Examples for running Llama 2 on Ray with Google Cloud TPUs.

• cluster: sample YAML files for creating your Ray cluster.
• notebooks: sample notebook to demonstrate interacting with Ray TPU clusters.
• docker: sample Docker files for quick env setup.
• serve: sample code for RayServe deployments.
• train: sample code for pretraining from scratch.
• scripts: sample scripts to automate common tasks.

To get started with this repo, a great option to start is to started with an interactive notebook environment. See notebooks.

If you are interested in large scale training runs, see train to get started.

If you are interested in serving, see serve to get started.

To quickly set up your environment, you can run

$ ./scripts/set_project_info.sh

and supply a base GCR/Docker path and GCP project ID. This will automatically set these values in cluster YAML files and scripts.