ray-ml flavor) come with a comprehensive set of packages frequently used for machine learning, you may still need to modify your environment when you need additional packages or when you need a specific version of a given package.
Domino allows you to easily package and manage dependencies as part of your Ray-enabled compute environments. This approach creates the flexibility to manage dependencies for individual projects or workloads without having to deal with the complexity of a shared cluster.
To add a new dependency, you must add the appropriate statements in the Docker Instructions section of the relevant Ray base and Ray execution compute environments.
For example, if you wanted to add a particular version of PyTorch you might include the following.
Use on-demand Ray
Manage Ray dependencies
Install additional packages or specific versions in both Ray cluster and execution Compute Environments through Docker Instructions.
While the Ray base images (especially the
Last modified on September 3, 2026
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