Python Client for MLflow

Python client for MLflow REST API.

Features:

• Minimal dependencies
• Unlike MLflow Tracking client all REST API methods and params are exposed to user.
• MLflow URL is passed via constructor argument instead of env variable, so multiple client instances could be created in the same Python interpreter.
• Basic and Bearer auth are supported (via constructor args too).
• All class fields are validated with pydantic.
• 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.
• Supported MLflow versions: from 1.17.0 to 1.23.0. It is possible to use client with older MLflow versions (e.g. 1.10.0), but this is not guaranteed.
• Only Python 3.7+ is supported. Python 3.6 and lower already reached end of life.

Documentation

See https://mlflow-rest-client.readthedocs.io/

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 release

Development 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

Development

Clone repo:

git clone git@github.com: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

Usage

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.