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MLflow Tensorflow Integration

Introduction​

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

MLflow has built-in support (we call it MLflow Tensorflow flavor) for Tensorflow workflow, at a high level in MLflow we provide a set of APIs for:

  • Simplified Experiment Tracking: Log parameters, metrics, and models during model training.
  • Experiments Management: Store your Tensorflow experiments in MLflow server, and you can view and share them from MLflow UI.
  • Effortless Deployment: Deploy Tensorflow models with simple API calls, catering to a variety of production environments.

5 Minute Quick Start with the MLflow Tensorflow Flavor​

Quickstart with MLflow Tensorflow Flavor

Learn how to leverage MLflow for tracking Tensorflow experiments and models.

Developer Guide of Tensorflow with MLflow​

To learn more about the nuances of the tensorflow flavor in MLflow, please read the developer guide. It will walk you through the following topics:

View the Developer Guide

  • Autologging Tensorflow Experiments with MLflow: How to left MLflow autolog Tensorflow experiments, and what metrics are logged.
  • Control MLflow Logging with Keras Callback: For people who don't like autologging, we offer an option to log experiments to MLflow using a custom Keras callback.
  • Log Your Tensorflow Models with MLflow: How to log your Tensorflow models with MLflow and how to load them back for inference.