sagemaker-mlflow-skinny 0.1.0.dev3

Skinny Version of AWS Plugin for MLflow with SageMaker

SageMaker MLflow Plugin

What does this Plugin do?

This plugin generates Signature V4 headers in each outgoing request to the Amazon SageMaker with MLflow capability, determines the URL of capability to connect to tracking servers, and registers models to the SageMaker Model Registry. It generates a token with the SigV4 Algorithm that the service will use to conduct Authentication and Authorization using AWS IAM.

Installation

To install this plugin, run the following command inside the directory:

pip install .

Eventually when the plugin gets distributed, it will be installed with:

pip install sagemaker-mlflow-skinny

Running this will install the Auth Plugin and mlflow.

To install a specific mlflow version

pip install .
pip install mlflow==2.13

Development details

setup.py

setup.py Contains the primary entry points for the sdk. install_requires Installs mlflow. entry_points Contains the entry points for the sdk. See https://mlflow.org/docs/latest/plugins.html#defining-a-plugin for more details.

Running tests

Setup

To run tests using tox, run:

pip install tox

Installing tox will enable users to run multi-environment tests. On the other hand, if running individual tests in a single environment, feel free to continue to use pytest instead.

Running format checks

tox -e flake8,black-check,typing,twine

Formatting code to comply with format checks

tox -e black-format

Running unit tests

tox --skip-env "black.*|flake8|typing|twine" -- test/unit

Running integration tests

tox --skip-env "black.*|flake8|typing|twine" -- test/integration

Available test environments by default

tox.ini contains support for py39, py310, py311, with mlflow 2.11.* and 2.12.*. To add test environments on tox for additional versions of python or mlflow, modify the environment configs in envlist, as well as deps and depends in [testenv].