---
title: "AI Notebooks - Getting started with Public Cloud AI"
description: "Create, configure, and manage Jupyter and VSCode Notebooks on OVHcloud AI Notebooks via the Control Panel, CLI, API, and Python SDK"
url: https://docs.ovhcloud.com/pl/guides/public-cloud/ai-machine-learning/ai-notebooks-definition
lang: pl
lastUpdated: 2026-01-28
---
# AI Notebooks - Getting started with Public Cloud AI
## Introduction
A **Notebook** is a document that integrates code, rich text elements, and multimedia, making it a convenient tool for data analysis, visualization, and Machine Learning tasks.
**OVHcloud AI Notebooks** is our managed solution for Jupyter and VSCode Notebooks. You can easily launch Notebooks with the required CPU and GPU resources while enjoying benefits like pre-installed dependencies and AI frameworks (e.g. TensorFlow, PyTorch, Hugging Face, Scikit-learn, ...), secure user access, and simplified data management.
The advantage compared to doing your own setup is that everything is already installed for you, and that you pay only for your AI Notebooks while they are running.
## Objective
This guide demonstrates how to create, configure, access, stop, restart and delete AI Notebooks from multiple interfaces:
- The **OVHcloud Control Panel (UI)** is user-friendly and ideal for beginners.
- The **ovhai** command-line interface (CLI)
- The **AI API**
- The **ovhai** Python SDK
Each method offers its distinct advantages, depending on your expertise and preferred workflow.
## Requirements
- A [Public Cloud project](https://www.ovhcloud.com/fr/public-cloud/) in your OVHcloud account
- A Public Cloud user with the **Administrator** or **AI Training Operator & Objectstore Operator** roles, see [here](/pl/guides/public-cloud/ai-machine-learning/ai-users.md) for more information
***
### Accès à l'espace client OVHcloud
- **Lien direct :** Projets Public Cloud
- **Pour accéder à vos services :** Public Cloud > Sélectionnez votre projet
***
## Instructions
### OVHcloud AI Solutions Authorization Process
**Using the Control Panel (UI)**
If you are using the UI, this step is not required. You can directly go to the next step which is the [Notebook creation](#launch-your-first-ai-notebook).
**Using CLI / AI API / SDK**
If you already have created an AI Notebook using the Control Panel (UI) on this Public Cloud project, you can directly go to the next step [Launch your first AI Notebook](#launch-your-first-ai-notebook).
However, if this is the first time you are using the AI Products on your Public Cloud project, you will need to complete an authorization process before using the AI API, ovhai CLI, or Python SDK. To do so, please follow these steps:
Click this link to access your Public Cloud project, then click on the AI & Machine Learning category in the left-hand vertical menu to access the AI Notebooks section.
From there, click the Create a Notebook button. The authorization process will take place silently in the background.
Once you have clicked this button, you can leave this page and continue to follow this documentation to launch your AI Notebook using the CLI, API, or SDK as desired. Indeed, you do not need to complete the entire Notebook creation process in the Control Panel. Clicking the Create a Notebook button is sufficient.
### Launch your first AI Notebook
To create an AI Notebook, follow these steps:
**Using the Control Panel (UI)**
The manager is the more intuitive way to launch an AI Notebook. To get started, follow these steps:
Click this link to access your Public Cloud project, then click on the AI & Machine Learning category in the left-hand vertical menu to access the AI Notebooks section.
Once there, you can create your first Notebook by clicking the Create a Notebook button.
Once you have clicked this button, you will be prompted to provide some information to specify the configuration of your AI Notebook (code editor, pre-installed framework, compute resources, etc.).
**1. Notebook name**
Give a name to your AI Notebook. This will make it easier to manage when you have multiple AI Notebooks created.
**2. Notebook location**
Next, select a location.
**3. Compute Resources**
Specify and adjust the type and amount of computing resources (CPU / GPU) for your AI Notebook. Use the + and - buttons to increase or decrease the number of CPUs and GPUs, depending on your needs.
**4. Pre-configured Frameworks**
Then, choose the AI framework you want to use, which will be pre-installed and ready to use when your AI Notebook is launched. A wide range of Machine Learning frameworks are available, in different versions. Select the version that suits your needs. This guide will use _PyTorch_ as an example.
**5. Live-code editor**
Choose the code editor you want.
In this example, we use _JupyterLab_, but you have the option to choose _Visual Studio Code_ as well. Both of them have their own pros and cons. Jupyter Notebook is generally considered easier to use for beginners.
**6. Privacy Settings**
Next, select your privacy settings.
:::warning
_Public access_ will expose your data and code to anyone getting the AI Notebook link. Be careful and don't use it with sensitive data. On the other hand, _Restricted access_ will ask a user and password combination or an AI token to access the Notebook content, ensuring a secure environment.
:::
**7. Notebook lifecycle**
:::warning
Our main motivation is to keep the platform up-to-date in terms of security patches and new feature alignment. To achieve this, we need to update and restart hosts from time to time, which requires them to be free from any customer workload.
:::
By default, your AI notebook will automatically shut down after 7 consecutive days of being in a RUNNING state. Rest assured that all your settings and data will be preserved. You have the option to enable Automatic Restart, which will automatically restart your notebook every 7 days, ensuring minimal disruption to your workflow. Alternatively, you can also contact [our support](https://help.ovhcloud.com/csm?id=csm_get_help) to extend this automatic restart period from 7 to 28 days.
**8. Advanced configuration**
_This step is optional._
By default, your AI Notebook comes with **ephemeral storage** (local storage). But in this step, you can also link Object Storage containers and Git repositories to your AI Notebook, to easily access your remote data.
If you want to learn more about configuring containers and Git repositories in the Notebook, you can refer to this [documentation](/pl/guides/public-cloud/ai-machine-learning/ai-notebooks-manage-data-ui.md). For now, we will launch a classic Notebook without any external volumes added to it.
After that, you have the option to set labels. This allows to specify one of your AI tokens and apply it to this new AI Notebook. This AI token can then be shared with anyone who should have access to the Notebook, allowing them to access it without requiring your own Public Cloud project credentials. This is useful if you want to provide access to the AI Notebook outside your Public Cloud project. More information [here](/pl/guides/public-cloud/ai-machine-learning/ai-cli-app-token.md).
Finally, **SSH public keys** allow you to access your Notebook remotely.
**9. Launch the AI Notebook**
At the end of the process, review your settings and click the Order button to confirm and launch the creation of your Notebook.
:::info
Note at the bottom of the screen the equivalent _ovhai_ CLI command. This command allows you to run the exact same Notebook using the CLI.
:::
When your Notebook is created, it will appear on your AI Notebooks tab:
**Using ovhai CLI**
If you prefer to use the command line interface to launch your AI Notebook, please follow these steps:
First, you will need to install the _ovhai_ CLI and log into it. Please follow [this documentation](/pl/guides/public-cloud/ai-machine-learning/ai-cli-install-client.md).
After installation and successful login, visit this [second documentation](/pl/guides/public-cloud/ai-machine-learning/ai-cli-run-notebook.md) to learn how to create your AI Notebook using commands.
**Using the AI API**
To create an AI Notebook using the OVHcloud AI API, follow these steps:
First, navigate to the AI API link for the desired region among the ones that are available for the AI Products usage:
- [GRA AI API](https://gra.training.ai.cloud.ovh.net/#/), for Gravelines, France.
- [BHS AI API](https://bhs.training.ai.cloud.ovh.net/#/), for Beauharnois, Canada.
Upon visiting the API URL, you will notice various endpoint categories on the left side of the screen. Click the Notebook category to display the endpoints related to AI Notebooks.
Among them, you will find a Submit a new notebook POST method. This is the endpoint for submitting a new AI Notebook.
_The exact link for this endpoint on GRA region is as follows: `https://gra.training.ai.cloud.ovh.net/#/operations/notebookNew`._
In the `Submit a new notebook` endpoint page, you will find the detailed schema for the `Notebook Specification` and its various possible parameters. Examples of responses (`200`, `400`, `401`, `402`, `403`) are also provided in case of success or different error scenarios.
Before using this method to create an AI Notebook, you will need to authenticate. This can be done by providing a bearer token, which you can create through the [AI Dashboard](/pl/guides/public-cloud/ai-machine-learning/ai-dashboard-getting-started.md) or by using the _ovhai_ CLI. For more detailed instructions on creating a token, please consult the [Manage access tokens](/pl/guides/public-cloud/ai-machine-learning/ai-cli-app-token.md) documentation.
Once you have created your token, provide your token in the 'Auth' panel, on the right-hand corner of the API page.
:::warning
Make sure to select `Bearer Auth` instead of `OAuth 2.0`, which is selected by default.
:::
After logging in, you may modify the body (Notebook specifications) for the AI Notebook you wish to create. A default example is provided. Feel free to adjust it.
Once your body is filled, click the Send API request button. If your body is correctly written, the Notebook will be created on the Public Cloud project from which the bearer token used for identification originates. You will receive all the necessary information about the created Notebook (`ID`, `access URL`, etc.) in the `Responses` field.
In addition to the API interface, the API provides Python code examples to help you use the endpoint and reproduce the same result programmatically. However, for an efficient Python experience, we recommend using the _ovhai_ Python SDK.
**Using the Python SDK**
The _ovhai_ library is a Python client that allows developers to easily use the OVHcloud AI API. With this SDK, you can run, manage, automate your AI Notebooks.
:::warning
**Alpha Warning**: This package is currently in the **alpha phase** of development. The APIs and functionalities of the package may not be fully tested.
:::
To launch an AI Notebook using the [ovhai Python SDK](https://pypi.org/project/ovhai/), you first need to install the _ovhai_ library. You can do this using `pip`. Open your terminal and run the following command:
```bash
pip install ovhai
```
Once installed, we invite you to follow the instructions provided in the [ovhai PyPI](https://pypi.org/project/ovhai/) page to learn how to create a token for authentication, understand the SDK's functionality, and launch an AI Notebook using the _ovhai_ Python SDK.
Here is a basic example of how to do it, making it easy for you to get started:
```python
from ovhai import AuthenticatedClient
from ovhai.api.notebook import notebook_new
from ovhai.models import NotebookSpec, Notebook
from ovhai.ovhai_types import Response
client = AuthenticatedClient(
base_url="https://gra.training.ai.cloud.ovh.net",
token="YOUR_AI_TOKEN",
)
# Define notebook parameters
editor_id = "jupyterlab"
framework_id = "conda"
framework_version = "conda-py311-cudaDevel11.8"
nb_cpu = 2
# Create the notebook creation request
notebook_specs = {
"env": {"editorId": editor_id, "frameworkId": framework_id, "frameworkVersion": framework_version},
"resources": {"cpu": nb_cpu},
}
with client as client:
response: Response[Notebook] = notebook_new.sync_detailed(
client=client, body=NotebookSpec.from_dict(notebook_specs)
)
import json
response_content = response.content.decode('utf-8') # Decode bytes to a string
response_dict = json.loads(response_content)
status_code = response.status_code
id_ = response_dict['id']
spec = response_dict['spec']
status = response_dict['status']
state = status['state']
info = status['info']
url = response_dict['status']['url']
print(f"Status code: {status_code}")
print(f"ID: {id_}")
print(f"Spec: {spec}")
print(f"State: {state}")
print(f"Info: {info}")
print(f"URL: {url}")
```
Please make sure to replace `YOUR_AI_TOKEN` with a bearer token. Bearer tokens can be created through the [AI Dashboard](/pl/guides/public-cloud/ai-machine-learning/ai-dashboard-getting-started.md) or by using the _ovhai_ CLI. For more detailed instructions on creating a token, please consult the [Manage access tokens](/pl/guides/public-cloud/ai-machine-learning/ai-cli-app-token.md) documentation.
Running this code will launch a new AI Notebook based on the specifications you provided. Feel free to adjust the specifications based on the Notebook you want to create.
### Accessing your AI Notebook
At this point your AI Notebook is created. You will need to wait a few seconds for the Notebook to start and reach the `RUNNING` status. Once it has, the Notebook URL will be accessible.
**Using the Control Panel (UI)**
You can access your Notebook by clicking the JupyterLab link, from the AI Notebooks board, in the `Access` column:
This JupyterLab button can also be accessed from the dedicated Notebook page, which can be reached by clicking the Notebook name in the AI Notebooks board page.
On your Notebook page, you will find a lot of information about your Notebook (specifications, billing, attached data, ...). There is also a monitoring URL, which allows you to view real-time metrics for your Notebook, such as the GPU average usage for example.
**Using ovhai CLI**
In the previous step, you ordered the creation of your Notebook. This should have displayed the `URL` of your Notebook in your terminal, allowing you to access it:
If you no longer have the Notebook `UUID` and `URL` displayed in your terminal, you can easily list all your existing AI Notebooks by running:
```bash
ovhai notebook list
```
This will allow you to retrieve the `URL` of the Notebook you want to access.
You can also get all the information related to a Notebook (Specifications, Access Url, Monitoring URL, ...) by running:
```bash
ovhai notebook get
```
Just make sure to replace the `` with the actual `UUID` of the Notebook you want to retrieve the information for.
**Using the AI API**
In the previous step, you ordered the creation of your Notebook. This should have displayed the `URL` of your Notebook in the 'Response' panel, allowing you to access it:
If you no longer have the Notebook `UUID` and `URL` displayed in the API page, you can list all your AI Notebooks by using the Paginated list of notebooks GET endpoint method, in the Notebook category.
This will allow you to retrieve the `URL` of the Notebook you want to access.
You can also get all the information related to a Notebook (Specifications, Access Url, Monitoring URL, ...) by using the Get notebook information GET endpoint method. You will just have to provide the `UUID` of the notebook from which you want to obtain information.
**Using the Python SDK**
If you no longer have the Notebook `UUID` and `URL` displayed in your Python IDE, you can easily list all your AI Notebooks by running:
```python
from ovhai.api.notebook import notebook_get_all
from ovhai.models import NotebookList
from ovhai.ovhai_types import Response
from ovhai import AuthenticatedClient
client = AuthenticatedClient(
base_url="https://gra.training.ai.cloud.ovh.net",
token="YOUR_AI_TOKEN",
)
with client as client:
response: Response[NotebookList] = notebook_get_all.sync_detailed(client=client)
import json
response = json.loads(response.content.decode())
for notebook_info in response["items"]:
print(f"ID: {notebook_info['id']}")
print(f"Name: {notebook_info['spec']['name']}")
print(f"Status: {notebook_info['status']['state']}")
print(f"Framework: {notebook_info['spec']['env']['frameworkId']}")
print(f"Framework version: {notebook_info['spec']['env']['frameworkVersion']}")
print(f"Editor: {notebook_info['spec']['env']['editorId']}")
print(f"Access link: {notebook_info['status']['url']}")
print("---------------")
```
This will allow you to retrieve the `URL` of the Notebook you want to access. Just make sure you are using the right API url, and replace `YOUR_AI_TOKEN` with your actual AI token.
You can also get all the information related to a Notebook (Specifications, Access Url, Monitoring URL, ...) by running the following code:
```python
from ovhai.api.notebook import notebook_get
from ovhai import AuthenticatedClient
import json
client = AuthenticatedClient(
base_url="https://gra.training.ai.cloud.ovh.net",
token="YOUR_AI_TOKEN",
)
id = "YOUR_NOTEBOOK_UUID"
with client as client:
response = notebook_get.sync_detailed(
id=id, client=client
)
print(json.loads(response.content.decode()))
```
### Connecting to your AI Notebook
Once you have clicked on the Notebook `URL`, you will be redirected to the corresponding Notebook page. For _restricted access_ Notebooks, you will need to authenticate to ensure you have the required rights for accessing it.
For _public access_ Notebooks, no authentication will be needed, so you will directly land on the notebook with your data and code exposed.
There are two methods of authentication for a restricted Notebook: using a **username and password** combination or using an **access token**.
**Using a Username and Password**
To authenticate using a username and password, please follow these steps:
Enter one of your Public Cloud project user's username and password in the respective fields, as shown in the screenshot below. The user needs to belong to the same Public Cloud project you used to create the Notebook and must have sufficient permissions.
If you have not yet created a Public Cloud user for your project, or require more information on how to do so, please refer to the following [guide](/pl/guides/public-cloud/ai-machine-learning/ai-users.md).
Once you have entered your username and password, click the Connect button to log in to your AI Notebook.
**Using an Access Token**
Using a token is an effective way to share the AI Notebook with anyone, without requiring them to have the credentials of one of your Public Cloud project users. This is useful when you want to provide access to the Notebook outside your Public Cloud project.
To authenticate using an access token, please follow these steps:
Click the Login with token button, located below the user and password fields. This will switch the access mode to use a token-based authentication method instead.
Make sure to use a token that you have created either from the AI Dashboard or from the ovhai CLI, API, or SDK. You can use tokens that were created without a label selector. If you specified a label selector during the Notebook creation, you can also use the token that matches the label selector specified. For more information on how to create an access token, please refer to this [documentation](/pl/guides/public-cloud/ai-machine-learning/ai-cli-app-token.md).
Enter the access token in the 'Access Token' field, as shown in the screenshot below.
Click the Connect button to log in to your AI Notebook.
Once you have successfully reached JupyterLab, you can create your first Notebook by clicking on the `Pytorch 2.4.0
` button (or a similar button name depending on the framework you have selected).
With your new Notebook open, enter some Python code in the first code cell. We can test it with a simple _Hello World_ :
```python
print("Hello World")
```
To execute the code, simply press the ▶️ button located in the toolbar above the code cell. You should then see the output:
```bash
Hello World
```
Alternatively, you can use the keyboard shortcut `Ctrl+Enter`, or `CMD+Enter`, to save the Notebook quickly.
Your code is executed in your browser and will consume the CPU and GPU resources linked to your AI Notebook.
To save your Notebook, click on the sub-menu `Save
` of the `File`
menu. Alternatively, you can use the keyboard shortcut `Ctrl+S`
, or `CMD+S`
, to save the Notebook quickly.
### Getting started with code examples
To help you get started with your AI Notebook, we provide a [GitHub repository](https://github.com/ovh/ai-training-examples) named `ai-training-examples`, containing code examples and tutorials. This repository is already cloned in your Notebook [/workspace](/pl/guides/public-cloud/ai-machine-learning/ai-notebooks-workspace.md) when you launch it, so you can start exploring the examples right away.
We currently provide a variety of tutorials, including for examples:
- [Tensorflow](https://www.tensorflow.org/) tutorials as `ipython notebooks`
- Basic computation using single CPU or GPU: accessible on `notebooks/getting-started/tensorflow/basic_gpu_cpu_benchmark.ipynb`. GitHub preview available [here](https://github.com/ovh/ai-training-examples/blob/main/notebooks/getting-started/tensorflow/basic_gpu_cpu_benchmark.ipynb).
- Basic computation using multiple GPUs: accessible on `notebooks/getting-started/tensorflow/multiple_gpus_computation.ipynb`. GitHub preview available [here](https://github.com/ovh/ai-training-examples/blob/main/notebooks/getting-started/tensorflow/multiple_gpus_computation.ipynb).
- A [PyTorch](https://pytorch.org/) version of this multi-GPU benchmarking tutorial, available [here](https://github.com/ovh/ai-training-examples/blob/main/notebooks/getting-started/pytorch/multi_gpu_benchmark.ipynb).
These tutorials demonstrate how to perform simple tensor computations and compare the performance of running them on CPU versus GPU.
In addition to these tutorials, we also provide examples for more advanced topics, including:
- Audio classification
- Image classification
- Image Generation
- Image Segmentation
- Object detection
You can also learn to compare AI models based on resource consumption, accuracy, and training time. Refer to [this tutorial](/pl/guides/public-cloud/ai-machine-learning/ai-training-models-comparaison-wandb.md) for more information.
### Stopping an AI Notebook
You can stop your AI Notebook at any time. This will release its compute resources but will keep your Notebook data and installed libraries. Therefore, you will not incur any further charges for compute unless you restart the Notebook. However, attached storage will be billed at the price of OVHcloud Object Storage. Consult the [AI Notebooks Billing documentation](/pl/guides/public-cloud/ai-machine-learning/ai-notebooks-billing.md) for more information.
Indeed, when you stop a Notebook, it will be unavailable for a certain period while your data is synchronized to the Object Storage, so that you can retrieve your work later. Once the synchronization is complete, the Notebook will reach the `STOPPED` state, at which point you can either start it again or delete it.
**Using the Control Panel (UI)**
Go back to the AI Notebooks board, from the Public Cloud section of the OVHcloud Control Panel.
Then, select the Notebook you want to stop by clicking on its `Name`.
From there, you will be able to click the 🔴 stop button:
**Using ovhai CLI**
You can easily stop your Notebook using the following command:
```bash
ovhai notebook stop ``
```
Make sure to replace `` with the Notebook UUID you want to stop.
If you do not know its UUID, remember that you can easily list all your existing AI Notebooks by running:
```bash
ovhai notebook list
```
**Using the AI API**
Select the Stop a running notebook endpoint, which is a PUT method. Provide the UUID of the Notebook you wish to stop as an input parameter. Then, click the Send API request button to send the request.
If the request is successful, the Notebook will be stopped and a 'No content' message will be displayed in the 'Response' panel. If the request is unsuccessful, an error message will be displayed there.
**Using the Python SDK**
Here is an example of how to stop a running Notebook using the Python SDK:
```python
from ovhai.api.notebook import notebook_stop
from ovhai import AuthenticatedClient
client = AuthenticatedClient(
base_url="https://gra.training.ai.cloud.ovh.net",
token="YOUR_AI_TOKEN",
)
id = "YOUR_NOTEBOOK_UUID"
with client as client:
response = notebook_stop.sync_detailed(client=client, id=id)
```
Please replace `YOUR_AI_TOKEN` with your actual AI token and `YOUR_NOTEBOOK_UUID` with the `UUID` of the Notebook you want to stop.
### Starting a stopped Notebook
Once the Notebook has reached the `Stopped` state, you may be interested in starting it in order to continue your work.
**Using the Control Panel (UI)**
Go back to the AI Notebooks board, from the Public Cloud section of the OVHcloud Control Panel.
Then, select the Notebook you want to start by clicking on its `Name`.
From there, you will be able to click the ▶️ button:
**Using ovhai CLI**
Use the following command and replace `` with your Notebook UUID to start your stopped Notebook:
```bash
ovhai notebook start ``
```
**Using the AI API**
Select the Start a stopped notebook endpoint, which is a PUT method. Provide the UUID of the Notebook you wish to start as an input parameter. Then, click the Send API request button to send the request.
If the request is successful, the Notebook will be started and a 'No content' message will be displayed in the 'Response' panel. If the request is unsuccessful, an error message will be displayed there.
**Using the Python SDK**
Here is an example of how to stop a running Notebook using the Python SDK:
```python
from ovhai.api.notebook import notebook_start
from ovhai import AuthenticatedClient
client = AuthenticatedClient(
base_url="https://gra.training.ai.cloud.ovh.net",
token="YOUR_AI_TOKEN",
)
id = "YOUR_NOTEBOOK_UUID"
with client as client:
response = notebook_start.sync_detailed(
id=id, client=client
)
```
Please replace `YOUR_AI_TOKEN` with your actual AI token and `YOUR_NOTEBOOK_UUID` with the `UUID` of the Notebook you want to stop.
Starting a Notebook will transition its state to `RUNNING`, once the Notebook is started. However, there will be a short waiting period while your data synchronizes back to your Notebook (internal workspace and remotely mounted volumes if any). During this data synchronization, the Notebook's state will display as `Pending` or `Starting` before entering the `RUNNING` state.
### Restarting a Notebook
When your Notebook is in the `RUNNING` state, you may want to restart it to refresh the environment or apply updates while preserving all your data and settings.
The restart operation **will not trigger a datasync process**, which means your notebook's data and settings will be preserved without requiring the time consuming process of copying data.
If you have enabled the Automatic Restart feature, your Notebook will automatically restart every 7 days without requiring manual intervention. You also have the option to extend this period to 28 days by contacting our [support](https://help.ovhcloud.com/csm?id=csm_get_help).
**Using the Control Panel (UI)**
Go back to the AI Notebooks board, from the Public Cloud section of the OVHcloud Control Panel.
Then, select the Notebook you want to start by clicking on its `Name`.
From there, you will be able to click the 🔄 restart button:
**Using ovhai CLI**
Use the following command and replace `` with your Notebook UUID to restart your running Notebook:
```bash
ovhai notebook restart ``
```
**Using the AI API**
Select the Restart a stopped notebook endpoint, which is a PUT method. Provide the UUID of the Notebook you wish to start as an input parameter. Then, click the Send API request button to send the request.
If the request is successful, the Notebook will be started and a 'No content' message will be displayed in the 'Response' panel. If the request is unsuccessful, an error message will be displayed there.
### Deleting a Notebook
If you no longer need your Notebook, you can delete it.
Deleting a Notebook also means that you will delete the Notebook's local storage. This data cannot be recovered once the Notebook has been deleted. However, any remote Object Storage that was mounted on the AI Notebook during its creation will not be deleted.
**Using the Control Panel (UI)**
If you want to completely **delete** your AI Notebook, just click on the ... button, and then select delete action.
**Using ovhai CLI**
If you want to completely **delete** your AI Notebook, just run the following command:
```bash
ovhai notebook delete ``
```
If the Notebook is still `RUNNING`, a confirmation message will appear, asking you to confirm the action. Confirm only if you're sure you want to delete the Notebook.
**Using the AI API**
Select the Delete permanently a notebook endpoint, which is a DELETE method. Provide the UUID of the Notebook you wish to delete. Use the `force` parameter if your Notebook is not `STOPPED` and still `RUNNING`. Then, click the Send API request button to send the request.
If the request is successful, the Notebook will be deleted and a 'No content' message will be displayed in the 'Response' panel. If the request is unsuccessful, an error message will be displayed there.
**Using the Python SDK**
Here is an example of how to delete a Notebook using the Python SDK:
```python
from ovhai.api.notebook import notebook_delete
from ovhai import AuthenticatedClient
client = AuthenticatedClient(
base_url="https://gra.training.ai.cloud.ovh.net",
token="YOUR_AI_TOKEN",
)
id = "YOUR_NOTEBOOK_UUID"
with client as client:
response = notebook_delete.sync_detailed(
id=id, client=client, force=False
)
print(response.content.decode())
```
Please replace `YOUR_AI_TOKEN` with your actual AI token and `YOUR_NOTEBOOK_UUID` with the `UUID` of the Notebook you want to delete. If the Notebook is still `RUNNING`, you will need to set the `force` parameter to `True` to delete the Notebook.
Once the AI Notebook has been deleted, you should no longer see it when listing your existing AI Notebooks.
## Going further
- Discover how to access your Object Storage data and Git repositories from your Notebooks using the UI [here](/pl/guides/public-cloud/ai-machine-learning/ai-notebooks-manage-data-ui.md).
- To learn about the technical features, capabilities, and limitations of the Public Cloud AI Notebooks offer, explore [this guide](/pl/guides/public-cloud/ai-machine-learning/ai-notebooks-capabilities.md).
- To get some hints on how to debug your Notebooks if things go wrong, see the [Troubleshooting documentation](/pl/guides/public-cloud/ai-machine-learning/ai-notebooks-debug.md).
- To discover the AI Notebooks lifecycle and billing, explore this [guide](/pl/guides/public-cloud/ai-machine-learning/ai-notebooks-capabilities.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/fr/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.
## Feedback
Please feel free to send us your questions, feedback, and suggestions regarding AI Notebooks:
- In the #ai-notebooks channel of the OVHcloud [Discord server](https://discord.gg/ovhcloud), where you can engage with the community and OVHcloud team members.