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Use the preinstalled environment

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How to use the preinstalled environment

Published on 25 March 2022

GPU Instances have different types of pre-installed environment, depending on the OS image you chose during creation of the Instance:

OS imageImage release typePreinstalled on imageWorking environment
Ubuntu Focal GPU OS 11LatestNvidia drivers, Nvidia Docker environment (launch Docker container to access working environment)pipenv virtual environment accessed via Docker
Ubuntu Bionic ML 10.1LegacyNvidia drivers, CUDA 10.1, Nvidia Docker environment, ready-to-use “ai” conda environmentconda environment
Ubuntu Bionic ML 9.2LegacyNvidia drivers, CUDA 9.1, Nvidia Docker environment, ready-to-use “ai” conda environmentconda environment

Using the latest Ubuntu Focal GPU OS11 image gives you a minimal OS installation on which you can launch one of our ready-made Docker images. This gives you access to a pre-installed Python environment managed with pipenv. A number of 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, other packages and tools will also be pre-installed, providing a convenient framework environment for you so that you can begin work immediately.

Using one of the legacy Ubuntu Bionic ML images gives you direct access to a ready-to-use Python environment pre-installed on the OS, managed with conda. As Docker is pre-installed, you can also choose to launch a Docker image to access a different working environment.

Identity and Access Management (IAM):

If you have activated IAM, you may need certain IAM permissions to carry out some actions described on this page. This means:

  • you are the Owner of the Scaleway Organization in which the actions will be carried out, or
  • you are an IAM user of the Organization, with a policy granting you the necessary permission sets

Working with the pre-installed environment on Ubuntu Bionic ML legacy images

  1. Connect to your Instance via SSH.

    You are now directly within the conda ai pre-installed environment, and can get right to work.

  2. Use the official conda documentation if you need any help managing your conda environment.


    For a full, detailed list of the Python packages and versions pre-installed in this environment, look at the content of the /root/conda-ai-env-requirements.frozen file.

    As Docker is also pre-installed, you could choose to launch one of Scaleway’s ready-made Docker images 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 pre-installed environment on Ubuntu Focal GPU OS 11

  1. Connect to your Instance via SSH.

    You are now connected to your Instance, and see your OS. A minimum of packages, including Docker, are installed. Pipenv is not pre-installed here. You must launch a Scaleway AI Docker container to access the preinstalled Pipenv environment.

  2. Launch one of our ready-made Docker images.

    You are now in the ai directoy 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.

Launching an application in your local browser

Some applications, such as Jupyter Lab, Tensorboard and Code Server, 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 to add 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:


    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
    [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 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 <ip-address> with the IP address of your Scaleway GPU Instance, and <my-token> with the token shown displayed in the last lines of terminal output after theh jupyter-lab command


    You can find the IP address of your Instance in the Scaleway console. In the side menu, click 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 in 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 pre-installed 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 the following command to leave the pre-installed ai environment.

    You are now outside the pre-installed 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:

  3. Type exit once more to disconnect from your GPU Instance.

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