Release 0.3.0

.github/workflows/check-test-release.yml

@@ -7,6 +7,7 @@ on:
 
 env:
   MLEM_TESTS: "true"
+  MLEM_DEBUG: "true"
 
 jobs:
   authorize:
@@ -58,7 +59,7 @@ jobs:
         # no HDF5 support installed for tables
         - os: windows-latest
           python: "3.9"
-      fail-fast: true
+      fail-fast: false
     steps:
     - uses: actions/checkout@v3
       with:
@@ -67,6 +68,10 @@ jobs:
     - uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python }}
+    - uses: conda-incubator/setup-miniconda@v2
+      with:
+        python-version: ${{ matrix.python }}
+        activate-environment: true
     - name: get pip cache dir
       id: pip-cache-dir
       run: |
@@ -92,6 +97,9 @@ jobs:
         pip install pre-commit .[tests]
     - run: pre-commit run pylint -a -v --show-diff-on-failure
       if: matrix.python != '3.7'
+    - name: Start minikube
+      if: matrix.os == 'ubuntu-latest' && matrix.python == '3.9'
+      uses: medyagh/setup-minikube@master
     - name: Run tests
       timeout-minutes: 40
       run: pytest

.pre-commit-config.yaml

@@ -1,8 +1,8 @@
 default_language_version:
   python: python3
 repos:
-  - repo: 'https://github.com/pre-commit/pre-commit-hooks'
-    rev: v4.0.1
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
     hooks:
       - id: check-added-large-files
       - id: check-case-conflict
@@ -17,8 +17,8 @@ repos:
       - id: mixed-line-ending
       - id: sort-simple-yaml
       - id: trailing-whitespace
-  - repo: 'https://github.com/pycqa/flake8'
-    rev: 4.0.1
+  - repo: https://github.com/pycqa/flake8
+    rev: 5.0.4
     hooks:
       - id: flake8
         args:
@@ -28,16 +28,16 @@ repos:
           - flake8-comprehensions
           - flake8-debugger
           - flake8-string-format
-  - repo: 'https://github.com/psf/black'
-    rev: 22.3.0
+  - repo: https://github.com/psf/black
+    rev: 22.10.0
     hooks:
       - id: black
   - repo: 'https://github.com/PyCQA/isort'
     rev: 5.10.1
     hooks:
       - id: isort
-  - repo: 'https://github.com/pre-commit/mirrors-mypy'
-    rev: v0.942
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v0.982
     hooks:
       - id: mypy
         additional_dependencies:
@@ -54,7 +54,11 @@ repos:
         entry: pylint -v
         language: system
         types: [ python ]
-  # - repo: https://github.com/PyCQA/bandit
-  #   rev: '1.7.0'
-  #   hooks:
-  #     - id: bandit
+  - repo: https://github.com/PyCQA/bandit
+    rev: 1.7.4
+    hooks:
+      - id: bandit
+        exclude: tests/
+        args:
+          - -iii  # high level
+          - -lll  # high confidence

.pylintrc

@@ -170,7 +170,8 @@ disable=print-statement,
         redefined-builtin, # TODO: https://github.com/iterative/mlem/issues/60
         no-self-use, # TODO: https://github.com/iterative/mlem/issues/60 maybe leave it
         import-outside-toplevel,
-        wrong-import-order # handeled by isort
+        wrong-import-order, # handeled by isort
+        cannot-enumerate-pytest-fixtures # TODO: https://github.com/iterative/mlem/issues/60
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option
@@ -369,7 +370,7 @@ indent-string='    '
 max-line-length=100
 
 # Maximum number of lines in a module.
-max-module-lines=1000
+max-module-lines=2000
 
 # Allow the body of a class to be on the same line as the declaration if body
 # contains single statement.
@@ -389,7 +390,7 @@ ignore-comments=yes
 ignore-docstrings=yes
 
 # Ignore imports when computing similarities.
-ignore-imports=no
+ignore-imports=yes
 
 # Ignore function signatures when computing similarities.
 ignore-signatures=no

README.md

@@ -45,7 +45,7 @@ The main reason to use MLEM instead of other tools is to adopt a **GitOps approa
 
 ## Usage
 
-This a quick walkthrough showcasing deployment and export functionality of MLEM.
+This a quick walkthrough showcasing deployment functionality of MLEM.
 
 Please read [Get Started guide](https://mlem.ai/doc/get-started) for a full version.
 
@@ -81,18 +81,34 @@ def main():
 
     save(
         rf,
-        "rf",
+        "models/rf",
         sample_data=data,
     )
 
 if __name__ == "__main__":
     main()
 ```
 
+### Productionization
+
+We'll show how to deploy your model with MLEM below, but let's briefly mention all the
+scenarios that MLEM enables with a couple lines of code:
+
+- **[Apply model](/doc/get-started/applying)** - load model in Python or get
+  prediction in command line.
+- **[Serve model](/doc/get-started/serving)** - create a service from your model
+  for online serving.
+- **[Build model](/doc/get-started/building)** - export model into Python
+  packages, Docker images, etc.
+- **[Deploy model](/doc/get-started/deploying)** - deploy your model to Heroku,
+  Sagemaker, Kubernetes, etc.
+
+### Codification
+
 Check out what we have:
 
 ```shell
-$ ls
+$ ls models/
 rf
 rf.mlem
 $ cat rf.mlem
@@ -153,51 +169,6 @@ model_type:
         - null
         - 3
         type: ndarray
-    sklearn_predict:
-      args:
-      - name: X
-        type_:
-          columns:
-          - sepal length (cm)
-          - sepal width (cm)
-          - petal length (cm)
-          - petal width (cm)
-          dtypes:
-          - float64
-          - float64
-          - float64
-          - float64
-          index_cols: []
-          type: dataframe
-      name: predict
-      returns:
-        dtype: int64
-        shape:
-        - null
-        type: ndarray
-    sklearn_predict_proba:
-      args:
-      - name: X
-        type_:
-          columns:
-          - sepal length (cm)
-          - sepal width (cm)
-          - petal length (cm)
-          - petal width (cm)
-          dtypes:
-          - float64
-          - float64
-          - float64
-          - float64
-          index_cols: []
-          type: dataframe
-      name: predict_proba
-      returns:
-        dtype: float64
-        shape:
-        - null
-        - 3
-        type: ndarray
   type: sklearn
 object_type: model
 requirements:
@@ -213,54 +184,31 @@ requirements:
 ### Deploying the model
 
 If you want to follow this Quick Start, you'll need to sign up on https://heroku.com,
-create an API_KEY and populate `HEROKU_API_KEY` env var.
-
-First, create an environment to deploy your model:
-
-```shell
-$ mlem declare env heroku staging
-💾 Saving env to staging.mlem
-```
+create an API_KEY and populate `HEROKU_API_KEY` env var (or run `heroku login` in command line).
+Besides, you'll need to run `heroku container:login`. This will log you in to Heroku
+container registry.
 
 Now we can [deploy the model with `mlem deploy`](https://mlem.ai/doc/get-started/deploying)
 (you need to use different `app_name`, since it's going to be published on https://herokuapp.com):
 
 ```shell
-$ mlem deployment run mydeploy -m rf -t staging -c app_name=mlem-quick-start
-⏳️ Loading deployment from .mlem/deployment/myservice.mlem
-🔗 Loading link to .mlem/env/staging.mlem
-🔗 Loading link to .mlem/model/rf.mlem
-💾 Updating deployment at .mlem/deployment/myservice.mlem
-🏛 Creating Heroku App example-mlem-get-started
-💾 Updating deployment at .mlem/deployment/myservice.mlem
+$ mlem deployment run heroku app.mlem \
+  --model models/rf \
+  --app_name example-mlem-get-started-app
+⏳️ Loading model from models/rf.mlem
+⏳️ Loading deployment from app.mlem
 🛠 Creating docker image for heroku
+  🛠 Building MLEM wheel file...
   💼 Adding model files...
   🛠 Generating dockerfile...
   💼 Adding sources...
   💼 Generating requirements file...
-  🛠 Building docker image registry.heroku.com/example-mlem-get-started/web...
-  ✅  Built docker image registry.heroku.com/example-mlem-get-started/web
-  🔼 Pushed image registry.heroku.com/example-mlem-get-started/web to remote registry at host registry.heroku.com
-💾 Updating deployment at .mlem/deployment/myservice.mlem
-🛠 Releasing app my-mlem-service formation
-💾 Updating deployment at .mlem/deployment/myservice.mlem
-✅  Service example-mlem-get-started is up. You can check it out at https://mlem-quick-start.herokuapp.com/
-```
-
-### Exporting the model
-
-You could easily [export the model to a different format using `mlem build`](https://mlem.ai/doc/get-started/building):
-
-```
-$ mlem build rf docker -c server.type=fastapi -c image.name=sklearn-model
-⏳️ Loading model from rf.mlem
-🛠 Building MLEM wheel file...
-💼 Adding model files...
-🛠 Generating dockerfile...
-💼 Adding sources...
-💼 Generating requirements file...
-🛠 Building docker image sklearn-model:latest...
-✅  Built docker image sklearn-model:latest
+  🛠 Building docker image registry.heroku.com/example-mlem-get-started-app/web...
+  ✅  Built docker image registry.heroku.com/example-mlem-get-started-app/web
+  🔼 Pushing image registry.heroku.com/example-mlem-get-started-app/web to registry.heroku.com
+  ✅  Pushed image registry.heroku.com/example-mlem-get-started-app/web to registry.heroku.com
+🛠 Releasing app example-mlem-get-started-app formation
+✅  Service example-mlem-get-started-app is up. You can check it out at https://example-mlem-get-started-app.herokuapp.com/
 ```
 
 ## Contributing

mlem/api/__init__.py

@@ -11,15 +11,13 @@
     import_object,
     init,
     link,
-    ls,
     serve,
 )
 
 __all__ = [
     "save",
     "load",
     "load_meta",
-    "ls",
     "clone",
     "init",
     "link",