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MLflow is an open-source platform for monitoring and managing machine learning experiments.
- Prerequisites
Make sure you have installed the MLflow library:
pip install mlflow
- Serveur MLflow
Launch your MLflow server. You can run it with the following command:
mlflow server --backend-store-uri mlflow_server
This will start a local server at http://127.0.0.1:5000 by default and save all mlflow logs to the mlflow_server directory at the location of the command execution.
To cut all instances of MLflow, you can run this command:
ps aux | grep 'mlflow' | grep -v 'grep' | awk '{print $2}' | xargs kill -9
- MLflow parameters
Set your server address in the MLFLOW_TRACKING_URI environment variable. If the address is not provided, a warning will be raised and the run will not be recorded.
Set the name of your experiment in the MLFLOW_EXPERIMENT_NAME environment variable. If no name is provided, the project name (--project of train.py) will be set by default.
Define the name of your run in the MLFLOW_RUN environment variable. If no name is provided, the run name (--name of train.py) will be set by default.
After that, your training sessions will be saved in your MLflow server!