Creating a custom Docker image for a project

You can configure the environment to run your code using Docker images.

Yandex DataSphere lets you create repositories of Docker images in a project and select an image for the project. The selected image will be used when running code in all project notebooks.

To create a Docker image:

1. Go to the Docker images tab.
2. Click the icon.
3. On the tab that opens:

   1. Enter a name for the repository, such as cuda.

   2. Set a tag for the image, such as 1.0.0.

   3. Edit the Build script block contents. For example, to install CUDA^® packages, add the following code after the line # Write your instructions here:

      ENV LD_LIBRARY_PATH /usr/local/cuda-11.2/lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64
      RUN\
        apt-get update &&\
        apt-get install -y -q xserver-xorg-core wget &&\
        wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin -O /etc/apt/preferences.d/cuda-repository-pin-600 &&\
        apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub &&\
        add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /" &&\
        apt-get update &&\
        apt-get -y install cuda libcudnn8 &&\
        exit
      

   4. Click Build.

      This creates a Docker image with CUDA^® packages to use the GPU in computations.

   5. To use the image for your project, click Set image to project and then click Ok.

Make sure that the custom image environment is available in your project. For example, for the image with CUDA^® packages, create and run a cell with the following code:

#!g1.1
%pip install tensorflow==2.4.1
import tensorflow as tf
tf.config.list_physical_devices('GPU')

Execution result:

...
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]