Python client for MLflow REST API
Project description
Python client for MLflow REST API.
Features:
Unlike MLflow Tracking client all REST API methods are exposed to user.
All class fields are validated with pydantic.
Basic and Bearer auth is supported.
All methods and classes are documented.
Limitations:
There is no integration with ML frameworks and libraries. You should use official MLflow client instead.
There is no integration with S3 or other artifact storage type. You should access it directly with boto3 or other client.
Only Python 3.7+ is supported. Python 3.6 and lower already reached end of life.
Documentation
Contribution guide
See CONTRIBUTING.rst
Security
See SECURITY.rst
Installation
Stable release
Stable version is released on every tag to master branch. Please use stable releases on production environment. Version example: 2.0.0
pip install mlflow-rest-client==2.0.0 # exact version
pip install mlflow-rest-client # latest releaseDevelopment release
Development version is released on every commit to dev branch. You can use them to test some new features before official release. Version example: 2.0.0.dev5
pip install mlflow-rest-client==2.0.0.dev5 # exact dev version
pip install --pre mlflow-rest-client # latest dev versionDevelopment
Clone repo:
git clone git@github.com:MobileTeleSystems/mlflow-rest-client.git
cd mlflow-rest-clientInstall dependencies for development:
pip install -r requirements-dev.txtInstall pre-commit hooks:
pre-commit install
pre-commit autoupdate
pre-commit install-hooksTest pre-commit hooks run:
pre-commit run --all-files -vUsage
Make sure you have an MLflow Tracking Server running.
from mlflow_rest_client import MLflowRESTClient
client = MLflowRESTClient("https://mlflow.domain", ignore_ssl_check=True)
experiment = client.get_or_create_experiment("experiment_name")
run = client.create_run(experiment.id)See sample.py for more examples.
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