> ## Documentation Index
> Fetch the complete documentation index at: https://docs.domino.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Manage Ray dependencies

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 given package.

Domino allows you to easily package and manage dependencies as part of your [Ray-enabled compute environments](/cloud/platform-capabilities/features/compute-clusters-hpc/ray/configure-prerequisites#creating_base_ray_env). 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.

```dockerfile theme={null}
### Optionally specify version if desired
RUN pip install torch==1.8.0 torchvision==0.9.0
```