While the Ray base images (especially the
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
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 need to 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 may include the following.
### Optionally specify version if desired RUN pip install torch==1.8.0 torchvision==0.9.0