--- title: "AI Notebooks - Tutorial - Track your ML models with MLflow inside notebooks" description: "Learn how to use MLflow for model tracking and versioning" url: https://docs.ovhcloud.com/en/guides/public-cloud/ai-machine-learning/ai-notebooks-mlflow lang: en lastUpdated: 2023-10-18 --- # 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**](https://mlflow.org/) 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?** ![MLflowOverview](/images/public-cloud/ai-machine-learning/notebook-tuto-16-mlflow/mlflow-inside-notebook.png) For this purpose, we will compare **image classification** models: [EfficientNets](https://paperswithcode.com/method/efficientnet). :::info EfficientNet models are pre-trained on [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 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 - An AI Notebooks project created inside a [Public Cloud project](https://www.ovhcloud.com/en-gb/public-cloud/) in your OVHcloud account - A user for AI Notebooks *** ### OVHcloud Control Panel Access - **Direct link:** Public Cloud Projects - **Navigation path:** Public Cloud > Select your project *** ## Instructions You can launch the notebook from the OVHcloud Control Panel or via the [ovhai CLI](/en/guides/public-cloud/ai-machine-learning/ai-cli-run-notebook.md) . ### 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. :::info Here, using `1 GPU` is sufficient. ::: ### Launching a Jupyter notebook with "TensorFlow" via CLI _If you do not use our CLI yet, follow [this guide](/en/guides/public-cloud/ai-machine-learning/ai-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 ``` :::info 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 ``. To launch your notebook, run the following command: ```console ovhai notebook run tensorflow jupyterlab \ --name \ --framework-version tf2.12-py311-cudaDevel11.8 \ --gpu ``` 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 There are many other methods to track your ML model. Check our other tutorials to learn how to: - [Use TensorBoard inside notebook](/en/guides/public-cloud/ai-machine-learning/ai-notebooks-tensorboard-embedded.md) - [Weights & Biases integration](/en/guides/public-cloud/ai-machine-learning/ai-notebooks-weights-biases.md) If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/en-gb/professional-services/) 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](https://discord.gg/ovhcloud)