--- title: "AI Notebooks - Features, capabilities, and limitations" description: "Learn about the technical features, capabilities, and limitations of the Public Cloud AI Notebooks offer." url: https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-notebooks-capabilities 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 - Features, capabilities, and limitations **Learn about the technical features, capabilities, and limitations of the Public Cloud AI Notebooks offer.** **Note:** AI Notebooks is covered by [**OVHcloud Public Cloud Special Conditions**](https://us.ovhcloud.com/legal/service-specific-terms/).   ## Features ### Available features AI Notebooks are Managed Jupyter or VS Code notebooks linked to compute resources (CPUs/GPUs) and storage. You can compare them to Google Colab or Amazon Sagemaker notebooks. | Feature | Details | | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Live code editor and AI environments** | | | Jupyter and VS Code | You can use Jupyter or VS Code as your preferred live-code editor. If you opt for VS Code, you can also set up a remote connection (for example, from your laptop). | | Preinstalled Machine Learning environments | AI Notebooks comes with a generic Python environment (Conda) or pre-installed ones, such as Pytorch, Tensorflow, HuggingFace, and more. | | Easy customization | AI Notebooks allows installation of almost any Conda or Pip packages. You can easily customize your environment to suit your needs. | | **Management** | | | Multiple ways to manage your notebooks | You can manage your AI Notebooks through the OVHcloud Control Panel, CLI, API or Python SDK. Depending on your needs, you can easily automate their creation and deletion as well. | | Easy start and Stop | You can start and stop a notebook in one click. Once stopped, your notebook environment is kept, and you can restart it later without losing your data and experiments. | | **Compute resources** | | | Guaranteed compute resources | Select the number of CPUs or GPUs required during the creation of the AI Notebooks. Once launched, you will keep these resources as long as your notebook is running. | | Background execution | Your tasks can be executed in the background, meaning that closing your web browser will not affect your work. | | No maximum runtime | Your tasks can last as long as your notebook is running. | | Monitoring tools | Each AI Notebooks service comes with a native Grafana dashboard, allowing you to keep track of and monitor your CPU, GPU, RAM, and storage resources. | | **Storage** | | | Fast and flexible storage | Each AI Notebooks service comes with local storage, but also the ability to attach remote storage from Object Storage. From a few GiB to multiple TiB, we push your data near our compute power on fast SSD storage for better performance. | | Git repositories importation | During the creation of your AI Notebooks, you can specify one or multiple Git repositories to download inside your notebook environment. | | **Security** | | | Open or restricted authentication | During the creation of your AI Notebooks, select open or restricted access to your notebook. If restricted, people can be granted access via token or credentials to securely access your environment. | | **Billing** | | | Easy billing | You only pay for what you consume, billed per minute with per-second granularity. |   #### Command line interface (CLI) AI Notebooks is compliant with the OVHcloud AI CLI. Discover how to [install the OVHcloud AI CLI](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/cli-install-client.md).   #### **Monitoring tools** To see information about your notebook, you can do so with the `ovhai` CLI using this command: ``` ovhai notebook get ``` You can access your metrics through the `Monitoring Url`. You are also able to check it from the OVHcloud Control Panel in your notebook's general information by clicking the `Go to Graph Dashboard` button.   ### Planned features We continuously improve our offers. You can follow, vote, and submit ideas to add to [our roadmap](https://github.com/orgs/ovh/projects/16/views/1?sliceBy%5Bvalue%5D=AI+Notebooks).   ## Capabilities and limitations ### Supported regions AI Notebooks can be used from any country in the world, as long as you have an OVHcloud account. Our AI & Machine Learning services are based in the `US-EAST-VA` (Vint Hill, Virginia) region.   ### Attached resources #### Compute resources You can choose the number of GPUs or CPUs for a notebook, but cannot use both. By default, a notebook uses one GPU. The memory resource is not customizable. If you choose `GPU`: - CPU, memory, and local storage resources are not customizable but scaled linearly with each additional GPU. If you choose `CPU`: - Memory and local storage resources are not customizable but scaled linearly with each additional CPU. The maximum amount of CPU/GPU, memory per CPU/GPU, and local storage is available on the [OVHcloud website](https://us.ovhcloud.com/public-cloud/prices/), Control Panel, and the `ovhai` CLI. ``` ovhai capabilities flavor list ``` For your information, the current limits are: - CPU: 12 per notebook. - GPU: 4 per notebook.   ##### **Available hardware for AI Notebooks** Currently, we provide: - `ai1-1-cpu` - `L4-1-gpu` - `L40s-1-gpu`   #### Available storage ##### **Local storage** Each AI Notebook comes with a local storage space, which is ephemeral. When you delete your notebook, this storage space is also deleted. This storage space depends on the selected instances during the notebook creation. Please refer to the compute resources section for more information. **Note:** **Local storage** is limited and not the recommended way to handle data, see the [OVHcloud documentation on data](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/data-concepts-best-practices.md) for more information.   ##### **Attached storage** When attaching data volumes to your AI Notebooks, you can use storage from Public Cloud Object Storage. This allows you to work with large datasets, while ensuring persistence even if you delete your notebooks. **Note:** For best performance, we recommend keeping the Object Storage bucket in the **same region** as your AI Notebook. Mounting a bucket from a different region also works, but adds cross-region network latency. If you use an external S3[\*](#go-further) datastore registered via the `ovhai` CLI with `--store-credentials-locally`, keep in mind that the datastore is only registered for the CLI region context (`ovhai config set `) it was created under, so you need to register it again under each region you use it from. Your Public Cloud Project can store unlimited data in Object Storage buckets. However, when you mount an Object Storage bucket as a volume, there is a **usage limit of 10 TB per Public Cloud Project**. This limit applies to the total storage consumed by all volumes attached simultaneously across your AI Notebooks, AI Training jobs, and AI Deploy apps. When the same volume is used across multiple resources, **enabling caching** allows shared access to the volume data, preventing multiple copies from consuming additional storage quota. Without caching, each instance will maintain a separate copy of the volume data, increasing the total storage usage linearly.   #### Maximum execution time **Warning:** **NOTE:** There is no duration limitation on AI Notebooks execution. However, by default, your AI Notebook will automatically shut down after **7 consecutive days of being in a RUNNING state**. All your settings and data are preserved and you can start it again anytime. You can also enable **Automatic Restart** to have it restart every 7 days.   ### Live-code editors You can choose between two **live-code editors** to launch and edit your notebook: - Jupyterlab - VS Code You cannot install your own code editor on AI Notebooks. With VS Code, you get the capability to use remote connections (from a local computer).   ### Pre-installed AI environments OVHcloud AI Notebooks come with pre-installed AI environments. List of available AI Environments: - AutoGluon + MXNet - FastAI - HuggingFace Transformers - JAX - Miniconda (Python generic) - Miniconda with Colab Compatibility - MLR3 - MXNet - One image to rule them all - PyTorch - Scikit-Learn - TensorFlow   #### Environment customization Each environment can be customized directly with PIP or CONDA (we support almost any package and library). **Limitations**: - You are **not the administrator (root)**. You cannot install Linux packages (such as _apt-get_). - AI Notebooks **does not allow the use of custom Docker images**. In case you need a very specific package or framework, you can bring your custom Docker images with **OVHcloud AI Training**. AI Training allows you to benefit from the same technology and pricing, but you can create notebooks directly with your own Docker images. If you want to build and use a custom Docker image, you can do it with AI Training by following this [tutorial](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-build-use-custom-image.md).   ### Network - **Public networking** can be used for all the AI Tools. - **Private networking (OVHcloud vRack)** is not supported.   #### Available ports to public network Each notebook has a public URL that accesses port `8080` of the notebook by default and cannot be changed. Notebook URL for accessing the default port (starting with the notebook's ID): - [https://00000000-0000-0000-0000-000000000000.notebook.us-east-va.ai.cloud.ovh.us](https://00000000-0000-0000-0000-000000000000.notebook.us-east-va.ai.cloud.ovh.us) However, you can access other ports than the default `8080` by appending its number to your notebook URL. For example, if you want to access port `8501` from your notebook, you will have to add `-8501` to your notebook URL. Your notebook URL for accessing the `8501` port will then be [https://00000000-0000-0000-0000-000000000000-8501.notebook.us-east-va.ai.cloud.ovh.us](https://00000000-0000-0000-0000-000000000000-8501.notebook.us-east-va.ai.cloud.ovh.us). Only the HTTP layer is accessible.   ### Quotas per Public Cloud project GPU and CPU availability is subject to the quota set on your Public Cloud Project. You can [reach out to our support team](https://docs.us.ovhcloud.com/en/account/account-information/contact-us-support.md) to increase your quota.   ## 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. \*S3 is a trademark filed by Amazon Technologies, Inc. OVHcloud's service is not sponsored by, endorsed by, or otherwise affiliated with Amazon Technologies, Inc.