[doc][clusters] add doc for setting up Ray and K8s

doc/source/_toc.yml

@@ -290,6 +290,7 @@ parts:
  sections:
  - file: cluster/kubernetes/user-guides/aws-eks-gpu-cluster.md
  - file: cluster/kubernetes/user-guides/gcp-gke-gpu-cluster.md
+ - file: cluster/kubernetes/user-guides/storage.md
  - file: cluster/kubernetes/user-guides/config.md
  - file: cluster/kubernetes/user-guides/configuring-autoscaling.md
  - file: cluster/kubernetes/user-guides/gke-gcs-bucket.md

doc/source/cluster/kubernetes/user-guides/storage.md

@@ -0,0 +1,83 @@
+(kuberay-storage)=
+
+# Storage and dependencies best practices with KubeRay
+
+This document contains recommendations for setting up storage and handling application dependencies for your Ray deployment on Kubernetes.
+
+When you set up Ray on Kubernetes, the [KubeRay documentation](kuberay-quickstart) provides an overview of how to configure the operator to execute and manage the Ray cluster lifecycle.
+However, administrators may still have questions with respect to actual user workflows. For example:
+
+* How do I ship or run code on the Ray cluster?
+* What type of storage system should I set up for artifacts?
+* How do I handle package dependencies for your application?
+
+The answers to these questions will vary between development and production. This table summarizes the recommended setup for both situations:
+
+| | Interactive Development | Production |
+|---|---|---|
+| Cluster Configuration | KubeRay YAML | KubeRay YAML |
+| Code | Run driver or Jupyter notebook on head node | Bake code into Docker image |
+| Artifact Storage | Set up an EFS <br /> or <br /> Cloud Storage (S3, GS) | Set up an EFS <br /> or <br /> Cloud Storage (S3, GS) |
+| Package Dependencies | Install onto NFS <br /> or <br /> Use runtime environments | Bake into docker image |
+
+Table 1: Table comparing recommended setup for development and production.
+
+## Interactive development
+
+To provide an interactive development environment for data scientists and ML practitioners, we recommend setting up the code, storage, and dependencies in a way that reduces context switches for developers and shortens iteration times.
+
+```{eval-rst}
+.. image:: ../images/interactive-dev.png
+ :align: center
+..
+ Find the source document here (https://whimsical.com/clusters-P5Y6R23riCuNb6xwXVXN72)
+```
+
+### Storage
+
+Use one of these two standard solutions for artifact and log storage during the development process, depending on your use case:
+
+* POSIX-compliant network file storage (like NFS and EFS): This approach is useful when you want to have artifacts or dependencies accessible across different nodes with low latency. For example, experiment logs of different models trained on different Ray tasks.
+* Cloud storage (like AWS S3 or GCP GS): This approach is useful for large artifacts or datasets that you need to access with high throughput.
+
+Ray's AI libraries such as Ray Data, Ray Train, and Ray Tune come with out-of-the-box capabilities to read and write from cloud storage and local/networked storage.
+### Driver script
+
+Run the main (driver) script on the head node of the cluster. Ray Core and library programs often assume that the driver is located on the head node and take advantage of the local storage. For example, Ray Tune will by default generate log files on the head node.
+
+A typical workflow can look like this:
+
+* Start a Jupyter server on the head node
+* SSH onto the head node and run the driver script or application there
+* Use the Ray Job Submission client to submit code from a local machine onto a cluster
+
+### Dependencies
+
+For local dependencies (for example, if you’re working in a mono-repo), or external dependencies (like a pip package), use one of the following options:
+
+* Put the code and install the packages onto your NFS. The benefit is that you can quickly interact with the rest of the codebase and dependencies without shipping it across a cluster every time.
+* Use the `runtime env` with the [Ray Job Submission Client](ray.job_submission.JobSubmissionClient), which can pull down code from S3 or ship code from your local working directory onto the remote cluster.
+* Bake remote and local dependencies into a published Docker image that all nodes will use ([guide](serve-custom-docker-images)). This is the most common way to deploy applications onto [Kubernetes](https://kube.academy/courses/building-applications-for-kubernetes), but it is also the highest friction option.
+
+## Production
+
+Our recommendations regarding production are more aligned with standard Kubernetes best practices. For production, we suggest the following configuration.
+
+
+```{eval-rst}
+.. image:: ../images/production.png
+ :align: center
+..
+ Find the source document here (https://whimsical.com/clusters-P5Y6R23riCuNb6xwXVXN72)
+```
+
+
+### Storage
+
+The choice of storage system remains the same across development and production.
+
+### Code and Dependencies
+
+Bake your code, remote, and local dependencies into a published Docker image for all nodes in the cluster. This is the most common way to deploy applications onto [Kubernetes](https://kube.academy/courses/building-applications-for-kubernetes). Here is a [guide](serve-custom-docker-images) for doing so.
+
+Using cloud storage and the `runtime_env` is a less preferred method but still viable as it may not be as reproducible as the container path. In this case, use the runtime environment option to download zip files containing code and other private modules from cloud storage, in addition to specifying the pip packages needed to run your application.