AI Notebooks - Tutorial - Track your ML models with MLflow inside notebooks

Objective

The aim of this tutorial is to show you how to integrate MLflow inside AI Notebooks in order to track and compare your Machine Learning models.

MLflow is an open-source platform for Machine Learning workflows management. You can use this tool for ML model tracking, versioning but also storage and deployment.

• How to use MLflow inside AI Notebooks?

For this purpose, we will compare image classification models: EfficientNets.

In this context, several EfficientNet models will be trained on the same dataset: (stanford_dogs). The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world.

The different models will be tracked and compared thanks to MLflow in order to be compare their performance:

• EfficientNet-b0
• EfficientNet-b1
• EfficientNet-b2
• EfficientNet-b3
• EfficientNet-b4
• EfficientNet-b5
• EfficientNet-b6
• EfficientNet-b7

Requirements

• Access to the OVHcloud Control Panel
• An AI Notebooks project created inside a Public Cloud project in your OVHcloud account
• A user for AI Notebooks

Instructions

You can launch the notebook from the OVHcloud Control Panel or via the ovhai CLI.

Launching a Jupyter notebook with "TensorFlow" via UI (Control Panel)

To launch your notebook from the OVHcloud Control Panel, refer to the following steps.

Code editor

Choose the Jupyterlab code editor.

Framework

In this tutorial, the TensorFlow framework is used. You can choose the 2.12 version: tf2.12-py311-cudaDevel11.8.

Resources

Using GPUs is recommended to train the EfficientNet models.

Launching a Jupyter notebook with "TensorFlow" via CLI

If you do not use our CLI yet, follow this guide to install it.

To launch your notebook with the OVHcloud AI CLI, choose the jupyterlab editor and the tensorflow framework.

To access the different versions of tensorflow available, run the following command:

ovhai capabilities framework get tensorflow -o yaml

You will also need to choose the number of GPUs to use in your notebook, using <nb-gpus>.

To launch your notebook, run the following command:

ovhai notebook run tensorflow jupyterlab \
		--name <notebook-name> \
		--framework-version tf2.12-py311-cudaDevel11.8 \
		--gpu <nb-gpus>

You can then reach your notebook's URL once the notebook is running.

Accessing the notebooks

Once our AI examples repository has been cloned in your environment, find the fine-tuning notebook tutorial by following this path: ai-training-examples > notebooks > go-further > mlflow > notebook-tutorial-mlflow-integration.ipynb.

A preview of this notebook can be found on GitHub here.

Go further

There are many other methods to track your ML model. Check our other tutorials to learn how to:

• Use TensorBoard inside notebook

• Weights & Biases integration

If you need training or technical assistance to implement our solutions, contact your sales representative or click on this link to get a quote and ask our Professional Services experts for a custom analysis of your project.

Feedback

Please send us your questions, feedback and suggestions to improve the service:

• On the OVHcloud Discord server