chore!: make mlflow as an optional dependency (#47)

Signed-off-by: Avik Basu <[email protected]>

.github/workflows/ci.yml

@@ -30,7 +30,7 @@ jobs:
  - name: Install dependencies
  run: |
  poetry env use ${{ matrix.python-version }}
- poetry install --no-root
+ poetry install --all-extras
 
  - name: Test with pytest
  run: make test

.github/workflows/coverage.yml

@@ -30,7 +30,7 @@ jobs:
  - name: Install dependencies
  run: |
  poetry env use ${{ matrix.python-version }}
- poetry install --no-root
+ poetry install --all-extras
 
  - name: Run Coverage
  run: |

.github/workflows/lint.yml

@@ -30,7 +30,7 @@ jobs:
  - name: Install dependencies
  run: |
  poetry env use ${{ matrix.python-version }}
- poetry install --no-root
+ poetry install
 
  - name: Black format check
  run: poetry run black --check .

.github/workflows/pypi.yml

@@ -30,7 +30,7 @@ jobs:
  - name: Install dependencies
  run: |
  poetry env use ${{ matrix.python-version }}
- poetry install --no-root
+ poetry install --all-extras
 
  - name: Build dist
  run: poetry build

Makefile

@@ -23,7 +23,7 @@ lint: format
 
 # install all dependencies
 setup:
- poetry install -v
+ poetry install --all-extras
 
 # test your application (tests in the tests/ directory)
 test:

README.md

@@ -35,10 +35,16 @@ the result further or drop it after a trigger request.
 
 ## Installation
 
+numalogic can be installed using pip.
 ```shell
 pip install numalogic
 ```
 
+If using mlflow for model registry, install using:
+```shell
+pip install numalogic[mlflow]
+```
+
 ### Build locally
 
 1. Install [Poetry](https://python-poetry.org/docs/):
@@ -53,13 +59,17 @@ pip install numalogic
  ```
  poetry install
  ```
+ If extra dependencies are needed:
+ ```
+ poetry install --all-extras
+ ```
 4. To run unit tests:
  ```
  make test
  ```
 5. To format code style using black:
  ```
- make format
+ make lint
  ```
 
 ## Usage
@@ -133,7 +143,7 @@ Now that the model is trained, let's save it. Numalogic has built in support
 for Mlflow's tracking and logging system.
 
 Let's first start the [mlflow server on localhost](https://www.mlflow.org/docs/latest/tracking.html#scenario-1-mlflow-on-localhost),
-which has already been installed via `poetry` dependency:
+which has already been installed optionally via `poetry` dependency:
 ```shell
 mlflow server \
  --default-artifact-root {directory}/mlruns --serve-artifacts \

numalogic/registry/__init__.py

@@ -1,5 +1,8 @@
 from numalogic.registry.artifact import ArtifactManager
-from numalogic.registry.mlflow_registry import MLflowRegistrar
 
-
-__all__ = ["ArtifactManager", "MLflowRegistrar"]
+try:
+ from numalogic.registry.mlflow_registry import MLflowRegistrar
+except ImportError:
+ __all__ = ["ArtifactManager"]
+else:
+ __all__ = ["ArtifactManager", "MLflowRegistrar"]

numalogic/tests/registry/_mlflow_utils.py

@@ -110,6 +110,7 @@ def mock_log_model_pytorch(*_, **__):
  signature_dict=None,
  utc_time_created="2022-05-23 22:35:59.557372",
  mlflow_version="1.26.0",
+ signature=None,
  )
 
 
@@ -133,6 +134,7 @@ def mock_log_model_sklearn(*_, **__):
  signature_dict=None,
  utc_time_created="2022-05-23 22:35:59.557372",
  mlflow_version="1.26.0",
+ signature=None,
  )
 
 

numalogic/tools/types.py

@@ -1,15 +1,12 @@
 from typing import Union, Dict, NewType, TypeVar, Sequence, Optional
 
-from mlflow.entities.model_registry import ModelVersion
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.pipeline import Pipeline
 from torch import nn
 
 Artifact = NewType("Artifact", Union[nn.Module, BaseEstimator, TransformerMixin, Pipeline])
 ArtifactDict = NewType(
  "ArtifactDict",
- Optional[
- Dict[str, Union[Sequence[Artifact], Dict[str, Artifact], Artifact, None, ModelVersion]]
- ],
+ Optional[Dict[str, Union[Sequence[Artifact], Dict[str, Artifact], Artifact, None]]],
 )
 AutoencoderModel = TypeVar("AutoencoderModel", bound="TorchAE")