---
title: "AI Training - Start a job with a notebook Docker image"
description: "Learn how to start a simple interactive notebook leveraging GPUs over AI Training service."
url: https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-start-use-notebooks
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 Training - Start a job with a notebook Docker image

**Learn how to start a simple interactive notebook leveraging GPUs over AI Training service.**

 

## Requirements

- Access to the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/)
- An **AI Training project** created inside a **public cloud** project
- A [user for AI Training](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/manage-users-roles.md)

 

## Instructions

### Step 1 - Begin as a classic job submission

Follow the same steps as a classic job submission described [here](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-submit-job.md) until you reach [the Docker image step](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-submit-job.md#step-5---providing-a-docker-image).

 

### Step 2 - Select the notebook corresponding to your needs

A `job` is basically a Docker container that is run within the OVHcloud infrastructure.

Notebooks are `daemon jobs`, meaning they will run indefinitely until the user requests an interruption.

AI Training offers several notebook images with different configurations. You can choose the configuration that best suits your needs.

Currently, the following configurations are available:

- **PyTorch**: An OVHcloud preset image including JupyterLab notebook, Visual Studio Code IDE, and `pytorch` libraries
- **Tensorflow 2**: An OVHcloud preset image containing JupyterLab notebook, Visual Studio Code IDE, and `tensorflow 2` libraries
- **Hugging Face Transformers**: An OVHcloud preset image containing JupyterLab notebook, Visual Studio Code IDEand `hugging face` libraries
- **MXNet**: An OVHcloud preset image containing JupyterLab notebook, Visual Studio Code IDE, and `mxnet` libraries
- **Fast.ai**: An OVHcloud preset image containing JupyterLab notebook, Visual Studio Code IDE, and `fast.ai` libraries
- **auto gluon**: An OVHcloud preset image containing JupyterLab notebook, Visual Studio Code IDE, and `AutoGluon` + `mxnet` libraries

 

### Step 3 - Continue as a classic job submission

Continue to follow the same steps as a classic job submission described [here](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-submit-job.md) until you reach [Step 10 - Consulting your job](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-submit-job.md#step-10---consulting-your-job).

**Warning:**

**NOTE:** If you want to be able to save your notebook files on your object storage, we strongly advise that you plug a **read** and **write** volume on your job before submitting. That volume will be synchronized with your object storage at the end of the job.

 

### Step 4 - Access notebook URL

Once your job is `Running`, in the job **Access** panel, you should see the `Container access` link. Click on it, and you will be redirected to your job URL.

 

### Step 5 - Log in as an AI Training user

If you are not authenticated as an AI Training user, you should see a screen asking for your username and password.

**Note:**

If you have never created a user for AI Training yet, you can follow the instructions [here](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/manage-users-roles.md).

Fill in the fields and click `Connect`.

 

### Step 6 - Use your notebook

In most provided preset images, you can choose which editor you prefer between JupyterLab and VisualStudio code.

Select the one that you want to use, and you will be redirected to the corresponding editor.

By default, the home directory of your job is located under `/workspace`. It means that you will have **read** and **write** access to that directory as well as your **read** and **write** mounted volumes.

**Warning:**

**NOTE:** If you are missing a library or a configuration, you can add it directly in the command line of the notebook's **console** as long as you don't need privileged access (root access). Example: `pip install <...>`

For installing specific libraries that require privileged access, you will have to build your own notebook image and use it as a **custom image** at [Step 2](#step-2-select-the-notebook-corresponding-to-your-needs) instead of a **preset image**. More information about creating your own Docker image can be found [here](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-build-use-custom-image.md).

**Note:**

If you open a **console** tab in your notebook and type `nvidia-smi`, you will see the available GPUs that you can use on your notebook.

From the Jupyter Notebook interface, the `!nvidia-smi` command will retrieve the same information.

 

### Step 7 - Stop your notebook

Once you are done working with your notebook, don't forget to stop it.

You can do it by selecting _more options_ `...` and then `Stop`.

Then, in the pop-up window, click `Stop`.

After some time, your job should go into an `Interrupted` state, meaning that the job has been stopped.

**Note:**

Before going into the `Interrupted` state, your job may run through the `Finalizing` state. During this phase, all data inside `read & write volumes` are saved inside their linked containers in your object storage.

 

## 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.