---
title: "AI Notebooks - Tutorial - Track your ML models with MLflow inside notebooks"
description: "Learn how to integrate MLflow inside AI Notebooks to track and compare your Machine Learning models."
url: https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-notebooks-mlflow
lang: en
---
> For AI agents: the complete documentation index is available at https://docs.us.ovhcloud.com/en/llms.txt, the full documentation bundle is available at https://docs.us.ovhcloud.com/en/llms-full.txt.

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

**Learn how to integrate [MLflow](https://mlflow.org/) inside AI Notebooks to track and compare your Machine Learning models.**

**MLflow** is an open-source platform for _Machine Learning workflow management_. You can use this tool for ML model tracking and versioning but also storage and deployment.

- **How to use MLflow inside AI Notebooks?**

For this purpose, we will compare **image classification** models: [EfficientNets](https://paperswithcode.com/method/efficientnet).

**Note:**

EfficientNet models are pre-trained on the [ImageNet](https://www.image-net.org/) dataset.

In this context, several EfficientNet models will be trained on the same dataset: ([stanford\_dogs](https://www.tensorflow.org/datasets/catalog/stanford_dogs#:~:text=The%20Stanford%20Dogs%20dataset%20contains,training%20and%208580%20for%20testing.)). The **Stanford Dogs** dataset contains images of 120 breeds of dogs from around the world.

The different models will be tracked and compared based on their performance thanks to **MLflow**:

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

 

## Requirements

- Access to the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/)
- An AI Notebooks project created inside a [Public Cloud project](https://us.ovhcloud.com/public-cloud/) in your OVHcloud account
- A user for AI Notebooks

 

## Instructions

You can launch the notebook from the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/) or via the [ovhai CLI](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/cli-run-notebook.md).

 

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

To launch your notebook from the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/), 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.

**Note:**

Here, using `1 GPU` is sufficient.

 

### Launching a Jupyter notebook with "TensorFlow" via CLI

_If you have not used our CLI yet, follow [this guide](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/cli-install-client.md) 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:

```console
ovhai capabilities framework get tensorflow -o yaml
```

**Note:**

Here, you can use the version `2.12` of TensorFlow (`tf2.12-py311-cudaDevel11.8`).

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:

```console
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](https://github.com/ovh/ai-training-examples/) 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](https://github.com/ovh/ai-training-examples/blob/main/notebooks/go-further/mlflow/notebook-tutorial-mlflow-integration.ipynb).

 

## Go further

Browse all the [AI & Machine Learning guides](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/landing-page-ai-machine-learning.md).

Browse the rest of the [OVHcloud US documentation](https://docs.us.ovhcloud.com/en/index.md) for guides on our other products and services.

Learn more about [AI & Machine Learning](https://us.ovhcloud.com/public-cloud/ai-machine-learning/) on the OVHcloud US website.

If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://us.ovhcloud.com/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.