You can set up a {{ ml-platform-name }} project environment on your own by using a Docker image with any software, libraries, environment variables, and configuration files. The Docker image you apply to your project will be used when running code in all its notebooks.
You cannot change Docker images after you create them.
Like other {{ ml-platform-name }} resources, a Docker image can be published in a community and used in multiple projects. To do this, you need at least the Editor role in the project and the Developer role in the community in which you want to publish it. You can open the access on the {{ ui-key.yc-ui-datasphere.common.access }} tab on the Docker image view page. The resource available to the community will appear on the community page under {{ ui-key.yc-ui-datasphere.spaces-page.community-resources }}.
For more information about working with Docker images, see {#T}.
For a Docker image to run and work correctly in {{ ml-platform-name }}, it must include:
- Python 3.8, 3.9, 3.10, or 3.11 installation
- Pip installation
--uid 1000 jupyteruser
{% include Docker hub limits %}
{% note info %}
The Docker image templates available in {{ ml-platform-name }} already meet these requirements. If you are downloading an image from a third-party source or writing your own, include the required commands.
{% endnote %}