError: if the Azure Key Vault API returns an unexpected error. """ azure_secret_id = self._get_azure_secret_id(secret_id) @@ -423,184 +258,48 @@ def get_secret( f"Error fetching secret with ID {secret_id} {e}" ) - # The _convert_azure_secret method raises a KeyError if the - # secret is tied to a workspace or user that no longer exists. Here we + # The _verify_secret_metadata method raises a KeyError if the + # secret is not valid or does not belong to this server. Here we # simply pass the exception up the stack, as if the secret was not found - # in the first place, knowing that it will be cascade-deleted soon. + # in the first place. assert azure_secret.properties.tags is not None - return self._convert_azure_secret( - tags=azure_secret.properties.tags, - values=azure_secret.value, - hydrate=hydrate, + self._verify_secret_metadata( + secret_id=secret_id, + metadata=azure_secret.properties.tags, ) - def list_secrets( - self, secret_filter_model: SecretFilter, hydrate: bool = False - ) -> Page[SecretResponse]: - """List all secrets matching the given filter criteria. - - Note that returned secrets do not include any secret values. To fetch - the secret values, use `get_secret`. - - Args: - secret_filter_model: All filter parameters including pagination - params. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - A list of all secrets matching the filter criteria, with pagination - information and sorted according to the filter criteria. The - returned secrets do not include any secret values, only metadata. To - fetch the secret values, use `get_secret` individually with each - secret. - - Raises: - ValueError: If the filter contains an out-of-bounds page number. - RuntimeError: If the Azure Key Vault API returns an unexpected - error. - """ - # The Azure Key Vault API does not natively support any of the - # filtering, sorting or pagination options that ZenML supports. The - # implementation of this method therefore has to fetch all secrets from - # the Key Vault, then apply the filtering, sorting and pagination on - # the client side. - - # The metadata will always contain at least the filter criteria - # required to exclude everything but Azure secrets that belong to the - # current ZenML deployment. - results: List[SecretResponse] = [] - - try: - all_secrets = self.client.list_properties_of_secrets() - for secret_property in all_secrets: - try: - # NOTE: we do not include the secret values in the - # response. We would need a separate API call to fetch - # them for each secret, which would be very inefficient - # anyway. - assert secret_property.tags is not None - secret_model = self._convert_azure_secret( - tags=secret_property.tags, hydrate=hydrate - ) - except KeyError: - # The _convert_azure_secret method raises a KeyError - # if the secret is tied to a workspace or user that no - # longer exists or if it is otherwise not valid. Here we - # pretend that the secret does not exist. - continue - - # Filter the secret on the client side. - if not secret_filter_model.secret_matches(secret_model): - continue - results.append(secret_model) - except HttpResponseError as e: - raise RuntimeError(f"Error listing Azure Key Vault secrets: {e}") - - # Sort the results - sorted_results = secret_filter_model.sort_secrets(results) + values = json.loads(azure_secret.value) if azure_secret.value else {} - # Paginate the results - total = len(sorted_results) - if total == 0: - total_pages = 1 - else: - total_pages = math.ceil(total / secret_filter_model.size) - - if secret_filter_model.page > total_pages: - raise ValueError( - f"Invalid page {secret_filter_model.page}. The requested page " - f"size is {secret_filter_model.size} and there are a total of " - f"{total} items for this query. The maximum page value " - f"therefore is {total_pages}." + if not isinstance(values, dict): + raise RuntimeError( + f"Azure Key Vault secret values for secret {azure_secret_id} " + "could not be retrieved: invalid type for values" ) - return Page[SecretResponse]( - total=total, - total_pages=total_pages, - items=sorted_results[ - (secret_filter_model.page - 1) - * secret_filter_model.size : secret_filter_model.page - * secret_filter_model.size - ], - index=secret_filter_model.page, - max_size=secret_filter_model.size, - ) - - def update_secret( - self, secret_id: UUID, secret_update: SecretUpdate - ) -> SecretResponse: - """Updates a secret. - - Secret values that are specified as `None` in the update that are - present in the existing secret are removed from the existing secret. - Values that are present in both secrets are overwritten. All other - values in both the existing secret and the update are kept (merged). + logger.debug(f"Retrieved Azure secret: {azure_secret_id}") - If the update includes a change of name or scope, the scoping rules - enforced in the secrets store are used to validate the update: + return values - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + def update_secret_values( + self, + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Updates secret values for an existing secret. Args: secret_id: The ID of the secret to be updated. - secret_update: The update to be applied. - - Returns: - The updated secret. + secret_values: The new secret values. Raises: - EntityExistsError: If the update includes a change of name or - scope and a secret with the same name already exists in the - same scope. - RuntimeError: If the Azure Key Vault API returns an unexpected + RuntimeError: if the Azure Key Vault API returns an unexpected error. """ - secret = self.get_secret(secret_id) - - if secret_update.name is not None: - self._validate_azure_secret_name(secret_update.name) - secret.name = secret_update.name - if secret_update.scope is not None: - secret.get_body().scope = secret_update.scope - if secret_update.values is not None: - # Merge the existing values with the update values. - # The values that are set to `None` in the update are removed from - # the existing secret when we call `.secret_values` later. - secret.get_body().values.update(secret_update.values) - - if secret_update.name is not None or secret_update.scope is not None: - # Check if a secret with the same name already exists in the same - # scope. - assert secret.user is not None - secret_exists, msg = self._check_secret_scope( - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace.id, - user=secret.user.id, - exclude_secret_id=secret.id, - ) - if secret_exists: - raise EntityExistsError(msg) - azure_secret_id = self._get_azure_secret_id(secret_id) - secret_value = json.dumps(secret.secret_values) + secret_value = json.dumps(secret_values) # Convert the ZenML secret metadata to Azure tags - metadata = self._get_secret_metadata_for_secret(secret) - - # We manage the created and updated times ourselves, so we don't need to - # rely on the Azure Key Vault API to set them. - updated = datetime.utcnow() - metadata[ZENML_AZURE_SECRET_UPDATED_KEY] = updated.isoformat() - metadata[ZENML_AZURE_SECRET_CREATED_KEY] = ( - secret.created.isoformat() - if secret.created - else metadata[ZENML_AZURE_SECRET_UPDATED_KEY] - ) + metadata = self._get_secret_metadata(secret_id=secret_id) try: self.client.set_secret( @@ -612,39 +311,25 @@ def update_secret( except HttpResponseError as e: raise RuntimeError(f"Error updating secret {secret_id}: {e}") - logger.debug("Updated Azure secret: %s", azure_secret_id) - - secret_model = SecretResponse( - id=secret_id, - name=secret.name, - body=SecretResponseBody( - user=secret.user, - created=secret.created, - updated=updated, - scope=secret.scope, - values=secret.secret_values, - ), - metadata=SecretResponseMetadata( - workspace=secret.workspace, - ), - ) - - return secret_model + logger.debug(f"Updated Azure secret: {azure_secret_id}") - def delete_secret(self, secret_id: UUID) -> None: - """Delete a secret. + def delete_secret_values(self, secret_id: UUID) -> None: + """Deletes secret values for an existing secret. Args: - secret_id: The id of the secret to delete. + secret_id: The ID of the secret. Raises: - KeyError: If the secret does not exist. - RuntimeError: If the Azure Key Vault API returns an unexpected + KeyError: if no secret values for the given ID are stored in the + secrets store. + RuntimeError: if the Azure Key Vault API returns an unexpected error. """ + azure_secret_id = self._get_azure_secret_id(secret_id) + try: self.client.begin_delete_secret( - self._get_azure_secret_id(secret_id), + azure_secret_id, ).wait() except ResourceNotFoundError: raise KeyError(f"Secret with ID {secret_id} not found") @@ -652,3 +337,5 @@ def delete_secret(self, secret_id: UUID) -> None: raise RuntimeError( f"Error deleting secret with ID {secret_id}: {e}" ) + + logger.debug(f"Deleted Azure secret: {azure_secret_id}") diff --git a/src/zenml/zen_stores/secrets_stores/base_secrets_store.py b/src/zenml/zen_stores/secrets_stores/base_secrets_store.py index ca5d0558e86..739d41b4542 100644 --- a/src/zenml/zen_stores/secrets_stores/base_secrets_store.py +++ b/src/zenml/zen_stores/secrets_stores/base_secrets_store.py @@ -13,37 +13,22 @@ # permissions and limitations under the License. """Base Secrets Store implementation.""" from abc import ABC -from datetime import datetime -from functools import partial from typing import ( TYPE_CHECKING, Any, ClassVar, Dict, Optional, - Tuple, Type, - Union, ) from uuid import UUID from pydantic import BaseModel from zenml.config.secrets_store_config import SecretsStoreConfiguration -from zenml.enums import SecretScope, SecretsStoreType +from zenml.enums import SecretsStoreType from zenml.logger import get_logger -from zenml.models import ( - SecretFilter, - SecretRequest, - SecretResponse, - SecretResponseBody, - SecretResponseMetadata, - UserResponse, - WorkspaceResponse, -) from zenml.utils import source_utils -from zenml.utils.pagination_utils import depaginate -from zenml.zen_stores.enums import StoreEvent from zenml.zen_stores.secrets_stores.secrets_store_interface import ( SecretsStoreInterface, ) @@ -56,13 +41,10 @@ ZENML_SECRET_LABEL = "zenml" ZENML_SECRET_ID_LABEL = "zenml_secret_id" ZENML_SECRET_NAME_LABEL = "zenml_secret_name" -ZENML_SECRET_SCOPE_LABEL = "zenml_secret_scope" -ZENML_SECRET_USER_LABEL = "zenml_secret_user" -ZENML_SECRET_WORKSPACE_LABEL = "zenml_secret_workspace" class BaseSecretsStore(BaseModel, SecretsStoreInterface, ABC): - """Base class for accessing and persisting ZenML secret objects. + """Base class for accessing and persisting ZenML secret values. Attributes: config: The configuration of the secret store. @@ -97,14 +79,6 @@ def __init__( super().__init__(**kwargs) self._zen_store = zen_store - self.zen_store.register_event_handler( - StoreEvent.WORKSPACE_DELETED, self._on_workspace_deleted - ) - - self.zen_store.register_event_handler( - StoreEvent.USER_DELETED, self._on_user_deleted - ) - try: self._initialize() except Exception as e: @@ -167,13 +141,6 @@ def get_store_class( return SqlSecretsStore - if store_config.type == SecretsStoreType.REST: - from zenml.zen_stores.secrets_stores.rest_secrets_store import ( - RestSecretsStore, - ) - - return RestSecretsStore - if store_config.type == SecretsStoreType.AWS: from zenml.zen_stores.secrets_stores.aws_secrets_store import ( AWSSecretsStore, @@ -253,160 +220,6 @@ def zen_store(self) -> "BaseZenStore": raise ValueError("Store not initialized") return self._zen_store - # -------------------- - # Store Event Handlers - # -------------------- - - def _on_workspace_deleted( - self, event: StoreEvent, workspace_id: UUID - ) -> None: - """Handle the deletion of a workspace. - - This method deletes all secrets associated with the given workspace. - - Args: - event: The store event. - workspace_id: The ID of the workspace that was deleted. - """ - logger.debug( - "Handling workspace deletion event for workspace %s", workspace_id - ) - - # Delete all secrets associated with the workspace. - secrets = depaginate( - partial( - self.list_secrets, - secret_filter_model=SecretFilter(workspace_id=workspace_id), - ) - ) - for secret in secrets: - try: - self.delete_secret(secret.id) - except KeyError: - pass - except Exception as e: - logger.warning("Failed to delete secret %s: %s", secret.id, e) - - def _on_user_deleted(self, event: StoreEvent, user_id: UUID) -> None: - """Handle the deletion of a user. - - This method deletes all secrets associated with the given user. - - Args: - event: The store event. - user_id: The ID of the user that was deleted. - """ - logger.debug("Handling user deletion event for user %s", user_id) - - # Delete all secrets associated with the user. - secrets = depaginate( - partial( - self.list_secrets, - secret_filter_model=SecretFilter(user_id=user_id), - ) - ) - for secret in secrets: - try: - self.delete_secret(secret.id) - except KeyError: - pass - except Exception as e: - logger.warning("Failed to delete secret %s: %s", secret.id, e) - - # ------------------------------------------ - # Common helpers for Secrets Store back-ends - # ------------------------------------------ - - def _validate_user_and_workspace( - self, user_id: UUID, workspace_id: UUID - ) -> Tuple[UserResponse, WorkspaceResponse]: - """Validates that the given user and workspace IDs are valid. - - This method calls the ZenML store to validate the user and workspace - IDs. It raises a KeyError exception if either the user or workspace - does not exist. - - Args: - user_id: The ID of the user to validate. - workspace_id: The ID of the workspace to validate. - - Returns: - The user and workspace. - """ - user = self.zen_store.get_user(user_id) - workspace = self.zen_store.get_workspace(workspace_id) - - return user, workspace - - def _check_secret_scope( - self, - secret_name: str, - scope: SecretScope, - workspace: UUID, - user: UUID, - exclude_secret_id: Optional[UUID] = None, - ) -> Tuple[bool, str]: - """Checks if a secret with the given name exists in the given scope. - - This method enforces the following scope rules: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. - - Args: - secret_name: The name of the secret. - scope: The scope of the secret. - workspace: The ID of the workspace to which the secret belongs. - user: The ID of the user to which the secret belongs. - exclude_secret_id: The ID of a secret to exclude from the check - (used e.g. during an update to exclude the existing secret). - - Returns: - True if a secret with the given name already exists in the given - scope, False otherwise, and an error message. - """ - filter = SecretFilter( - name=secret_name, - scope=scope, - page=1, - size=2, # We only need to know if there is more than one secret - ) - - if scope in [SecretScope.WORKSPACE, SecretScope.USER]: - filter.workspace_id = workspace - if scope == SecretScope.USER: - filter.user_id = user - - existing_secrets = self.list_secrets(secret_filter_model=filter).items - if exclude_secret_id is not None: - existing_secrets = [ - s for s in existing_secrets if s.id != exclude_secret_id - ] - - if existing_secrets: - existing_secret_model = existing_secrets[0] - - msg = ( - f"Found an existing {scope.value} scoped secret with the " - f"same '{secret_name}' name" - ) - if scope in [SecretScope.WORKSPACE, SecretScope.USER]: - msg += ( - f" in the same '{existing_secret_model.workspace.name}' " - f"workspace" - ) - if scope == SecretScope.USER: - assert existing_secret_model.user - msg += ( - f" for the same '{existing_secret_model.user.name}' user" - ) - - return True, msg - - return False, "" - # -------------------------------------------------------- # Helpers for Secrets Store back-ends that use tags/labels # -------------------------------------------------------- @@ -414,32 +227,21 @@ def _check_secret_scope( def _get_secret_metadata( self, secret_id: Optional[UUID] = None, - secret_name: Optional[str] = None, - scope: Optional[SecretScope] = None, - workspace: Optional[UUID] = None, - user: Optional[UUID] = None, ) -> Dict[str, str]: """Get a dictionary with metadata that can be used as tags/labels. This utility method can be used with Secrets Managers that can associate metadata (e.g. tags, labels) with a secret. The metadata can - be configured alongside each secret and then used as a filter criteria - when running queries against the backend e.g. to retrieve all the - secrets within a given scope or to retrieve all secrets with a given - name within a given scope. + be configured alongside each secret. NOTE: the ZENML_SECRET_LABEL is always included in the metadata to distinguish ZenML secrets from other secrets that might be stored in the same backend, as well as to distinguish between different ZenML deployments using the same backend. Its value is set to the ZenML - deployment ID, and it should be included in all queries to the backend. + deployment ID. Args: secret_id: Optional secret ID to include in the metadata. - secret_name: Optional secret name to include in the metadata. - scope: Optional scope to include in the metadata. - workspace: Optional workspace ID to include in the metadata. - user: Optional user ID to include in the scope metadata. Returns: Dictionary with secret metadata information. @@ -452,82 +254,27 @@ def _get_secret_metadata( ZENML_SECRET_LABEL: str(self.zen_store.get_store_info().id) } - if secret_id: + # Include the secret ID if provided. + if secret_id is not None: metadata[ZENML_SECRET_ID_LABEL] = str(secret_id) - if secret_name: - metadata[ZENML_SECRET_NAME_LABEL] = secret_name - if scope: - metadata[ZENML_SECRET_SCOPE_LABEL] = scope.value - if workspace: - metadata[ZENML_SECRET_WORKSPACE_LABEL] = str(workspace) - if user: - metadata[ZENML_SECRET_USER_LABEL] = str(user) return metadata - def _get_secret_metadata_for_secret( - self, - secret: Union[SecretRequest, SecretResponse], - secret_id: Optional[UUID] = None, - ) -> Dict[str, str]: - """Get a dictionary with the secrets metadata describing a secret. - - This utility method can be used with Secrets Managers that can - associate metadata (e.g. tags, labels) with a secret. The metadata can - be configured alongside each secret and then used as a filter criteria - when running queries against the backend. - - Args: - secret: The secret to get the metadata for. - secret_id: Optional secret ID to include in the metadata (if not - already included in the secret). - - Returns: - Dictionary with secret metadata information. - """ - if isinstance(secret, SecretRequest): - return self._get_secret_metadata( - secret_id=secret_id, - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace, - user=secret.user, - ) - - return self._get_secret_metadata( - secret_id=secret.id, - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace.id, - user=secret.user.id if secret.user else None, - ) - - def _create_secret_from_metadata( + def _verify_secret_metadata( self, + secret_id: UUID, metadata: Dict[str, str], - created: datetime, - updated: datetime, - values: Optional[Dict[str, str]] = None, - hydrate: bool = False, - ) -> SecretResponse: - """Create a ZenML secret model from metadata stored in the secrets store backend. + ) -> None: + """Verify that the given metadata corresponds to a valid ZenML secret. Args: + secret_id: The ID of the secret. metadata: ZenML secret metadata collected from the backend secret (e.g. from secret tags/labels). - created: The secret creation time. - updated: The secret last updated time. - values: The secret values (optional). - hydrate: Flag deciding whether to hydrate the output model - by including metadata fields in the response. - - Returns: - The ZenML secret. Raises: - KeyError: If the secret does not have the required metadata, if it - is not managed by this ZenML instance or if it is linked to a - user or workspace that no longer exists. + KeyError: If the secret does not have the required metadata or if it + is not managed by this ZenML instance. """ # Double-check that the secret is managed by this ZenML instance. if metadata.get(ZENML_SECRET_LABEL) != str( @@ -537,56 +284,18 @@ def _create_secret_from_metadata( # Recover the ZenML secret fields from the input secret metadata. try: - secret_id = UUID(metadata[ZENML_SECRET_ID_LABEL]) - name = metadata[ZENML_SECRET_NAME_LABEL] - scope = SecretScope(metadata[ZENML_SECRET_SCOPE_LABEL]) - workspace_id = UUID(metadata[ZENML_SECRET_WORKSPACE_LABEL]) - user_id = UUID(metadata[ZENML_SECRET_USER_LABEL]) + stored_secret_id = UUID(metadata[ZENML_SECRET_ID_LABEL]) except KeyError as e: raise KeyError( f"Secret could not be retrieved: missing required metadata: {e}" ) - try: - user, workspace = self._validate_user_and_workspace( - user_id, workspace_id - ) - except KeyError as e: - # The user or workspace associated with the secret no longer - # exists. This can happen if the user or workspace is being - # deleted nearly at the same time as this call. In this case, we - # raise a KeyError exception. The caller should handle this - # exception by assuming that the secret no longer exists. - logger.warning( - f"Secret with ID {secret_id} is associated with a " - f"non-existent user or workspace. Silently ignoring the " - f"secret: {e}" - ) + if secret_id != stored_secret_id: raise KeyError( - f"Secret with ID {secret_id} could not be retrieved: " - f"the secret is associated with a non-existent user or " - f"workspace: {e}" + f"Secret could not be retrieved: secret ID mismatch: " + f"expected {secret_id}, got {stored_secret_id}" ) - secret_model = SecretResponse( - id=secret_id, - name=name, - body=SecretResponseBody( - user=user, - created=created, - updated=updated, - scope=scope, - values=values if values else {}, - ), - metadata=SecretResponseMetadata( - workspace=workspace, - ) - if hydrate - else None, - ) - - return secret_model - class Config: """Pydantic configuration class.""" diff --git a/src/zenml/zen_stores/secrets_stores/gcp_secrets_store.py b/src/zenml/zen_stores/secrets_stores/gcp_secrets_store.py index eb0bca2d952..8c68f50203a 100644 --- a/src/zenml/zen_stores/secrets_stores/gcp_secrets_store.py +++ b/src/zenml/zen_stores/secrets_stores/gcp_secrets_store.py @@ -15,20 +15,13 @@ import json -import math import os -import re -import uuid -from datetime import datetime from typing import ( Any, ClassVar, Dict, - List, Optional, - Tuple, Type, - Union, cast, ) from uuid import UUID @@ -37,12 +30,9 @@ from google.cloud.secretmanager import SecretManagerServiceClient from pydantic import root_validator -from zenml.analytics.enums import AnalyticsEvent -from zenml.analytics.utils import track_decorator from zenml.enums import ( SecretsStoreType, ) -from zenml.exceptions import EntityExistsError from zenml.integrations.gcp import ( GCP_CONNECTOR_TYPE, GCP_RESOURCE_TYPE, @@ -51,15 +41,6 @@ GCPAuthenticationMethods, ) from zenml.logger import get_logger -from zenml.models import ( - Page, - SecretFilter, - SecretRequest, - SecretResponse, - SecretResponseBody, - SecretResponseMetadata, - SecretUpdate, -) from zenml.zen_stores.secrets_stores.service_connector_secrets_store import ( ServiceConnectorSecretsStore, ServiceConnectorSecretsStoreConfiguration, @@ -71,9 +52,6 @@ GCP_ZENML_SECRET_NAME_PREFIX = "zenml" ZENML_GROUP_KEY = "zenml-group-key" ZENML_GCP_SECRET_SCOPE_PATH_SEPARATOR = "-" -ZENML_GCP_DATE_FORMAT_STRING = "%Y-%m-%d-%H-%M-%S" -ZENML_GCP_SECRET_CREATED_KEY = "zenml-secret-created" -ZENML_GCP_SECRET_UPDATED_KEY = "zenml-secret-updated" class GCPSecretsStoreConfiguration(ServiceConnectorSecretsStoreConfiguration): @@ -195,47 +173,6 @@ def parent_name(self) -> str: """ return f"projects/{self.config.project_id}" - def _get_secret_labels( - self, secret: Union[SecretRequest, SecretResponse] - ) -> List[Tuple[str, str]]: - """Return a list of Google secret label values for a given secret. - - Args: - secret: the secret object - - Returns: - A list of Google secret label values - """ - metadata = self._get_secret_metadata_for_secret(secret) - return list(metadata.items()) - - def _validate_gcp_secret_name(self, name: str) -> None: - """Validate a secret name. - - Given that we save secret names as labels, we are also limited by the - limitation that Google imposes on label values: max 63 characters and - must only contain lowercase letters, numerals and the hyphen (-) and - underscore (_) characters. - - Args: - name: the secret name - - Raises: - ValueError: if the secret name is invalid - """ - if not re.fullmatch(r"[a-z0-9_\-]+", name): - raise ValueError( - f"Invalid secret name '{name}'. Must contain " - f"only lowercase alphanumeric characters and the hyphen (-) and " - f"underscore (_) characters." - ) - - if name and len(name) > 63: - raise ValueError( - f"Invalid secret name '{name}'. The length is " - f"limited to maximum 63 characters." - ) - def _get_gcp_secret_name( self, secret_id: UUID, @@ -254,133 +191,24 @@ def _get_gcp_secret_name( """ return f"{GCP_ZENML_SECRET_NAME_PREFIX}-{str(secret_id)}" - def _convert_gcp_secret( + def store_secret_values( self, - labels: Dict[str, str], - values: Optional[Dict[str, str]] = None, - hydrate: bool = False, - ) -> SecretResponse: - """Create a ZenML secret model from data stored in an GCP secret. - - If the GCP secret cannot be converted, the method acts as if the - secret does not exist and raises a KeyError. - - Args: - labels: The GCP secret labels. - values: The GCP secret values. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - The ZenML secret model. - - Raises: - KeyError: if the GCP secret cannot be converted. - """ - # Recover the ZenML secret metadata from the AWS secret tags. - - # The GCP secret labels do not really behave like a dictionary: when - # a key is not found, it does not raise a KeyError, but instead - # returns an empty string. That's why we make this conversion. - label_dict = dict(labels) - - try: - created = datetime.strptime( - label_dict[ZENML_GCP_SECRET_CREATED_KEY], - ZENML_GCP_DATE_FORMAT_STRING, - ) - updated = datetime.strptime( - label_dict[ZENML_GCP_SECRET_UPDATED_KEY], - ZENML_GCP_DATE_FORMAT_STRING, - ) - except KeyError as e: - raise KeyError( - f"Invalid GCP secret: missing required tag '{e}'" - ) from e - - return self._create_secret_from_metadata( - metadata=label_dict, - created=created, - updated=updated, - values=values, - hydrate=hydrate, - ) - - def _get_gcp_filter_string(self, secret_filter_model: SecretFilter) -> str: - """Convert a SecretFilter to a GCP filter string. - - Args: - secret_filter_model: The secret filter model. - - Returns: - The GCP filter string. - """ - operator_map = { - "equals": ":", - } - filter_terms = [] - for filter in secret_filter_model.list_of_filters: - filter_terms.append( - f"{filter.column}{operator_map[filter.operation.value]}{filter.value}" - ) - - return f" {secret_filter_model.logical_operator.name} ".join( - filter_terms - ) - - @track_decorator(AnalyticsEvent.CREATED_SECRET) - def create_secret(self, secret: SecretRequest) -> SecretResponse: - """Create a new secret. - - The new secret is also validated against the scoping rules enforced in - the secrets store: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Store secret values for a new secret. Args: - secret: The secret to create. - - Returns: - The created secret. + secret_id: ID of the secret. + secret_values: Values for the secret. Raises: - RuntimeError: if the secret was unable to be created. - EntityExistsError: If a secret with the same name already exists - in the same scope. + RuntimeError: if the GCP Secrets Manager API returns an unexpected + error. """ - self._validate_gcp_secret_name(secret.name) - - user, workspace = self._validate_user_and_workspace( - secret.user, secret.workspace - ) - - # Check if a secret with the same name already exists in the same - # scope. - secret_exists, msg = self._check_secret_scope( - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace, - user=secret.user, - ) - if secret_exists: - raise EntityExistsError(msg) + secret_value = json.dumps(secret_values) - secret_id = uuid.uuid4() - secret_value = json.dumps(secret.secret_values) - - created = datetime.utcnow().replace(tzinfo=None, microsecond=0) - labels = self._get_secret_metadata_for_secret( - secret=secret, secret_id=secret_id - ) - labels[ZENML_GCP_SECRET_CREATED_KEY] = created.strftime( - ZENML_GCP_DATE_FORMAT_STRING - ) - labels[ZENML_GCP_SECRET_UPDATED_KEY] = created.strftime( - ZENML_GCP_DATE_FORMAT_STRING - ) + labels = self._get_secret_metadata(secret_id=secret_id) try: gcp_secret = self.client.create_secret( @@ -394,7 +222,7 @@ def create_secret(self, secret: SecretRequest) -> SecretResponse: } ) - logger.debug("Created empty parent secret: %s", gcp_secret.name) + logger.debug(f"Created empty GCP parent secret: {gcp_secret.name}") self.client.add_secret_version( request={ @@ -402,42 +230,26 @@ def create_secret(self, secret: SecretRequest) -> SecretResponse: "payload": {"data": secret_value.encode()}, } ) + + logger.debug(f"Added value to GCP secret {gcp_secret.name}") except Exception as e: raise RuntimeError(f"Failed to create secret.: {str(e)}") from e - logger.debug("Added value to secret.") - - return SecretResponse( - id=secret_id, - name=secret.name, - body=SecretResponseBody( - user=user, - created=created, - updated=created, - scope=secret.scope, - values=secret.secret_values, - ), - metadata=SecretResponseMetadata( - workspace=workspace, - ), - ) + logger.debug(f"Created GCP secret {gcp_secret.name}") - def get_secret( - self, secret_id: UUID, hydrate: bool = True - ) -> SecretResponse: - """Get a secret by ID. + def get_secret_values(self, secret_id: UUID) -> Dict[str, str]: + """Get the secret values for an existing secret. Args: - secret_id: The ID of the secret to fetch. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. + secret_id: ID of the secret. Returns: - The secret. + The secret values. Raises: - KeyError: If the secret does not exist. - RuntimeError: If the GCP Secrets Manager API returns an unexpected + KeyError: if no secret values for the given ID are stored in the + secrets store. + RuntimeError: if the GCP Secrets Manager API returns an unexpected error. """ gcp_secret_name = self.client.secret_path( @@ -452,189 +264,67 @@ def get_secret( ) except google_exceptions.NotFound as e: raise KeyError( - f"Can't find the specified secret for secret_id '{secret_id}': {str(e)}" + f"Can't find the specified secret for secret_id '{secret_id}': " + f"{str(e)}" ) from e except Exception as e: raise RuntimeError( f"Error fetching secret with ID {secret_id} {e}" ) - secret_values = json.loads( - secret_version_values.payload.data.decode("UTF-8") + # The GCP secret labels do not really behave like a dictionary: when + # a key is not found, it does not raise a KeyError, but instead + # returns an empty string. That's why we make this conversion. + metadata = dict(secret.labels) + + # The _verify_secret_metadata method raises a KeyError if the + # secret is not valid or does not belong to this server. Here we + # simply pass the exception up the stack, as if the secret was not found + # in the first place. + self._verify_secret_metadata( + secret_id=secret_id, + metadata=metadata, ) - return self._convert_gcp_secret( - labels=secret.labels, - values=secret_values, - hydrate=hydrate, + secret_values = json.loads( + secret_version_values.payload.data.decode("UTF-8") ) - def list_secrets( - self, secret_filter_model: SecretFilter, hydrate: bool = False - ) -> Page[SecretResponse]: - """List all secrets matching the given filter criteria. - - Note that returned secrets do not include any secret values. To fetch - the secret values, use `get_secret`. - - Args: - secret_filter_model: The filter criteria. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - A list of all secrets matching the filter criteria, with pagination - information and sorted according to the filter criteria. The - returned secrets do not include any secret values, only metadata. To - fetch the secret values, use `get_secret` individually with each - secret. - - Raises: - ValueError: If the filter contains an out-of-bounds page number. - RuntimeError: If the Azure Key Vault API returns an unexpected - error. - """ - # TODO: implement filter method for server-side filtering - # convert the secret_filter_model to a GCP filter string - gcp_filters = "" - # gcp_filters = self._get_gcp_filter_string( - # secret_filter_model=secret_filter_model - # ) - - try: - # get all the secrets and their labels (for their names) from GCP - # (use the filter string to limit what doesn't match the filter) - secrets = [] - for secret in self.client.list_secrets( - request={ - "parent": self.parent_name, - "filter": gcp_filters, - } - ): - try: - secrets.append( - self._convert_gcp_secret( - secret.labels, hydrate=hydrate - ) - ) - except KeyError: - # keep going / ignore if this secret version doesn't exist - # or isn't a ZenML secret - continue - except Exception as e: - raise RuntimeError(f"Error listing GCP secrets: {e}") from e - - # do client filtering for anything not covered by the filter string - filtered_secrets = [ - secret - for secret in secrets - if secret_filter_model.secret_matches(secret) - ] - - # sort the results - sorted_results = secret_filter_model.sort_secrets(filtered_secrets) - - # paginate the results - secret_count = len(sorted_results) - if secret_count == 0: - total_pages = 1 - else: - total_pages = math.ceil(secret_count / secret_filter_model.size) - if secret_filter_model.page > total_pages: - raise ValueError( - f"Invalid page {secret_filter_model.page}. The requested page " - f"size is {secret_filter_model.size} and there are a total of " - f"{secret_count} items for this query. The maximum page value " - f"therefore is {total_pages}." + if not isinstance(secret_values, dict): + raise RuntimeError( + f"Google secret values for secret ID {gcp_secret_name} could " + "not be decoded: expected a dictionary." ) - return Page[SecretResponse]( - total=secret_count, - total_pages=total_pages, - items=sorted_results[ - (secret_filter_model.page - 1) - * secret_filter_model.size : secret_filter_model.page - * secret_filter_model.size - ], - index=secret_filter_model.page, - max_size=secret_filter_model.size, - ) - - def update_secret( - self, secret_id: UUID, secret_update: SecretUpdate - ) -> SecretResponse: - """Update a secret. - Secret values that are specified as `None` in the update that are - present in the existing secret are removed from the existing secret. - Values that are present in both secrets are overwritten. All other - values in both the existing secret and the update are kept (merged). + logger.debug(f"Fetched GCP secret: {gcp_secret_name}") - If the update includes a change of name or scope, the scoping rules - enforced in the secrets store are used to validate the update: + return secret_values - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + def update_secret_values( + self, + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Updates secret values for an existing secret. Args: - secret_id: The ID of the secret to update. - secret_update: The update to apply to the secret. - - Returns: - The updated secret. + secret_id: The ID of the secret to be updated. + secret_values: The new secret values. Raises: - RuntimeError: If the secret update is invalid. - EntityExistsError: If the update includes a change of name or - scope and a secret with the same name already exists in the - same scope. + RuntimeError: if the GCP Secrets Manager API returns an unexpected + error. """ - secret = self.get_secret(secret_id=secret_id) gcp_secret_name = self.client.secret_path( self.config.project_id, self._get_gcp_secret_name(secret_id=secret_id), ) - if secret_update.name is not None: - self._validate_gcp_secret_name(secret_update.name) - secret.name = secret_update.name - if secret_update.scope is not None: - secret.get_body().scope = secret_update.scope - if secret_update.values is not None: - # Merge the existing values with the update values. - # The values that are set to `None` in the update are removed from - # the existing secret when we call `.secret_values` later. - secret.get_body().values.update(secret_update.values) - - if secret_update.name is not None or secret_update.scope is not None: - # Check if a secret with the same name already exists in the same - # scope. - assert secret.user is not None - secret_exists, msg = self._check_secret_scope( - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace.id, - user=secret.user.id, - exclude_secret_id=secret.id, - ) - if secret_exists: - raise EntityExistsError(msg) - # Convert the ZenML secret metadata to GCP labels - updated = datetime.utcnow().replace(tzinfo=None, microsecond=0) - metadata = self._get_secret_metadata_for_secret(secret) - metadata[ZENML_GCP_SECRET_UPDATED_KEY] = updated.strftime( - ZENML_GCP_DATE_FORMAT_STRING - ) - metadata[ZENML_GCP_SECRET_CREATED_KEY] = ( - secret.created.strftime(ZENML_GCP_DATE_FORMAT_STRING) - if secret.created - else metadata[ZENML_GCP_SECRET_UPDATED_KEY] - ) + metadata = self._get_secret_metadata(secret_id) try: - # UPDATE THE SECRET METADATA + # Update the secret metadata update_secret = { "name": gcp_secret_name, "labels": metadata, @@ -646,8 +336,8 @@ def update_secret( "update_mask": update_mask, } ) - # ADD A NEW SECRET VERSION - secret_value = json.dumps(secret.secret_values) + # Add a new secret version + secret_value = json.dumps(secret_values) self.client.add_secret_version( request={ "parent": gcp_updated_secret.name, @@ -657,32 +347,19 @@ def update_secret( except Exception as e: raise RuntimeError(f"Error updating secret: {e}") from e - logger.debug("Updated GCP secret: %s", gcp_secret_name) - - return SecretResponse( - id=secret_id, - name=secret.name, - body=SecretResponseBody( - user=secret.user, - created=secret.created, - updated=updated, - scope=secret.scope, - values=secret.secret_values, - ), - metadata=SecretResponseMetadata( - workspace=secret.workspace, - ), - ) + logger.debug(f"Updated GCP secret: {gcp_secret_name}") - def delete_secret(self, secret_id: UUID) -> None: - """Delete a secret. + def delete_secret_values(self, secret_id: UUID) -> None: + """Deletes secret values for an existing secret. Args: - secret_id: The ID of the secret to delete. + secret_id: The ID of the secret. Raises: - KeyError: If the secret could not be found. - RuntimeError: If the secret could not be deleted. + KeyError: if no secret values for the given ID are stored in the + secrets store. + RuntimeError: if the GCP Secrets Manager API returns an unexpected + error. """ gcp_secret_name = self.client.secret_path( self.config.project_id, @@ -695,3 +372,5 @@ def delete_secret(self, secret_id: UUID) -> None: raise KeyError(f"Secret with ID {secret_id} not found") except Exception as e: raise RuntimeError(f"Failed to delete secret: {str(e)}") from e + + logger.debug(f"Deleted GCP secret: {gcp_secret_name}") diff --git a/src/zenml/zen_stores/secrets_stores/hashicorp_secrets_store.py b/src/zenml/zen_stores/secrets_stores/hashicorp_secrets_store.py index 67a770a6215..6eb5bf4121c 100644 --- a/src/zenml/zen_stores/secrets_stores/hashicorp_secrets_store.py +++ b/src/zenml/zen_stores/secrets_stores/hashicorp_secrets_store.py @@ -13,16 +13,9 @@ # permissions and limitations under the License. """HashiCorp Vault Secrets Store implementation.""" -import logging -import math -import re -import uuid -from datetime import datetime from typing import ( - Any, ClassVar, Dict, - List, Optional, Type, ) @@ -35,23 +28,11 @@ ) from pydantic import SecretStr -from zenml.analytics.enums import AnalyticsEvent -from zenml.analytics.utils import track_decorator from zenml.config.secrets_store_config import SecretsStoreConfiguration from zenml.enums import ( SecretsStoreType, ) -from zenml.exceptions import EntityExistsError from zenml.logger import get_logger -from zenml.models import ( - Page, - SecretFilter, - SecretRequest, - SecretResponse, - SecretResponseBody, - SecretResponseMetadata, - SecretUpdate, -) from zenml.zen_stores.secrets_stores.base_secrets_store import ( BaseSecretsStore, ) @@ -62,8 +43,6 @@ HVAC_ZENML_SECRET_NAME_PREFIX = "zenml" ZENML_VAULT_SECRET_VALUES_KEY = "zenml_secret_values" ZENML_VAULT_SECRET_METADATA_KEY = "zenml_secret_metadata" -ZENML_VAULT_SECRET_CREATED_KEY = "zenml_secret_created" -ZENML_VAULT_SECRET_UPDATED_KEY = "zenml_secret_updated" class HashiCorpVaultSecretsStoreConfiguration(SecretsStoreConfiguration): @@ -108,32 +87,11 @@ class HashiCorpVaultSecretsStore(BaseSecretsStore): * the name/ID of an HashiCorp Vault secret is derived from the ZenML secret UUID and a `zenml` prefix in the form `zenml/{zenml_secret_uuid}`. This clearly identifies a secret as being managed by ZenML in the HashiCorp Vault - server. This also allows use to reduce the scope of `list_secrets` to cover - only secrets managed by ZenML by using `zenml/` as the path prefix. + server. * given that HashiCorp Vault secrets do not support attaching arbitrary metadata in the form of label or tags, we store the entire ZenML secret - metadata (e.g. name, scope, etc.) alongside the secret values in the - HashiCorp Vault secret value. - - * when a user or workspace is deleted, the secrets associated with it are - deleted automatically via registered event handlers. - - Known challenges and limitations: - - * HashiCorp Vault secrets do not support filtering secrets by metadata - attached to secrets in the form of label or tags. This means that we cannot - filter secrets server-side based on their metadata (e.g. name, scope, etc.). - Instead, we have to retrieve all ZenML managed secrets and filter them - client-side. - - * HashiCorp Vault secrets are versioned. This means that when a secret is - updated, a new version is created which has its own creation timestamp. - Furthermore, older secret versions are deleted automatically after a certain - configurable number of versions is reached. To work around this, we also - manage `created` and `updated` timestamps here and store them in the secret - value itself. - + metadata alongside the secret values in the HashiCorp Vault secret value. Attributes: config: The configuration of the HashiCorp Vault secrets store. @@ -196,25 +154,6 @@ def _initialize(self) -> None: # Secrets # ------ - @staticmethod - def _validate_vault_secret_name(name: str) -> None: - """Validate a secret name. - - HashiCorp Vault secret names must contain only alphanumeric characters - and the characters _+=.@-/. - - Args: - name: the secret name - - Raises: - ValueError: if the secret name is invalid - """ - if not re.fullmatch(r"[a-zA-Z0-9_+=\.@\-/]*", name): - raise ValueError( - f"Invalid secret name or namespace '{name}'. Must contain " - f"only alphanumeric characters and the characters _+=.@-/." - ) - @staticmethod def _get_vault_secret_id( secret_id: UUID, @@ -233,107 +172,32 @@ def _get_vault_secret_id( """ return f"{HVAC_ZENML_SECRET_NAME_PREFIX}/{str(secret_id)}" - def _convert_vault_secret( + def store_secret_values( self, - vault_secret: Dict[str, Any], - hydrate: bool = False, - ) -> SecretResponse: - """Create a ZenML secret model from data stored in an HashiCorp Vault secret. - - If the HashiCorp Vault secret cannot be converted, the method acts as if - the secret does not exist and raises a KeyError. - - Args: - vault_secret: The HashiCorp Vault secret in JSON form. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - The ZenML secret. - - Raises: - KeyError: if the HashiCorp Vault secret cannot be converted. - """ - try: - metadata = vault_secret[ZENML_VAULT_SECRET_METADATA_KEY] - values = vault_secret[ZENML_VAULT_SECRET_VALUES_KEY] - created = datetime.fromisoformat( - vault_secret[ZENML_VAULT_SECRET_CREATED_KEY], - ) - updated = datetime.fromisoformat( - vault_secret[ZENML_VAULT_SECRET_UPDATED_KEY], - ) - except (KeyError, ValueError) as e: - raise KeyError( - f"Secret could not be retrieved: missing required metadata: {e}" - ) - - return self._create_secret_from_metadata( - metadata=metadata, - created=created, - updated=updated, - values=values, - hydrate=hydrate, - ) - - @track_decorator(AnalyticsEvent.CREATED_SECRET) - def create_secret(self, secret: SecretRequest) -> SecretResponse: - """Creates a new secret. - - The new secret is also validated against the scoping rules enforced in - the secrets store: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Store secret values for a new secret. Args: - secret: The secret to create. - - Returns: - The newly created secret. + secret_id: ID of the secret. + secret_values: Values for the secret. Raises: - EntityExistsError: If a secret with the same name already exists - in the same scope. RuntimeError: If the HashiCorp Vault API returns an unexpected error. """ - self._validate_vault_secret_name(secret.name) - user, workspace = self._validate_user_and_workspace( - secret.user, secret.workspace - ) - - # Check if a secret with the same name already exists in the same - # scope. - secret_exists, msg = self._check_secret_scope( - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace, - user=secret.user, - ) - if secret_exists: - raise EntityExistsError(msg) - - # Generate a new UUID for the secret - secret_id = uuid.uuid4() vault_secret_id = self._get_vault_secret_id(secret_id) - metadata = self._get_secret_metadata_for_secret( - secret, secret_id=secret_id - ) + metadata = self._get_secret_metadata(secret_id=secret_id) - created = datetime.utcnow() try: self.client.secrets.kv.v2.create_or_update_secret( path=vault_secret_id, # Store the ZenML secret metadata alongside the secret values secret={ - ZENML_VAULT_SECRET_VALUES_KEY: secret.secret_values, + ZENML_VAULT_SECRET_VALUES_KEY: secret_values, ZENML_VAULT_SECRET_METADATA_KEY: metadata, - ZENML_VAULT_SECRET_CREATED_KEY: created.isoformat(), - ZENML_VAULT_SECRET_UPDATED_KEY: created.isoformat(), }, # Do not allow overwriting an existing secret cas=0, @@ -341,40 +205,20 @@ def create_secret(self, secret: SecretRequest) -> SecretResponse: except VaultError as e: raise RuntimeError(f"Error creating secret: {e}") - logger.debug("Created HashiCorp Vault secret: %s", vault_secret_id) - - secret_model = SecretResponse( - id=secret_id, - name=secret.name, - body=SecretResponseBody( - user=user, - created=created, - updated=created, - scope=secret.scope, - values=secret.secret_values, - ), - metadata=SecretResponseMetadata( - workspace=workspace, - ), - ) - - return secret_model + logger.debug(f"Created HashiCorp Vault secret: {vault_secret_id}") - def get_secret( - self, secret_id: UUID, hydrate: bool = True - ) -> SecretResponse: - """Get a secret by ID. + def get_secret_values(self, secret_id: UUID) -> Dict[str, str]: + """Get the secret values for an existing secret. Args: - secret_id: The ID of the secret to fetch. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. + secret_id: ID of the secret. Returns: - The secret. + The secret values. Raises: - KeyError: If the secret does not exist. + KeyError: if no secret values for the given ID are stored in the + secrets store. RuntimeError: If the HashiCorp Vault API returns an unexpected error. """ @@ -395,213 +239,62 @@ def get_secret( f"Error fetching secret with ID {secret_id} {e}" ) - # The _convert_vault_secret method raises a KeyError if the - # secret is tied to a workspace or user that no longer exists. Here we - # simply pass the exception up the stack, as if the secret was not found - # in the first place, knowing that it will be cascade-deleted soon. - return self._convert_vault_secret( - vault_secret, - hydrate=hydrate, - ) - - def list_secrets( - self, secret_filter_model: SecretFilter, hydrate: bool = False - ) -> Page[SecretResponse]: - """List all secrets matching the given filter criteria. - - Note that returned secrets do not include any secret values. To fetch - the secret values, use `get_secret`. - - Args: - secret_filter_model: All filter parameters including pagination - params. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - A list of all secrets matching the filter criteria, with pagination - information and sorted according to the filter criteria. The - returned secrets do not include any secret values, only metadata. To - fetch the secret values, use `get_secret` individually with each - secret. - - Raises: - ValueError: If the filter contains an out-of-bounds page number. - RuntimeError: If the HashiCorp Vault API returns an unexpected - error. - """ - # The HashiCorp Vault API does not natively support any of the - # filtering, sorting or pagination options that ZenML supports. The - # implementation of this method therefore has to fetch all secrets from - # the Key Vault, then apply the filtering, sorting and pagination on - # the client side. - - results: List[SecretResponse] = [] - try: - # List all ZenML secrets in the Vault - all_secrets = ( - self.client.secrets.kv.v2.list_secrets( - path=HVAC_ZENML_SECRET_NAME_PREFIX - ) - .get("data", {}) - .get("keys", []) + metadata = vault_secret[ZENML_VAULT_SECRET_METADATA_KEY] + values = vault_secret[ZENML_VAULT_SECRET_VALUES_KEY] + except (KeyError, ValueError) as e: + raise KeyError( + f"Secret could not be retrieved: missing required metadata: {e}" ) - except InvalidPath: - # no secrets created yet - pass - except VaultError as e: - raise RuntimeError(f"Error listing HashiCorp Vault secrets: {e}") - else: - # Convert the Vault secrets to ZenML secrets - for secret_uuid in all_secrets: - vault_secret_id = ( - f"{HVAC_ZENML_SECRET_NAME_PREFIX}/{secret_uuid}" - ) - try: - vault_secret = ( - self.client.secrets.kv.v2.read_secret( - path=vault_secret_id - ) - .get("data", {}) - .get("data", {}) - ) - except (InvalidPath, VaultError) as e: - logging.warning( - f"Error fetching secret with ID {vault_secret_id}: {e}", - ) - continue - - try: - secret_model = self._convert_vault_secret( - vault_secret, - hydrate=hydrate, - ) - except KeyError as e: - # The _convert_vault_secret method raises a KeyError - # if the secret is tied to a workspace or user that no - # longer exists or if it is otherwise not valid. Here we - # pretend that the secret does not exist. - logging.warning( - f"Error fetching secret with ID {vault_secret_id}: {e}", - ) - continue - - # Filter the secret on the client side. - if not secret_filter_model.secret_matches(secret_model): - continue - - # Remove the secret values from the response - secret_model.get_body().values = {} - results.append(secret_model) - - # Sort the results - sorted_results = secret_filter_model.sort_secrets(results) - - # Paginate the results - total = len(sorted_results) - if total == 0: - total_pages = 1 - else: - total_pages = math.ceil(total / secret_filter_model.size) - - if secret_filter_model.page > total_pages: - raise ValueError( - f"Invalid page {secret_filter_model.page}. The requested page " - f"size is {secret_filter_model.size} and there are a total of " - f"{total} items for this query. The maximum page value " - f"therefore is {total_pages}." + + if not isinstance(values, dict) or not isinstance(metadata, dict): + raise RuntimeError( + f"HashiCorp Vault secret values for secret {vault_secret_id} " + "could not be retrieved: invalid type for metadata or values" ) - return Page[SecretResponse]( - total=total, - total_pages=total_pages, - items=sorted_results[ - (secret_filter_model.page - 1) - * secret_filter_model.size : secret_filter_model.page - * secret_filter_model.size - ], - index=secret_filter_model.page, - max_size=secret_filter_model.size, + # The _verify_secret_metadata method raises a KeyError if the + # secret is not valid or does not belong to this server. Here we + # simply pass the exception up the stack, as if the secret was not found + # in the first place. + self._verify_secret_metadata( + secret_id=secret_id, + metadata=metadata, ) - def update_secret( - self, secret_id: UUID, secret_update: SecretUpdate - ) -> SecretResponse: - """Updates a secret. + logger.debug(f"Fetched HashiCorp Vault secret: {vault_secret_id}") - Secret values that are specified as `None` in the update that are - present in the existing secret are removed from the existing secret. - Values that are present in both secrets are overwritten. All other - values in both the existing secret and the update are kept (merged). + return values - If the update includes a change of name or scope, the scoping rules - enforced in the secrets store are used to validate the update: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + def update_secret_values( + self, + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Updates secret values for an existing secret. Args: secret_id: The ID of the secret to be updated. - secret_update: The update to be applied. - - Returns: - The updated secret. + secret_values: The new secret values. Raises: - KeyError: If the secret does not exist. - EntityExistsError: If the update includes a change of name or - scope and a secret with the same name already exists in the - same scope. + KeyError: if no secret values for the given ID are stored in the + secrets store. RuntimeError: If the HashiCorp Vault API returns an unexpected error. """ - secret = self.get_secret(secret_id) - - if secret_update.name is not None: - self._validate_vault_secret_name(secret_update.name) - secret.name = secret_update.name - if secret_update.scope is not None: - secret.get_body().scope = secret_update.scope - if secret_update.values is not None: - # Merge the existing values with the update values. - # The values that are set to `None` in the update are removed from - # the existing secret when we call `.secret_values` later. - secret.get_body().values.update(secret_update.values) - - if secret_update.name is not None or secret_update.scope is not None: - # Check if a secret with the same name already exists in the same - # scope. - assert secret.user is not None - secret_exists, msg = self._check_secret_scope( - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace.id, - user=secret.user.id, - exclude_secret_id=secret.id, - ) - if secret_exists: - raise EntityExistsError(msg) - vault_secret_id = self._get_vault_secret_id(secret_id) # Convert the ZenML secret metadata to HashiCorp Vault tags - metadata = self._get_secret_metadata_for_secret(secret) + metadata = self._get_secret_metadata(secret_id=secret_id) - updated = datetime.utcnow() try: self.client.secrets.kv.v2.create_or_update_secret( path=vault_secret_id, # Store the ZenML secret metadata alongside the secret values secret={ - ZENML_VAULT_SECRET_VALUES_KEY: secret.secret_values, + ZENML_VAULT_SECRET_VALUES_KEY: secret_values, ZENML_VAULT_SECRET_METADATA_KEY: metadata, - ZENML_VAULT_SECRET_CREATED_KEY: secret.created.isoformat() - if secret.created - else updated.isoformat(), - ZENML_VAULT_SECRET_UPDATED_KEY: updated.isoformat(), }, ) except InvalidPath: @@ -609,39 +302,25 @@ def update_secret( except VaultError as e: raise RuntimeError(f"Error updating secret {secret_id}: {e}") - logger.debug("Updated HashiCorp Vault secret: %s", vault_secret_id) - - secret_model = SecretResponse( - id=secret_id, - name=secret.name, - body=SecretResponseBody( - user=secret.user, - created=secret.created, - updated=updated, - scope=secret.scope, - values=secret.secret_values, - ), - metadata=SecretResponseMetadata( - workspace=secret.workspace, - ), - ) + logger.debug(f"Updated HashiCorp Vault secret: {vault_secret_id}") - return secret_model - - def delete_secret(self, secret_id: UUID) -> None: - """Delete a secret. + def delete_secret_values(self, secret_id: UUID) -> None: + """Deletes secret values for an existing secret. Args: - secret_id: The id of the secret to delete. + secret_id: The ID of the secret. Raises: - KeyError: If the secret does not exist. + KeyError: if no secret values for the given ID are stored in the + secrets store. RuntimeError: If the HashiCorp Vault API returns an unexpected error. """ + vault_secret_id = self._get_vault_secret_id(secret_id) + try: self.client.secrets.kv.v2.delete_metadata_and_all_versions( - path=self._get_vault_secret_id(secret_id), + path=vault_secret_id, ) except InvalidPath: raise KeyError(f"Secret with ID {secret_id} does not exist.") @@ -649,3 +328,5 @@ def delete_secret(self, secret_id: UUID) -> None: raise RuntimeError( f"Error deleting secret with ID {secret_id}: {e}" ) + + logger.debug(f"Deleted HashiCorp Vault secret: {vault_secret_id}") diff --git a/src/zenml/zen_stores/secrets_stores/rest_secrets_store.py b/src/zenml/zen_stores/secrets_stores/rest_secrets_store.py deleted file mode 100644 index a34a1c2df9a..00000000000 --- a/src/zenml/zen_stores/secrets_stores/rest_secrets_store.py +++ /dev/null @@ -1,266 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. -"""REST Secrets Store implementation.""" - -from typing import ( - TYPE_CHECKING, - Any, - ClassVar, - Type, -) -from uuid import UUID - -from zenml.config.secrets_store_config import SecretsStoreConfiguration -from zenml.constants import SECRETS -from zenml.enums import ( - SecretsStoreType, -) -from zenml.exceptions import ( - IllegalOperationError, -) -from zenml.logger import get_logger -from zenml.models import ( - Page, - SecretFilter, - SecretRequest, - SecretResponse, - SecretUpdate, -) -from zenml.zen_stores.secrets_stores.base_secrets_store import ( - BaseSecretsStore, -) - -logger = get_logger(__name__) - -if TYPE_CHECKING: - from zenml.zen_stores.base_zen_store import BaseZenStore - from zenml.zen_stores.rest_zen_store import RestZenStore - - -class RestSecretsStoreConfiguration(SecretsStoreConfiguration): - """REST secrets store configuration. - - Attributes: - type: The type of the store. - """ - - type: SecretsStoreType = SecretsStoreType.REST - - class Config: - """Pydantic configuration class.""" - - # Don't validate attributes when assigning them. This is necessary - # because the certificate attributes can be expanded to the contents - # of the certificate files. - validate_assignment = False - # Forbid extra attributes set in the class. - extra = "forbid" - - -class RestSecretsStore(BaseSecretsStore): - """Secrets store implementation that uses the REST ZenML store as a backend. - - This secrets store piggybacks on the REST ZenML store. It uses the same - REST client configuration as the REST ZenML store. - - Attributes: - config: The configuration of the REST secrets store. - TYPE: The type of the store. - CONFIG_TYPE: The type of the store configuration. - """ - - config: RestSecretsStoreConfiguration - TYPE: ClassVar[SecretsStoreType] = SecretsStoreType.REST - CONFIG_TYPE: ClassVar[ - Type[SecretsStoreConfiguration] - ] = RestSecretsStoreConfiguration - - def __init__( - self, - zen_store: "BaseZenStore", - **kwargs: Any, - ) -> None: - """Create and initialize the REST secrets store. - - Args: - zen_store: The ZenML store that owns this REST secrets store. - **kwargs: Additional keyword arguments to pass to the Pydantic - constructor. - - Raises: - IllegalOperationError: If the ZenML store to which this secrets - store belongs is not a REST ZenML store. - """ - from zenml.zen_stores.rest_zen_store import RestZenStore - - if not isinstance(zen_store, RestZenStore): - raise IllegalOperationError( - "The REST secrets store can only be used with the REST ZenML " - "store." - ) - super().__init__(zen_store, **kwargs) - - @property - def zen_store(self) -> "RestZenStore": - """The ZenML store that this REST secrets store is using as a back-end. - - Returns: - The ZenML store that this REST secrets store is using as a back-end. - - Raises: - ValueError: If the store is not initialized. - """ - from zenml.zen_stores.rest_zen_store import RestZenStore - - if not self._zen_store: - raise ValueError("Store not initialized") - assert isinstance(self._zen_store, RestZenStore) - return self._zen_store - - # ==================================== - # Secrets Store interface implementation - # ==================================== - - # -------------------------------- - # Initialization and configuration - # -------------------------------- - - def _initialize(self) -> None: - """Initialize the secrets REST store.""" - logger.debug("Initializing RestSecretsStore") - - # Nothing else to do here, the REST ZenML store back-end is already - # initialized - - # ------ - # Secrets - # ------ - - def create_secret(self, secret: SecretRequest) -> SecretResponse: - """Creates a new secret. - - The new secret is also validated against the scoping rules enforced in - the secrets store: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. - - Args: - secret: The secret to create. - - Returns: - The newly created secret. - """ - return self.zen_store._create_workspace_scoped_resource( - resource=secret, - route=SECRETS, - response_model=SecretResponse, - ) - - def get_secret( - self, secret_id: UUID, hydrate: bool = True - ) -> SecretResponse: - """Get a secret by ID. - - Args: - secret_id: The ID of the secret to fetch. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - The secret. - """ - return self.zen_store._get_resource( - resource_id=secret_id, - route=SECRETS, - response_model=SecretResponse, - params={"hydrate": hydrate}, - ) - - def list_secrets( - self, secret_filter_model: SecretFilter, hydrate: bool = False - ) -> Page[SecretResponse]: - """List all secrets matching the given filter criteria. - - Note that returned secrets do not include any secret values. To fetch - the secret values, use `get_secret`. - - Args: - secret_filter_model: All filter parameters including pagination - params. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - A list of all secrets matching the filter criteria, with pagination - information and sorted according to the filter criteria. The - returned secrets do not include any secret values, only metadata. To - fetch the secret values, use `get_secret` individually with each - secret. - """ - return self.zen_store._list_paginated_resources( - route=SECRETS, - response_model=SecretResponse, - filter_model=secret_filter_model, - params={"hydrate": hydrate}, - ) - - def update_secret( - self, secret_id: UUID, secret_update: SecretUpdate - ) -> SecretResponse: - """Updates a secret. - - Secret values that are specified as `None` in the update that are - present in the existing secret are removed from the existing secret. - Values that are present in both secrets are overwritten. All other - values in both the existing secret and the update are kept (merged). - - If the update includes a change of name or scope, the scoping rules - enforced in the secrets store are used to validate the update: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. - - Args: - secret_id: The ID of the secret to be updated. - secret_update: The update to be applied. - - Returns: - The updated secret. - """ - return self.zen_store._update_resource( - resource_id=secret_id, - resource_update=secret_update, - route=SECRETS, - response_model=SecretResponse, - # The default endpoint behavior is to replace all secret values - # with the values in the update. We want to merge the values - # instead. - params=dict(patch_values=True), - ) - - def delete_secret(self, secret_id: UUID) -> None: - """Delete a secret. - - Args: - secret_id: The id of the secret to delete. - """ - self.zen_store._delete_resource( - resource_id=secret_id, - route=SECRETS, - ) diff --git a/src/zenml/zen_stores/secrets_stores/secrets_store_interface.py b/src/zenml/zen_stores/secrets_stores/secrets_store_interface.py index 0736733635d..2601dac02a2 100644 --- a/src/zenml/zen_stores/secrets_stores/secrets_store_interface.py +++ b/src/zenml/zen_stores/secrets_stores/secrets_store_interface.py @@ -13,16 +13,9 @@ # permissions and limitations under the License. """ZenML secrets store interface.""" from abc import ABC, abstractmethod +from typing import Dict from uuid import UUID -from zenml.models import ( - Page, - SecretFilter, - SecretRequest, - SecretResponse, - SecretUpdate, -) - class SecretsStoreInterface(ABC): """ZenML secrets store interface. @@ -47,116 +40,58 @@ def _initialize(self) -> None: # --------- @abstractmethod - def create_secret( + def store_secret_values( self, - secret: SecretRequest, - ) -> SecretResponse: - """Creates a new secret. - - The new secret is also validated against the scoping rules enforced in - the secrets store: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Store secret values for a new secret. Args: - secret: The secret to create. - - Returns: - The newly created secret. - - Raises: - KeyError: if the user or workspace does not exist. - EntityExistsError: If a secret with the same name already exists in - the same scope. - ValueError: if the secret is invalid. + secret_id: ID of the secret. + secret_values: Values for the secret. """ @abstractmethod - def get_secret( - self, secret_id: UUID, hydrate: bool = True - ) -> SecretResponse: - """Get a secret with a given name. + def get_secret_values(self, secret_id: UUID) -> Dict[str, str]: + """Get the secret values for an existing secret. Args: secret_id: ID of the secret. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. Returns: - The secret. + The secret values. Raises: - KeyError: if the secret does not exist. - """ - - @abstractmethod - def list_secrets( - self, secret_filter_model: SecretFilter, hydrate: bool = False - ) -> Page[SecretResponse]: - """List all secrets matching the given filter criteria. - - Note that returned secrets do not include any secret values. To fetch - the secret values, use `get_secret`. - - Args: - secret_filter_model: All filter parameters including pagination - params. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - A list of all secrets matching the filter criteria, with pagination - information and sorted according to the filter criteria. The - returned secrets do not include any secret values, only metadata. To - fetch the secret values, use `get_secret` individually with each - secret. + KeyError: if no secret values for the given ID are stored in the + secrets store. """ @abstractmethod - def update_secret( + def update_secret_values( self, secret_id: UUID, - secret_update: SecretUpdate, - ) -> SecretResponse: - """Updates a secret. - - Secret values that are specified as `None` in the update that are - present in the existing secret are removed from the existing secret. - Values that are present in both secrets are overwritten. All other - values in both the existing secret and the update are kept (merged). - - If the update includes a change of name or scope, the scoping rules - enforced in the secrets store are used to validate the update: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + secret_values: Dict[str, str], + ) -> None: + """Updates secret values for an existing secret. Args: secret_id: The ID of the secret to be updated. - secret_update: The update to be applied. - - Returns: - The updated secret. + secret_values: The new secret values. Raises: - KeyError: if the secret doesn't exist. - EntityExistsError: If a secret with the same name already exists in - the same scope. - ValueError: if the secret is invalid. + KeyError: if no secret values for the given ID are stored in the + secrets store. """ @abstractmethod - def delete_secret(self, secret_id: UUID) -> None: - """Deletes a secret. + def delete_secret_values(self, secret_id: UUID) -> None: + """Deletes secret values for an existing secret. Args: - secret_id: The ID of the secret to delete. + secret_id: The ID of the secret. Raises: - KeyError: if the secret doesn't exist. + KeyError: if no secret values for the given ID are stored in the + secrets store. """ diff --git a/src/zenml/zen_stores/secrets_stores/service_connector_secrets_store.py b/src/zenml/zen_stores/secrets_stores/service_connector_secrets_store.py index 250eafa1038..385e2f46a87 100644 --- a/src/zenml/zen_stores/secrets_stores/service_connector_secrets_store.py +++ b/src/zenml/zen_stores/secrets_stores/service_connector_secrets_store.py @@ -64,9 +64,19 @@ def validate_auth_config(cls, values: Dict[str, Any]) -> Dict[str, Any]: Returns: The validated configuration values. + + Raises: + ValueError: If the authentication configuration is not a valid + JSON object. """ if isinstance(values.get("auth_config"), str): - values["auth_config"] = json.loads(values["auth_config"]) + try: + values["auth_config"] = json.loads(values["auth_config"]) + except json.JSONDecodeError as e: + raise ValueError( + f"The authentication configuration is not a valid JSON " + f"object: {e}" + ) return values diff --git a/src/zenml/zen_stores/secrets_stores/sql_secrets_store.py b/src/zenml/zen_stores/secrets_stores/sql_secrets_store.py index 5dc5ff4376a..288e9aafaa7 100644 --- a/src/zenml/zen_stores/secrets_stores/sql_secrets_store.py +++ b/src/zenml/zen_stores/secrets_stores/sql_secrets_store.py @@ -17,8 +17,8 @@ TYPE_CHECKING, Any, ClassVar, + Dict, Optional, - Tuple, Type, ) from uuid import UUID @@ -28,25 +28,14 @@ from sqlalchemy_utils.types.encrypted.encrypted_type import AesGcmEngine from sqlmodel import Session, select -from zenml.analytics.enums import AnalyticsEvent -from zenml.analytics.utils import track_decorator from zenml.config.secrets_store_config import SecretsStoreConfiguration from zenml.enums import ( - SecretScope, SecretsStoreType, ) from zenml.exceptions import ( - EntityExistsError, IllegalOperationError, ) from zenml.logger import get_logger -from zenml.models import ( - Page, - SecretFilter, - SecretRequest, - SecretResponse, - SecretUpdate, -) from zenml.zen_stores.schemas import ( SecretSchema, ) @@ -179,140 +168,48 @@ def _initialize(self) -> None: # Secrets # ------ - def _check_sql_secret_scope( + def store_secret_values( self, - session: Session, - secret_name: str, - scope: SecretScope, - workspace: UUID, - user: UUID, - exclude_secret_id: Optional[UUID] = None, - ) -> Tuple[bool, str]: - """Checks if a secret with the given name already exists in the given scope. - - This method enforces the following scope rules: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. - - Args: - session: The SQLAlchemy session. - secret_name: The name of the secret. - scope: The scope of the secret. - workspace: The ID of the workspace to which the secret belongs. - user: The ID of the user to which the secret belongs. - exclude_secret_id: The ID of a secret to exclude from the check - (used e.g. during an update to exclude the existing secret). - - Returns: - True if a secret with the given name already exists in the given - scope, False otherwise, and an error message. - """ - scope_filter = ( - select(SecretSchema) - .where(SecretSchema.name == secret_name) - .where(SecretSchema.scope == scope.value) - ) - - if scope in [SecretScope.WORKSPACE, SecretScope.USER]: - scope_filter = scope_filter.where( - SecretSchema.workspace_id == workspace - ) - if scope == SecretScope.USER: - scope_filter = scope_filter.where(SecretSchema.user_id == user) - if exclude_secret_id is not None: - scope_filter = scope_filter.where( - SecretSchema.id != exclude_secret_id - ) - - existing_secret = session.exec(scope_filter).first() - - if existing_secret is not None: - existing_secret_model = existing_secret.to_model( - encryption_engine=self._encryption_engine - ) - - msg = ( - f"Found an existing {scope.value} scoped secret with the " - f"same '{secret_name}' name" - ) - if scope in [SecretScope.WORKSPACE, SecretScope.USER]: - msg += ( - f" in the same '{existing_secret_model.workspace.name}' " - f"workspace" - ) - if scope == SecretScope.USER: - assert existing_secret_model.user - msg += ( - f" for the same '{existing_secret_model.user.name}' user" - ) - - return True, msg - - return False, "" - - @track_decorator(AnalyticsEvent.CREATED_SECRET) - def create_secret(self, secret: SecretRequest) -> SecretResponse: - """Creates a new secret. - - The new secret is also validated against the scoping rules enforced in - the secrets store: + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Store secret values for a new secret. - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + The secret is already created in the database by the SQL Zen store, this + method only stores the secret values. Args: - secret: The secret to create. - - Returns: - The newly created secret. + secret_id: ID of the secret. + secret_values: Values for the secret. Raises: - EntityExistsError: If a secret with the same name already exists in - the same scope. + KeyError: if a secret for the given ID is not found. """ with Session(self.engine) as session: - # Check if a secret with the same name already exists in the same - # scope. - secret_exists, msg = self._check_sql_secret_scope( - session=session, - secret_name=secret.name, - scope=secret.scope, - workspace=secret.workspace, - user=secret.user, - ) - if secret_exists: - raise EntityExistsError(msg) - - new_secret = SecretSchema.from_request( - secret, encryption_engine=self._encryption_engine + secret_in_db = session.exec( + select(SecretSchema).where(SecretSchema.id == secret_id) + ).first() + if secret_in_db is None: + raise KeyError(f"Secret with ID {secret_id} not found.") + secret_in_db.set_secret_values( + secret_values=secret_values, + encryption_engine=self._encryption_engine, ) - session.add(new_secret) + session.add(secret_in_db) session.commit() - return new_secret.to_model( - encryption_engine=self._encryption_engine, hydrate=True - ) - - def get_secret( - self, secret_id: UUID, hydrate: bool = True - ) -> SecretResponse: - """Get a secret by ID. + def get_secret_values(self, secret_id: UUID) -> Dict[str, str]: + """Get the secret values for an existing secret. Args: - secret_id: The ID of the secret to fetch. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. + secret_id: ID of the secret. Returns: - The secret. + The secret values. Raises: - KeyError: if the secret doesn't exist. + KeyError: if no secret values for the given ID are stored in the + secrets store. """ with Session(self.engine) as session: secret_in_db = session.exec( @@ -320,129 +217,39 @@ def get_secret( ).first() if secret_in_db is None: raise KeyError(f"Secret with ID {secret_id} not found.") - return secret_in_db.to_model( - encryption_engine=self._encryption_engine, hydrate=hydrate - ) - - def list_secrets( - self, secret_filter_model: SecretFilter, hydrate: bool = False - ) -> Page[SecretResponse]: - """List all secrets matching the given filter criteria. - - Note that returned secrets do not include any secret values. To fetch - the secret values, use `get_secret`. - - Args: - secret_filter_model: All filter parameters including pagination - params. - hydrate: Flag deciding whether to hydrate the output model(s) - by including metadata fields in the response. - - Returns: - A list of all secrets matching the filter criteria, with pagination - information and sorted according to the filter criteria. The - returned secrets do not include any secret values, only metadata. To - fetch the secret values, use `get_secret` individually with each - secret. - """ - with Session(self.engine) as session: - query = select(SecretSchema) - return self.zen_store.filter_and_paginate( - session=session, - query=query, - table=SecretSchema, - filter_model=secret_filter_model, - custom_schema_to_model_conversion=lambda secret: secret.to_model( - include_values=False, hydrate=hydrate - ), + return secret_in_db.get_secret_values( + encryption_engine=self._encryption_engine, ) - def update_secret( - self, secret_id: UUID, secret_update: SecretUpdate - ) -> SecretResponse: - """Updates a secret. - - Secret values that are specified as `None` in the update that are - present in the existing secret are removed from the existing secret. - Values that are present in both secrets are overwritten. All other - values in both the existing secret and the update are kept (merged). - - If the update includes a change of name or scope, the scoping rules - enforced in the secrets store are used to validate the update: - - - only one workspace-scoped secret with the given name can exist - in the target workspace. - - only one user-scoped secret with the given name can exist in the - target workspace for the target user. + def update_secret_values( + self, + secret_id: UUID, + secret_values: Dict[str, str], + ) -> None: + """Updates secret values for an existing secret. Args: secret_id: The ID of the secret to be updated. - secret_update: The update to be applied. - - Returns: - The updated secret. - - Raises: - KeyError: if the secret doesn't exist. - EntityExistsError: If a secret with the same name already exists in - the same scope. + secret_values: The new secret values. """ - with Session(self.engine) as session: - existing_secret = session.exec( - select(SecretSchema).where(SecretSchema.id == secret_id) - ).first() - - if not existing_secret: - raise KeyError(f"Secret with ID {secret_id} not found.") - - # A change in name or scope requires a check of the scoping rules. - if ( - secret_update.name is not None - and existing_secret.name != secret_update.name - or secret_update.scope is not None - and existing_secret.scope != secret_update.scope - ): - secret_exists, msg = self._check_sql_secret_scope( - session=session, - secret_name=secret_update.name or existing_secret.name, - scope=secret_update.scope - or SecretScope(existing_secret.scope), - workspace=existing_secret.workspace.id, - user=existing_secret.user.id, - exclude_secret_id=secret_id, - ) - - if secret_exists: - raise EntityExistsError(msg) - - existing_secret.update( - secret_update=secret_update, - encryption_engine=self._encryption_engine, - ) - session.add(existing_secret) - session.commit() - - # Refresh the Model that was just created - session.refresh(existing_secret) - return existing_secret.to_model( - encryption_engine=self._encryption_engine, hydrate=True - ) + self.store_secret_values(secret_id, secret_values) - def delete_secret(self, secret_id: UUID) -> None: - """Delete a secret. + def delete_secret_values(self, secret_id: UUID) -> None: + """Deletes secret values for an existing secret. Args: - secret_id: The id of the secret to delete. + secret_id: The ID of the secret. Raises: - KeyError: if the secret doesn't exist. + KeyError: if no secret values for the given ID are stored in the + secrets store. """ with Session(self.engine) as session: try: secret_in_db = session.exec( select(SecretSchema).where(SecretSchema.id == secret_id) ).one() - session.delete(secret_in_db) + secret_in_db.values = None session.commit() except NoResultFound: raise KeyError(f"Secret with ID {secret_id} not found.") diff --git a/src/zenml/zen_stores/sql_zen_store.py b/src/zenml/zen_stores/sql_zen_store.py index 10accb8d1fc..b3ecefad046 100644 --- a/src/zenml/zen_stores/sql_zen_store.py +++ b/src/zenml/zen_stores/sql_zen_store.py @@ -83,6 +83,7 @@ LoggingLevels, ModelStages, SecretScope, + SecretsStoreType, SorterOps, StackComponentType, StepRunInputArtifactType, @@ -177,6 +178,8 @@ ScheduleUpdate, SecretFilter, SecretRequest, + SecretResponse, + SecretUpdate, ServerDatabaseType, ServerModel, ServiceAccountFilter, @@ -228,7 +231,6 @@ from zenml.zen_stores.base_zen_store import ( BaseZenStore, ) -from zenml.zen_stores.enums import StoreEvent from zenml.zen_stores.migrations.alembic import ( ZENML_ALEMBIC_START_REVISION, Alembic, @@ -254,6 +256,7 @@ PipelineSchema, RunMetadataSchema, ScheduleSchema, + SecretSchema, ServiceConnectorSchema, StackComponentSchema, StackSchema, @@ -270,6 +273,7 @@ ArtifactVisualizationSchema, ) from zenml.zen_stores.schemas.logs_schemas import LogsSchema +from zenml.zen_stores.secrets_stores.base_secrets_store import BaseSecretsStore from zenml.zen_stores.secrets_stores.sql_secrets_store import ( SqlSecretsStoreConfiguration, ) @@ -712,6 +716,25 @@ class SqlZenStore(BaseZenStore): _engine: Optional[Engine] = None _alembic: Optional[Alembic] = None + _secrets_store: Optional[BaseSecretsStore] = None + + @property + def secrets_store(self) -> "BaseSecretsStore": + """The secrets store associated with this store. + + Returns: + The secrets store associated with this store. + + Raises: + NotImplementedError: If no secrets store is configured. + """ + if self._secrets_store is None: + raise NotImplementedError( + "No secrets store is configured. Please configure a secrets " + "store to create and manage ZenML secrets." + ) + + return self._secrets_store @property def engine(self) -> Engine: @@ -928,6 +951,24 @@ def _initialize(self) -> None: ): self.migrate_database() + secrets_store_config = self.config.secrets_store + + # Initialize the secrets store + if ( + secrets_store_config + and secrets_store_config.type != SecretsStoreType.NONE + ): + secrets_store_class = BaseSecretsStore.get_store_class( + secrets_store_config + ) + self._secrets_store = secrets_store_class( + zen_store=self, + config=secrets_store_config, + ) + # Update the config with the actual secrets store config + # to reflect the default values in the saved configuration + self.config.secrets_store = self._secrets_store.config + def _initialize_database(self) -> None: """Initialize the database on first use.""" self._get_or_create_default_workspace() @@ -3735,6 +3776,372 @@ def delete_schedule(self, schedule_id: UUID) -> None: session.delete(schedule) session.commit() + # ------------------------- Secrets ------------------------- + + def _check_sql_secret_scope( + self, + session: Session, + secret_name: str, + scope: SecretScope, + workspace: UUID, + user: UUID, + exclude_secret_id: Optional[UUID] = None, + ) -> Tuple[bool, str]: + """Checks if a secret with the given name already exists in the given scope. + + This method enforces the following scope rules: + + - only one workspace-scoped secret with the given name can exist + in the target workspace. + - only one user-scoped secret with the given name can exist in the + target workspace for the target user. + + Args: + session: The SQLAlchemy session. + secret_name: The name of the secret. + scope: The scope of the secret. + workspace: The ID of the workspace to which the secret belongs. + user: The ID of the user to which the secret belongs. + exclude_secret_id: The ID of a secret to exclude from the check + (used e.g. during an update to exclude the existing secret). + + Returns: + True if a secret with the given name already exists in the given + scope, False otherwise, and an error message. + """ + scope_filter = ( + select(SecretSchema) + .where(SecretSchema.name == secret_name) + .where(SecretSchema.scope == scope.value) + ) + + if scope in [SecretScope.WORKSPACE, SecretScope.USER]: + scope_filter = scope_filter.where( + SecretSchema.workspace_id == workspace + ) + if scope == SecretScope.USER: + scope_filter = scope_filter.where(SecretSchema.user_id == user) + if exclude_secret_id is not None: + scope_filter = scope_filter.where( + SecretSchema.id != exclude_secret_id + ) + + existing_secret = session.exec(scope_filter).first() + + if existing_secret is not None: + existing_secret_model = existing_secret.to_model(hydrate=True) + + msg = ( + f"Found an existing {scope.value} scoped secret with the " + f"same '{secret_name}' name" + ) + if scope in [SecretScope.WORKSPACE, SecretScope.USER]: + msg += ( + f" in the same '{existing_secret_model.workspace.name}' " + f"workspace" + ) + if scope == SecretScope.USER: + assert existing_secret_model.user + msg += ( + f" for the same '{existing_secret_model.user.name}' user" + ) + + return True, msg + + return False, "" + + def _set_secret_values( + self, secret_id: UUID, values: Dict[str, str] + ) -> None: + """Sets the values of a secret in the configured secrets store. + + Args: + secret_id: The ID of the secret to set the values of. + values: The values to set. + """ + self.secrets_store.store_secret_values( + secret_id=secret_id, secret_values=values + ) + + def _get_secret_values(self, secret_id: UUID) -> Dict[str, str]: + """Gets the values of a secret in the configured secrets store. + + Args: + secret_id: The ID of the secret to set the values of. + + Returns: + The values of the secret. + """ + return self.secrets_store.get_secret_values( + secret_id=secret_id, + ) + + def _update_secret_values( + self, secret_id: UUID, values: Dict[str, Optional[str]] + ) -> Dict[str, str]: + """Updates the values of a secret in the configured secrets store. + + This method will update the existing values with the new values + and drop `None` values. + + Args: + secret_id: The ID of the secret to set the values of. + values: The updated values to set. + + Returns: + The updated values. + """ + existing_values = self._get_secret_values(secret_id=secret_id) + + for k, v in values.items(): + if v is not None: + existing_values[k] = v + # Drop values removed in the update + if v is None and k in existing_values: + del existing_values[k] + + self.secrets_store.update_secret_values( + secret_id=secret_id, secret_values=existing_values + ) + + return existing_values + + def _delete_secret_values( + self, + secret_id: UUID, + ) -> None: + """Deletes the values of a secret in the configured secrets store. + + Args: + secret_id: The ID of the secret for which to delete the values. + """ + self.secrets_store.delete_secret_values(secret_id=secret_id) + + @track_decorator(AnalyticsEvent.CREATED_SECRET) + def create_secret(self, secret: SecretRequest) -> SecretResponse: + """Creates a new secret. + + The new secret is also validated against the scoping rules enforced in + the secrets store: + + - only one workspace-scoped secret with the given name can exist + in the target workspace. + - only one user-scoped secret with the given name can exist in the + target workspace for the target user. + + Args: + secret: The secret to create. + + Returns: + The newly created secret. + + Raises: + EntityExistsError: If a secret with the same name already exists in + the same scope. + """ + with Session(self.engine) as session: + # Check if a secret with the same name already exists in the same + # scope. + secret_exists, msg = self._check_sql_secret_scope( + session=session, + secret_name=secret.name, + scope=secret.scope, + workspace=secret.workspace, + user=secret.user, + ) + if secret_exists: + raise EntityExistsError(msg) + + new_secret = SecretSchema.from_request( + secret, + ) + session.add(new_secret) + session.commit() + + secret_model = new_secret.to_model(hydrate=True) + + try: + # Set the secret values in the configured secrets store + self._set_secret_values( + secret_id=new_secret.id, values=secret.secret_values + ) + except: + # If setting the secret values fails, delete the secret from the + # database. + with Session(self.engine) as session: + session.delete(new_secret) + session.commit() + raise + + secret_model.set_secrets(secret.secret_values) + return secret_model + + def get_secret( + self, secret_id: UUID, hydrate: bool = True + ) -> SecretResponse: + """Get a secret by ID. + + Args: + secret_id: The ID of the secret to fetch. + hydrate: Flag deciding whether to hydrate the output model(s) + by including metadata fields in the response. + + Returns: + The secret. + + Raises: + KeyError: if the secret doesn't exist. + """ + with Session(self.engine) as session: + secret_in_db = session.exec( + select(SecretSchema).where(SecretSchema.id == secret_id) + ).first() + if secret_in_db is None: + raise KeyError(f"Secret with ID {secret_id} not found.") + secret_model = secret_in_db.to_model(hydrate=hydrate) + + secret_model.set_secrets(self._get_secret_values(secret_id=secret_id)) + + return secret_model + + def list_secrets( + self, secret_filter_model: SecretFilter, hydrate: bool = False + ) -> Page[SecretResponse]: + """List all secrets matching the given filter criteria. + + Note that returned secrets do not include any secret values. To fetch + the secret values, use `get_secret`. + + Args: + secret_filter_model: All filter parameters including pagination + params. + hydrate: Flag deciding whether to hydrate the output model(s) + by including metadata fields in the response. + + Returns: + A list of all secrets matching the filter criteria, with pagination + information and sorted according to the filter criteria. The + returned secrets do not include any secret values, only metadata. To + fetch the secret values, use `get_secret` individually with each + secret. + """ + with Session(self.engine) as session: + query = select(SecretSchema) + return self.filter_and_paginate( + session=session, + query=query, + table=SecretSchema, + filter_model=secret_filter_model, + hydrate=hydrate, + ) + + def update_secret( + self, secret_id: UUID, secret_update: SecretUpdate + ) -> SecretResponse: + """Updates a secret. + + Secret values that are specified as `None` in the update that are + present in the existing secret are removed from the existing secret. + Values that are present in both secrets are overwritten. All other + values in both the existing secret and the update are kept (merged). + + If the update includes a change of name or scope, the scoping rules + enforced in the secrets store are used to validate the update: + + - only one workspace-scoped secret with the given name can exist + in the target workspace. + - only one user-scoped secret with the given name can exist in the + target workspace for the target user. + + Args: + secret_id: The ID of the secret to be updated. + secret_update: The update to be applied. + + Returns: + The updated secret. + + Raises: + KeyError: if the secret doesn't exist. + EntityExistsError: If a secret with the same name already exists in + the same scope. + """ + with Session(self.engine) as session: + existing_secret = session.exec( + select(SecretSchema).where(SecretSchema.id == secret_id) + ).first() + + if not existing_secret: + raise KeyError(f"Secret with ID {secret_id} not found.") + + # A change in name or scope requires a check of the scoping rules. + if ( + secret_update.name is not None + and existing_secret.name != secret_update.name + or secret_update.scope is not None + and existing_secret.scope != secret_update.scope + ): + secret_exists, msg = self._check_sql_secret_scope( + session=session, + secret_name=secret_update.name or existing_secret.name, + scope=secret_update.scope + or SecretScope(existing_secret.scope), + workspace=existing_secret.workspace.id, + user=existing_secret.user.id, + exclude_secret_id=secret_id, + ) + + if secret_exists: + raise EntityExistsError(msg) + + existing_secret.update( + secret_update=secret_update, + ) + session.add(existing_secret) + session.commit() + + # Refresh the Model that was just created + session.refresh(existing_secret) + secret_model = existing_secret.to_model(hydrate=True) + + if secret_update.values is not None: + # Update the secret values in the configured secrets store + updated_values = self._update_secret_values( + secret_id=secret_id, + values=secret_update.get_secret_values_update(), + ) + secret_model.set_secrets(updated_values) + else: + secret_model.set_secrets(self._get_secret_values(secret_id)) + + return secret_model + + def delete_secret(self, secret_id: UUID) -> None: + """Delete a secret. + + Args: + secret_id: The id of the secret to delete. + + Raises: + KeyError: if the secret doesn't exist. + """ + # Delete the secret values in the configured secrets store + try: + self._delete_secret_values(secret_id=secret_id) + except KeyError: + # If the secret values don't exist in the secrets store, we don't + # need to raise an error. + pass + + with Session(self.engine) as session: + try: + secret_in_db = session.exec( + select(SecretSchema).where(SecretSchema.id == secret_id) + ).one() + session.delete(secret_in_db) + session.commit() + except NoResultFound: + raise KeyError(f"Secret with ID {secret_id} not found.") + # ------------------------- Service Accounts ------------------------- @track_decorator(AnalyticsEvent.CREATED_SERVICE_ACCOUNT) @@ -3988,9 +4395,9 @@ def create_service_connector( session.refresh(new_service_connector) except Exception: # Delete the secret if it was created - if secret_id and self.secrets_store: + if secret_id: try: - self.secrets_store.delete_secret(secret_id) + self.delete_secret(secret_id) except Exception: # Ignore any errors that occur while deleting the # secret @@ -4286,11 +4693,9 @@ def delete_service_connector(self, service_connector_id: UUID) -> None: else: session.delete(service_connector) - if service_connector.secret_id and self.secrets_store: + if service_connector.secret_id: try: - self.secrets_store.delete_secret( - service_connector.secret_id - ) + self.delete_secret(service_connector.secret_id) except KeyError: # If the secret doesn't exist anymore, we can ignore # this error @@ -4351,19 +4756,10 @@ def _create_connector_secret( Returns: The ID of the newly created secret or None, if the service connector does not contain any secret credentials. - - Raises: - NotImplementedError: If a secrets store is not configured or - supported. """ if not secrets: return None - if not self.secrets_store: - raise NotImplementedError( - "A secrets store is not configured or supported." - ) - # Generate a unique name for the secret # Replace all non-alphanumeric characters with a dash because # the secret name must be a valid DNS subdomain name in some @@ -4373,14 +4769,14 @@ def _create_connector_secret( # that is not already in use while True: secret_name = f"connector-{connector_name}-{random_str(4)}".lower() - existing_secrets = self.secrets_store.list_secrets( + existing_secrets = self.list_secrets( SecretFilter( name=secret_name, ) ) if not existing_secrets.size: try: - return self.secrets_store.create_secret( + return self.create_secret( SecretRequest( name=secret_name, user=user, @@ -4476,16 +4872,7 @@ def _update_connector_secret( Returns: The ID of the updated secret or None, if the new service connector does not contain any secret credentials. - - Raises: - NotImplementedError: If a secrets store is not configured or - supported. """ - if not self.secrets_store: - raise NotImplementedError( - "A secrets store is not configured or supported." - ) - if updated_connector.secrets is None: # If the connector update does not contain a secrets update, keep # the existing secret (if any) @@ -4494,7 +4881,7 @@ def _update_connector_secret( # Delete the existing secret (if any), to be replaced by the new secret if existing_connector.secret_id: try: - self.secrets_store.delete_secret(existing_connector.secret_id) + self.delete_secret(existing_connector.secret_id) except KeyError: # Ignore if the secret no longer exists pass @@ -5530,10 +5917,8 @@ def _get_resource_references( and attr not in # These are not resources owned by the user or are resources that - # are deleted automatically when the user is deleted. Secrets in - # particular are left out because they are automatically deleted - # even when stored in an external secret store. - ["api_keys", "auth_devices", "secrets"] + # are deleted automatically when the user is deleted. + ["api_keys", "auth_devices"] ] # This next part is crucial in preserving scalability: we don't fetch @@ -5837,8 +6222,6 @@ def delete_user(self, user_name_or_id: Union[str, UUID]) -> None: "account or consider deactivating it instead." ) - self._trigger_event(StoreEvent.USER_DELETED, user_id=user.id) - session.delete(user) session.commit() @@ -6027,10 +6410,6 @@ def delete_workspace(self, workspace_name_or_id: Union[str, UUID]) -> None: "The default workspace cannot be deleted." ) - self._trigger_event( - StoreEvent.WORKSPACE_DELETED, workspace_id=workspace.id - ) - session.delete(workspace) session.commit() diff --git a/src/zenml/zen_stores/zen_store_interface.py b/src/zenml/zen_stores/zen_store_interface.py index 4a8bbb4e3c4..d308f4e96ad 100644 --- a/src/zenml/zen_stores/zen_store_interface.py +++ b/src/zenml/zen_stores/zen_store_interface.py @@ -84,6 +84,10 @@ ScheduleRequest, ScheduleResponse, ScheduleUpdate, + SecretFilter, + SecretRequest, + SecretResponse, + SecretUpdate, ServerModel, ServiceAccountFilter, ServiceAccountRequest, @@ -1337,6 +1341,121 @@ def delete_schedule(self, schedule_id: UUID) -> None: KeyError: if the schedule doesn't exist. """ + # -------------------- Secrets -------------------- + + @abstractmethod + def create_secret( + self, + secret: SecretRequest, + ) -> SecretResponse: + """Creates a new secret. + + The new secret is also validated against the scoping rules enforced in + the secrets store: + + - only one workspace-scoped secret with the given name can exist + in the target workspace. + - only one user-scoped secret with the given name can exist in the + target workspace for the target user. + + Args: + secret: The secret to create. + + Returns: + The newly created secret. + + Raises: + KeyError: if the user or workspace does not exist. + EntityExistsError: If a secret with the same name already exists in + the same scope. + """ + + @abstractmethod + def get_secret( + self, secret_id: UUID, hydrate: bool = True + ) -> SecretResponse: + """Get a secret with a given name. + + Args: + secret_id: ID of the secret. + hydrate: Flag deciding whether to hydrate the output model(s) + by including metadata fields in the response. + + Returns: + The secret. + + Raises: + KeyError: if the secret does not exist. + """ + + @abstractmethod + def list_secrets( + self, secret_filter_model: SecretFilter, hydrate: bool = False + ) -> Page[SecretResponse]: + """List all secrets matching the given filter criteria. + + Note that returned secrets do not include any secret values. To fetch + the secret values, use `get_secret`. + + Args: + secret_filter_model: All filter parameters including pagination + params. + hydrate: Flag deciding whether to hydrate the output model(s) + by including metadata fields in the response. + + Returns: + A list of all secrets matching the filter criteria, with pagination + information and sorted according to the filter criteria. The + returned secrets do not include any secret values, only metadata. To + fetch the secret values, use `get_secret` individually with each + secret. + """ + + @abstractmethod + def update_secret( + self, + secret_id: UUID, + secret_update: SecretUpdate, + ) -> SecretResponse: + """Updates a secret. + + Secret values that are specified as `None` in the update that are + present in the existing secret are removed from the existing secret. + Values that are present in both secrets are overwritten. All other + values in both the existing secret and the update are kept (merged). + + If the update includes a change of name or scope, the scoping rules + enforced in the secrets store are used to validate the update: + + - only one workspace-scoped secret with the given name can exist + in the target workspace. + - only one user-scoped secret with the given name can exist in the + target workspace for the target user. + + Args: + secret_id: The ID of the secret to be updated. + secret_update: The update to be applied. + + Returns: + The updated secret. + + Raises: + KeyError: if the secret doesn't exist. + EntityExistsError: If a secret with the same name already exists in + the same scope. + """ + + @abstractmethod + def delete_secret(self, secret_id: UUID) -> None: + """Deletes a secret. + + Args: + secret_id: The ID of the secret to delete. + + Raises: + KeyError: if the secret doesn't exist. + """ + # -------------------- Service Accounts -------------------- @abstractmethod diff --git a/tests/integration/functional/zen_stores/test_secrets_store.py b/tests/integration/functional/zen_stores/test_secrets_store.py index 6607aefb68b..bc3d79af393 100644 --- a/tests/integration/functional/zen_stores/test_secrets_store.py +++ b/tests/integration/functional/zen_stores/test_secrets_store.py @@ -26,15 +26,9 @@ ) from zenml.client import Client from zenml.enums import SecretScope, SecretsStoreType, StoreType -from zenml.exceptions import EntityExistsError +from zenml.exceptions import EntityExistsError, IllegalOperationError from zenml.models import SecretFilter, SecretUpdate -# The AWS secrets store takes some time to reflect new and updated secrets in -# the `list_secrets` API. This is the number of seconds to wait before making -# `list_secrets` calls after creating/updating a secret to ensure that the -# latest secret information is returned. -AWS_SECRET_REFRESH_SLEEP = 10 - def _get_secrets_store_type() -> SecretsStoreType: """Returns the secrets store back-end type that is used by the test @@ -623,12 +617,6 @@ def test_update_scope_succeeds(): ), ) - if _get_secrets_store_type() == SecretsStoreType.AWS: - # The AWS secrets store returns before the secret is actually - # updated in the backend, so we need to wait a bit before - # running `list_secrets`. - time.sleep(AWS_SECRET_REFRESH_SLEEP) - assert updated_secret.name == secret.name assert updated_secret.scope == SecretScope.USER @@ -670,12 +658,6 @@ def test_update_scope_succeeds(): ), ) - if _get_secrets_store_type() == SecretsStoreType.AWS: - # The AWS secrets store returns before the secret is actually - # updated in the backend, so we need to wait a bit before - # running `list_secrets`. - time.sleep(AWS_SECRET_REFRESH_SLEEP) - assert updated_secret.name == secret.name assert updated_secret.scope == SecretScope.WORKSPACE @@ -1466,14 +1448,8 @@ def test_list_secrets_pagination_and_sorting(): ), ) - if _get_secrets_store_type() == SecretsStoreType.AWS: - # The AWS secrets store returns before the secret is actually - # updated in the backend, so we need to wait a bit before - # running `list_secrets`. - time.sleep(AWS_SECRET_REFRESH_SLEEP) - else: - assert secret_one.updated > secret_one.created - assert secret_two.updated > secret_two.created + assert secret_one.updated > secret_one.created + assert secret_two.updated > secret_two.created secrets = store.list_secrets( SecretFilter( @@ -1618,8 +1594,8 @@ def test_secret_is_deleted_with_workspace(): assert len(all_workspace_secrets) == 0 -def test_secret_is_deleted_with_user(): - """Tests that deleting a user automatically deletes all its secrets.""" +def test_delete_user_with_secrets(): + """Tests that deleting a user is not possible while it owns secrets.""" client = Client() store = client.zen_store @@ -1679,9 +1655,34 @@ def test_secret_is_deleted_with_user(): assert len(user_secrets) == 1 assert other_secret.id == user_secrets[0].id - store.delete_user(user.id) + with pytest.raises(IllegalOperationError): + store.delete_user(user.id) + + with does_not_raise(): + store.get_secret(other_secret.id) + + all_secrets = store.list_secrets( + SecretFilter(name=secret.name), + ).items + assert len(all_secrets) == 2 + assert secret.id in [s.id for s in all_secrets] + assert other_secret.id in [s.id for s in all_secrets] + + user_secrets = store.list_secrets( + SecretFilter( + name=secret.name, + scope=SecretScope.USER, + user_id=user.id, + workspace_id=client.active_workspace.id, + ), + ).items + assert len(user_secrets) == 1 + + # Delete the secret + store.delete_secret(other_secret.id) - # New secret has been deleted at this point with the user + with does_not_raise(): + store.delete_user(user.id) with pytest.raises(KeyError): store.get_secret(other_secret.id) diff --git a/tests/integration/functional/zen_stores/test_zen_store.py b/tests/integration/functional/zen_stores/test_zen_store.py index 6d7e7813d18..811877e8d1a 100644 --- a/tests/integration/functional/zen_stores/test_zen_store.py +++ b/tests/integration/functional/zen_stores/test_zen_store.py @@ -478,10 +478,18 @@ def test_delete_user_with_resources_fails(): zen_store.delete_user(user.id) with UserContext(delete=False, login=login) as user: - with SecretContext(user_id=user.id, delete=False): + secret_context = SecretContext(user_id=user.id, delete=False) + with secret_context: + # We only use the context as a shortcut to create the resource pass - # Secrets are deleted when the user is deleted + # Can't delete because owned resources exist + with pytest.raises(IllegalOperationError): + zen_store.delete_user(user.id) + + secret_context.cleanup() + + # Can delete because owned resources have been removed with does_not_raise(): zen_store.delete_user(user.id) @@ -815,10 +823,20 @@ def test_delete_service_account_with_resources_fails(): zen_store.delete_service_account(service_account.id) with ServiceAccountContext(delete=False, login=login) as service_account: - with SecretContext(user_id=service_account.id, delete=False): + secret_context = SecretContext( + user_id=service_account.id, delete=False + ) + with secret_context: + # We only use the context as a shortcut to create the resource pass - # Secrets are deleted when the service_account is deleted + # Can't delete because owned resources exist + with pytest.raises(IllegalOperationError): + zen_store.delete_service_account(service_account.id) + + secret_context.cleanup() + + # Can delete because owned resources have been removed with does_not_raise(): zen_store.delete_service_account(service_account.id) diff --git a/tests/integration/functional/zen_stores/utils.py b/tests/integration/functional/zen_stores/utils.py index 40735b04ef2..c7fd2a2e87b 100644 --- a/tests/integration/functional/zen_stores/utils.py +++ b/tests/integration/functional/zen_stores/utils.py @@ -496,12 +496,15 @@ def __enter__(self): self.created_secret = self.store.create_secret(new_secret) return self.created_secret + def cleanup(self): + try: + self.store.delete_secret(self.created_secret.id) + except KeyError: + pass + def __exit__(self, exc_type, exc_value, exc_traceback): if self.delete: - try: - self.store.delete_secret(self.created_secret.id) - except KeyError: - pass + self.cleanup() class CodeRepositoryContext: From 3342af1a88df6b7e44747fec8a78303a44515690 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 12 Jan 2024 09:10:28 +0100 Subject: [PATCH 12/16] Add `latest_version_id` to the `ModelResponse` (#2266) * add `latest_version_id` * Auto-update of NLP template * Update src/zenml/models/v2/core/model.py Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> * update test case * Revert "update test case" This reverts commit eda0e6bd8345a2a52324812a9b220e6b0ef7c9fa. * update test case --------- Co-authored-by: GitHub Actions Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> --- src/zenml/cli/model.py | 2 +- src/zenml/models/v2/core/model.py | 18 ++++++++++++++---- src/zenml/zen_stores/schemas/model_schemas.py | 12 +++++++----- tests/integration/functional/test_client.py | 13 +++++++++++++ .../functional/zen_stores/test_zen_store.py | 11 +++++++++-- 5 files changed, 44 insertions(+), 12 deletions(-) diff --git a/src/zenml/cli/model.py b/src/zenml/cli/model.py index 2fe1d633c5d..da0f2410f29 100644 --- a/src/zenml/cli/model.py +++ b/src/zenml/cli/model.py @@ -38,7 +38,7 @@ def _model_to_print(model: ModelResponse) -> Dict[str, Any]: return { "id": model.id, "name": model.name, - "latest_version": model.latest_version, + "latest_version": model.latest_version_name, "description": model.description, "tags": [t.name for t in model.tags], "use_cases": model.use_cases, diff --git a/src/zenml/models/v2/core/model.py b/src/zenml/models/v2/core/model.py index cd88c4276a8..b27a1faecd0 100644 --- a/src/zenml/models/v2/core/model.py +++ b/src/zenml/models/v2/core/model.py @@ -116,7 +116,8 @@ class ModelResponseBody(WorkspaceScopedResponseBody): tags: List["TagResponse"] = Field( title="Tags associated with the model", ) - latest_version: Optional[str] + latest_version_name: Optional[str] + latest_version_id: Optional[UUID] created: datetime = Field( title="The timestamp when this component was created." ) @@ -200,13 +201,22 @@ def tags(self) -> List["TagResponse"]: return self.get_body().tags @property - def latest_version(self) -> Optional[str]: - """The `latest_version` property. + def latest_version_name(self) -> Optional[str]: + """The `latest_version_name` property. Returns: the value of the property. """ - return self.get_body().latest_version + return self.get_body().latest_version_name + + @property + def latest_version_id(self) -> Optional[UUID]: + """The `latest_version_id` property. + + Returns: + the value of the property. + """ + return self.get_body().latest_version_id @property def created(self) -> datetime: diff --git a/src/zenml/zen_stores/schemas/model_schemas.py b/src/zenml/zen_stores/schemas/model_schemas.py index 3190b60c4d1..843c8876253 100644 --- a/src/zenml/zen_stores/schemas/model_schemas.py +++ b/src/zenml/zen_stores/schemas/model_schemas.py @@ -145,11 +145,12 @@ def to_model( if self.model_versions: version_numbers = [mv.number for mv in self.model_versions] - latest_version = self.model_versions[ - version_numbers.index(max(version_numbers)) - ].name + latest_version_idx = version_numbers.index(max(version_numbers)) + latest_version_name = self.model_versions[latest_version_idx].name + latest_version_id = self.model_versions[latest_version_idx].id else: - latest_version = None + latest_version_name = None + latest_version_id = None metadata = None if hydrate: @@ -171,7 +172,8 @@ def to_model( created=self.created, updated=self.updated, tags=tags, - latest_version=latest_version, + latest_version_name=latest_version_name, + latest_version_id=latest_version_id, ) return ModelResponse( diff --git a/tests/integration/functional/test_client.py b/tests/integration/functional/test_client.py index 5398b6fd3df..3b5dd08ef1f 100644 --- a/tests/integration/functional/test_client.py +++ b/tests/integration/functional/test_client.py @@ -1310,6 +1310,19 @@ def test_name_is_mutable(self, clean_client: "Client"): model = clean_client.get_model(model.id) assert model.name == "bar" + def test_latest_version_retrieval(self, clean_client: "Client"): + """Test that model response has proper latest version in it.""" + model = clean_client.create_model(name=self.MODEL_NAME) + mv1 = clean_client.create_model_version(model.id, name="foo") + model_ = clean_client.get_model(model.id) + assert model_.latest_version_name == mv1.name + assert model_.latest_version_id == mv1.id + + mv2 = clean_client.create_model_version(model.id, name="bar") + model_ = clean_client.get_model(model.id) + assert model_.latest_version_name == mv2.name + assert model_.latest_version_id == mv2.id + class TestModelVersion: MODEL_NAME = "foo" diff --git a/tests/integration/functional/zen_stores/test_zen_store.py b/tests/integration/functional/zen_stores/test_zen_store.py index 811877e8d1a..ea26320be43 100644 --- a/tests/integration/functional/zen_stores/test_zen_store.py +++ b/tests/integration/functional/zen_stores/test_zen_store.py @@ -3610,7 +3610,8 @@ def test_latest_version_properly_fetched(self): """Test that latest version can be properly fetched.""" with ModelVersionContext() as created_model: zs = Client().zen_store - assert zs.get_model(created_model.id).latest_version is None + assert zs.get_model(created_model.id).latest_version_name is None + assert zs.get_model(created_model.id).latest_version_id is None for name in ["great one", "yet another one"]: mv = zs.create_model_version( ModelVersionRequest( @@ -3620,7 +3621,13 @@ def test_latest_version_properly_fetched(self): name=name, ) ) - assert zs.get_model(created_model.id).latest_version == mv.name + assert ( + zs.get_model(created_model.id).latest_version_name + == mv.name + ) + assert ( + zs.get_model(created_model.id).latest_version_id == mv.id + ) time.sleep(1) # thanks to MySQL again! def test_update_name(self, clean_client: "Client"): From 625c46c73906454c9a554688b914b37ee1ad425b Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Fri, 12 Jan 2024 09:47:27 +0100 Subject: [PATCH 13/16] remove manual linkage from docs (#2272) --- .../data-management/model-management.md | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/docs/book/user-guide/advanced-guide/data-management/model-management.md b/docs/book/user-guide/advanced-guide/data-management/model-management.md index 7140685dde4..48d868c2aa4 100644 --- a/docs/book/user-guide/advanced-guide/data-management/model-management.md +++ b/docs/book/user-guide/advanced-guide/data-management/model-management.md @@ -323,17 +323,5 @@ artifact, and you specify whether it's for a model or deployment by using the `is_model_artifact` and `is_deployment_artifact` flags (as shown above) else it will be assumed to be a data artifact. -### Manual Linkage - -Finally, artifacts can be linked to an existing model version manually using the -SDK: - -```python -model_version = ModelVersion(name="my_model", version="1.0.0") -model_version.link_artifact(my_artifact, name="new_artifact") -``` - -The `link_artifact` method handles creating this connection. -
ZenML Scarf
From 9b2a3aa9b61068a6ca7af206059d5912f07564a0 Mon Sep 17 00:00:00 2001 From: Christian Versloot Date: Fri, 12 Jan 2024 09:53:03 +0100 Subject: [PATCH 14/16] Improve action by adding advice to KeyError when configured steps are not present in pipeline (#2265) * Improve action by adding advice to KeyError * Reformatting --- src/zenml/config/compiler.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/zenml/config/compiler.py b/src/zenml/config/compiler.py index c3f74c5c748..238ab7c29fa 100644 --- a/src/zenml/config/compiler.py +++ b/src/zenml/config/compiler.py @@ -210,7 +210,9 @@ def _apply_run_configuration( for invocation_id in config.steps: if invocation_id not in pipeline.invocations: - raise KeyError(f"No step invocation with id {invocation_id}.") + raise KeyError( + f"Configuration for step {invocation_id} cannot be applied to any pipeline step. Make sure that all configured steps are present in your pipeline." + ) # Override `enable_cache` of all steps if set at run level if config.enable_cache is not None: From a0fd100e247d1c1dd9a74bb2634fd139355ed159 Mon Sep 17 00:00:00 2001 From: Safoine El Khabich <34200873+safoinme@users.noreply.github.com> Date: Fri, 12 Jan 2024 12:12:36 +0100 Subject: [PATCH 15/16] Allow multi step configuration for skypilot (#2166) * allow multi step configuration for skypilot * fixes * Auto-update of Starter template * update skypilot to allow running using an orchestrating VM * add ability to create an aws config when configuring local client * Add advanced AWS configurations to config.yaml * Fix cluster name concatenation in skypilot orchestrator entrypoint * Fix AWS service connector and Skypilot orchestrator issues * Add APT packages to Skypilot integrations and sanitize cluster name * Remove advanced AWS configurations from config.yaml * Update Skypilot orchestrator code * Auto-update of Starter template * Refactor cluster name sanitization in SkypilotBaseOrchestrator * Refactor SkypilotBaseOrchestrator and SkypilotOrchestratorEntrypoint * Remove commented out code and update Skypilot orchestrator entrypoint * Fix GCP service connector support for local gcloud CLI login * Fix Skypilot orchestrator cluster name generation * Refactor cluster creation logic in SkypilotBaseOrchestrator * Allow custom docker run args when using the Skypilot orchestrator * Handle exception and log error message in SkypilotBaseOrchestrator * Refactor AWS service connector to improve file handling * Refactor Skypilot integrations for ZenML * Add encoding parameter to subprocess call * Update Skypilot integration for AWS, GCP, and Azure * Add note about configuring pipeline resources for specific orchestrator flavors * Update src/zenml/integrations/gcp/service_connectors/gcp_service_connector.py Co-authored-by: Alex Strick van Linschoten * Add error handling for running code in a notebook and update Skypilot orchestrator entrypoint --------- Co-authored-by: Alex Strick van Linschoten Co-authored-by: GitHub Actions Co-authored-by: Stefan Nica Co-authored-by: Michael Schuster --- .../orchestrators/skypilot-vm.md | 54 +++- .../aws_service_connector.py | 16 + .../gcp_service_connector.py | 108 +++++-- src/zenml/integrations/skypilot/__init__.py | 36 +-- .../skypilot_orchestrator_base_vm_config.py | 4 +- .../skypilot_base_vm_orchestrator.py | 95 ++++-- .../skypilot_orchestrator_entrypoint.py | 277 ++++++++++++++++++ ...t_orchestrator_entrypoint_configuration.py | 77 +++++ src/zenml/orchestrators/step_launcher.py | 4 +- 9 files changed, 595 insertions(+), 76 deletions(-) create mode 100644 src/zenml/integrations/skypilot/orchestrators/skypilot_orchestrator_entrypoint.py create mode 100644 src/zenml/integrations/skypilot/orchestrators/skypilot_orchestrator_entrypoint_configuration.py diff --git a/docs/book/stacks-and-components/component-guide/orchestrators/skypilot-vm.md b/docs/book/stacks-and-components/component-guide/orchestrators/skypilot-vm.md index ad7fd9d1ef8..3d250d5cdd9 100644 --- a/docs/book/stacks-and-components/component-guide/orchestrators/skypilot-vm.md +++ b/docs/book/stacks-and-components/component-guide/orchestrators/skypilot-vm.md @@ -65,16 +65,16 @@ To use the SkyPilot VM Orchestrator, you need: * One of the SkyPilot integrations installed. You can install the SkyPilot integration for your cloud provider of choice using the following command: ```shell # For AWS - pip install "zenml[connectors-gcp]" - zenml integration install aws vm_aws + pip install "zenml[connectors-aws]" + zenml integration install aws skypilot_aws # for GCP pip install "zenml[connectors-gcp]" - zenml integration install gcp vm_gcp # for GCP + zenml integration install gcp skypilot_gcp # for GCP # for Azure pip install "zenml[connectors-azure]" - zenml integration install azure vm_azure # for Azure + zenml integration install azure skypilot_azure # for Azure ``` * [Docker](https://www.docker.com) installed and running. * A [remote artifact store](../artifact-stores/artifact-stores.md) as part of your stack. @@ -91,7 +91,7 @@ We need first to install the SkyPilot integration for AWS and the AWS connectors ```shell pip install "zenml[connectors-aws]" - zenml integration install aws vm_aws + zenml integration install aws skypilot_aws ``` To provision VMs on AWS, your VM Orchestrator stack component needs to be configured to authenticate with [AWS Service Connector](../../../stacks-and-components/auth-management/aws-service-connector.md). @@ -141,7 +141,7 @@ We need first to install the SkyPilot integration for GCP and the GCP extra for ```shell pip install "zenml[connectors-gcp]" - zenml integration install gcp vm_gcp + zenml integration install gcp skypilot_gcp ``` To provision VMs on GCP, your VM Orchestrator stack component needs to be configured to authenticate with [GCP Service Connector](../../../stacks-and-components/auth-management/gcp-service-connector.md) @@ -199,7 +199,7 @@ We need first to install the SkyPilot integration for Azure and the Azure extra ```shell pip install "zenml[connectors-azure]" - zenml integration install azure vm_azure + zenml integration install azure skypilot_azure ``` To provision VMs on Azure, your VM Orchestrator stack component needs to be configured to authenticate with [Azure Service Connector](../../../stacks-and-components/auth-management/azure-service-connector.md) @@ -371,6 +371,46 @@ skypilot_settings = SkypilotAzureOrchestratorSettings( {% endtab %} {% endtabs %} + +One of the key features of the SkyPilot VM Orchestrator is the ability to run each step of a pipeline on a separate VM with its own specific settings. This allows for fine-grained control over the resources allocated to each step, ensuring that each part of your pipeline has the necessary compute power while optimizing for cost and efficiency. + +## Configuring Step-Specific Resources + +The SkyPilot VM Orchestrator allows you to configure resources for each step individually. This means you can specify different VM types, CPU and memory requirements, and even use spot instances for certain steps while using on-demand instances for others. + +To configure step-specific resources, you can pass a `SkypilotBaseOrchestratorSettings` object to the `settings` parameter of the `@step` decorator. This object allows you to define various attributes such as `instance_type`, `cpus`, `memory`, `use_spot`, `region`, and more. + +Here's an example of how to configure specific resources for a step for the AWS cloud: + +```python +from zenml.integrations.skypilot.flavors.skypilot_orchestrator_aws_vm_flavor import SkypilotAWSOrchestratorSettings + +# Settings for a specific step that requires more resources +high_resource_settings = SkypilotAWSOrchestratorSettings( + instance_type='t2.2xlarge', + cpus=8, + memory=32, + use_spot=False, + region='us-east-1', + # ... other settings +) + +@step(settings={"orchestrator.vm_aws": high_resource_settings}) +def my_resource_intensive_step(): + # Step implementation + pass +``` + +{% hint style="warning" %} +When configuring pipeline or step-specific resources, you can use the `settings` +parameter to specifically target the orchestrator flavor you want to use +`orchestrator.STACK_COMPONENT_FLAVOR` and not orchestrator component name +`orchestrator.STACK_COMPONENT_NAME`. For example, if you want to configure +resources for the `vm_gcp` flavor, you can use `settings={"orchestrator.vm_gcp": ...}`. +{% endhint %} + +By using the `settings` parameter, you can tailor the resources for each step according to its specific needs. This flexibility allows you to optimize your pipeline execution for both performance and cost. + Check out the [SDK docs](https://sdkdocs.zenml.io/latest/integration\_code\_docs/integrations-skypilot/#zenml.integrations.skypilot.flavors.skypilot\_orchestrator\_base\_vm\_flavor.SkypilotBaseOrchestratorSettings) for a full list of available attributes and [this docs page](/docs/book/user-guide/advanced-guide/pipelining-features/configure-steps-pipelines.md) for more diff --git a/src/zenml/integrations/aws/service_connectors/aws_service_connector.py b/src/zenml/integrations/aws/service_connectors/aws_service_connector.py index fd17fb3ec00..7ce03867fc6 100644 --- a/src/zenml/integrations/aws/service_connectors/aws_service_connector.py +++ b/src/zenml/integrations/aws/service_connectors/aws_service_connector.py @@ -27,6 +27,7 @@ import base64 import datetime import json +import os import re from typing import Any, Dict, List, Optional, Tuple, cast @@ -1371,6 +1372,21 @@ def _configure_local_client( "aws_session_token" ] = credentials.token + aws_credentials_path = os.path.join( + users_home, ".aws", "credentials" + ) + + # Create the file as well as the parent dir if needed. + dirname = os.path.split(aws_credentials_path)[0] + if not os.path.isdir(dirname): + os.makedirs(dirname) + with os.fdopen( + os.open(aws_credentials_path, os.O_WRONLY | os.O_CREAT, 0o600), + "w", + ): + pass + + # Write the credentials to the file common.rewrite_credentials_file(all_profiles, users_home) logger.info( diff --git a/src/zenml/integrations/gcp/service_connectors/gcp_service_connector.py b/src/zenml/integrations/gcp/service_connectors/gcp_service_connector.py index 577f935dbf0..94b37496575 100644 --- a/src/zenml/integrations/gcp/service_connectors/gcp_service_connector.py +++ b/src/zenml/integrations/gcp/service_connectors/gcp_service_connector.py @@ -23,6 +23,9 @@ import json import os import re +import shutil +import subprocess +import tempfile from typing import Any, Dict, List, Optional, Tuple import google.api_core.exceptions @@ -1062,6 +1065,7 @@ def _configure_local_client( NotImplementedError: If the connector instance does not support local configuration for the configured resource type or authentication method.registry + AuthorizationException: If the local client configuration fails. """ resource_type = self.resource_type @@ -1070,16 +1074,8 @@ def _configure_local_client( # There is no way to configure the local gcloud CLI to use # temporary OAuth 2.0 tokens. However, we can configure it to use - # the user account credentials or service account credentials - if self.auth_method == GCPAuthenticationMethods.USER_ACCOUNT: - assert isinstance(self.config, GCPUserAccountConfig) - # Use the user account credentials JSON to configure the - # local gcloud CLI - gcloud_config_json = ( - self.config.user_account_json.get_secret_value() - ) - - elif self.auth_method == GCPAuthenticationMethods.SERVICE_ACCOUNT: + # the service account credentials + if self.auth_method == GCPAuthenticationMethods.SERVICE_ACCOUNT: assert isinstance(self.config, GCPServiceAccountConfig) # Use the service account credentials JSON to configure the # local gcloud CLI @@ -1090,7 +1086,66 @@ def _configure_local_client( if gcloud_config_json: from google.auth import _cloud_sdk - # Dump the user account or service account credentials JSON to + if not shutil.which("gcloud"): + raise AuthorizationException( + "The local gcloud CLI is not installed. Please " + "install the gcloud CLI to use this feature." + ) + + # Write the credentials JSON to a temporary file + with tempfile.NamedTemporaryFile( + mode="w", suffix=".json", delete=True + ) as f: + f.write(gcloud_config_json) + f.flush() + adc_path = f.name + + try: + # Run the gcloud CLI command to configure the local + # gcloud CLI to use the credentials JSON + subprocess.run( + [ + "gcloud", + "auth", + "login", + "--cred-file", + adc_path, + ], + check=True, + stderr=subprocess.STDOUT, + encoding="utf-8", + stdout=subprocess.PIPE, + ) + except subprocess.CalledProcessError as e: + raise AuthorizationException( + f"Failed to configure the local gcloud CLI to use " + f"the credentials JSON: {e}\n" + f"{e.stdout.decode()}" + ) + + try: + # Run the gcloud CLI command to configure the local gcloud + # CLI to use the credentials project ID + subprocess.run( + [ + "gcloud", + "config", + "set", + "project", + self.config.project_id, + ], + check=True, + stderr=subprocess.STDOUT, + stdout=subprocess.PIPE, + ) + except subprocess.CalledProcessError as e: + raise AuthorizationException( + f"Failed to configure the local gcloud CLI to use " + f"the project ID: {e}\n" + f"{e.stdout.decode()}" + ) + + # Dump the service account credentials JSON to # the local gcloud application default credentials file adc_path = ( _cloud_sdk.get_application_default_credentials_path() @@ -1099,8 +1154,8 @@ def _configure_local_client( f.write(gcloud_config_json) logger.info( - f"Updated the local gcloud default application " - f"credentials file at '{adc_path}'" + "Updated the local gcloud CLI and application default " + f"credentials file ({adc_path})." ) return @@ -1108,7 +1163,6 @@ def _configure_local_client( raise NotImplementedError( f"Local gcloud client configuration for resource type " f"{resource_type} is only supported if the " - f"'{GCPAuthenticationMethods.USER_ACCOUNT}' or " f"'{GCPAuthenticationMethods.SERVICE_ACCOUNT}' authentication " f"method is used and only if the generation of temporary OAuth " f"2.0 tokens is disabled by setting the " @@ -1257,13 +1311,27 @@ def _auto_configure( "GOOGLE_APPLICATION_CREDENTIALS" ) if service_account_json_file is None: - # Shouldn't happen since google.auth.default() should - # already have loaded the credentials from the environment + # No explicit service account JSON file was specified in the + # environment, meaning that the credentials were loaded from + # the GCP application default credentials (ADC) file. + from google.auth import _cloud_sdk + + # Use the location of the gcloud application default + # credentials file + service_account_json_file = ( + _cloud_sdk.get_application_default_credentials_path() + ) + + if not service_account_json_file or not os.path.isfile( + service_account_json_file + ): raise AuthorizationException( - "No GCP service account credentials found in the " - "environment. Please set the " - "GOOGLE_APPLICATION_CREDENTIALS environment variable " - "to the path of the service account JSON file." + "No GCP service account credentials were found in the " + "environment or the application default credentials " + "path. Please set the GOOGLE_APPLICATION_CREDENTIALS " + "environment variable to the path of the service " + "account JSON file or run 'gcloud auth application-" + "default login' to generate a new ADC file." ) with open(service_account_json_file, "r") as f: service_account_json = f.read() diff --git a/src/zenml/integrations/skypilot/__init__.py b/src/zenml/integrations/skypilot/__init__.py index 5c0330770a2..1b096c4e08c 100644 --- a/src/zenml/integrations/skypilot/__init__.py +++ b/src/zenml/integrations/skypilot/__init__.py @@ -30,52 +30,52 @@ SKYPILOT_GCP_ORCHESTRATOR_FLAVOR = "vm_gcp" SKYPILOT_AZURE_ORCHESTRATOR_FLAVOR = "vm_azure" +class SkypilotGCPIntegration(Integration): + """Definition of Skypilot Integration for ZenML.""" -class SkypilotAWSIntegration(Integration): - """Definition of Skypilot AWS Integration for ZenML.""" - - NAME = SKYPILOT_AWS - REQUIREMENTS = ["skypilot[aws]"] + NAME = SKYPILOT_GCP + REQUIREMENTS = ["skypilot[aws,gcp,azure]<=0.4.1"] + APT_PACKAGES = ["openssh-client","rsync"] @classmethod def flavors(cls) -> List[Type[Flavor]]: - """Declare the stack component flavors for the Skypilot AWS integration. + """Declare the stack component flavors for the Skypilot GCP integration. Returns: List of stack component flavors for this integration. """ from zenml.integrations.skypilot.flavors import ( - SkypilotAWSOrchestratorFlavor, + SkypilotGCPOrchestratorFlavor, ) - return [SkypilotAWSOrchestratorFlavor] - + return [SkypilotGCPOrchestratorFlavor] -class SkypilotGCPIntegration(Integration): - """Definition of Skypilot Integration for ZenML.""" +class SkypilotAWSIntegration(Integration): + """Definition of Skypilot AWS Integration for ZenML.""" - NAME = SKYPILOT_GCP - REQUIREMENTS = ["skypilot[gcp]"] + NAME = SKYPILOT_AWS + REQUIREMENTS = ["skypilot[aws,gcp,azure]<=0.4.1"] + APT_PACKAGES = ["openssh-client","rsync"] @classmethod def flavors(cls) -> List[Type[Flavor]]: - """Declare the stack component flavors for the Skypilot GCP integration. + """Declare the stack component flavors for the Skypilot AWS integration. Returns: List of stack component flavors for this integration. """ from zenml.integrations.skypilot.flavors import ( - SkypilotGCPOrchestratorFlavor, + SkypilotAWSOrchestratorFlavor, ) - return [SkypilotGCPOrchestratorFlavor] - + return [SkypilotAWSOrchestratorFlavor] class SkypilotAzureIntegration(Integration): """Definition of Skypilot Integration for ZenML.""" NAME = SKYPILOT_AZURE - REQUIREMENTS = ["skypilot[azure]"] + REQUIREMENTS = ["skypilot[aws,gcp,azure]<=0.4.1"] + APT_PACKAGES = ["openssh-client","rsync"] @classmethod def flavors(cls) -> List[Type[Flavor]]: diff --git a/src/zenml/integrations/skypilot/flavors/skypilot_orchestrator_base_vm_config.py b/src/zenml/integrations/skypilot/flavors/skypilot_orchestrator_base_vm_config.py index daca191d7fe..6f04b0445ec 100644 --- a/src/zenml/integrations/skypilot/flavors/skypilot_orchestrator_base_vm_config.py +++ b/src/zenml/integrations/skypilot/flavors/skypilot_orchestrator_base_vm_config.py @@ -13,7 +13,7 @@ # permissions and limitations under the License. """Skypilot orchestrator base config and settings.""" -from typing import Dict, Literal, Optional, Union +from typing import Dict, List, Literal, Optional, Union from zenml.config.base_settings import BaseSettings from zenml.logger import get_logger @@ -107,6 +107,8 @@ class SkypilotBaseOrchestratorSettings(BaseSettings): down: bool = True stream_logs: bool = True + docker_run_args: List[str] = [] + class SkypilotBaseOrchestratorConfig( # type: ignore[misc] # https://github.com/pydantic/pydantic/issues/4173 BaseOrchestratorConfig, SkypilotBaseOrchestratorSettings diff --git a/src/zenml/integrations/skypilot/orchestrators/skypilot_base_vm_orchestrator.py b/src/zenml/integrations/skypilot/orchestrators/skypilot_base_vm_orchestrator.py index 48990bada12..f1ca4db56d9 100644 --- a/src/zenml/integrations/skypilot/orchestrators/skypilot_base_vm_orchestrator.py +++ b/src/zenml/integrations/skypilot/orchestrators/skypilot_base_vm_orchestrator.py @@ -14,24 +14,26 @@ """Implementation of the Skypilot base VM orchestrator.""" import os -import time +import re from abc import abstractmethod from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, cast -from uuid import uuid4 import sky -from zenml.entrypoints import PipelineEntrypointConfiguration from zenml.enums import StackComponentType +from zenml.environment import Environment from zenml.integrations.skypilot.flavors.skypilot_orchestrator_base_vm_config import ( SkypilotBaseOrchestratorSettings, ) +from zenml.integrations.skypilot.orchestrators.skypilot_orchestrator_entrypoint_configuration import ( + SkypilotOrchestratorEntrypointConfiguration, +) from zenml.logger import get_logger from zenml.orchestrators import ( ContainerizedOrchestrator, ) +from zenml.orchestrators.utils import get_orchestrator_run_name from zenml.stack import StackValidator -from zenml.utils import string_utils if TYPE_CHECKING: from zenml.models import PipelineDeploymentResponse @@ -136,7 +138,7 @@ def prepare_or_run_pipeline( stack: "Stack", environment: Dict[str, str], ) -> Any: - """Runs all pipeline steps in Skypilot containers. + """Runs each pipeline step in a separate Skypilot container. Args: deployment: The pipeline deployment to prepare or run. @@ -146,7 +148,18 @@ def prepare_or_run_pipeline( Raises: Exception: If the pipeline run fails. + RuntimeError: If the code is running in a notebook. """ + # First check whether the code is running in a notebook. + if Environment.in_notebook(): + raise RuntimeError( + "The Skypilot orchestrator cannot run pipelines in a notebook " + "environment. The reason is that it is non-trivial to create " + "a Docker image of a notebook. Please consider refactoring " + "your notebook cells into separate scripts in a Python module " + "and run the code outside of a notebook when using this " + "orchestrator." + ) if deployment.schedule: logger.warning( "Skypilot Orchestrator currently does not support the" @@ -154,32 +167,42 @@ def prepare_or_run_pipeline( "and the pipeline will be run immediately." ) - # Set up some variables for configuration - orchestrator_run_id = str(uuid4()) - environment[ - ENV_ZENML_SKYPILOT_ORCHESTRATOR_RUN_ID - ] = orchestrator_run_id - settings = cast( SkypilotBaseOrchestratorSettings, self.get_settings(deployment), ) - entrypoint = PipelineEntrypointConfiguration.get_entrypoint_command() - entrypoint_str = " ".join(entrypoint) - arguments = PipelineEntrypointConfiguration.get_entrypoint_arguments( - deployment_id=deployment.id + pipeline_name = deployment.pipeline_configuration.name + orchestrator_run_name = get_orchestrator_run_name(pipeline_name) + + assert stack.container_registry + + # Get Docker image for the orchestrator pod + try: + image = self.get_image(deployment=deployment) + except KeyError: + # If no generic pipeline image exists (which means all steps have + # custom builds) we use a random step image as all of them include + # dependencies for the active stack + pipeline_step_name = next(iter(deployment.step_configurations)) + image = self.get_image( + deployment=deployment, step_name=pipeline_step_name + ) + + # Build entrypoint command and args for the orchestrator pod. + # This will internally also build the command/args for all step pods. + command = SkypilotOrchestratorEntrypointConfiguration.get_entrypoint_command() + entrypoint_str = " ".join(command) + args = SkypilotOrchestratorEntrypointConfiguration.get_entrypoint_arguments( + run_name=orchestrator_run_name, + deployment_id=deployment.id, ) - arguments_str = " ".join(arguments) + arguments_str = " ".join(args) - # Set up docker run command - image = self.get_image(deployment=deployment) docker_environment_str = " ".join( f"-e {k}={v}" for k, v in environment.items() ) - start_time = time.time() - instance_type = settings.instance_type or self.DEFAULT_INSTANCE_TYPE # Set up credentials @@ -188,8 +211,7 @@ def prepare_or_run_pipeline( # Guaranteed by stack validation assert stack is not None and stack.container_registry is not None - docker_creds = stack.container_registry.credentials - if docker_creds: + if docker_creds := stack.container_registry.credentials: docker_username, docker_password = docker_creds setup = ( f"docker login --username $DOCKER_USERNAME --password " @@ -232,6 +254,7 @@ def prepare_or_run_pipeline( ) ) + # Set the cluster name cluster_name = settings.cluster_name if cluster_name is None: # Find existing cluster @@ -243,6 +266,10 @@ def prepare_or_run_pipeline( logger.info( f"Found existing cluster {cluster_name}. Reusing..." ) + if cluster_name is None: + cluster_name = self.sanitize_cluster_name( + f"{orchestrator_run_name}" + ) # Launch the cluster sky.launch( @@ -252,17 +279,29 @@ def prepare_or_run_pipeline( idle_minutes_to_autostop=settings.idle_minutes_to_autostop, down=settings.down, stream_logs=settings.stream_logs, + detach_setup=True, ) except Exception as e: - raise e + logger.error(f"Pipeline run failed: {e}") + raise finally: # Unset the service connector AWS profile ENV variable self.prepare_environment_variable(set=False) - run_duration = time.time() - start_time - logger.info( - "Pipeline run has finished in `%s`.", - string_utils.get_human_readable_time(run_duration), - ) + def sanitize_cluster_name(self, name: str) -> str: + """Sanitize the value to be used in a cluster name. + + Args: + name: Arbitrary input cluster name. + + Returns: + Sanitized cluster name. + """ + name = re.sub( + r"[^a-z0-9-]", "-", name.lower() + ) # replaces any character that is not a lowercase letter, digit, or hyphen with a hyphen + name = re.sub(r"^[-]+", "", name) # trim leading hyphens + name = re.sub(r"[-]+$", "", name) # trim trailing hyphens + return name diff --git a/src/zenml/integrations/skypilot/orchestrators/skypilot_orchestrator_entrypoint.py b/src/zenml/integrations/skypilot/orchestrators/skypilot_orchestrator_entrypoint.py new file mode 100644 index 00000000000..ada74e63bb4 --- /dev/null +++ b/src/zenml/integrations/skypilot/orchestrators/skypilot_orchestrator_entrypoint.py @@ -0,0 +1,277 @@ +# Copyright (c) ZenML GmbH 2024. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at: +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express +# or implied. See the License for the specific language governing +# permissions and limitations under the License. +"""Entrypoint of the Skypilot master/orchestrator VM.""" + +import argparse +import socket +import time +from typing import Dict, cast + +import sky + +from zenml.client import Client +from zenml.entrypoints.step_entrypoint_configuration import ( + StepEntrypointConfiguration, +) +from zenml.enums import ExecutionStatus +from zenml.integrations.skypilot.flavors.skypilot_orchestrator_base_vm_config import ( + SkypilotBaseOrchestratorSettings, +) +from zenml.integrations.skypilot.orchestrators.skypilot_base_vm_orchestrator import ( + ENV_ZENML_SKYPILOT_ORCHESTRATOR_RUN_ID, + SkypilotBaseOrchestrator, +) +from zenml.logger import get_logger +from zenml.orchestrators.dag_runner import ThreadedDagRunner +from zenml.orchestrators.utils import get_config_environment_vars + +logger = get_logger(__name__) + + +def parse_args() -> argparse.Namespace: + """Parse entrypoint arguments. + + Returns: + Parsed args. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--run_name", type=str, required=True) + parser.add_argument("--deployment_id", type=str, required=True) + return parser.parse_args() + + +def main() -> None: + """Entrypoint of the Skypilot master/orchestrator VM. + + This is the entrypoint of the Skypilot master/orchestrator VM. It is + responsible for provisioning the VM and running the pipeline steps in + separate VMs. + + The VM is provisioned using the `sky` library. The pipeline steps are run + using the `sky` library as well. + + Raises: + TypeError: If the active stack's orchestrator is not an instance of + SkypilotBaseOrchestrator. + ValueError: If the active stack's container registry is None. + """ + # Log to the container's stdout so it can be streamed by the client. + logger.info("Skypilot orchestrator VM started.") + + # Parse / extract args. + args = parse_args() + + orchestrator_run_id = socket.gethostname() + + deployment = Client().get_deployment(args.deployment_id) + + pipeline_dag = { + step_name: step.spec.upstream_steps + for step_name, step in deployment.step_configurations.items() + } + step_command = StepEntrypointConfiguration.get_entrypoint_command() + entrypoint_str = " ".join(step_command) + + active_stack = Client().active_stack + + orchestrator = active_stack.orchestrator + if not isinstance(orchestrator, SkypilotBaseOrchestrator): + raise TypeError( + "The active stack's orchestrator is not an instance of SkypilotBaseOrchestrator." + ) + + # Set up credentials + orchestrator.setup_credentials() + + # Set the service connector AWS profile ENV variable + orchestrator.prepare_environment_variable(set=True) + + # get active container registry + container_registry = active_stack.container_registry + if container_registry is None: + raise ValueError("Container registry cannot be None.") + + if docker_creds := container_registry.credentials: + docker_username, docker_password = docker_creds + setup = ( + f"docker login --username $DOCKER_USERNAME --password " + f"$DOCKER_PASSWORD {container_registry.config.uri}" + ) + task_envs = { + "DOCKER_USERNAME": docker_username, + "DOCKER_PASSWORD": docker_password, + } + else: + setup = None + task_envs = None + + unique_resource_configs: Dict[str, str] = {} + for step_name, step in deployment.step_configurations.items(): + settings = cast( + SkypilotBaseOrchestratorSettings, + orchestrator.get_settings(step), + ) + # Handle both str and Dict[str, int] types for accelerators + if isinstance(settings.accelerators, dict): + accelerators_hashable = frozenset(settings.accelerators.items()) + elif isinstance(settings.accelerators, str): + accelerators_hashable = frozenset({(settings.accelerators, 1)}) + else: + accelerators_hashable = None + resource_config = ( + settings.instance_type, + settings.cpus, + settings.memory, + settings.disk_size, # Assuming disk_size is part of the settings + settings.disk_tier, # Assuming disk_tier is part of the settings + settings.use_spot, + settings.spot_recovery, + settings.region, + settings.zone, + accelerators_hashable, + ) + cluster_name_parts = [ + orchestrator.sanitize_cluster_name(str(part)) + for part in resource_config + if part is not None + ] + cluster_name = f"cluster-{orchestrator_run_id}" + "-".join( + cluster_name_parts + ) + unique_resource_configs[step_name] = cluster_name + + run = Client().list_pipeline_runs( + sort_by="asc:created", + size=1, + deployment_id=args.deployment_id, + status=ExecutionStatus.INITIALIZING, + )[0] + + logger.info("Fetching pipeline run: %s", run.id) + + def run_step_on_skypilot_vm(step_name: str) -> None: + """Run a pipeline step in a separate Skypilot VM. + + Args: + step_name: Name of the step. + """ + cluster_name = unique_resource_configs[step_name] + + image = SkypilotBaseOrchestrator.get_image( + deployment=deployment, step_name=step_name + ) + + step_args = StepEntrypointConfiguration.get_entrypoint_arguments( + step_name=step_name, deployment_id=deployment.id + ) + arguments_str = " ".join(step_args) + + step = deployment.step_configurations[step_name] + settings = cast( + SkypilotBaseOrchestratorSettings, + orchestrator.get_settings(step), + ) + env = get_config_environment_vars() + env[ENV_ZENML_SKYPILOT_ORCHESTRATOR_RUN_ID] = orchestrator_run_id + + docker_environment_str = " ".join( + f"-e {k}={v}" for k, v in env.items() + ) + custom_run_args = " ".join(settings.docker_run_args) + if custom_run_args: + custom_run_args += " " + + # Set up the task + run_command = f"docker run --rm {custom_run_args}{docker_environment_str} {image} {entrypoint_str} {arguments_str}" + task_name = f"{deployment.id}-{step_name}-{time.time()}" + task = sky.Task( + run=run_command, + setup=setup, + envs=task_envs, + name=task_name, + ) + task = task.set_resources( + sky.Resources( + cloud=orchestrator.cloud, + instance_type=settings.instance_type + or orchestrator.DEFAULT_INSTANCE_TYPE, + cpus=settings.cpus, + memory=settings.memory, + disk_size=settings.disk_size, + disk_tier=settings.disk_tier, + accelerators=settings.accelerators, + accelerator_args=settings.accelerator_args, + use_spot=settings.use_spot, + spot_recovery=settings.spot_recovery, + region=settings.region, + zone=settings.zone, + image_id=settings.image_id, + ) + ) + + sky.launch( + task, + cluster_name, + retry_until_up=settings.retry_until_up, + idle_minutes_to_autostop=settings.idle_minutes_to_autostop, + down=settings.down, + stream_logs=settings.stream_logs, + detach_setup=True, + detach_run=True, + ) + + # Wait for pod to finish. + logger.info(f"Waiting for pod of step `{step_name}` to start...") + + current_run = Client().get_pipeline_run(run.id) + + step_is_finished = False + while not step_is_finished: + time.sleep(10) + current_run = Client().get_pipeline_run(run.id) + try: + step_is_finished = current_run.steps[ + step_name + ].status.is_finished + except KeyError: + # Step is not yet in the run, so we wait for it to appear + continue + + # Pop the resource configuration for this step + unique_resource_configs.pop(step_name) + + if cluster_name in unique_resource_configs.values(): + # If there are more steps using this configuration, skip deprovisioning the cluster + logger.info( + f"Resource configuration for cluster '{cluster_name}' " + "is used by subsequent steps. Skipping the deprovisioning of " + "the cluster." + ) + else: + # If there are no more steps using this configuration, down the cluster + logger.info( + f"Resource configuration for cluster '{cluster_name}' " + "is not used by subsequent steps. deprovisioning the cluster." + ) + sky.down(cluster_name) + + logger.info(f"Running step `{step_name}` on a VM is completed.") + + ThreadedDagRunner(dag=pipeline_dag, run_fn=run_step_on_skypilot_vm).run() + + logger.info("Orchestration VM provisioned.") + + +if __name__ == "__main__": + main() diff --git a/src/zenml/integrations/skypilot/orchestrators/skypilot_orchestrator_entrypoint_configuration.py b/src/zenml/integrations/skypilot/orchestrators/skypilot_orchestrator_entrypoint_configuration.py new file mode 100644 index 00000000000..2df18d58d61 --- /dev/null +++ b/src/zenml/integrations/skypilot/orchestrators/skypilot_orchestrator_entrypoint_configuration.py @@ -0,0 +1,77 @@ +# Copyright (c) ZenML GmbH 2024. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at: +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express +# or implied. See the License for the specific language governing +# permissions and limitations under the License. +"""Entrypoint configuration for the Skypilot master/orchestrator VM.""" + +from typing import TYPE_CHECKING, List, Set + +if TYPE_CHECKING: + from uuid import UUID + +RUN_NAME_OPTION = "run_name" +DEPLOYMENT_ID_OPTION = "deployment_id" + + +class SkypilotOrchestratorEntrypointConfiguration: + """Entrypoint configuration for the Skypilot master/orchestrator VM.""" + + @classmethod + def get_entrypoint_options(cls) -> Set[str]: + """Gets all the options required for running this entrypoint. + + Returns: + Entrypoint options. + """ + options = { + RUN_NAME_OPTION, + DEPLOYMENT_ID_OPTION, + } + return options + + @classmethod + def get_entrypoint_command(cls) -> List[str]: + """Returns a command that runs the entrypoint module. + + Returns: + Entrypoint command. + """ + command = [ + "python", + "-m", + "zenml.integrations.skypilot.orchestrators.skypilot_orchestrator_entrypoint", + ] + return command + + @classmethod + def get_entrypoint_arguments( + cls, + run_name: str, + deployment_id: "UUID", + ) -> List[str]: + """Gets all arguments that the entrypoint command should be called with. + + Args: + run_name: Name of the ZenML run. + deployment_id: ID of the deployment. + + Returns: + List of entrypoint arguments. + """ + args = [ + f"--{RUN_NAME_OPTION}", + run_name, + f"--{DEPLOYMENT_ID_OPTION}", + str(deployment_id), + ] + + return args diff --git a/src/zenml/orchestrators/step_launcher.py b/src/zenml/orchestrators/step_launcher.py index 6da14aa48d5..b3b9fd1d2cd 100644 --- a/src/zenml/orchestrators/step_launcher.py +++ b/src/zenml/orchestrators/step_launcher.py @@ -212,8 +212,8 @@ def launch(self) -> None: execution_needed, step_run = self._prepare( step_run=step_run ) - except: # noqa: E722 - logger.error( + except: + logger.exception( f"Failed preparing run step `{self._step_name}`." ) step_run.status = ExecutionStatus.FAILED From 542009544677739038b2ba4db3cb20bbf329ab6a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bar=C4=B1=C5=9F=20Can=20Durak?= <36421093+bcdurak@users.noreply.github.com> Date: Fri, 12 Jan 2024 13:11:23 +0100 Subject: [PATCH 16/16] Reworking the examples (#2259) * removed two examples * updated the README * reworking the toc for docs * removed the init file * fixing links after name changes * minor fix --- docs/book/learning/examples.md | 10 - docs/book/learning/projects.md | 57 --- docs/book/toc.md | 17 +- examples/README.md | 80 ++-- examples/__init__.py | 13 - examples/generative_chat/README.md | 137 ------- .../generative_chat/pipelines/__init__.py | 13 - .../pipelines/index_builder.py | 23 -- examples/generative_chat/requirements.txt | 15 - examples/generative_chat/run.py | 90 ----- examples/generative_chat/steps/__init__.py | 13 - 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-# 🔏 Examples - -ZenML examples are small examples that focus on particular aspects of ZenML, and can be found in the [examples directory of the open source repository](https://github.com/zenml-io/zenml/blob/main/examples/). - - -
ZenML Scarf
diff --git a/docs/book/learning/projects.md b/docs/book/learning/projects.md deleted file mode 100644 index 3a2b87aa845..00000000000 --- a/docs/book/learning/projects.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -description: Resources to learn how to use ZenML practically. ---- - -# 🧩 Projects - -ZenML Projects are small-scale production-grade ML use cases powered by ZenML. They are fully fleshed-out, end-to-end, projects that showcase ZenML's capabilities. They can also serve as a template from which to start similar projects. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Predicting customer satisfactionAnalyze customer feedback to predict satisfaction levels using ZenML.customer_satisfaction.pnghttps://github.com/zenml-io/zenml-projects/tree/main/customer-satisfaction
Build an LLM-powered community Slack botProductionalize LangChain and LlamaIndex with a ZenML MLOps Pipeline to support community Slack channels.llm.pnghttps://github.com/zenml-io/zenml-projects/tree/main/langchain-llamaindex-slackbot
Use ZenML seamlessly with GitHub ActionsAutomate machine-learning workflows with GitHub Actions and ZenML for continuous integration and deployment.Remote_with_git_ops.pnghttps://github.com/zenml-io/zenml-gitflow
Forecasting Time SeriesPredict future time-series events like power generation or stock prices with the help of ZenML's pipeline.pexels_pixabay_433308_0665b2fb5b.webphttps://github.com/zenml-io/zenml-projects/tree/main/time-series-forecast
Detect Sign Language alphabet in real-time images using Yolov5Utilize ZenML and Yolov5 to detect and recognize American Sign Language alphabets in real-time image streams.pexels_shvets_production_7516363_8bec88f86d.webphttps://github.com/zenml-io/zenml-projects/tree/main/sign-language-detection-yolov5
- -## Start working on your own projects with ZenML Templates - -If you like the projects listed above and you are interested in creating your own project, you can take a look at our [ZenML Project Templates](https://github.com/zenml-io/zenml-project-templates). The goal of these templates is to help you generate ready-to-use scaffolding for your own projects, with everything you need to get started: steps, pipelines, stack configurations, and other useful resources. - - -
ZenML Scarf
diff --git a/docs/book/toc.md b/docs/book/toc.md index cc541016a46..8a0a710d44b 100644 --- a/docs/book/toc.md +++ b/docs/book/toc.md @@ -158,19 +158,12 @@ * [Implement a custom stack component](stacks-and-components/custom-solutions/implement-a-custom-stack-component.md) * [Implement a custom integration](stacks-and-components/custom-solutions/implement-a-custom-integration.md) -## Learning +## Examples -* [🚀 Quickstart](https://github.com/zenml-io/zenml/blob/main/examples/quickstart/README.md) -* [🔏 Examples](learning/examples.md) - * [e2e Batch Inference using classical ML](https://github.com/zenml-io/zenml/tree/main/examples/e2e) - * [Generative Chat with LLMs](https://github.com/zenml-io/zenml/tree/main/examples/generative_chat) - * [Basic NLP with BERT](https://github.com/zenml-io/zenml/tree/main/examples/nlp-case) -* [🧩 Projects](learning/projects.md) - * [Predicting customer satisfaction](https://github.com/zenml-io/zenml-projects/tree/main/customer-satisfaction) - * [Build an LLM-powered community Slack bot](https://github.com/zenml-io/zenml-projects/tree/main/langchain-llamaindex-slackbot) - * [Use ZenML seamlessly with GitHub Actions](https://github.com/zenml-io/zenml-gitflow) - * [Forecasting Time Series](https://github.com/zenml-io/zenml-projects/tree/main/time-series-forecast) - * [Detect Sign Language alphabet in real-time images using Yolov5](https://github.com/zenml-io/zenml-projects/tree/main/sign-language-detection-yolov5) +* [🚀 Quickstart](https://github.com/zenml-io/zenml/blob/main/examples/quickstart) +* [🔏 End-to-End Batch Inference](https://github.com/zenml-io/zenml/tree/main/examples/e2e) +* [📚 Basic NLP with BERT](https://github.com/zenml-io/zenml/tree/main/examples/e2e_nlp) +* [🧩 More Projects...](https://github.com/zenml-io/zenml-projects) ## Reference diff --git a/examples/README.md b/examples/README.md index 82006bf5342..d174d48bb7d 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,39 +1,43 @@ -# ZenML Use Cases - -This folder contains examples of ZenML pipelines. These are more involved use -cases with production-ready pipelines, with the exception of -some that will be indicated in the table below. - -These core demonstrations of ZenML showcase the power of the framework in real -scenarios that use cloud infrastructure or other cloud components. -Explanations for how to use and run the use cases can be found in the README -associated with each sub-folder. - -{% hint style="info" %} -This directory previously contained examples of ZenML integrations and how to -use them. These have been ingested into the core library as integration tests. -It is therefore still possible to view the code at -[`/tests/integration/examples`](https://github.com/zenml-io/zenml/tree/main/tests/integration/examples) -but for explanations on how to use them, please refer to our dedicated [Component -guide](https://docs.zenml.io/stacks-and-components/component-guide) in our -documentation which has been updated with all the latest information. -{% endhint %} - -Note that our full use cases are all implemented as templates so you can start -with our code and then adapt the specifics to your individual needs. To learn -more about how to do this, please [visit our dedicated documentation -page](https://docs.zenml.io/user-guide/starter-guide/using-project-templates) on -this. - -| Name | Description | Integrations | Core Project | -| ---- | ----------- | ------------ | ------------ | -| quickstart | This is our quickstart example showcasing basic functionality and a workflow to get you started with the ZenML framework | mlflow | ✅ | -| e2e | Trains one or more scikit-learn classification models to make predictions on tabular classification datasets | scikit-learn | ✅ | -| generative_chat | LEGACY: constructs a vector store to be used by a LLM-based chatbot based on documentation and data from a variety of sources | langchain, llama-index, slack | | -| label_studio_annotation | LEGACY: shows how to use the Label Studio integration for annotation in a computer-vision use case and series of pipelines | label_studio, pillow | | - -## ❓ Questions / Further Assistance - -If you have any questions about how to get going with these use cases, or if -you're wondering how you can adapt them to your particular needs, please do +# ZenML Examples + +Welcome to the `examples` folder of ZenML! This directory contains a collection +of examples that demonstrate the use of ZenML in various settings. Whether +you're a beginner looking to explore ZenML's capabilities or an experienced +user seeking inspiration, these examples cover a range of scenarios to help +you get started quickly. + +## Structure + +Each project in this folder is organized in a standardized structure to +showcase ZenML's best practices. Moreover, each example listed below is a +materialized form of one of our project templates. If you like any of the +examples here, and you want to set up something similar, just grab the template +and kickstart your own example. It's that simple! + +Here's a brief overview of the featured examples: + +| Name | Description | Template | +|-------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------| +| **[Quickstart](quickstart)** | Our quickstart example showcasing the basic functionality and a workflow to get you started with the ZenML framework. | [starter](https://github.com/zenml-io/template-starter) | +| **[End-to-End Training with Batch Predictions](e2e)** | A comprehensive supervised ML project to train scikit-learn classification models and make predictions on tabular datasets. | [e2e-batch](https://github.com/zenml-io/template-e2e-batch) | +| **[NLP Training, Promotion and Deployment](e2e_nlp)** | An NLP pipeline that walks through tokenization, training, HP tuning, evaluation and deployment. | [nlp](https://github.com/zenml-io/template-nlp) | + +## More Projects & Practical Examples + +If you're eager to discover more projects leveraging ZenML, you can check out +our [zenml-projects](https://github.com/zenml-io/zenml-projects) repository. +It hosts a wide variety of projects that showcase the practical application +of ZenML pipelines in different real-world examples. + +Furthermore, if you are interested in targeted examples featuring a particular integration, +you can check out the [component guide](https://docs.zenml.io/stacks-and-components/component-guide) +in our docs or the [integration tests](https://github.com/zenml-io/zenml/tree/main/tests/integration/examples) +in our repository. + +## Support & Feedback + +If you have questions or need assistance with any of the examples, feel free to [reach out to us on Slack](https://zenml.io/slack-invite/)! + +Happy experimenting with ZenML! + diff --git a/examples/__init__.py b/examples/__init__.py deleted file mode 100644 index 5e058539166..00000000000 --- a/examples/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. diff --git a/examples/generative_chat/README.md b/examples/generative_chat/README.md deleted file mode 100644 index aaba81a9d0c..00000000000 --- a/examples/generative_chat/README.md +++ /dev/null @@ -1,137 +0,0 @@ -# 💬 Generative Chat with Langchain and Llama Index: Enhancing Large Language Models with Custom Data - -Large language models (LLMs) have become a cornerstone of natural language -processing, offering unparalleled capabilities for knowledge generation and -reasoning. However, despite their immense potential, incorporating custom, -private data into these models remains a challenge. This is where tools like -[LangChain](https://github.com/hwchase17/langchain) and -[LlamaIndex](https://github.com/jerryjliu/llama_index) (formerly 'GPT Index') -come into play, offering innovative solutions for data ingestion and indexing, -enabling developers to augment LLMs with their unique datasets. - -LangChain and LlamaIndex facilitate in-context learning, an emerging paradigm -that allows developers to insert context into input prompts, leveraging LLM's -reasoning capabilities for generating more relevant and accurate responses. This -differs from finetuning, which requires retraining models using custom datasets, -often demanding significant computational resources and time. - -By addressing data ingestion and indexing, LangChain and LlamaIndex provide a -streamlined framework for integrating custom data into LLMs. Their flexible -design simplifies incorporating external data sources, enabling developers to -focus on creating powerful applications that harness LLMs' full potential. - -These tools bridge the gap between external data and LLMs, ensuring seamless -integration while maintaining performance. By utilizing LangChain and -LlamaIndex, developers can unlock LLMs' true potential and build cutting-edge -applications tailored to specific use cases and datasets. - -## 🗺 Overview - -This example showcases a simple way to use the core features of both frameworks, -ingesting data and building an index. There are lots of demos that you can plug -your index into, and we recommend you check out Langchain's [documentation on -use cases](https://langchain.readthedocs.io/en/latest/index.html#use-cases) for -more information on how to use your index. - -**COMING SOON:** We also recommend you check out our [more advanced project -implementation](https://github.com/zenml-io/zenml-projects) which showcases -additional benefits of integrating these tools with ZenML. - - -## 🧰 How the example is implemented - -This example is made up of the following steps: - -- a Langchain loader step to get documentation from a GitBook website -- a LlamaIndex loader step to download messages from a Slack workspace and - channel -- a Langchain index generator step that builds an index from the GitBook docs - and the Slack messages - -The index can then be accessed using ZenML's [post-execution workflow], for -example: - -```python -from zenml.post_execution import get_pipeline - -# Retrieve the pipeline for generating ZenML docs index -pipeline = get_pipeline("docs_to_index_pipeline") - -# Access the last pipeline run -pipeline_run = pipeline.runs[-1] - -# Retrieve the last step of the relevant run -last_step_of_relevant_run = pipeline_run.steps[-1] - -# Read the vector index from the last step's output -vector_index = last_step_of_relevant_run.outputs.read() -``` - -# 🖥 Run it locally - -## 👣 Step-by-Step - -### 📄 Prerequisites - -For this example you'll need the following: - -- A Slack API key / token which will allow you to download messages from a Slack - workspace and channel. You can get one by following the instructions - [here](https://api.slack.com/authentication/basics). -- [An OpenAI API - key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) - to interface with their embeddings in the VectorIndex. (Alternatively, you - could use a different embedding provider as described [in the Langchain - documentation](https://langchain.readthedocs.io/en/latest/modules/indexes/examples/embeddings.html).) - -In addition to those requirements, in order to run this example, you need to -install and initialize ZenML: - -```shell -# install CLI -pip install zenml[server] - -# install ZenML integrations -zenml integration install langchain llama_index - -# install additional dependencies for this example -pip install -r requirements.txt - -# make your API key and token available in the environment -export SLACK_BOT_TOKEN= -export OPENAI_API_KEY= - -# clone the ZenML repository -git clone https://github.com/zenml-io/zenml.git -cd examples/generative_chat - -# Initialize ZenML repo -zenml init - -# Start the ZenServer to enable dashboard access -zenml up -``` - -### ▶️ Run the Code - -Now we're ready. Execute: - -```shell -python run.py -``` - -This will run it with default arguments which are to query the ZenML docs and -Slack channel. For your own use case you'll want to change the arguments passed -in via a CLI and you can see the arguments available to you by running: - -```shell -python run.py --help -``` - -### 🧽 Clean up - -In order to clean up, delete the remaining ZenML references. - -```shell -rm -rf zenml_examples -``` diff --git a/examples/generative_chat/pipelines/__init__.py b/examples/generative_chat/pipelines/__init__.py deleted file mode 100644 index 7d170426951..00000000000 --- a/examples/generative_chat/pipelines/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. diff --git a/examples/generative_chat/pipelines/index_builder.py b/examples/generative_chat/pipelines/index_builder.py deleted file mode 100644 index 4b99ae3b148..00000000000 --- a/examples/generative_chat/pipelines/index_builder.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - - -from zenml.pipelines import pipeline - - -@pipeline() -def docs_to_index_pipeline(document_loader, slack_loader, index_generator): - documents = document_loader() - slack_docs = slack_loader() - index_generator(documents, slack_docs) diff --git a/examples/generative_chat/requirements.txt b/examples/generative_chat/requirements.txt deleted file mode 100644 index 7ae7452d8eb..00000000000 --- a/examples/generative_chat/requirements.txt +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - -faiss-cpu== "^1" diff --git a/examples/generative_chat/run.py b/examples/generative_chat/run.py deleted file mode 100644 index ed9cb168b63..00000000000 --- a/examples/generative_chat/run.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. -import argparse -import logging - -from pipelines.index_builder import ( - docs_to_index_pipeline, -) -from steps.docs_loader import DocsLoaderParameters, docs_loader -from steps.index_generator import IndexGeneratorParameters, index_generator -from steps.slack_loader import SlackLoaderParameters, slack_loader -from steps.utils import get_channel_id_from_name, get_release_date, page_exists - -docs_version = "0.35.0" -base_url = "https://docs.zenml.io" -docs_url = f"https://docs.zenml.io/v/{docs_version}/" -channel_names = ["general"] -channel_ids = [ - get_channel_id_from_name(channel_name) for channel_name in channel_names -] - -if not page_exists(docs_url): - print(f"Couldn't find docs page for zenml version '{docs_version}'.") - -release_date, next_release_date = get_release_date("zenml", docs_version) - -parser = argparse.ArgumentParser( - description="Build ZenML documentation index." -) -parser.add_argument( - "--docs_version", - type=str, - default=docs_version, - help="Docs version number", -) -parser.add_argument( - "--base_url", - type=str, - default=base_url, - help="Base URL for documentation site", -) -parser.add_argument( - "--docs_url", - type=str, - default=docs_url, - help="URL for documentation site", -) -args = parser.parse_args() - -docs_version = args.docs_version -base_url = args.base_url -docs_url = args.docs_url - -docs_to_index_pipeline = docs_to_index_pipeline( - document_loader=docs_loader( - params=DocsLoaderParameters(docs_uri=docs_url, base_url=base_url) - ), - index_generator=index_generator(params=IndexGeneratorParameters()), - slack_loader=slack_loader( - params=SlackLoaderParameters( - channel_ids=channel_ids, - earliest_date=release_date, - latest_date=next_release_date, - ) - ), -) - - -def main(): - try: - docs_to_index_pipeline.run() - except Exception as e: - print(f"Failed to build index for docs version '{docs_version}'.") - print(e) - - -if __name__ == "__main__": - logging.basicConfig(level="INFO") - main() diff --git a/examples/generative_chat/steps/__init__.py b/examples/generative_chat/steps/__init__.py deleted file mode 100644 index 7d170426951..00000000000 --- a/examples/generative_chat/steps/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. diff --git a/examples/generative_chat/steps/docs_loader.py b/examples/generative_chat/steps/docs_loader.py deleted file mode 100644 index 27c8e46fc7f..00000000000 --- a/examples/generative_chat/steps/docs_loader.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - - -from typing import List - -from langchain.docstore.document import Document -from langchain.document_loaders import GitbookLoader - -from zenml.steps import BaseParameters, step - - -class DocsLoaderParameters(BaseParameters): - """Params for Gitbook loader. - - Attributes: - docs_uri: URI of the docs. - docs_base_url: Base URL of the docs. - """ - - docs_uri: str = "https://docs.zenml.io" - docs_base_url: str = "https://docs.zenml.io" - - -@step(enable_cache=True) -def docs_loader(params: DocsLoaderParameters) -> List[Document]: - """Langchain loader for Gitbook docs. - - Args: - params: Parameters for the step. - - Returns: - List of langchain documents. - """ - loader = GitbookLoader( - web_page=params.docs_uri, - base_url=params.docs_base_url, - load_all_paths=True, - ) - return loader.load() diff --git a/examples/generative_chat/steps/index_generator.py b/examples/generative_chat/steps/index_generator.py deleted file mode 100644 index b130abf4c39..00000000000 --- a/examples/generative_chat/steps/index_generator.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - -import random -from typing import List - -from langchain.docstore.document import Document -from langchain.embeddings import OpenAIEmbeddings -from langchain.text_splitter import ( - CharacterTextSplitter, - RecursiveCharacterTextSplitter, -) -from langchain.vectorstores import FAISS, VectorStore -from openai.error import InvalidRequestError - -from zenml.logger import get_logger -from zenml.steps import step -from zenml.steps.base_parameters import BaseParameters - -logger = get_logger(__name__) - - -class IndexGeneratorParameters(BaseParameters): - """Params for index generator. - - Attributes: - document_chunk_size: Size of the document chunks. - document_chunk_overlap: Overlap of the document chunks. - slack_chunk_size: Size of the slack chunks. - slack_chunk_overlap: Overlap of the slack chunks. - """ - - document_chunk_size: int = 1000 - document_chunk_overlap: int = 0 - slack_chunk_size: int = 1000 - slack_chunk_overlap: int = 200 - - -@step(enable_cache=True) -def index_generator( - documents: List[Document], - slack_documents: List[Document], - params: IndexGeneratorParameters, -) -> VectorStore: - """Generates a FAISS index from a list of documents. - - Args: - documents: List of langchain documents. - slack_documents: List of langchain documents. - - Returns: - FAISS index. - """ - embeddings = OpenAIEmbeddings() - - # chunk the documents into smaller chunks - docs_text_splitter = CharacterTextSplitter( - chunk_size=params.document_chunk_size, - chunk_overlap=params.document_chunk_overlap, - ) - split_documents = docs_text_splitter.split_documents(documents) - - # chunk the slack documents into smaller chunks - slack_text_splitter = RecursiveCharacterTextSplitter( - chunk_size=params.slack_chunk_size, - chunk_overlap=params.slack_chunk_overlap, - ) - slack_texts = slack_text_splitter.split_documents(slack_documents) - - split_documents.extend(slack_texts) # merges the two document lists - - try: - return FAISS.from_documents(documents, embeddings) - except InvalidRequestError: - random.shuffle(documents) - shorter_documents = documents[: len(documents) // 2] - logger.warning( - "Too many tokens passed to OpenAI. Trying with smaller set of documents" - ) - return FAISS.from_documents(shorter_documents, embeddings) diff --git a/examples/generative_chat/steps/slack_loader.py b/examples/generative_chat/steps/slack_loader.py deleted file mode 100644 index c87b3be0491..00000000000 --- a/examples/generative_chat/steps/slack_loader.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - - -import datetime -import os -from typing import List, Optional - -from langchain.docstore.document import Document -from llama_index.readers.slack import SlackReader - -from zenml.steps import BaseParameters, step - - -class SlackLoaderParameters(BaseParameters): - """Params for Slack loader. - - Attributes: - channel_ids: List of channel IDs to load. - earliest_date: Earliest date to load. - latest_date: Latest date to load. - """ - - channel_ids: List[str] = [] - earliest_date: Optional[datetime.datetime] = None - latest_date: Optional[datetime.datetime] = None - - -@step(enable_cache=True) -def slack_loader(params: SlackLoaderParameters) -> List[Document]: - """Langchain loader for Slack. - - Args: - params: Parameters for the step. - - Returns: - List of langchain documents. - """ - loader = SlackReader( - slack_token=os.environ["SLACK_BOT_TOKEN"], - earliest_date=params.earliest_date, - latest_date=params.latest_date, - ) - documents = loader.load_data(channel_ids=params.channel_ids) - return [d.to_langchain_format() for d in documents] diff --git a/examples/generative_chat/steps/utils.py b/examples/generative_chat/steps/utils.py deleted file mode 100644 index 39f4cfc0f56..00000000000 --- a/examples/generative_chat/steps/utils.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright (c) ZenML GmbH 2023. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - -import datetime -import os -from typing import Optional, Tuple - -import requests -from slack_sdk import WebClient - - -def get_channel_id_from_name(name: str) -> str: - """Gets a channel ID from a Slack channel name. - - Args: - name: Name of the channel. - - Returns: - Channel ID. - """ - client = WebClient(token=os.environ["SLACK_BOT_TOKEN"]) - response = client.conversations_list() - conversations = response["channels"] - if id := [c["id"] for c in conversations if c["name"] == name][0]: - return id - else: - raise ValueError(f"Channel {name} not found.") - - -SLACK_CHANNEL_IDS = [get_channel_id_from_name("general")] - - -def page_exists(url: str) -> bool: - import requests - - r = requests.get(url, timeout=5) - return r.status_code == 200 - - -def get_release_date( - package_name: str, version: str -) -> Tuple[datetime.datetime, Optional[datetime.datetime]]: - """Get the release date of a package version. - - Args: - package_name: Name of the package. - version: Version of the package. - - Returns: - The release date of the package version, and the date of the next - release if it exists (or None). - """ - # Get the package's release information from the PyPI API - response = requests.get( - f"https://pypi.org/pypi/{package_name}/json", timeout=5 - ) - - # Parse the JSON data - data = response.json() - - # Get a list of the package's release versions - release_info = data["releases"].get(version) - - if not release_info: - raise ValueError( - f"Version {version} not found for package {package_name}." - ) - release_upload_time = datetime.datetime.strptime( - data["releases"][version][0]["upload_time"], "%Y-%m-%dT%H:%M:%S" - ) - - two_weeks_later = release_upload_time + datetime.timedelta(weeks=2) - if two_weeks_later > datetime.datetime.now(): - two_weeks_later = datetime.datetime.now() - - return ( - release_upload_time, - two_weeks_later, - ) diff --git a/examples/label_studio_annotation/README.md b/examples/label_studio_annotation/README.md deleted file mode 100644 index e854e3432ab..00000000000 --- a/examples/label_studio_annotation/README.md +++ /dev/null @@ -1,347 +0,0 @@ -# 🏷 Continuous Annotation with Label Studio - -Data annotation / labeling is a core part of MLOps that is frequently left out -of the conversation. ZenML offers a way to build continuous annotation -(combining training and annotation into a loop) with Label Studio. This uses a -combination of user-defined steps as well as some built-in steps that ZenML -provides. - -## 🗺 Overview - -NOTE: This example currently only runs with a cloud artifact store. [This guide -by Label Studio](https://labelstud.io/guide/storage.html) contains more details -if you have trouble understanding the instructions below. - -For a full video walkthrough on how to run this example, please check out [our -community hour demo](https://www.youtube.com/watch?v=bLFGnoApWeU) in which we -show how to run the various pipelines, in what order, all using an AWS stack. - -# 🖥 Run the example - -In order to run this example, you need to install and initialize ZenML and Label -Studio. - -```shell -pip install "zenml[server]" torchvision - -# clone the ZenML repository -git clone https://github.com/zenml-io/zenml.git -cd examples/label_studio_annotation - -# Initialize ZenML repo -zenml init - -# Start the ZenServer to enable dashboard access -zenml up -``` - -You will need to install and start Label Studio locally: - -```shell -zenml integration install label_studio - -# start label-studio locally -label-studio start -p 8093 -``` - -To connect ZenML to your local Label Studio server, you also need an API key. -This will give you access to the web annotation interface. - -First order of business is to sign up if you haven't done so already, using an -email and a password. Then visit [http://localhost:8093/](http://localhost:8093/) to log in, and then -visit [http://localhost:8093/user/account](http://localhost:8093/user/account) and get your Label Studio API key (from -the upper right hand corner). You will need it for later. Keep the -Label Studio server running, because the ZenML Label Studio annotator will use -it as the backend. - -## 👣 Step-by-Step Infrastructure Setup - -Pick your preferred infrastructure provider from Azure, GCP and AWS. Then follow -the relevant guide below for how to set up the infrastructure required by this -example. - -### 🥞 Set up your stack for Microsoft Azure - -You should install the relevant integrations: - -```shell -zenml integration install pytorch azure pillow -``` - -Some setup for your stack is required. - -A resource group will allow you to clean up any associated resources easily, so -either create one with the following command, or reference an existing one in -what follows. The same applies to the storage account and container that we'll -create and use: - -```bash -az group create --name --location northeurope - -az storage account create --name --resource-group --location northeurope --sku Standard_ZRS --encryption-services blob - -az storage container create \ - --account-name \ - --name \ - --auth-mode login -``` - -At this point you'll want to get a storage account key with the following -command: - -```bash -az storage account keys list --account-name -``` - -You can pick either of the 'value' properties to use for the `--account-key` -value for the following command that adds CORS permissions to your storage -account: - -```bash -az storage cors add --origins '*' --methods GET --allowed-headers '*' --exposed-headers 'Access-Control-Allow-Origin' --max-age 3600 --services blob --account-key --account-name -``` - -Now we can set up all the necessary ZenML components: - -```shell -zenml stack copy default - -zenml stack set - -zenml secret create --account_name="" --account_key="" - -zenml artifact-store register -f azure --path="az://" --authentication_secret="" - -zenml stack update -a - -zenml secret create --api_key="" - -zenml annotator register --flavor label_studio --authentication_secret="" - -zenml stack update -an -``` - -At this point you should stop (`CTRL-C`) Label Studio, set the following two -environment variables (or do it wherever is appropriate if you have a deployed -Label Studio instance) before restarting Label Studio: - -```bash -export AZURE_BLOB_ACCOUNT_NAME="" -export AZURE_BLOB_ACCOUNT_KEY="" -``` - -### 🥞 Set up your stack for GCP - -You should install the relevant integrations: - -```shell -zenml integration install label_studio pytorch gcp pillow -``` - -This setup guide assumes that you have installed and are able to use the -`gcloud` CLI to run commands. - -Create your artifact store and set up the necessary permissions and service -accounts: - -```shell -gsutil mb -p -l -c standard "gs://" - -gcloud iam service-accounts create - -gcloud projects add-iam-policy-binding --member="serviceAccount:@.iam.gserviceaccount.com" --role="roles/storage.admin" - -gcloud projects add-iam-policy-binding --member="serviceAccount:@.iam.gserviceaccount.com" --role="roles/iam.serviceAccountTokenCreator" - -gcloud iam service-accounts keys create ls-annotation-credentials.json --iam-account=@.iam.gserviceaccount.com - -gsutil cors set cloud_config/gcp/cors-config.json gs:// -``` - -Now you have a credentials `json` file that you can use to authenticate with -Label Studio. Make sure to note down the path where this was created. - -Now you can set up the rest of your stack: - -```shell -zenml stack copy default - -zenml stack set - -zenml secret create --token="@PATH/TO/JSON/FILE/CREATED/ABOVE" - -zenml artifact-store register gcp_artifact_store -f gcp --path="gs://" --authentication_secret="" - -zenml stack update -a - -zenml secret create --api_key="" - -zenml annotator register --flavor label_studio --authentication_secret="" - -zenml stack update -an -``` - -Be sure also to set the environment variable for your GCP credentials and then -restart Label Studio: - -```shell -export GOOGLE_APPLICATION_CREDENTIALS="" -label-studio start -p 8093 -``` - -### 🥞 Set up your stack for AWS - -You should install the relevant integrations: - -```shell -# assuming label_studio was installed earlier -zenml integration install pytorch s3 pillow -``` - -Create your basic S3 bucket via CLI command: - -```shell -REGION= -S3_BUCKET_NAME= - -aws s3api create-bucket --bucket=$S3_BUCKET_NAME --region=$REGION --create-bucket-configuration=LocationConstraint=$REGION -``` - -Label Studio also needs you to set up cross-origin resource sharing (CORS) -access to your bucket, using a policy that allows `GET` access from the same -host name as your Label Studio deployment. You can use the CORS file we have -pre-populated for you inside the examples repo as follows: - -```shell -cd cloud_config/aws -aws s3api put-bucket-cors --bucket $S3_BUCKET_NAME --cors-configuration file://cors.json -cd ../.. -``` - -You will also need to assign a policy to your IAM user that allows it to access -the S3 bucket in the way that Label Studio needs. You can do this using [the -instructions described in the Label Studio -documentation](https://labelstud.io/guide/storage.html#Configure-access-to-your-S3-bucket) -(step two, specifically). - -Now you can get to the fun part of setting up Label Studio and working with -ZenML: - -```shell -YOUR_AWS_ZENML_STACK_NAME= -YOUR_ARTIFACT_STORE_NAME= - -zenml stack copy default $YOUR_AWS_ZENML_STACK_NAME - -zenml stack set $YOUR_AWS_ZENML_STACK_NAME - -# use your standard access key id and secret access key from ~/.aws/credentials here -zenml secret create --aws_access_key_id="" --aws_secret_access_key="" - -zenml artifact-store register $YOUR_ARTIFACT_STORE_NAME --flavor=s3 --path=s3://$S3_BUCKET_NAME --authentication_secret="" - -zenml stack update $YOUR_AWS_ZENML_STACK_NAME -a $YOUR_ARTIFACT_STORE_NAME - -zenml secret create --api_key="" - -zenml annotator register --flavor label_studio --authentication_secret="" - -zenml stack update $YOUR_AWS_ZENML_STACK_NAME -an -``` - -### ▶️ Run the Code - -There are several parts to running the pipeline. Start with: - -```shell -python run.py --train -``` - -On the first time you run this, this will fetch a pretrained model and amend the -head so that it is able to perform binary image classification. It does not get -finetuned at this point, however. - -You can use the CLI command to view our newly created dataset: - -``` -zenml annotator dataset list -``` - -It will show that we now have an `aria_detector` dataset. - -Then you can use this model to perform inference on some images (located in -`data/batch_1`). These 'pre-annotations' will then be synchronized with Label -Studio. - -```shell -python run.py --inference -``` - -At this point you should do some annotation using the Label Studio web -interface, making sure to annotate all the ten images. You can launch the -annotation interface with the CLI: - -```shell -zenml annotator dataset launch aria_detector -``` - -When you're done, you can get the stats on the annotation work you did by using -the ZenML CLI: - -```shell -zenml annotator dataset stats aria_detector -``` - -This will tell you how many labeled and unlabeled tasks you have for your Label -Studio dataset. - -With the annotations complete, you should rerun the training step. This time -instead of just pulling a pretrained model, the step will finetune our -previously-downloaded pretrained model using the ten annotations we just made. -This will improve our model's performance (a bit). - -```shell -python run.py --train --rerun -``` - -Now we can rerun the inference step, though using a different set of data -(`data/batch_2`). - -```shell -python run.py --inference --rerun -``` - -Once we've rerun this step, we can inspect the predictions our newly fine-tuned -model made within Label Studio. - -## CLI Commands for the Label Studio Integration - -Once you've run the pipeline for the first time, you'll be able to use some -ZenML CLI commands to interact with your Label Studio annotations and the -dataset: - -```shell -# the obvious ones to try -zenml annotator describe -zenml annotator dataset list -zenml annotator dataset stats -``` - -### 🧽 Clean up - -In order to clean up, delete any relevant cloud infrastructure that was created -above (the cloud storage bucket is the main one, along with any permissions and -whatever secrets or secrets vaults you created) and the remaining ZenML -references. If you created assets under a GCP project, or an Azure resource -group, then the newly created assets should be easy to find. With Amazon, -the IAM rules and the S3 bucket are what you need to delete. - -# 📜 Learn more - -If you want to learn more about annotation in general or about how to use your -own annotation tool in ZenML check out our -[docs](https://docs.zenml.io/stacks-and-components/component-guide/annotators/annotators). - -To explore other available CLI commands related to the annotator stack -components, check out the [CLI docs annotator -section](https://apidocs.zenml.io/latest/cli/#zenml.cli). diff --git a/examples/label_studio_annotation/__init__.py b/examples/label_studio_annotation/__init__.py deleted file mode 100644 index 5e058539166..00000000000 --- a/examples/label_studio_annotation/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. diff --git a/examples/label_studio_annotation/cloud_config/aws/cors.json b/examples/label_studio_annotation/cloud_config/aws/cors.json deleted file mode 100644 index 4a334d320db..00000000000 --- a/examples/label_studio_annotation/cloud_config/aws/cors.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "CORSRules": - [ - { - "AllowedHeaders": [ - "*" - ], - "AllowedMethods": [ - "GET" - ], - "AllowedOrigins": [ - "*" - ], - "ExposeHeaders": [ - "x-amz-server-side-encryption", - "x-amz-request-id", - "x-amz-id-2" - ], - "MaxAgeSeconds": 3000 - } - ] -} diff --git a/examples/label_studio_annotation/cloud_config/gcp/cors-config.json b/examples/label_studio_annotation/cloud_config/gcp/cors-config.json deleted file mode 100644 index 4068af99215..00000000000 --- a/examples/label_studio_annotation/cloud_config/gcp/cors-config.json +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "origin": ["*"], - "method": 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All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. -from pipelines.inference_pipeline import inference_pipeline -from pipelines.training_pipeline import training_pipeline diff --git a/examples/label_studio_annotation/pipelines/inference_pipeline.py b/examples/label_studio_annotation/pipelines/inference_pipeline.py deleted file mode 100644 index 17948cfc28f..00000000000 --- a/examples/label_studio_annotation/pipelines/inference_pipeline.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - -from steps.get_or_create_dataset import get_or_create_the_dataset -from steps.load_image_data_step import load_image_data -from steps.prediction_steps import ( - prediction_service_loader, - predictor, -) -from steps.sync_new_data_to_label_studio import data_sync - -from zenml import pipeline -from zenml.logger import get_logger - -logger = get_logger(__name__) - - -@pipeline -def inference_pipeline(rerun: bool = False): - dataset_name = get_or_create_the_dataset() - new_images, new_images_uri = load_image_data( - dir_name="batch_2" if rerun else "batch_1" - ) - model_deployment_service = prediction_service_loader() - preds = predictor(model_deployment_service, new_images) - data_sync(uri=new_images_uri, dataset_name=dataset_name, predictions=preds) diff --git a/examples/label_studio_annotation/pipelines/training_pipeline.py b/examples/label_studio_annotation/pipelines/training_pipeline.py deleted file mode 100644 index a81d5808300..00000000000 --- a/examples/label_studio_annotation/pipelines/training_pipeline.py +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - - -from steps.convert_annotations_step import convert_annotations -from steps.get_or_create_dataset import get_or_create_the_dataset -from steps.pytorch_trainer import ( - pytorch_model_trainer, -) - -from zenml import pipeline -from zenml.integrations.label_studio.steps.label_studio_standard_steps import ( - get_labeled_data, -) - - -@pipeline(enable_cache=False) -def training_pipeline(): - dataset_name = get_or_create_the_dataset() - labeled_data = get_labeled_data(dataset_name) - training_images, training_annotations = convert_annotations(labeled_data) - pytorch_model_trainer(training_images, training_annotations) diff --git a/examples/label_studio_annotation/run.py b/examples/label_studio_annotation/run.py deleted file mode 100644 index 20a91e3b90d..00000000000 --- a/examples/label_studio_annotation/run.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - -import click -from pipelines import inference_pipeline, training_pipeline - - -@click.command() -@click.option( - "--train", - "pipeline", - flag_value="train", - default=True, - help="Run the training pipeline.", -) -@click.option( - "--inference", - "pipeline", - flag_value="inference", - help="Run the inference pipeline.", -) -@click.option( - "--rerun", - is_flag=True, - default=False, -) -def main(pipeline, rerun): - """Simple CLI interface for annotation example.""" - if pipeline == "train": - training_pipeline() - elif pipeline == "inference": - inference_pipeline(rerun=rerun) - - -if __name__ == "__main__": - main() diff --git a/examples/label_studio_annotation/steps/__init__.py b/examples/label_studio_annotation/steps/__init__.py deleted file mode 100644 index 5e058539166..00000000000 --- a/examples/label_studio_annotation/steps/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. diff --git a/examples/label_studio_annotation/steps/convert_annotations_step.py b/examples/label_studio_annotation/steps/convert_annotations_step.py deleted file mode 100644 index ea8bcd57ebf..00000000000 --- a/examples/label_studio_annotation/steps/convert_annotations_step.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. -from typing import Any, Dict, List, Tuple - -from typing_extensions import Annotated - -from zenml import step -from zenml.logger import get_logger - -logger = get_logger(__name__) - - -@step -def convert_annotations( - label_studio_annotations: List[Dict[Any, Any]], -) -> Tuple[Annotated[List[str], "image_urls"], Annotated[List[str], "labels"]]: - """Converts the annotation from Label Studio to a dictionary.""" - image_urls, labels = [], [] - for annotation in label_studio_annotations: - image_url = annotation["storage_filename"] - label = annotation["annotations"][0]["result"][0]["value"]["choices"][ - 0 - ] - image_urls.append(image_url) - labels.append(label) - return image_urls, labels diff --git a/examples/label_studio_annotation/steps/get_or_create_dataset.py b/examples/label_studio_annotation/steps/get_or_create_dataset.py deleted file mode 100644 index 23b7138bd6a..00000000000 --- a/examples/label_studio_annotation/steps/get_or_create_dataset.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. -from zenml.integrations.label_studio.label_config_generators import ( - generate_image_classification_label_config, -) -from zenml.integrations.label_studio.steps import ( - get_or_create_dataset, -) - -LABELS = ["aria", "not_aria"] - -label_config, _ = generate_image_classification_label_config(LABELS) - -get_or_create_the_dataset = get_or_create_dataset.with_options( - parameters=dict( - label_config=label_config, - dataset_name="aria_detector", - ) -) diff --git a/examples/label_studio_annotation/steps/load_image_data_step.py b/examples/label_studio_annotation/steps/load_image_data_step.py deleted file mode 100644 index a1fa0639853..00000000000 --- a/examples/label_studio_annotation/steps/load_image_data_step.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. - -import glob -import os -from pathlib import Path -from typing import Annotated, Dict, Optional, Tuple - -from PIL import Image - -from zenml import get_step_context, step -from zenml.integrations.pillow.materializers.pillow_image_materializer import ( - DEFAULT_IMAGE_FILENAME, -) - - -@step(enable_cache=False) -def load_image_data( - base_path: Optional[str] = None, - dir_name: str = "batch_1", -) -> Tuple[Annotated[Dict[str, Image.Image], "images"], Annotated[str, "uri"]]: - """Gets images from a cloud artifact store directory.""" - if base_path is None: - base_path = str( - Path(__file__).parent.absolute().parent.absolute() / "data" - ) - image_dir_path = os.path.join(base_path, dir_name) - image_files = glob.glob(f"{image_dir_path}/*.jpeg") - context = get_step_context() - uri = context.get_output_artifact_uri("images") - - images = {} - for i, image_file in enumerate(image_files): - image = Image.open(image_file) - image.load() - artifact_filepath = ( - f"{uri}/1/{i}/{DEFAULT_IMAGE_FILENAME}.{image.format}" - ) - - images[artifact_filepath] = image - - return images, uri diff --git a/examples/label_studio_annotation/steps/prediction_steps.py b/examples/label_studio_annotation/steps/prediction_steps.py deleted file mode 100644 index 6d7c9f136e5..00000000000 --- a/examples/label_studio_annotation/steps/prediction_steps.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. -from typing import Dict, List - -import torch -from steps.pytorch_trainer import LABEL_MAPPING, load_mobilenetv3_transforms -from typing_extensions import Annotated - -from zenml import step -from zenml.client import Client - -REVERSE_LABEL_MAPPING = {value: key for key, value in LABEL_MAPPING.items()} -PIPELINE_NAME = "training_pipeline" -PIPELINE_STEP_NAME = "model_trainer" - - -@step -def prediction_service_loader( - training_pipeline_name: str = "training_pipeline", - training_pipeline_step_name: str = "pytorch_model_trainer", -) -> torch.nn.Module: - train_run = Client().get_pipeline(training_pipeline_name).last_run - return train_run.steps[training_pipeline_step_name].output.load() - - -@step(enable_cache=False) -def predictor( - model: torch.nn.Module, - images: Dict, -) -> Annotated[List[Dict[str, str]], "predictions"]: - preprocess = load_mobilenetv3_transforms() - preds = [] - for file_name, image in images.items(): - image = preprocess(image) - image = image.unsqueeze(0) - pred = model(image) - pred = pred.squeeze(0).softmax(0) - class_id = pred.argmax().item() - class_name = REVERSE_LABEL_MAPPING[class_id] - label_studio_output = { - "filename": file_name, - "result": [ - { - "value": {"choices": [class_name]}, - "from_name": "choice", - "to_name": "image", - "type": "choices", - "origin": "manual", - }, - ], - } - preds.append(label_studio_output) - return preds diff --git a/examples/label_studio_annotation/steps/pytorch_trainer.py b/examples/label_studio_annotation/steps/pytorch_trainer.py deleted file mode 100644 index 8e9b6921e28..00000000000 --- a/examples/label_studio_annotation/steps/pytorch_trainer.py +++ /dev/null @@ -1,252 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. -import os -import tempfile -from typing import List, Optional -from urllib.parse import urlparse - -import numpy as np -import torch -import torch.nn as nn -import torch.optim as optim -from PIL import Image -from steps.get_or_create_dataset import LABELS -from torchvision import models, transforms - -from zenml import get_step_context, step -from zenml.client import Client -from zenml.integrations.label_studio.label_studio_utils import ( - get_file_extension, - is_azure_url, - is_s3_url, -) -from zenml.io import fileio -from zenml.utils import io_utils - -LABEL_MAPPING = {label: idx for idx, label in enumerate(LABELS)} - -PIPELINE_NAME = "training_pipeline" -PIPELINE_STEP_NAME = "pytorch_model_trainer" - - -def train_model( - model, - dataloaders, - criterion, - optimizer, - num_epochs=25, - device="cpu", -): - """Simplified version of https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html.""" - for epoch in range(num_epochs): - print(f"Epoch {epoch}/{num_epochs - 1}") - print("-" * 10) - - # Each epoch has a training and validation phase - for phase in ["train", "val"]: - if phase == "train": - model.train() # Set model to training mode - else: - model.eval() # Set model to evaluate mode - - running_loss = 0.0 - running_corrects = 0 - - # Iterate over data. - for inputs, labels in dataloaders[phase]: - inputs = inputs.to(device) - labels = labels.to(device) - - # zero the parameter gradients - optimizer.zero_grad() - - # forward; track history only if in train - with torch.set_grad_enabled(phase == "train"): - outputs = model(inputs) - loss = criterion(outputs, labels) - _, preds = torch.max(outputs, 1) - - # backward; optimize only if in training phase - if phase == "train": - loss.backward() - optimizer.step() - - # statistics - running_loss += loss.item() * inputs.size(0) - running_corrects += torch.sum(preds == labels.data) - - epoch_loss = running_loss / len(dataloaders[phase].dataset) - epoch_acc = running_corrects.double() / len( - dataloaders[phase].dataset - ) - print(f"{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}") - - return model - - -class CustomDataset: - """Creates a dataset to be used in the PyTorch model training.""" - - def __init__( - self, image_urls, labels, transforms, artifact_store_path: str - ) -> None: - assert len(image_urls) == len(labels) - self.transforms = transforms - - # Download all images from the artifact store as np.ndarray - self.images = [] - temp_dir = tempfile.TemporaryDirectory() - for i, image_url in enumerate(image_urls): - parts = image_url.split("/") - if is_s3_url(image_url): - file_url = f"{artifact_store_path}{urlparse(image_url).path}" - elif is_azure_url(image_url): - file_url = "az://" + "/".join(parts[3:]) - else: - url_path = urlparse(image_url).path - file_url = f"{artifact_store_path}/{url_path}" - file_extension = get_file_extension(urlparse(image_url).path) - path = os.path.join(temp_dir.name, f"{i}{file_extension}") - - io_utils.copy(file_url, path) - with fileio.open(path, "rb") as f: - image = np.asarray(Image.open(f)) - self.images.append(image) - fileio.rmtree(temp_dir.name) - - # Define class-label mapping and map labels - self.class_label_mapping = LABEL_MAPPING - self.labels = [self.class_label_mapping[label] for label in labels] - - def __getitem__(self, idx): - image = self.transforms(self.images[idx]) - label = self.labels[idx] - return (image, label) - - def __len__(self): - return len(self.images) - - -def _load_last_model() -> Optional[nn.Module]: - """Return the most recently trained model from this pipeline, or None.""" - last_run = Client().get_pipeline(PIPELINE_NAME).last_successful_run - return last_run.steps[PIPELINE_STEP_NAME].output.load() - - -def _is_new_data_available(image_urls: List[str]) -> bool: - """Find whether new data is available since the last run.""" - # If there are no samples, nothing can be new. - num_samples = len(image_urls) - if num_samples == 0: - return False - - # Else, we check whether we had the same number of samples before. - last_run = Client().get_pipeline(PIPELINE_NAME).last_successful_run - last_inputs = last_run.steps[PIPELINE_STEP_NAME].inputs - last_image_urls = last_inputs["image_urls"].load() - return len(last_image_urls) != len(image_urls) - - -def load_pretrained_mobilenetv3(num_classes: int = 2): - """Load a pretrained mobilenetv3 with fresh classification head.""" - model = models.mobilenet_v3_small(pretrained=True) - for param in model.parameters(): - param.requires_grad = False - model.classifier[-1] = nn.Linear(1024, num_classes) - model.num_classes = num_classes - return model - - -def load_mobilenetv3_transforms(): - """Load the transforms required before running mobilenetv3 on data.""" - weights = models.MobileNet_V3_Small_Weights.DEFAULT - return transforms.Compose([transforms.ToTensor(), weights.transforms()]) - - -@step(enable_cache=False) -def pytorch_model_trainer( - image_urls: List[str], - labels: List[str], - batch_size=1, - num_epochs=2, - learning_rate=5e-3, - device="cpu", - shuffle=True, - num_workers=1, - seed=42, # don't change: this seed ensures a good train/val split -) -> nn.Module: - """ZenML step which finetunes or loads a pretrained mobilenetv3 model.""" - # Try to load a model from a previous run, otherwise use a pretrained net - model = _load_last_model() - if model is None: - model = load_pretrained_mobilenetv3() - - # If there is no new data, just return the model - if not _is_new_data_available(image_urls): - return model - - context = get_step_context() - artifact_store_path = context.stack.artifact_store.path - - # Otherwise finetune the model on the current data - # Write images and labels to torch dataset - dataset = CustomDataset( - image_urls=image_urls, - labels=labels, - transforms=load_mobilenetv3_transforms(), - artifact_store_path=artifact_store_path, - ) - - # Split dataset into train and val - train_size = int(0.8 * len(dataset)) - val_size = len(dataset) - train_size - train_dataset, val_dataset = torch.utils.data.random_split( - dataset=dataset, - lengths=[train_size, val_size], - generator=torch.Generator().manual_seed(seed), - ) - dataset_dict = { - "train": train_dataset, - "val": val_dataset, - } - - # Create training and validation dataloaders - dataloaders_dict = { - dataset_type: torch.utils.data.DataLoader( - dataset, - batch_size=batch_size, - shuffle=shuffle, - num_workers=num_workers, - ) - for dataset_type, dataset in dataset_dict.items() - } - - # Define optimizer - optimizer_ft = optim.Adam( - params=model.classifier[-1].parameters(), lr=learning_rate - ) - - # Define loss - criterion = nn.CrossEntropyLoss() - - # Train and evaluate - model = train_model( - model, - dataloaders_dict, - criterion, - optimizer_ft, - num_epochs=num_epochs, - device=device, - ) - return model diff --git a/examples/label_studio_annotation/steps/sync_new_data_to_label_studio.py b/examples/label_studio_annotation/steps/sync_new_data_to_label_studio.py deleted file mode 100644 index 75df78ebce6..00000000000 --- a/examples/label_studio_annotation/steps/sync_new_data_to_label_studio.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright (c) ZenML GmbH 2022. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing -# permissions and limitations under the License. -from zenml.integrations.label_studio.label_config_generators import ( - generate_image_classification_label_config, -) -from zenml.integrations.label_studio.steps import ( - LabelStudioDatasetSyncParameters, - sync_new_data_to_label_studio, -) - -IMAGE_REGEX_FILTER = ".*(jpe?g|png|JPE?G|PNG)" - - -_, label_config_type = generate_image_classification_label_config( - ["aria", "not_aria"] -) - - -zenml_sync_params = LabelStudioDatasetSyncParameters( - label_config_type=label_config_type, - regex_filter=IMAGE_REGEX_FILTER, -) - -data_sync = sync_new_data_to_label_studio.with_options( - parameters={"params": zenml_sync_params}, -)