diff --git a/.github/workflows/documentation.yaml b/.github/workflows/documentation-publish.yml similarity index 66% rename from .github/workflows/documentation.yaml rename to .github/workflows/documentation-publish.yml index 26193d6..eea60ce 100644 --- a/.github/workflows/documentation.yaml +++ b/.github/workflows/documentation-publish.yml @@ -1,9 +1,14 @@ -name: Docs -on: [push, pull_request, workflow_dispatch] +name: Generate and Publish Documentation for OSML Model Runner Test + +on: + workflow_call: + permissions: contents: write + jobs: docs: + if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }} runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 @@ -13,14 +18,14 @@ jobs: pip install sphinx python -m pip install sphinx-autoapi python -m pip install sphinx_rtd_theme + python -m pip install tox tox-gh-actions - name: Sphinx build run: | - sphinx-build doc _build + tox -e docs - name: Deploy uses: peaceiris/actions-gh-pages@v3.9.3 - if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }} with: publish_branch: gh-pages github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: _build/ + publish_dir: .tox/docs/tmp/html/ force_orphan: true diff --git a/.github/workflows/notify-guidance-repo.yml b/.github/workflows/notify-guidance-repo.yml new file mode 100644 index 0000000..8f3bc47 --- /dev/null +++ b/.github/workflows/notify-guidance-repo.yml @@ -0,0 +1,18 @@ +name: Notify Guidance Overhead Imagery Repo + +on: + workflow_call: + +jobs: + notify: + runs-on: ubuntu-latest + steps: + - name: Short Sha + uses: benjlevesque/short-sha@v2.2 + - name: Invoke Guidance OSML Submodule Workflow + uses: benc-uk/workflow-dispatch@v1.2.2 + with: + repo: aws-solutions-library-samples/guidance-for-overhead-imagery-inference-on-aws + workflow: osml_update_submodules.yml + token: ${{ secrets.GUIDANCE_OSML_SUBMODULES_ACTION_10_24 }} + inputs: '{ "DISPATCH_REPO_NAME" : "${{ github.event.repository.name }}", "DISPATCH_REPO_SHA": "${{ env.SHA }}" }' diff --git a/.github/workflows/osml-model-runner-test-build.yml b/.github/workflows/osml-model-runner-test-build.yml new file mode 100644 index 0000000..1a5df76 --- /dev/null +++ b/.github/workflows/osml-model-runner-test-build.yml @@ -0,0 +1,10 @@ +name: "OSML Model Runner Test Build Workflow" + +on: + pull_request: + branches: ["main", "dev"] + +jobs: + Build_Validate_Tox: + uses: ./.github/workflows/python-tox.yml + secrets: inherit diff --git a/.github/workflows/osml-model-runner-test-publish.yml b/.github/workflows/osml-model-runner-test-publish.yml new file mode 100644 index 0000000..316a169 --- /dev/null +++ b/.github/workflows/osml-model-runner-test-publish.yml @@ -0,0 +1,22 @@ +name: "OSML Model Runner Test Build and Publish Workflow" + +on: + push: + branches: ["main", "dev"] + +jobs: + Build_Validate_Tox: + uses: ./.github/workflows/python-tox.yml + secrets: inherit + Publish_Python: + needs: [Build_Validate_Tox] + uses: ./.github/workflows/python-publish.yml + secrets: inherit + Publish_Documentation: + needs: [Build_Validate_Tox] + uses: ./.github/workflows/documentation-publish.yml + secrets: inherit + Notify_Guidance_Repo: + needs: [Build_Validate_Tox] + uses: ./.github/workflows/notify-guidance-repo.yml + secrets: inherit diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml new file mode 100644 index 0000000..f1e2cfa --- /dev/null +++ b/.github/workflows/python-publish.yml @@ -0,0 +1,33 @@ +# This workflow will upload a Python Package using Twine when a release is created +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries + +name: Publish Python Package + +on: + workflow_call: + release: + types: [published] + +permissions: + contents: read + id-token: write + +jobs: + deploy: + if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }} + runs-on: ubuntu-latest + environment: release + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: '3.x' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install build + - name: Build package + run: python -m build + - name: Publish package + uses: pypa/gh-action-pypi-publish@v1.8.10 diff --git a/.github/workflows/python-tox.yml b/.github/workflows/python-tox.yml new file mode 100644 index 0000000..00abac0 --- /dev/null +++ b/.github/workflows/python-tox.yml @@ -0,0 +1,30 @@ +# This workflow will install Python dependencies, run tests and lint with a single version of Python +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Build/Validation with Tox + +on: + workflow_call: + +permissions: + contents: read + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + with: + lfs: 'true' + ssh-key: ${{ secrets.git_ssh_key }} + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + - name: Install dependencies + run: | + python -m pip install --upgrade pip tox + - name: Validate package with tox + run: | + # stop the build if there are Python syntax errors or undefined names + tox diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md old mode 100644 new mode 100755 index c4b6a1c..b879f17 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,59 +1,260 @@ -# Contributing Guidelines +# OSML Contributing Guidelines -Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional -documentation, we greatly value feedback and contributions from our community. +Thank you for your interest in contributing to our project! This document will guide you through the process of +contributing to our repository using a release-based GitFlow workflow. Please read the guidelines carefully in order to +submit your pull requests effectively. -Please read through this document before submitting any issues or pull requests to ensure we have all the necessary -information to effectively respond to your bug report or contribution. +## Table of Contents +- [Release-based GitFlow](#release-based-gitflow) +- [Branches](#branches) +- [Linting](#linting) +- [Use Cases](#use-cases) + - [Develop a new feature](#develop-a-new-feature) + - [Develop multiple features in parallel](#develop-multiple-features-in-parallel) + - [Create and deploy a release](#create-and-deploy-a-release) + - [Production hot fix](#production-hot-fix) +- [Code Style](#code-style) +- [Commit Messages](#commit-messages) +- [Issue Tracking](#issue-tracking) -## Reporting Bugs/Feature Requests +## Release-based GitFlow -We welcome you to use the GitHub issue tracker to report bugs or suggest features. +We follow a release-based GitFlow branching model to manage our codebase. It involves different types of branches to +ensure proper version control and collaboration. The primary branches in this workflow are: -When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already -reported the issue. Please try to include as much information as you can. Details like these are incredibly useful: +note- `main`: This branch represents the latest production-ready state of the project code. +- `dev`: This branch is used as the integration branch, combining all feature branches for a specific release. +- `release/vX.Y.Z`: Branches in the format of `release/vX.Y.Z` are used to prepare a specific release. +- `feature/*`: Feature branches are created for the development of new features or significant enhancements. +- `hotfix/*`: Hotfix branches are created for bug fixes against production code. -* A reproducible test case or series of steps -* The version of our code being used -* Any modifications you've made relevant to the bug -* Anything unusual about your environment or deployment +## Branches -## Contributing via Pull Requests -Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that: +Here's a brief description of the supported branches in our repository: -1. You are working against the latest source on the *main* branch. -2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already. -3. You open an issue to discuss any significant work - we would hate for your time to be wasted. +![Release Deployment workflow](images/release-deployment.png) -To send us a pull request, please: +| Branch | Protected? | Base Branch | Description | +|:-----------------|:-----------|:-------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `main` | YES | N/A | What is live in production (**stable**).
A pull request is required to merge code into `main`. | +| `dev` | YES | `main` | The latest state of development (**unstable**).
A pull request is required to merge code into `dev` | +| `feature/*` | NO | `dev` | Cutting-edge features (**unstable**). These branches are used for any maintenance features / active development. | +| `release/vX.Y.Z` | NO | `dev` | A temporary release branch that follows the [semver](http://semver.org/) versioning. This is what is sent to UAT.
A pull request is required to merge code into any `release/vX.Y.Z` branch. | +| `hotfix/*` | NO | `main` | These are bug fixes against production.
This is used because develop might have moved on from the last published state.
Remember to merge this back into develop and any release branches. | -1. Fork the repository. -2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change. -3. Ensure local tests pass. -4. Commit to your fork using clear commit messages. -5. Send us a pull request, answering any default questions in the pull request interface. -6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation. -GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and -[creating a pull request](https://help.github.com/articles/creating-a-pull-request/). +## Linting +This package uses a number of tools to enforce formatting, linting, and general best practices: +* [eslint](https://github.com/pre-commit/mirrors-eslint) to check pep8 compliance and logical errors in code +* [prettier](https://github.com/pre-commit/mirrors-prettier) to check pep8 compliance and logical errors in code +* [pre-commit](https://github.com/pre-commit/pre-commit-hooks) to install and control linters in githooks -## Finding contributions to work on -Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start. +After pulling the repository, you should enable auto linting git commit hooks by running: +```bash +python3 -m pip install pre-commit +pre-commit install +``` -## Code of Conduct -This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). -For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact -opensource-codeofconduct@amazon.com with any additional questions or comments. +In addition to running the linters against files included in a commit, you can perform linting on all the files +in the package by running: +```bash +pre-commit run --all-files --show-diff-on-failure +``` +or if using tox +```bash +tox -e lint +```` +## Use Cases -## Security issue notifications -If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue. +### Develop a new feature +1. Make sure your `dev` branch is up-to-date -## Licensing +2. Create a feature branch based off of `dev` -See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution. + ```bash + $ git checkout dev + $ git checkout -b feature/new-documentation + $ git push --set-upstream feature/new-documentation + ``` + +3. Develop the code for the new feature and commit. Push your changes often. This + allows others to see your changes and suggest improvements/changes as well as + provides a safety net should your hard drive crash. + + ```bash + $ ... make changes + $ git add -A . + $ git commit -m "Add new documentation files" + $ ... make more changes + $ git add -A . + $ git commit -m "Fix some spelling errors" + $ git push + ``` + +4. Navigate to the project on [GitHub](https://github.com) and open a pull request with + the following branch settings: + * Base: `dev` + * Compare: `feature/new-documentation` + +5. When the pull request was reviewed, merge and close it and delete the + `feature/new-documentation` branch. + +### Develop multiple features in parallel + +There's nothing special about that. Each developer follows the above [Develop a new feature](#develop-a-new-feature) process. + +### Create and deploy a release +1. Merge `main` into `dev` to ensure the new release will contain the + latest production code. This reduces the chance of a merge conflict during + the release. + + ```bash + $ git checkout dev + $ git merge main + ``` + +2. Create a new `release/vX.Y.Z` release branch off of `dev`. + + ```bash + $ git checkout -b release/vX.Y.Z + $ git push --set-upstream release/vX.Y.Z + ``` + +3. Stabilize the release by using bugfix branches off of the `release/vX.Y.Z` branch + (the same way you would do a feature branch off of `dev`). + + ```bash + $ git checkout release/vX.Y.Z + $ git checkout -b hotfix/fix-label-alignment + $ git push --set-upstream hotfix/fix-label-alignment + ... do work + $ git commit -m "Adjust label to align with button" + $ git push + ``` + +4. When the code is ready to release, navigate to the project on + [GitHub](https://github.com) and open a pull request with the following branch + settings: + * Base: `main` + * Compare: `release/vX.Y.Z` + Paste the Release Checklist into the PR body. Each project should define a release + checklist. TBD - will link release checklist here. + +5. At some point in the checklist you will merge the release branch into `main`. + You can do this by using the "Merge pull request" button on the release PR. + +6. Now you are ready to create the actual release. Navigate to the project page + on GitHub and draft a new release with the following settings: + * Tag version: `vX.Y.Z` + * Target: `main` + * Release title: `Release vX.Y.Z` + * Description: Includes a high-level list of things changed in this release. + Click `Publish release`. + +7. Merge the `release/vX.Y.Z` into `dev`. + + ```bash + $ git checkout dev + $ git merge release/vX.Y.Z + $ git push + ``` + +8. Finish off the tasks in the release checklist. Once everything is done, close + the release PR. + +9. Delete the release branch on GitHub. + +### Production hot fix + +A production hotfix is very similar to a full-scale release except that you do +your work in a branch taken directly off of `main`. Hotfixes are useful in cases +where you want to patch a bug in a released version, but `dev` has unreleased +code in it already. + +**TBD: Insert diagram** + +1. Create a hot fix branch based off of `main`. + + ```bash + $ git checkout main + $ git checkout -b hotfix/documentation-broken-links + $ git push --set-upstream origin hotfix/documentation-broken-links + ``` + +2. Add a test case to validate the bug, fix the bug, and commit. + + ```bash + ... add test, fix bug, verify + $ git add -A . + $ git commit -m "Fix broken links" + $ git push + ``` + +3. Navigate to the project on [GitHub](https://github.com) and open a pull request + with the following branch settings: + * Base: `main` + * Compare: `hotfix/documentation-broken-links` + Paste your release checklist into the PR and work through the PR to get the + hotfix into production. + +4. At some point in the checklist you will merge the hotfix branch into `main`. + You can do this by using the "Merge pull request" button on the release PR. + +5. Now that the hotfix code is in `main` you are ready to create the actual + release. Navigate to the project page on GitHub and draft a new release with + the following settings: + * Tag version: `vX.Y.Z` + * Target: `main` + * Release title: `Release vX.Y.Z (hotfix)` + * Description: Includes a high-level list of things changed in this release. + +Click `Publish release`. + +*Note: Hotfix releases _are_ actual releases. You should bump at least the +patch part of the version when releasing a hotfix, and so even hotfixes go +through the process of creating a release like this.* + +1. Merge the `hotfix/documentation-broken-links` into `dev`. + + ```bash + $ git checkout dev + $ git merge hotfix/documentation-broken-links + $ git push + ``` + +2. Finish off the tasks in the release checklist. Once everything is done, close + the hotfix PR. + +## Code Style + +We maintain a consistent code style throughout the project, so please ensure your changes align with our existing style. +Take a look at the existing codebase to understand the patterns and conventions we follow. + +## Commit Messages + +The [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/#summary) specification is a +lightweight convention on top of commit messages. It provides an easy set of rules for creating an explicit +commit history, which makes it easier to write automated tools on top of. This convention dovetails with SemVer +by describing the features, fixes, and breaking changes made in commit messages. Please refer to the linked +documentation for examples and additional context. + +<type>[optional scope]: <description> + +[optional body] + +[optional footer(s)] + + +## Issue Tracking + +We utilize the issue tracking functionality of GitHub to manage our project's roadmap and track bugs or feature requests. +If you encounter any problems or have a new idea, please search the issues to ensure it hasn't already been reported. +If necessary, open a new issue providing a clear description, steps to reproduce, and any relevant information. + +We greatly appreciate your effort and contribution to our project! Let's build something awesome together! diff --git a/README.md b/README.md index cec679e..8c309f8 100755 --- a/README.md +++ b/README.md @@ -32,15 +32,13 @@ First, ensure you have installed the following tools locally git clone https://github.com/aws-solutions-library-samples/osml-model-runner-test.git ``` -2. Run `tox` to create a virtual environment +1. Run `tox` to create a virtual environment ```sh cd osml-model-runner-test tox ``` - - ### Documentation You can find documentation for this library in the `./doc` directory. Sphinx is used to construct a searchable HTML @@ -50,30 +48,6 @@ version of the API documents. tox -e docs ``` - -## Linting/Formatting - -This package uses a number of tools to enforce formatting, linting, and general best practices: - -- [black](https://github.com/psf/black) -- [isort](https://github.com/PyCQA/isort) for formatting with a max line length of 100 -- [mypy](https://github.com/pre-commit/mirrors-mypy) to enforce static type checking -- [flake8](https://github.com/PyCQA/flake8) to check pep8 compliance and logical errors in code -- [autopep](https://github.com/pre-commit/mirrors-autopep8) to check pep8 compliance and logical errors in code -- [eslint](https://github.com/pre-commit/mirrors-eslint) to check pep8 compliance and logical errors in code -- [prettier](https://github.com/pre-commit/mirrors-prettier) to check pep8 compliance and logical errors in code -- [pre-commit](https://github.com/pre-commit/pre-commit-hooks) to install and control linters in githooks - -```bash -python3 -m pip install pre-commit -pre-commit install -``` - -Additionally, you can perform linting on all the files in the package by running: -```bash -pre-commit run --all-files --show-diff-on-failure -``` - ### Build and Local Testing You can run the hydra tests against your dev account by exporting the required parameters and using the pytest CLI by @@ -112,14 +86,15 @@ python3 bin/process_image.py --help ``` ### Running Tests in Docker -Arguments can be passed in to docker run in the same way that they are porvided to the shell script. For example, the container can be built and run as follows: +Arguments can be passed in to docker run in the same way that they are provided to the shell script. +For example, the container can be built and run as follows: ``` docker build . -t model-runner-hydra-test:latest docker run -v ~/.aws:/root/.aws model-runner-hydra-test:latest small centerpoint NITF JPEG us-west-2 $ACCOUNT_NUMBER ``` -Credentials from the users account are volume mounted into the container's root directory. +Credentials from the user's account are volume mounted into the container's root directory. ### Running LoadTest @@ -144,7 +119,7 @@ If you are interested in contributing to OversightML Model Runner, see the [CONT ## Security -See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. +See [CONTRIBUTING](CONTRIBUTING.md) for more information. ## License diff --git a/bin/get_cost_report.py b/bin/get_cost_report.py new file mode 100644 index 0000000..e1d2677 --- /dev/null +++ b/bin/get_cost_report.py @@ -0,0 +1,88 @@ +import argparse +from typing import Any, Dict, List + +import boto3 +import pandas as pd +from tabulate import tabulate + + +def get_combined_cost_data(start_time: str, end_time: str) -> pd.DataFrame: + """ + Fetches AWS cost data for a specified time range and combines it into a DataFrame. + + :param start_time: Start time in ISO8601 format (e.g., '2023-10-20T09:00:00Z'). + :param end_time: End time in ISO8601 format (e.g., '2023-10-20T11:00:00Z'). + :return: A DataFrame containing cost data for the specified time range. + """ + print("Getting cost data for {} to {}".format(start_time, end_time)) + session = boto3.Session(profile_name="default", region_name="us-west-2") # Replace with your AWS profile name + + # AWS Cost Explorer client + ce = session.client("ce") + + # Initialize an empty list to store data for all hours + combined_data = [] + + # Calculate the number of hours between start and end times + num_hours = (pd.to_datetime(end_time) - pd.to_datetime(start_time)).total_seconds() / 3600 + + # Iterate through each hour and fetch data + for hour in range(int(num_hours) + 1): + hour_start = pd.to_datetime(start_time) + pd.DateOffset(hours=hour) + hour_end = hour_start + pd.DateOffset(hours=1) + + # Get an AWS cost and usage report with the service filter for the current hour + response = ce.get_cost_and_usage( + TimePeriod={"Start": hour_start.strftime("%Y-%m-%dT%H:%M:%SZ"), "End": hour_end.strftime("%Y-%m-%dT%H:%M:%SZ")}, + Granularity="HOURLY", + Metrics=["UnblendedCost"], + GroupBy=[{"Type": "DIMENSION", "Key": "SERVICE"}], + ) + + # Append the data for the current hour to the combined data list + combined_data += response["ResultsByTime"] + + # Create a DataFrame from the combined data + df_list: List[Dict[str, Any]] = [] + + # Initialize total cost + total_cost = 0.0 + + for period in combined_data: + for group in period.get("Groups", []): + service_name = group["Keys"][0] + cost_amount = float(group["Metrics"]["UnblendedCost"]["Amount"]) + df_list.append({"Service": service_name, "Cost": cost_amount, "TimePeriod": period["TimePeriod"]["Start"]}) + total_cost += cost_amount # Accumulate the total cost + + # Add a row for total cost + df_list.append({"Service": "Total", "Cost": total_cost, "TimePeriod": "Total"}) + + df = pd.DataFrame(df_list) + return df + + +def main(): + """ + Fetches AWS cost data for a specified time range and combines it into a DataFrame for display. + :return: + """ + parser = argparse.ArgumentParser(description="Fetch AWS cost data for a specified time range.") + parser.add_argument("start_time", help="Start time in ISO8601 format (e.g., 2023-10-20T09:00:00Z)") + parser.add_argument("end_time", help="End time in ISO8601 format (e.g., 2023-10-20T11:00:00Z)") + args = parser.parse_args() + + print("Please ensure that the cost explorer has been updated for the request time window!") + + # Get combined cost data for the specified time range + df = get_combined_cost_data(args.start_time, args.end_time) + + # Create a pretty table for cost breakdown + table = tabulate(df, headers="keys", tablefmt="fancy_grid") + + # Display the pretty table + print(table) + + +if __name__ == "__main__": + main() diff --git a/bin/process_image.py b/bin/process_image.py index ecd3d49..5393ac0 100644 --- a/bin/process_image.py +++ b/bin/process_image.py @@ -40,16 +40,17 @@ # set up a cli tool for the script using argparse parser = argparse.ArgumentParser("process_image") -parser.add_argument("--image", help="The target image URL to process with OSML Model Runner.", type=str, default="small") -parser.add_argument("--model", help="The target model to use for object detection.", type=str, default="centerpoint") +parser.add_argument("--image", help="Target image URL to process with OSML Model Runner.", type=str, default="small") +parser.add_argument("--model", help="Target model to use for object detection.", type=str, default="centerpoint") parser.add_argument("--skip_integ", help="Whether or not to compare image with known results.", action="store_true") -parser.add_argument("--tile_format", help="The target tile format to use for tiling.", type=str) -parser.add_argument("--tile_compression", help="The compression used for the target image.", type=str) -parser.add_argument("--tile_size", help="The tile size to split the image into for model processing.", type=str) -parser.add_argument("--tile_overlap", help="The tile overlap to consider when processing regions.", type=str) -parser.add_argument("--feature_selection_options", help="The feature selection options JSON string.", type=str) -parser.add_argument("--region", help="The AWS region OSML is deployed to.", type=str, default=default_region) -parser.add_argument("--account", help="The AWS account OSML is deployed to.", type=str, default=default_account) +parser.add_argument("--tile_format", help="Target tile format to use for tiling.", type=str) +parser.add_argument("--tile_compression", help="Compression used for the target image.", type=str) +parser.add_argument("--tile_size", help="Tile size to split the image into for model processing.", type=str) +parser.add_argument("--tile_overlap", help="Tile overlap to consider when processing regions.", type=str) +parser.add_argument("--feature_selection_options", help="Feature selection options JSON string.", type=str) +parser.add_argument("--region", help="AWS region OSML is deployed to.", type=str, default=default_region) +parser.add_argument("--account", help="AWS account OSML is deployed to.", type=str, default=default_account) +parser.add_argument("--endpoint_type", help="Type of model endpoint to test, sm or http.", type=str, default="sm") args = parser.parse_args() # standard test images deployed by CDK @@ -62,9 +63,13 @@ "tile_ntf": f"s3://{image_bucket}/tile.ntf", "tile_jpeg": f"s3://{image_bucket}/tile.jpeg", "tile_png": f"s3://{image_bucket}/tile.png", + "sicd_capella_chip_ntf": f"s3://{image_bucket}/sicd-capella-chip.ntf", + "sicd_umbra_chip_ntf": f"s3://{image_bucket}/sicd-umbra-chip.ntf", + "sicd_interferometric_hh_ntf": f"s3://{image_bucket}/sicd-interferometric-hh.nitf", + "wbid": f"s3://{image_bucket}/wbid.ntf", } -# call into root directory of this package so that we can run this script from anywhere. +# call into the root directory of this package so that we can run this script from anywhere. os.chdir(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")) # set the python path to include the project source @@ -92,6 +97,6 @@ test = "src/aws/osml/process_image/test_process_image.py" else: # run integration test against known results - test = f"src/aws/osml/integ/{args.model}/test_{args.model}_model.py" + test = f"src/aws/osml/integ/{args.endpoint_type}_{args.model}/test_{args.endpoint_type}_{args.model}_model.py" subprocess.run(["python3", "-m", "pytest", "-o", "log_cli=true", "-vv", test]) diff --git a/bin/run_load_test.py b/bin/run_load_test.py index 511ed59..067dce9 100644 --- a/bin/run_load_test.py +++ b/bin/run_load_test.py @@ -16,20 +16,20 @@ # set up a cli tool for the script using argparse parser = argparse.ArgumentParser("process_image") -parser.add_argument("--image_bucket", help="The target image bucket to process with OSML Model Runner.", type=str) -parser.add_argument("--result_bucket", help="The target result bucket to store outputs from OSML Model Runner", type=str) -parser.add_argument("--model", help="The target model to use for object detection.", type=str, default="aircraft") +parser.add_argument("--image_bucket", help="Target image bucket to process with OSML Model Runner.", type=str) +parser.add_argument("--result_bucket", help="Target result bucket to store outputs from OSML Model Runner", type=str) +parser.add_argument("--model", help="Target model to use for object detection.", type=str, default="aircraft") parser.add_argument("--processing_window", help="How long do we want to run the test for (in hours)", type=str) -parser.add_argument("--periodic_sleep", help="Periodically send a new image request (in seconds)", type=str, default="60") -parser.add_argument("--tile_format", help="The target tile format to use for tiling.", type=str) -parser.add_argument("--tile_compression", help="The compression used for the target image.", type=str) -parser.add_argument("--tile_size", help="The tile size to split the image into for model processing.", type=str) -parser.add_argument("--tile_overlap", help="The tile overlap to consider when processing regions.", type=str) -parser.add_argument("--region", help="The AWS region OSML is deployed to.", type=str, default=default_region) -parser.add_argument("--account", help="The AWS account OSML is deployed to.", type=str, default=default_account) +parser.add_argument("--periodic_sleep", help="Periodically send a new image request (seconds)", type=str, default="60") +parser.add_argument("--tile_format", help="Target tile format to use for tiling.", type=str) +parser.add_argument("--tile_compression", help="Compression used for the target image.", type=str) +parser.add_argument("--tile_size", help="Tile size to split the image into for model processing.", type=str) +parser.add_argument("--tile_overlap", help="Tile overlap to consider when processing regions.", type=str) +parser.add_argument("--region", help="AWS region OSML is deployed to.", type=str, default=default_region) +parser.add_argument("--account", help="AWS account OSML is deployed to.", type=str, default=default_account) args = parser.parse_args() -# call into root directory of this package so that we can run this script from anywhere. +# call into the root directory of this package so that we can run this script from anywhere. os.chdir(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")) # set the python path to include the project source @@ -53,7 +53,7 @@ os.environ["S3_LOAD_TEST_RESULT_BUCKET"] = result_bucket os.environ["S3_RESULTS_BUCKET"] = result_bucket os.environ["PERIODIC_SLEEP_SECS"] = args.periodic_sleep -os.environ["PROCESSING_WINDOW_HRS"] = args.processing_window +os.environ["PROCESSING_WINDOW_MIN"] = args.processing_window if args.tile_format: os.environ["TILE_FORMAT"] = args.tile_format @@ -63,6 +63,8 @@ os.environ["TILE_SIZE"] = args.tile_size if args.tile_overlap: os.environ["TILE_OVERLAP"] = args.tile_overlap +if args.model: + os.environ["SM_LOAD_TEST_MODEL"] = args.model test = "src/aws/osml/load/load_test.py" diff --git a/bin/run_test.sh b/bin/run_test.sh deleted file mode 100755 index 4562ace..0000000 --- a/bin/run_test.sh +++ /dev/null @@ -1,90 +0,0 @@ -#!/bin/bash - -# -# Copyright 2023 Amazon.com, Inc. or its affiliates. -# - -# This is a utility script to help building and uploading the OSML default test model to an accounts ECR -# repository deployed by the AWSOversightMLCDK package. While this script can be run without providing inputs, -# a user can pass in the following optional parameters to modify default behavior: -# $1 = test_image = image to run test against -# $2 = MODEL = options: < centerpoint | flood | process | aircraft > -# $2 = test_tile_format = tile format to use -# $3 = test_tile_compression = tile compression to use -# $4 = region = region OSML is deployed in -# $5 = account = account to test with -# $6 = remote account = see description below - -# If you want to test cross account access provide a remote account (ex. 555986723962) -# If the value is set it will use images from the gamma integ account -# and will write results to the stream and bucket located in that account -# You'll need to run the tests using creds for the gamma deployment account or -# add your account as a trusted principal to the necessary roles in the remote account - - -# Grab user inputs or set default values -export TARGET_IMAGE=${1} -export MODEL=${2:-centerpoint} -export TEST_TILE_FORMAT=${3:-NITF} -export TEST_TILE_COMPRESSION=${4:-J2K} -export REGION=${5:-us-west-2} -export ACCOUNT=${6:-$(aws sts get-caller-identity --query Account --output text)} -export REMOTE_ACCOUNT=${7:-} - -# Standard test images deployed by CDK currently -case $TARGET_IMAGE in - "small") - TARGET_IMAGE="s3://test-images-${ACCOUNT}/small.tif" - ;; - - "large") - TARGET_IMAGE="s3://test-images-${ACCOUNT}/large.tif" - TEST_TILE_COMPRESSION=NONE - ;; - - "meta") - TARGET_IMAGE="s3://test-images-${ACCOUNT}/meta.ntf" - ;; - - "tile_tif") - TARGET_IMAGE="s3://test-images-${ACCOUNT}/tile.tif" - ;; - - "tile_ntf") - TARGET_IMAGE="s3://test-images-${ACCOUNT}/tile.ntf" - ;; - - "tile_jpeg") - TARGET_IMAGE="s3://test-images-${ACCOUNT}/tile.jpeg" - ;; - - "tile_png") - TARGET_IMAGE="s3://test-images-${ACCOUNT}/tile.png" - ;; - -esac - -# Expected values from our CDK package -export IMAGE_QUEUE_NAME=ImageRequestQueue -export TEST_RESULTS_BUCKET=test-results-${ACCOUNT} -export TEST_STREAM=test-stream-${ACCOUNT} -export JOB_STATUS_TABLE=ImageProcessingJobStatus -export REGION_REQUEST_TABLE=RegionProcessingJobStatus -export FEATURE_TABLE=ImageProcessingFeatures -export SM_CENTER_POINT_MODEL=centerpoint -export SM_FLOOD_MODEL=flood -export SM_AIRCRAFT_MODEL=aircraft -export TILE_FORMAT=${TEST_TILE_FORMAT} -export TILE_COMPRESSION=${TEST_TILE_COMPRESSION} -export TEST_STATUS_QUEUE=ImageStatusQueue -export TARGET_MODEL=${MODEL} - -# Call into root directory of this pacakge so that we can -# run this script from anywhere. -LOCAL_DIR="$( dirname -- "$0"; )" -cd "${LOCAL_DIR}/.." || exit 1 - -export PYTHONPATH="${PYTHONPATH}:./src/" - -# Run integration tests with pytest -time python3 -m pytest -o log_cli=true -vv src/aws/osml/integ/"${MODEL}"/test_"${MODEL}"_model.py diff --git a/bin/translate_images.py b/bin/translate_images.py new file mode 100644 index 0000000..d5c0222 --- /dev/null +++ b/bin/translate_images.py @@ -0,0 +1,139 @@ +# Copyright 2023 Amazon.com, Inc. or its affiliates. + +""" +################################################################################################### + +This script provides functionality to convert GeoTIFF images to NITF (National Imagery Transmission Format) format. +The script supports two target NITF formats: GREYSCALE (16bit) and WBID (Wideband Image Data). +It uses the GDAL (Geospatial Data Abstraction Library) library for the conversion process. + +The script defines three main functions: 1. `Translate_to_greyscale`: Converts a GeoTIFF dataset to a greyscale NITF +format. 2. `Translate_to_WBID`: Converts a GeoTIFF dataset to a NITF file in the WBID format. 3. +`Convert_geotiff_to_nitf`: Converts GeoTIFF files in a specified input directory to the desired NITF format (either +greyscale or WBID) and saves them in an output directory. + +The script accepts command-line arguments for specifying input and output directories, target format, inclusion of +XML metadata, and compression method. + +Usage: python script_name.py --input_dir input_directory --output_format {GREYSCALE/WBID} [--output_dir +output_directory] [--include_xml {NO/YES}] [--compression compression_method] + +Example Usage: python3 translate_images.py --input_dir input_data --output_format GREYSCALE --output_dir output_data +--include_xml YES --compression LZW + +Please ensure you have the GDAL library and its Python bindings (osgeo.gdal) installed before running this script. +You can customize the script's behavior by modifying the function parameters and GDAL options as needed. +###################################################################################################""" + +import argparse +import glob +import os +from enum import Enum, auto +from typing import Optional + +from osgeo import gdal, gdalconst +from osgeo.gdal import Translate + + +class TargetFormat(Enum): + """ + Enum for specifying the target NITF format for conversion. + """ + + GREYSCALE = auto() + WBID = auto() + + +def translate_to_greyscale(out_file: str, ds: gdal.Dataset) -> gdal.Dataset: + """ + Translate a GeoTIFF dataset to a greyscale NITF format. + + :param out_file: The path to the output NITF file to be created. + :param ds: The input GeoTIFF dataset to be translated. + :return: The translated NITF dataset. + """ + return Translate( + destName=out_file, + srcDS=ds, + bandList=[float(1)], + format="NITF", + outputType=gdalconst.GDT_Int16, + creationOptions=["IC=C8", "PROFILE=NPJE_NUMERICALLY_LOSSLESS"], + ) + + +def translate_to_wbid(out_file: str, ds: gdal.Dataset) -> gdal.Dataset: + """ + Converts a GeoTIFF dataset to a NITF file in the WBID format. + + :param out_file: The path to the output NITF file to be created. + :param ds: The input GeoTIFF dataset to be converted. + :return: The converted NITF dataset. + """ + return Translate( + destName=out_file, + srcDS=ds, + bandList=[1], + format="NITF", + outputType=gdalconst.GDT_Byte, + creationOptions=["IC=C8", "PROFILE=NPJE_NUMERICALLY_LOSSLESS"], + ) + + +def convert_geotiff_to_nitf(input_dir: str, target_format: TargetFormat, output_dir: Optional[str] = None) -> None: + """ + Convert GeoTIFF files in the input directory to NITF format (either greyscale or WBID). + + :param input_dir: The directory containing the input GeoTIFF files. + :param target_format: The target format for conversion, either GREYSCALE or WBID. + :param output_dir: The directory where converted NITF files will be saved. + If not provided, a default 'output' directory will be created + inside the input directory. + :raises Exception: If an invalid target format is provided. + """ + if output_dir is None: + output_dir = os.path.join(input_dir, "output") + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + input_files = glob.glob(os.path.join(input_dir, "*.tif")) + + for input_file in input_files: + output_file = os.path.join(output_dir, os.path.splitext(os.path.basename(input_file))[0] + ".ntf") + src_ds = gdal.Open(input_file) + + if target_format == TargetFormat.GREYSCALE: + translate_to_greyscale(output_file, src_ds) + elif target_format == TargetFormat.WBID: + translate_to_wbid(output_file, src_ds) + else: + raise Exception(f"Invalid target format: {target_format}") + + print("Conversion completed.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Convert GeoTIFF images to a target NITF format.") + parser.add_argument("input_dir", help="Input directory containing GeoTIFF images") + parser.add_argument("--output_format", help="Target format to convert to (options: GREYSCALE/WBID)") + parser.add_argument( + "--output_dir", help="Output directory for NITF images (default: input_dir/output)", type=str, default=None + ) + parser.add_argument( + "--include_xml", help="Output directory for NITF images (default: NO, options: NO/YES)", type=str, default="NO" + ) + parser.add_argument( + "--compression", help="Target compression to use for imagery (default: NONE)", type=str, default=None + ) + args = parser.parse_args() + + if args.compression is not None: + gdal.SetConfigOption("COMPRESS_OVERVIEW", args.compression) + + gdal.SetConfigOption("GDAL_PAM_ENABLED", args.include_xml) + + usr_input_dir = args.input_dir + usr_output_dir = args.output_dir + + convert_geotiff_to_nitf(usr_input_dir, TargetFormat[args.output_format], usr_output_dir) diff --git a/environment-py311.yml b/environment-py311.yml new file mode 100644 index 0000000..ab86302 --- /dev/null +++ b/environment-py311.yml @@ -0,0 +1,7 @@ +name: osml_model_runner_test +channels: + - conda-forge +dependencies: + - conda-forge::python=3.11 + - conda-forge::gdal=3.7.2 + - conda-forge::proj=9.3.0 diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..80f6d82 --- /dev/null +++ b/environment.yml @@ -0,0 +1,6 @@ +name: osml_models +channels: + - conda-forge +dependencies: + - conda-forge::gdal=3.7.2 + - conda-forge::proj=9.3.0 diff --git a/images/release-deployment.png b/images/release-deployment.png new file mode 100644 index 0000000..65dcc2b Binary files /dev/null and b/images/release-deployment.png differ diff --git a/images/release_deployment.sketch b/images/release_deployment.sketch new file mode 100644 index 0000000..1d43e59 Binary files /dev/null and b/images/release_deployment.sketch differ diff --git a/logs/.gitignore b/logs/.gitignore new file mode 100644 index 0000000..72e8ffc --- /dev/null +++ b/logs/.gitignore @@ -0,0 +1 @@ +* diff --git a/setup.cfg b/setup.cfg index aeec4ec..cb21e95 100755 --- a/setup.cfg +++ b/setup.cfg @@ -5,7 +5,7 @@ description = Package to run tests against osml-model-runner deployments. long_description = file: README.md long_description_content_type = text/markdown author = Amazon Web Services -author_email = todo-public-library-poc@amazon.com +author_email = aws-osml-admin@amazon.com license = MIT No Attribution @@ -24,8 +24,8 @@ license = IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. project_urls = - Source = https://github.com/aws/todo-public-library - Tracker = https://github.com/aws/todo-public-library/issues + Source = https://github.com/aws-solutions-library-samples/osml-model-runner-test + Tracker = https://github.com/aws-solutions-library-samples/osml-model-runner-test/issues classifiers = Programming Language :: Python :: 3 Operating System :: OS Independent @@ -35,15 +35,16 @@ zip_safe = False package_dir= =src packages=find_namespace: -python_requires = >=3.8 +python_requires = >=3.9 include_package_data = True install_requires = - boto3==1.28.1 - botocore==1.31.1 + boto3==1.28.57 + botocore==1.31.57 geojson==3.0.1 pytest==7.3.1 setuptools==68.0.0 + toml==0.10.2 [options.packages.find] @@ -56,5 +57,7 @@ package_data = = ["py.typed"] [options.extras_require] +gdal = + gdal>=3.7.0 test = tox diff --git a/src/aws/osml/integ/aircraft/test_aircraft_model.py b/src/aws/osml/integ/aircraft/test_aircraft_model.py deleted file mode 100644 index ef2a432..0000000 --- a/src/aws/osml/integ/aircraft/test_aircraft_model.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2023 Amazon.com, Inc. or its affiliates. - -import logging - -from aws.osml.utils import ( - OSMLConfig, - count_features, - ddb_client, - kinesis_client, - run_model_on_image, - sqs_client, - validate_expected_feature_count, -) - -logger = logging.getLogger() -logger.setLevel(logging.INFO) - - -def test_model_runner_aircraft_model() -> None: - """ - Run the test using the Aircraft and validate the number of features - - :return: None - """ - - # Launch our image request and validate it completes - image_id, job_id, image_processing_request, kinesis_shard = run_model_on_image( - sqs_client(), OSMLConfig.SM_AIRCRAFT_MODEL, kinesis_client() - ) - - # Count the features that were create in the table for this image - feature_count = count_features(image_id=image_id, ddb_client=ddb_client()) - - # Validate the number of features we created match expected values - validate_expected_feature_count(feature_count) diff --git a/src/aws/osml/integ/__init__.py b/src/aws/osml/integ/http_centerpoint/__init__.py similarity index 100% rename from src/aws/osml/integ/__init__.py rename to src/aws/osml/integ/http_centerpoint/__init__.py diff --git a/src/aws/osml/integ/http_centerpoint/test_http_centerpoint_model.py b/src/aws/osml/integ/http_centerpoint/test_http_centerpoint_model.py new file mode 100644 index 0000000..926f575 --- /dev/null +++ b/src/aws/osml/integ/http_centerpoint/test_http_centerpoint_model.py @@ -0,0 +1,69 @@ +# Copyright 2023 Amazon.com, Inc. or its affiliates. + +import logging + +from aws.osml.utils import ( + OSMLConfig, + count_features, + count_region_request_items, + ddb_client, + elb_client, + kinesis_client, + run_model_on_image, + s3_client, + sqs_client, + validate_expected_region_request_items, + validate_features_match, +) + +logger = logging.getLogger() +logger.setLevel(logging.INFO) + + +def test_model_runner_centerpoint_http_model() -> None: + """ + Run the test using the CenterPointModel and validate the number of features + and region requests using the HTTP endpoint + + :return: None + """ + + if OSMLConfig.HTTP_CENTERPOINT_MODEL_URL: + http_endpoint_url = OSMLConfig.HTTP_CENTERPOINT_MODEL_URL + else: + http_endpoint_dns = get_load_balancer_dns_url(OSMLConfig.HTTP_CENTERPOINT_MODEL_ELB_NAME) + http_endpoint_url = f"http://{http_endpoint_dns}{OSMLConfig.HTTP_CENTERPOINT_MODEL_INFERENCE_PATH}" + + # launch our image request and validate it completes + image_id, job_id, image_processing_request, shard_iter = run_model_on_image( + sqs_client(), http_endpoint_url, "HTTP_ENDPOINT", kinesis_client() + ) + + # count the created features in the table for this image + count_features(image_id=image_id, ddb_client=ddb_client()) + + # verify the results we created in the appropriate syncs + validate_features_match( + image_processing_request=image_processing_request, + job_id=job_id, + shard_iter=shard_iter, + s3_client=s3_client(), + kinesis_client=kinesis_client(), + ) + + # validate the number of region requests that were created in the process and check if they are succeeded + region_request_count = count_region_request_items(image_id=image_id, ddb_client=ddb_client()) + validate_expected_region_request_items(region_request_count) + + +def get_load_balancer_dns_url(load_balancer_name: str) -> str: + """ + Get the DNS URL for the given load balancer + :param load_balancer_name: The name of the load balancer + :return: The DNS URL for the load balancer + """ + logger.debug("Retrieving DNS name for '{}'...".format(load_balancer_name)) + res = elb_client().describe_load_balancers(Names=[load_balancer_name]) + dns_name = res.get("LoadBalancers", [])[0].get("DNSName") + logger.debug("Found DNS name: {}".format(dns_name)) + return dns_name diff --git a/src/aws/osml/integ/aircraft/__init__.py b/src/aws/osml/integ/sm_aircraft/__init__.py similarity index 100% rename from src/aws/osml/integ/aircraft/__init__.py rename to src/aws/osml/integ/sm_aircraft/__init__.py diff --git a/src/aws/osml/integ/sm_aircraft/test_sm_aircraft_model.py b/src/aws/osml/integ/sm_aircraft/test_sm_aircraft_model.py new file mode 100644 index 0000000..041b09e --- /dev/null +++ b/src/aws/osml/integ/sm_aircraft/test_sm_aircraft_model.py @@ -0,0 +1,30 @@ +# Copyright 2023 Amazon.com, Inc. or its affiliates. + +import logging + +from aws.osml.utils import OSMLConfig, kinesis_client, run_model_on_image, s3_client, sqs_client, validate_features_match + +logger = logging.getLogger() +logger.setLevel(logging.INFO) + + +def test_model_runner_aircraft_model() -> None: + """ + Run the test using the Aircraft and validate the number of features + + :return: None + """ + + # Launch our image request and validate it completes + image_id, job_id, image_processing_request, kinesis_shard = run_model_on_image( + sqs_client(), OSMLConfig.SM_AIRCRAFT_MODEL, "SM_ENDPOINT", kinesis_client() + ) + + # Verify the results we created in the appropriate syncs + validate_features_match( + image_processing_request=image_processing_request, + job_id=job_id, + shard_iter=kinesis_shard, + s3_client=s3_client(), + kinesis_client=kinesis_client(), + ) diff --git a/src/aws/osml/integ/centerpoint/__init__.py b/src/aws/osml/integ/sm_centerpoint/__init__.py similarity index 100% rename from src/aws/osml/integ/centerpoint/__init__.py rename to src/aws/osml/integ/sm_centerpoint/__init__.py diff --git a/src/aws/osml/integ/centerpoint/test_centerpoint_model.py b/src/aws/osml/integ/sm_centerpoint/test_sm_centerpoint_model.py similarity index 82% rename from src/aws/osml/integ/centerpoint/test_centerpoint_model.py rename to src/aws/osml/integ/sm_centerpoint/test_sm_centerpoint_model.py index 6a9562b..6a2f9e1 100644 --- a/src/aws/osml/integ/centerpoint/test_centerpoint_model.py +++ b/src/aws/osml/integ/sm_centerpoint/test_sm_centerpoint_model.py @@ -29,10 +29,10 @@ def test_model_runner_center_point_model() -> None: # launch our image request and validate it completes image_id, job_id, image_processing_request, shard_iter = run_model_on_image( - sqs_client(), OSMLConfig.SM_CENTERPOINT_MODEL, kinesis_client() + sqs_client(), OSMLConfig.SM_CENTERPOINT_MODEL, "SM_ENDPOINT", kinesis_client() ) - # count the features that were create in the table for this image + # count the features created in the table for this image count_features(image_id=image_id, ddb_client=ddb_client()) # verify the results we created in the appropriate syncs @@ -44,6 +44,6 @@ def test_model_runner_center_point_model() -> None: kinesis_client=kinesis_client(), ) - # validate the number of region request that were created in the process and check if they are succeeded + # validate the number of region requests that were created in the process and check if they are succeeded region_request_count = count_region_request_items(image_id=image_id, ddb_client=ddb_client()) validate_expected_region_request_items(region_request_count) diff --git a/src/aws/osml/integ/flood/__init__.py b/src/aws/osml/integ/sm_flood/__init__.py similarity index 100% rename from src/aws/osml/integ/flood/__init__.py rename to src/aws/osml/integ/sm_flood/__init__.py diff --git a/src/aws/osml/integ/flood/test_flood_model.py b/src/aws/osml/integ/sm_flood/test_sm_flood_model.py similarity index 89% rename from src/aws/osml/integ/flood/test_flood_model.py rename to src/aws/osml/integ/sm_flood/test_sm_flood_model.py index ea53e66..7137179 100644 --- a/src/aws/osml/integ/flood/test_flood_model.py +++ b/src/aws/osml/integ/sm_flood/test_sm_flood_model.py @@ -28,10 +28,10 @@ def test_model_runner_flood_model() -> None: # Launch our image request and validate it completes image_id, job_id, image_processing_request, kinesis_shard = run_model_on_image( - sqs_client(), OSMLConfig.SM_FLOOD_MODEL, kinesis_client() + sqs_client(), OSMLConfig.SM_FLOOD_MODEL, "SM_ENDPOINT", kinesis_client() ) - # Count the features that were create in the table for this image + # Count the features created in the table for this image feature_count = count_features(image_id=image_id, ddb_client=ddb_client()) # Validate the number of features we created match expected values diff --git a/src/aws/osml/load/load_test.py b/src/aws/osml/load/load_test.py index adc4541..aa8c627 100644 --- a/src/aws/osml/load/load_test.py +++ b/src/aws/osml/load/load_test.py @@ -2,23 +2,38 @@ import json import logging +import os import sys from datetime import datetime, timedelta from threading import Thread from time import sleep -from typing import Dict, List, Tuple +from typing import Dict, List, Union from botocore.exceptions import ClientError from osgeo import gdal -from aws.osml.utils.clients import cw_client, s3_client, sm_client, sqs_client +from aws.osml.utils.clients import s3_client, sm_client, sqs_client from aws.osml.utils.integ_utils import build_image_processing_request, queue_image_processing_job from aws.osml.utils.osml_config import OSMLConfig, OSMLLoadTestConfig +job_summary_log_file = "logs/job_summary.json" +job_status_log_file = "logs/job_status.json" +job_output_log_file = "logs/job_log.log" + + logger = logging.getLogger() logger.setLevel(logging.INFO) +logging.getLogger("botocore").setLevel(logging.CRITICAL) + +# create the log file if it doesn't exist +if os.path.isfile(job_output_log_file): + # Creates a new file + with open(job_output_log_file, "w") as fp: + pass + # To write data to new file uncomment + # this fp.write("New file created") -fh = logging.FileHandler("loadtest-{:%s}.log".format(datetime.now())) +fh = logging.FileHandler(job_output_log_file) fh.setLevel(logging.INFO) logger.addHandler(fh) @@ -35,72 +50,13 @@ class ImageRequestStatus(str): FAILED = "FAILED" -def get_cw_metrics(start_time: datetime) -> Tuple[int, int]: +def get_s3_images(bucket_name: str) -> Union[List[Dict[str, str]], None]: """ - Get total tiles processed and region processed from start time to now + Get all s3 images within the bucket to iterate through for load testing - :param start_time: DateTime = start time of loadtest + :param bucket_name: Name of source bucket to use for testing images - :return: Tuple[int, int] = total tiles and regions in tuple format - """ - try: - tiles_processed_response = cw_client().get_metric_data( - MetricDataQueries=[ - { - "Id": "tiles1", - "MetricStat": { - "Metric": { - "Namespace": "OSML", - "MetricName": "TilesProcessed", - "Dimensions": [{"Name": "ImageFormat", "Value": "TIFF"}], - }, - "Period": 60, - "Stat": "Sum", - }, - }, - ], - StartTime=start_time, - EndTime=datetime.now(), - ) - - total_tiles_processed = sum(tiles_processed_response["MetricDataResults"][0]["Values"]) - - regions_processed_response = cw_client().get_metric_data( - MetricDataQueries=[ - { - "Id": "regions1", - "MetricStat": { - "Metric": { - "Namespace": "OSML", - "MetricName": "RegionsProcessed", - "Dimensions": [{"Name": "ImageFormat", "Value": "TIFF"}], - }, - "Period": 60, - "Stat": "Sum", - }, - }, - ], - StartTime=start_time, - EndTime=datetime.now(), - ) - - total_regions_processed = sum(regions_processed_response["MetricDataResults"][0]["Values"]) - - return (total_tiles_processed, total_regions_processed) - - except ClientError as error: - logger.error(f"Cannot fetch CloudWatch metric data - {error}") - - return (0, 0) - - -def get_s3_images(bucket_name: str) -> List[Dict[str, int]]: - """ - Get all s3 images within the bucket - - :param bucket_name: str = name of the bucket - - :return: Dict[str, str] = a list of s3 images + :return: List of s3 images to process for load test """ try: response = s3_client().list_objects_v2(Bucket=bucket_name) @@ -132,9 +88,9 @@ def check_s3_bucket(bucket_name: str) -> bool: """ Check to see if the S3 Bucket exists - :param bucket_name: str = name of the bucket + :param bucket_name: Name of the bucket - :return: bool = does the bucket exist or not + :return: True if the bucket exists, False otherwise """ try: s3_client().head_bucket(Bucket=bucket_name) @@ -148,13 +104,13 @@ def get_model_instance_type(sm_model: str) -> str: """ Get Model Instance Type - :param sm_model: str = name of the SageMaker Model + :param sm_model: Name of the SageMaker Model - :return: str = model instance type + :return: Model instance type """ try: list_endpoints_response = sm_client().list_endpoint_configs( - NameContains=f"MRTestingOSML{sm_model}ModelEndpointOSML{sm_model}Mode-", + NameContains=f"{sm_model}", ) endpoint_name = list_endpoints_response["EndpointConfigs"][0]["EndpointConfigName"] @@ -171,71 +127,79 @@ def get_model_instance_type(sm_model: str) -> str: raise error +def is_complete(job_status_dict: Dict) -> bool: + for image_id, value in job_status_dict.items(): + if value["completed"] is False: + return False + return True + + def monitor_job_status(job_status_dict: Dict, expected_end_time: datetime) -> None: """ Polls the messages from SQS, check every message in the SQS to determine the status of it. Then - update the item in the job_status dict and goes on to the next one. + update the item in the job_status dict and go on to the next one. - :param job_status_dict: Dict = dict containing job information - :param expected_end_time: datetime = expected end time to end the thread + :param job_status_dict: Dict containing job information + :param expected_end_time: Expected end time to end the thread :return: None """ - try: - queue = sqs_client().get_queue_by_name( - QueueName=OSMLConfig.SQS_IMAGE_STATUS_QUEUE, QueueOwnerAWSAccountId=OSMLConfig.ACCOUNT - ) - except ClientError as error: - logger.error(f"[Background Thread] Error encountered attempting to access SQS - {OSMLConfig.SQS_IMAGE_STATUS_QUEUE}") - raise error - while True: if not job_status_dict: - logger.info("[Background Thread] There's no images processing! Continuing...") - else: - logger.info( - f"[Background Thread] There are {len(job_status_dict)} items... Checking the status of each items..." - ) + logger.debug("[Background Thread] There's no images processing! Waiting 5 seconds...") + sleep(5) + continue try: - messages = queue.receive_messages() - for message in messages: - message_attributes = json.loads(message.body).get("MessageAttributes", {}) - message_image_id = message_attributes.get("image_id", {}).get("Value") - message_image_status = message_attributes.get("image_status", {}).get("Value") - - if message_image_id in job_status_dict.keys(): - # if the job already succeeded, we do not need to check, go to the next one - if not job_status_dict[message_image_id]["completed"]: - # update the status - job_status_dict[message_image_id]["status"] = message_image_status - - # mark the item in the dict completed - if message_image_status in [ - ImageRequestStatus.SUCCESS, - ImageRequestStatus.FAILED, - ImageRequestStatus.PARTIAL, - ]: - job_status_dict[message_image_id]["completed"] = True - else: - logger.warning(f"[Background Thread] {message_image_id} does not exist in job_status_dict yet") + image_status_queue = sqs_client().get_queue_by_name( + QueueName=OSMLConfig.SQS_IMAGE_STATUS_QUEUE, QueueOwnerAWSAccountId=OSMLConfig.ACCOUNT + ) + while True: + messages = image_status_queue.receive_messages() + if len(messages) == 0: + break + for message in messages: + message_attributes = json.loads(message.body).get("MessageAttributes", {}) + message_image_id = message_attributes.get("image_id", {}).get("Value") + message_image_status = message_attributes.get("image_status", {}).get("Value") + + if message_image_id in job_status_dict.keys(): + if job_status_dict[message_image_id]["completed"] is False: + # update the status + job_status_dict[message_image_id]["status"] = message_image_status + + # mark the item in the dict completed + if message_image_status in [ + ImageRequestStatus.SUCCESS, + ImageRequestStatus.FAILED, + ImageRequestStatus.PARTIAL, + ]: + start_time = datetime.strptime( + job_status_dict[message_image_id]["start_time"], "%m/%d/%Y/%H:%M:%S" + ) + processing_duration = (datetime.now() - start_time).total_seconds() + job_status_dict[message_image_id]["completed"] = True + job_status_dict[message_image_id]["processing_duration"] = processing_duration + else: + logger.warning(f"[Background Thread] {message_image_id} does not exist in job_status_dict yet") except ClientError as error: logging.warning(f"[Background Thread] {error}") except KeyError as error: logging.warning(f"[Background Thread] {error}") - if datetime.now() >= expected_end_time: + if datetime.now() >= expected_end_time and is_complete(job_status_dict): + logger.info("[Background Thread] All images have been processed!") break - sleep(15) + sleep(5) -def display_image_results(job_status_dict: Dict, total_tiles_processed: int, total_regions_processed: int) -> None: +def display_image_results(job_status_dict: Dict) -> Dict: """ Calculate and display the result of LoadTest - :param job_status_dict: Dict = dict containing job information + :param job_status_dict: Dictionary containing job information - :return: None + :return: Dict = dictionary containing the results """ total_image_sent = len(job_status_dict) total_image_in_progress = 0 @@ -243,7 +207,7 @@ def display_image_results(job_status_dict: Dict, total_tiles_processed: int, tot total_image_failed = 0 total_image_succeeded = 0 total_size_processed = 0 - total_pixels_processed = (0, 0) + total_pixels_processed = 0 for image_id, value in job_status_dict.items(): if value["completed"]: @@ -256,13 +220,13 @@ def display_image_results(job_status_dict: Dict, total_tiles_processed: int, tot total_size_processed += value["size"] - image_pixel = value["pixels"] - total_pixels_processed = tuple(sum(item) for item in zip(total_pixels_processed, image_pixel)) + total_pixels_processed += value["pixels"] else: total_image_in_progress += 1 - total_size = "%.3f GB" % (total_size_processed / 1024 / 1024 / 1024) + # convert the size to GB + total_gb_processed = total_size_processed / 1024 / 1024 / 1024 logger.info( f""" @@ -271,21 +235,29 @@ def display_image_results(job_status_dict: Dict, total_tiles_processed: int, tot Total Images Processed: {total_image_processed} Total Images Succeeded: {total_image_succeeded} Total Images Failed: {total_image_failed} - Total Size Processed: {total_size} - Total Pixels Processed: {total_pixels_processed[0]}x{total_pixels_processed[1]} - Total Tiles Processed: {total_tiles_processed} - Total Regions Processed: {total_regions_processed} + Total GB Processed: {total_gb_processed} + Total Pixels Processed: {total_pixels_processed} """ ) + return { + "total_image_sent": total_image_sent, + "total_image_in_progress": total_image_in_progress, + "total_image_processed": total_image_processed, + "total_image_succeeded": total_image_succeeded, + "total_image_failed": total_image_failed, + "total_gb_processed": total_gb_processed, + "total_pixels_processed": total_pixels_processed, + } + def start_workflow() -> None: """ The workflow to build an image request for a specific SM endpoint and then place it - on the corresponding SQS queue every definied seconds for ModelRunner to pick up and process. - Once the image has been completed return the associated image_id and image_request object for analysis. + on the corresponding SQS queue every defined second for ModelRunner to pick up and process. + Once the image has been completed, return the associated image_id and image_request object for analysis. Then it will display the results for all the images have been sent for ModelRunner and calculate - how many has succeeded or failed. + how many have succeeded or failed. :return: None """ @@ -296,14 +268,14 @@ def start_workflow() -> None: sm_endpoint_model = OSMLLoadTestConfig.SM_LOAD_TEST_MODEL sm_instance_type = get_model_instance_type(sm_endpoint_model) periodic_sleep = int(OSMLLoadTestConfig.PERIODIC_SLEEP_SECS) - processing_window_hrs = int(OSMLLoadTestConfig.PROCESSING_WINDOW_HRS) + processing_window_min = int(OSMLLoadTestConfig.PROCESSING_WINDOW_MIN) - # checking if images bucket exist and result bucket exist + # checking if images bucket exists and result bucket exists if not check_s3_bucket(s3_image_bucket) or not check_s3_bucket(s3_result_bucket): sys.exit(1) start_time = datetime.now() - expected_end_time = start_time + timedelta(hours=processing_window_hrs) + expected_end_time = start_time + timedelta(minutes=processing_window_min) logger.info( f"""Starting {start_time} the load test with the following parameters: @@ -311,14 +283,14 @@ def start_workflow() -> None: Output S3 Result Bucket: {s3_result_bucket} SageMaker Instance Type: {sm_instance_type} SageMaker Model: {sm_endpoint_model} - LoadTest will stop at {expected_end_time}, at least {processing_window_hrs} hours from now + LoadTest will stop at {expected_end_time}, at least {processing_window_min} minutes from now """ ) job_status_dict = {} # keep track of jobs and its status image_index = 0 - images_list = get_s3_images(s3_image_bucket) # get list of images from the bucket + images_list = get_s3_images(s3_image_bucket) # get a list of images from the bucket if not images_list: logger.error(f"There's no images in {s3_image_bucket} bucket!") @@ -326,13 +298,21 @@ def start_workflow() -> None: logger.info(f"There are {len(images_list)} images in {s3_image_bucket} bucket") + # it will keep looping until the time is greater than expected_end_time, will stop + # and generate cost_report after it is done # spinning up a daemon thread (aka monitoring the job status and keep tracking of it) daemon = Thread(target=monitor_job_status, args=(job_status_dict, expected_end_time), daemon=True, name="Background") daemon.start() - - # it will keep looping until the time is greater than expected_end_time, will stop - # and generate cost_report after it while datetime.now() <= expected_end_time: + image_request_queue = sqs_client().get_queue_by_name( + QueueName=OSMLConfig.SQS_IMAGE_REQUEST_QUEUE, QueueOwnerAWSAccountId=OSMLConfig.ACCOUNT + ) + while int(image_request_queue.attributes.get("ApproximateNumberOfMessages")) > 3: + image_request_queue = sqs_client().get_queue_by_name( + QueueName=OSMLConfig.SQS_IMAGE_REQUEST_QUEUE, QueueOwnerAWSAccountId=OSMLConfig.ACCOUNT + ) + logger.info(f"ApproximateNumberOfMessages is greater than 3... waiting {periodic_sleep} seconds..") + sleep(periodic_sleep) # build an image processing request image_url = images_list[image_index]["image_name"] image_size = images_list[image_index]["image_size"] @@ -344,43 +324,50 @@ def start_workflow() -> None: # grab the job id from the image request job_id = image_processing_request["jobId"] - # recreate the image_id that will be associated with the image request - # note this logic must match the strategy used to construct the image ID in the Model Runner from the - # image processing request. See AWSOversightMLModelRunner src/aws_oversightml_model_runner/model_runner_api.py - image_id = job_id + ":" + image_processing_request["imageUrls"][0] - - logger.info(f"Image Id: {image_id}") - - # get the pixel count (h x w) - # gdal has this capability which allow to open file virtually without needing to download or pull in. + # gdal has this capability which allows to open file virtually without needing to download or pull in. gdal_info = gdal.Open(image_url.replace("s3:/", "/vsis3", 1)) - pixels = (gdal_info.RasterXSize, gdal_info.RasterYSize) + pixels = gdal_info.RasterXSize * gdal_info.RasterYSize + image_id = job_id + ":" + image_url # compile into dict format and store it in the list for future tracking job_status_dict[image_id] = { "job_id": job_id, + "image_url": image_url, "message_id": message_id, "status": ImageRequestStatus.STARTED, "completed": False, "size": image_size, "pixels": pixels, + "start_time": datetime.now().strftime("%m/%d/%Y/%H:%M:%S"), + "processing_duration": None, } logger.info(f"Total image sent {len(job_status_dict)}") - # sleep for defined seconds, then send another image request - sleep(periodic_sleep) - image_index += 1 - # if we reached to the end, reset it back to zero + # if we reached to the end, reset it back to zero, # we would want to keep looping until the timer runs out if image_index == len(images_list): image_index = 0 # occasionally display image results - total_tiles_processed, total_regions_processed = get_cw_metrics(start_time) - display_image_results(job_status_dict, total_tiles_processed, total_regions_processed) + display_image_results(job_status_dict) + + # Writing to job status file + with open(job_status_log_file, "w") as outfile: + outfile.write(json.dumps(job_status_dict, indent=4)) + + sleep(1) + + # ensure jobs completed + while not is_complete(job_status_dict): + # Writing to sample.json + with open(job_status_log_file, "w") as outfile: + outfile.write(json.dumps(job_status_dict, indent=4)) + display_image_results(job_status_dict) + logger.info("Waiting for jobs to complete...") + sleep(5) actual_end_time = datetime.now() @@ -392,8 +379,17 @@ def start_workflow() -> None: logger.info(f"Total elapsed time: {total_elapsed_time.total_seconds()} seconds") # final display image results - total_tiles_processed, total_regions_processed = get_cw_metrics(start_time) - display_image_results(job_status_dict, total_tiles_processed, total_regions_processed) + job_status_summary = display_image_results(job_status_dict) + + # Writing to job status file + with open(job_status_log_file, "w") as outfile: + outfile.write(json.dumps(job_status_dict, indent=4)) + + # Writing final job_status.json + with open(job_summary_log_file, "w") as outfile: + outfile.write(json.dumps(job_status_summary, indent=4)) + + logger.info(f"Load finished, completed in {total_elapsed_time.total_seconds()} seconds!") start_workflow() diff --git a/src/aws/osml/utils/__init__.py b/src/aws/osml/utils/__init__.py index 198f723..15ea227 100644 --- a/src/aws/osml/utils/__init__.py +++ b/src/aws/osml/utils/__init__.py @@ -4,7 +4,7 @@ # __init__.py file. # flake8: noqa -from .clients import cw_client, ddb_client, kinesis_client, s3_client, sm_client, sqs_client +from .clients import cw_client, ddb_client, elb_client, kinesis_client, s3_client, sm_client, sqs_client from .integ_utils import ( build_image_processing_request, count_features, diff --git a/src/aws/osml/utils/clients.py b/src/aws/osml/utils/clients.py index 62cda0b..ab1d9f8 100644 --- a/src/aws/osml/utils/clients.py +++ b/src/aws/osml/utils/clients.py @@ -1,9 +1,12 @@ # Copyright 2023 Amazon.com, Inc. or its affiliates. +import logging import boto3 from .osml_config import OSMLConfig +logging.getLogger("botocore").setLevel(logging.CRITICAL) + def get_session_credentials() -> boto3.session.Session: """ @@ -72,3 +75,13 @@ def cw_client() -> boto3.client: """ session = get_session_credentials() return session.client("cloudwatch", region_name=OSMLConfig.REGION) + + +def elb_client() -> boto3.client: + """ + Get resources from the default ElasticLoadBalancing session + + :return: boto3.client = ELB client + """ + session = get_session_credentials() + return session.client("elbv2", region_name=OSMLConfig.REGION) diff --git a/src/aws/osml/utils/integ_utils.py b/src/aws/osml/utils/integ_utils.py index b50168b..8a5bdc0 100644 --- a/src/aws/osml/utils/integ_utils.py +++ b/src/aws/osml/utils/integ_utils.py @@ -16,16 +16,17 @@ def run_model_on_image( - sqs_client: boto3.resource, sm_endpoint: str, kinesis_client: Optional[boto3.resource] + sqs_client: boto3.resource, endpoint: str, endpoint_type: str, kinesis_client: Optional[boto3.resource] ) -> Tuple[str, str, Dict[str, Any], Optional[Dict[str, Any]]]: """ - The workflow to build an image request for a specific SM endpoint and then place it + The workflow to build an image request for a specific model endpoint and then place it on the corresponding SQS queue for ModelRunner to pick up and process. Once the image - has been completed return the associated image_id and image_request object for analysis. + has been completed, return the associated image_id and image_request object for analysis. - :param sqs_client: boto3.resource = the sqs client fixture passed in - :param sm_endpoint: str = the SM endpoint you wish to run your image against - :param kinesis_client: Optional[boto3.resource] = the optional kinesis client fixture passed in + :param endpoint_type: The type of endpoint you want to build the image_request for SM/HTTP + :param sqs_client: SQS client fixture passed in + :param endpoint: endpoint you wish to run your image against + :param kinesis_client: Optional kinesis client fixture passed in :return: Tuple[str, str, Dict[str, Any], Dict[str, Any]] = the generated image_id, job_id, image_request, and kinesis shard. @@ -33,7 +34,7 @@ def run_model_on_image( image_url = OSMLConfig.TARGET_IMAGE # get image_url # Build an image processing request from the test environment - image_processing_request = build_image_processing_request(sm_endpoint, image_url) + image_processing_request = build_image_processing_request(endpoint, endpoint_type, image_url) # Get the current Kinesis shard iterator to listen to for results since our start time shard_iter = get_kinesis_shard(kinesis_client) @@ -61,8 +62,8 @@ def queue_image_processing_job(sqs_client: boto3.resource, image_processing_requ Place an image_request object onto the associated SQS queue for ModelRunner pick up for processing. - :param sqs_client: boto3.resource = the sqs client fixture passed in - :param image_processing_request: Dict[str, Any] = the image request to place in the queue. + :param sqs_client: Sqs client fixture passed in + :param image_processing_request: Image request to place in the queue. :return: None """ @@ -81,13 +82,11 @@ def queue_image_processing_job(sqs_client: boto3.resource, image_processing_requ logging.error(f"Unable to send job request to SQS queue: {OSMLConfig.SQS_IMAGE_REQUEST_QUEUE}") logging.error(f"{error}") assert False - return None except ParamValidationError as error: logging.error("Invalid SQS API request; validation failed") logging.error(f"{error}") assert False - return None def monitor_job_status(sqs_client: boto3.resource, image_id: str) -> None: @@ -95,8 +94,8 @@ def monitor_job_status(sqs_client: boto3.resource, image_id: str) -> None: Monitors the status of the image request on the corresponding SQS queue and returns once the image request associated with the given image_id has completed. - :param sqs_client: boto3.resource = the sqs client fixture passed in - :param image_id: str = the image_id associated with the image request to monitor + :param sqs_client: The sqs client fixture passed in + :param image_id: Image_id associated with the image request to monitor :return: None """ done = False @@ -149,7 +148,6 @@ def get_kinesis_shard(kinesis_client: boto3.client) -> Dict[str, Any]: """ Get uniquely identified sequence of data records in a kinesis stream - :param date_time: datetime.datetime: the time at which the test started running and read results starting from :param kinesis_client: boto3.client = the kinesis client fixture passed in :return: Dict[str, Any] = uniquely identified sequence of data records @@ -171,7 +169,7 @@ def validate_features_match( kinesis_client: boto3.client = None, ) -> None: """ - Compares known standard results ( features ) against the ones generated from the tests. + Compares known standard results (features) against the ones generated from the tests. :param image_processing_request: Dict[str, Any] = the image processing request to validate against :param job_id: str = the job-id associated with the request @@ -191,7 +189,7 @@ def validate_features_match( expected_features = geojson.load(geojson_file)["features"] while not done and max_retries > 0: - # This is really a List[Sink] but we don't have a common library to pull + # This is really a List[Sink], but we don't have a common library to pull # this from so using "any" outputs: List[Dict[str, Any]] = image_processing_request["outputs"] # Reset found outputs on each retry so that we don't count a single output @@ -218,12 +216,12 @@ def validate_s3_features_match(bucket: str, prefix: str, expected_features: List """ Checks a s3 output sync against known good feature results for a given test image. - :param bucket: str = a folder inside s3 - :param prefix: str = string of characters at the beginning of the object key name - :param expected_features: List[Features] = the list of features - :param s3_client: boto3.client = the s3 client fixture passed in + :param bucket: Folder inside s3 + :param prefix: String of characters at the beginning of the object key name + :param expected_features: The list of features + :param s3_client: S3 client fixture passed in - :return: bool = s3 features matches the expected features + :return: True if S3 features match the expected features """ logging.info(f"Checking S3 at '{bucket}/{prefix}' for results.") for object_key in get_matching_s3_keys( @@ -255,13 +253,13 @@ def validate_kinesis_features_match( """ Checks a Kinesis output sync against know good feature results for a given test image - :param job_id: str = the job-id associated with the request - :param stream: str = kinesis stream name - :param shard_iter: Dict[str, Any] = uniquely identified sequence of data records - :param expected_features: List[Feature] = the list of features - :param kinesis_client: boto3.client = the kinesis client fixture passed in + :param job_id: Job-id associated with the request + :param stream: Kinesis stream name + :param shard_iter: Uniquely identified record sequence + :param expected_features: List of features expected by test + :param kinesis_client: Kinesis client fixture passed in - :return: bool = kinesis features matches the expected features + :return: Kinesis features matches the expected features """ logging.info(f"Checking Kinesis Stream '{stream}' for results.") records = kinesis_client.get_records(ShardIterator=shard_iter, Limit=10000)["Records"] @@ -286,10 +284,10 @@ def get_matching_s3_keys(s3_client: boto3.client, bucket: str, prefix: str = "", """ Generate the keys in an S3 bucket. - :param s3_client: boto3.client = Boto3 S3 Client. - :param bucket: str = Name of the S3 bucket. - :param prefix: str = Only fetch keys that start with this prefix (optional). - :param suffix: str = Only fetch keys that end with this suffix (optional). + :param s3_client: Boto3 S3 Client. + :param bucket: Name of the S3 bucket. + :param prefix: Only fetches keys that start with this prefix (optional). + :param suffix: Only fetches keys that end with this suffix (optional). :return: None """ @@ -310,14 +308,14 @@ def get_matching_s3_keys(s3_client: boto3.client, bucket: str, prefix: str = "", def feature_equal(expected: geojson.Feature, actual: geojson.Feature) -> bool: """ Determines if two features are roughly equivalent. We can't compare the full object because - each feature include a hex token as a top level property. Instead, we compare every other part of + each feature includes a hex token as a top level property. Instead, we compare every other part of the feature to make sure they match. The expected data located in ./src/data has a placeholder value, $IMAGE_ID$, in place of an actual image id so that we can swap it in. - :param expected: geojson.Feature = the feature we expect to match the result - :param actual: geojson.Feature = the feature that was generated by the test + :param expected: Feature we expect to match the result + :param actual: Feature that was generated by the test - :return: bool = whether the features match + :return: Whether the features match """ actual_pixel_coords = actual.get("properties", {}).get("detection", {}).get("pixelCoordinates") expected_pixel_coords = expected.get("properties", {}).get("detection", {}).get("pixelCoordinates") @@ -337,16 +335,16 @@ def feature_collections_equal(expected: List[geojson.Feature], actual: List[geoj """ Determines whether the supplied unordered feature lists are equal - :param expected: List[geojson.Feature] = unordered list of expected features - :param actual: List[geojson.Feature] = unordered list of detected features + :param expected: An unordered list of expected features + :param actual: An unordered list of detected features - :return: bool = check to see if expected and actual are equal + :return: Check to see if expected and actual are equal """ if not len(expected) == len(actual): logging.info(f"Expected {len(expected)} features but found {len(actual)}") return False # The order of features will depend on the order that regions/tiles were fed to the model. - # While the order should roughly be the same there isn't a guarantee, we're just checking + # While the order should roughly be the same, there isn't a guarantee, we're just checking # that all expected features were detected here as order within the collection isn't # actually important. expected.sort(key=lambda x: str(x.get("properties", {}).get("detection", {}).get("pixelCoordinates"))) @@ -360,17 +358,18 @@ def feature_collections_equal(expected: List[geojson.Feature], actual: List[geoj return True -def build_image_processing_request(sm_endpoint: str, image_url: str) -> Dict[str, Any]: +def build_image_processing_request(endpoint: str, endpoint_type: str, image_url: str) -> Dict[str, Any]: """ Build an image_processing_request meant to be placed on the corresponding ModelRunner SQS queue. - The image request is configured from the hydra provisioned hydra environment. In the future - this could, and probably should, be extended to build more variant image requests for additional + The image request is configured from test environment. + In the future this could, and probably should, be extended to build more variant image requests for additional testing configurations. - :param image_url: the URL to the image you want to process - :param sm_endpoint: str = the SM model endpoint that you want to build the image_request for + :param endpoint_type: The type of endpoint you want to build the image_request for SM/HTTP + :param image_url: URL to the image you want to process + :param endpoint: Model endpoint that you want to build the image_request for - :return: Dict[str, Any] = the dictionary representation of the image request + :return: Dictionary representation of the image request """ if OSMLConfig.KINESIS_RESULTS_STREAM: result_stream = OSMLConfig.KINESIS_RESULTS_STREAM @@ -384,7 +383,7 @@ def build_image_processing_request(sm_endpoint: str, image_url: str) -> Dict[str logging.info(f"Starting ModelRunner image job in {OSMLConfig.REGION}") logging.info(f"Image: {image_url}") - logging.info(f"Model: {sm_endpoint}") + logging.info(f"Model: {endpoint}, Type:{endpoint_type}") job_id = token_hex(16) job_name = f"test-{job_id}" @@ -399,7 +398,7 @@ def build_image_processing_request(sm_endpoint: str, image_url: str) -> Dict[str {"type": "S3", "bucket": result_bucket, "prefix": f"{job_name}/"}, {"type": "Kinesis", "stream": result_stream, "batchSize": 1000}, ], - "imageProcessor": {"name": sm_endpoint, "type": "SM_ENDPOINT"}, + "imageProcessor": {"name": endpoint, "type": endpoint_type}, "imageProcessorTileSize": OSMLConfig.TILE_SIZE, "imageProcessorTileOverlap": OSMLConfig.TILE_OVERLAP, "imageProcessorTileFormat": OSMLConfig.TILE_FORMAT, @@ -415,10 +414,10 @@ def count_features(image_id: str, ddb_client: boto3.resource) -> int: Counts the features present in the given DDB table associated with an image_id (hash_key) - :param image_id: str = the image_id the features are associated with - :param ddb_client: boto3.resource = the DDB client fixture + :param image_id: Image_id features are associated with + :param ddb_client: DDB client fixture - :return: int = count of the features found in the table for the image id + :return: Count of the features found in the table for the image id """ ddb_table = ddb_client.Table(OSMLConfig.DDB_FEATURES_TABLE) @@ -442,7 +441,7 @@ def validate_expected_feature_count(feature_count: int) -> None: """ Validate the number of features created match expected values - :param feature_count: int = the numer of features found for an image + :param feature_count: Number of features found for an image :return: None """ @@ -460,16 +459,21 @@ def validate_expected_feature_count(feature_count: int) -> None: def get_expected_image_feature_count(image: str) -> int: """ Get the number of expected features that are created for each image, - since these are random we want to just count the features in the table + since these are random. + We want to just count the features in the table, not match them against any known results. - :return: int = features we expect for a given image using the flood model + :return: Features we expect for a given image using the flood model """ # Set the expected if "large" in image: - return 99200 + return 112200 elif "tile" in image: return 2 + elif "sicd-capella-chip" in image or "sicd-umbra-chip" in image: + return 100 + elif "sicd-interferometric" in image: + return 15300 else: raise Exception(f"Could not determine expected features for image: {image}") @@ -478,9 +482,9 @@ def query_items(ddb_table: boto3.resource, hash_key: str, hash_value: str) -> Li """ Query the table for all items of a given hash_key and hash_value. - :param ddb_table: boto3.resource = the DynamoDB table you wish to query - :param hash_key: str = the hash key of the items you wish to scan - :param hash_value: str = the hash key value of the items you wish to scan + :param ddb_table: DynamoDB table you wish to query + :param hash_key: The Hash key associated with the items you wish to scan + :param hash_value: The Hash key value of the items you wish to scan :return: List[Dict[str, Any]] = the list of dictionary responses corresponding to the items returned """ @@ -512,10 +516,10 @@ def count_region_request_items(image_id: str, ddb_client: boto3.resource) -> int Counts the region request present in the given DDB table associated with an image_id (hash_key) - :param image_id: str the image_id the region request are associated with - :param ddb_client: boto3.resource = the DDB client fixture + :param image_id: Image_id region request is associated with + :param ddb_client: DDB client fixture - :return: int = count of the region request found in the table for the image id + :return: Count of the region request found in the table for the image id """ ddb_region_request_table = ddb_client.Table(OSMLConfig.DDB_REGION_REQUEST_TABLE) items = query_items(ddb_region_request_table, "image_id", image_id) @@ -531,9 +535,9 @@ def count_region_request_items(image_id: str, ddb_client: boto3.resource) -> int def validate_expected_region_request_items(region_request_count: int) -> None: """ - Validate the number of region request created match expected values + Validate the number of region requests created match-expected values - :param region_request_count: int = the numer of region request found for an image + :param region_request_count: Number of region requests found for an image :return: None """ @@ -550,19 +554,25 @@ def validate_expected_region_request_items(region_request_count: int) -> None: def get_expected_region_request_count(image: str) -> int: """ - Get the expected number of region request were created for each image + Get the expected number of region requests was created for each image - :return: int = region request we expect for a given image using the flood model + :return: Region request we expect for a given image using the flood model """ expected_count = 0 if "small" in image: - expected_count = 4 + expected_count = 1 elif "meta" in image: expected_count = 1 elif "large" in image: expected_count = 49 elif "tile" in image: expected_count = 1 + elif "sicd-capella-chip" in image or "sicd-umbra-chip" in image: + expected_count = 1 + elif "sicd-interferometric" in image: + expected_count = 8 + elif "wbid" in image: + expected_count = 1 # Check that we got a valid region request count if expected_count != 0: diff --git a/src/aws/osml/utils/osml_config.py b/src/aws/osml/utils/osml_config.py index 9430aa2..e23d862 100644 --- a/src/aws/osml/utils/osml_config.py +++ b/src/aws/osml/utils/osml_config.py @@ -26,6 +26,11 @@ class OSMLConfig: SM_FLOOD_MODEL: str = os.getenv("SM_FLOOD_MODEL", "flood") SM_AIRCRAFT_MODEL: str = os.getenv("SM_AIRCRAFT_MODEL", "aircraft") + # HTTP model config + HTTP_CENTERPOINT_MODEL_URL: str = os.getenv("HTTP_CENTER_POINT_MODEL_URL", None) + HTTP_CENTERPOINT_MODEL_ELB_NAME: str = os.getenv("HTTP_CENTER_POINT_MODEL_ELB_NAME", "test-http-model-endpoint") + HTTP_CENTERPOINT_MODEL_INFERENCE_PATH = os.getenv("HTTP_CENTERPOINT_MODEL_INFERENCE_PATH", "/invocations") + # bucket name prefixes S3_RESULTS_BUCKET: str = os.getenv("S3_RESULTS_BUCKET") S3_RESULTS_BUCKET_PREFIX: str = os.getenv("S3_RESULTS_BUCKET_PREFIX", "test-results") @@ -45,7 +50,7 @@ class OSMLConfig: TILE_FORMAT: str = os.environ.get("TILE_FORMAT", "GTIFF") TILE_COMPRESSION: str = os.environ.get("TILE_COMPRESSION", "NONE") TILE_SIZE: int = int(os.environ.get("TILE_SIZE", "512")) - TILE_OVERLAP: int = int(os.environ.get("TILE_OVERLAP", "32")) + TILE_OVERLAP: int = int(os.environ.get("TILE_OVERLAP", "128")) FEATURE_SELECTION_OPTIONS: str = os.environ.get( "FEATURE_SELECTION_OPTIONS", '{"algorithm": "NMS", "iou_threshold": 0.75, "skip_box_threshold": 0.0001, "sigma": .1}', @@ -62,5 +67,5 @@ class OSMLLoadTestConfig: S3_LOAD_TEST_RESULT_BUCKET: str = os.getenv("S3_LOAD_TEST_RESULT_BUCKET") # processing workflow - PERIODIC_SLEEP_SECS: str = os.getenv("PERIODIC_SLEEP_SECS", "60") # in seconds - PROCESSING_WINDOW_HRS: str = os.getenv("PROCESSING_WINDOW_HRS", "1") # in hours + PERIODIC_SLEEP_SECS: str = os.getenv("PERIODIC_SLEEP_SECS", "60") + PROCESSING_WINDOW_MIN: str = os.getenv("PROCESSING_WINDOW_MIN", "1") diff --git a/src/data/aircraft.tile.jpeg.geojson b/src/data/aircraft.tile.jpeg.geojson new file mode 100644 index 0000000..b7f9a88 --- /dev/null +++ b/src/data/aircraft.tile.jpeg.geojson @@ -0,0 +1 @@ +{"type": "FeatureCollection", "features": [{"type": "Feature", "id": "26a4813c-b770-459c-9daf-89f939e56264", "geometry": {"type": "Point", "coordinates": [0.0, 0.0]}, "properties": {"geom_imcoords": [[230.0, 315.0], [229.0, 316.0], [228.0, 316.0], [227.0, 316.0], [226.0, 316.0], [225.0, 316.0], [224.0, 316.0], [223.0, 316.0], [222.0, 317.0], [221.0, 317.0], [221.0, 318.0], [220.0, 319.0], [219.0, 320.0], [218.0, 321.0], [217.0, 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to specify which tests to run, e.g.: tox -- -s commands = - pytest --cov-config .coveragerc --cov aws.osml --cov-report term-missing {posargs} + pytest --cov-config .coveragerc --cov test --cov-report term-missing {posargs} {env:IGNORE_COVERAGE:} coverage report --rcfile .coveragerc {env:IGNORE_COVERAGE:} coverage html --rcfile .coveragerc