--- title: How to use the preinstalled environment description: This page explains how to use the preinstalled environment on a Scaleway Docker image for a GPU Instance tags: preinstalled environment preinstalled-environment gpu dates: validation: 2025-07-21 posted: 2022-03-25 --- import Requirements from '@macros/iam/requirements.mdx' import image from './assets/scaleway-jupyter-lab.webp' GPU Instances have different types of preinstalled environments, depending on the OS image you chose during [creation of the Instance](/gpu/how-to/create-manage-gpu-instance/): | OS image | Image release type | Preinstalled on image | Working environment | |:----------------------:|:------------------:|:---------------------------------------------------------------:|------------------------------------------------| | Ubuntu Focal GPU OS 12 | Legacy | NVIDIA drivers, NVIDIA Docker environment (launch Docker container to access working environment) | Pipenv virtual environment accessed via Docker | | Ubuntu Noble GPU OS 13 (NVIDIA) | Latest | NVIDIA drivers, Nvidia Docker environment (launch Docker container to access working environment) | Pipenv virtual environment accessed via Docker | The Ubuntu Noble GPU OS 13 (NVIDIA) contains the latest version of the [NVIDIA open-source drivers](https://developer.nvidia.com/blog/nvidia-releases-open-source-gpu-kernel-modules/#:~:text=This%20release%20is%20a%20significant,modules%20increase%20ease%20of%20use) required for Blackwell GPU architecture. Using the latest Ubuntu Noble GPU OS13 image gives you a minimal OS installation on which you can [launch](/gpu/how-to/use-gpu-with-docker/) one of our ready-made [Docker images](/gpu/reference-content/docker-images/). This gives you access to a preinstalled Python environment managed with pipenv. Several useful AI core packages and tools are installed, including `scipy`, `numpy`, `scikit-learn`, `jupyter`, `tensorflow`, and the Scaleway SDK. Depending on the [Docker image you choose](/gpu/reference-content/docker-images/), other packages and tools will also be preinstalled, providing a convenient framework for you to begin work immediately. - A Scaleway account logged into the [console](https://console.scaleway.com) - [Owner](/iam/concepts/#owner) status or [IAM permissions](/iam/concepts/#permission) allowing you to perform actions in the intended Organization - A [GPU Instance](/gpu/how-to/create-manage-gpu-instance/) - An [SSH key](/organizations-and-projects/how-to/create-ssh-key/) added to your account ## Working with the preinstalled environment on Ubuntu Bionic ML legacy images 1. [Connect to your Instance via SSH](/instances/how-to/connect-to-instance/). You are now directly within the conda `ai` preinstalled environment. 2. Use the [official conda documentation](https://docs.conda.io/projects/conda/en/latest/commands.html) if you need any help managing your conda environment. For a full, detailed list of the Python packages and versions preinstalled in this environment, look at the content of the `/root/conda-ai-env-requirements.frozen` file. As Docker is also preinstalled, you could choose to [launch one of Scaleway's ready-made Docker images](/gpu/how-to/use-gpu-with-docker/) to access our latest working environments, if you wish. 3. Type `exit` to disconnect from your GPU Instance when you have finished your work. ## Working with the preinstalled environment on Ubuntu Focal GPU OS 12 1. [Connect to your Instance via SSH](/instances/how-to/connect-to-instance/). You are now connected to your Instance, and see your OS. A minimum of packages, including Docker, are installed. Pipenv is **not** preinstalled here. You must launch a Scaleway AI Docker container to access the preinstalled Pipenv environment. 2. [Launch](/gpu/how-to/use-gpu-with-docker/) one of our ready-made [Docker images](/gpu/reference-content/docker-images/). You are now in the `ai` directory of the Docker container, in the activated Pipenv virtual environment, and can get right to work! Use the command `pipenv graph` to see a list of all installed packages and their versions, as well as all the dependencies of each package. For more help with pipenv, see our [dedicated documentation](/gpu/how-to/use-pipenv/). ### Launching an application in your local browser Some applications, such as [Jupyter Lab](https://jupyter.org/), [Tensorboard](https://www.tensorflow.org/tensorboard) and [Code Server](https://code-server.dev/), require a browser to run. You can launch these from the `ai` virtual environment of your Docker container, and view them in the browser of your local machine. This is thanks to the possibility of adding port mapping arguments when launching a container with the `docker run` command. In our example, we added the port mapping arguments `-p 8888:8888 -p 6006:6006` when we launched our container, mapping `8888:8888` for Jupyter Lab and `6006:6006` for Tensorboard. Code Server runs in Jupyter Lab via Jupyter Hub, so does not need port mapping in this case. You can add other port mapping arguments for other applications as you wish. 1. Launch an application. Here, we launch Jupyter Lab: `jupyter-lab` Within the output, you should see something similar to the following: ``` [I 2022-04-06 11:38:40.554 ServerApp] Serving notebooks from local directory: /home/jovyan/ai [I 2022-04-06 11:38:40.554 ServerApp] Jupyter Server 1.15.6 is running at: [I 2022-04-06 11:38:40.554 ServerApp] http://7d783f7cf615:8888/lab?token=e0c21db2665ac58c3cf124abf43927a9d27a811449cb356b [I 2022-04-06 11:38:40.555 ServerApp] or http://127.0.0.1:8888/lab?token=e0c21db2665ac58c3cf124abf43927a9d27a811449cb356b [I 2022-04-06 11:38:40.555 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). ``` Jupyter Lab is launched automatically when you run any Scaleway container image. You will see a message upon start-up telling you how to access the notebook in your browser. To override Jupyter Lab being launched automatically in this way, add `/bin/bash` to the end of your `docker run` command, e.g. `docker run --runtime=nvidia -it --rm -p 8888:8888 -p 6006:6006 rg.fr-par.scw.cloud/scw-ai/pytorch:latest /bin/bash`. This preempts the launch of Jupyter Lab at container startup, and replaces it with the specified command, in this case, a bash shell. 2. Open a browser window on your local computer, and enter the following URL. Replace `` with the IP address of your Scaleway GPU Instance, and `` with the token shown in the last lines of terminal output after the `jupyter-lab` command. ``` :8888/lab?token= ``` You can find the IP address of your Instance in the Scaleway console. In the side menu, click **CPU & GPU Instances** to see a list of your Instances. The IP address of each of them is shown in the list that displays. Jupyter Lab now displays in your browser. You can use the Notebook, Console, or other features as required: You can display the GPU Dashboard in Jupyter Lab to view information about CPU and GPU resource usage. This is accessed via the **System Dashboards** icon on the left side menu (the third icon from the top). 3. Use CTRL+C in the terminal window of your GPU Instance / Docker container to close the Jupyter server when you have finished. ### Exiting the preinstalled environment and the container When you are in the activated Pipenv virtual environment, your command line prompt will normally be prefixed by the name of the environment. Here, for example, from `(ai) jovyan@d73f1fa6bf4d:~/ai` we see that we are in the activated `ai` environment, and from `jovyan@d73f1fa6bf4d:~/ai` that we are in the `~/ai` directory of our container: 1. Type `exit` to leave the preinstalled `ai` environment. You are now outside the preinstalled virtual environment. 2. Type `exit` again to exit the Docker container. Your prompt should now look similar to the following. You are still connected to your GPU Instance, but you have left the Docker container: ``` root@scw-name-of-instance:~# ``` 3. Type `exit` once more to disconnect from your GPU Instance.