Python client for MLflow REST API
Python client for MLflow REST API.
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.
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.
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 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 version
git clone firstname.lastname@example.org:MobileTeleSystems/mlflow-rest-client.git cd mlflow-rest-client
Install dependencies for development:
pip install -r requirements-dev.txt
Install pre-commit hooks:
pre-commit install pre-commit autoupdate pre-commit install-hooks
Test pre-commit hooks run:
pre-commit run --all-files -v
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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Hashes for mlflow_rest_client-2.0.0-py3-none-any.whl