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This PR consists of the following clean-up items for KubeRay autoscaler integration: Remove the docker/kuberay directory Move the Python files formerly in docker/kuberay to the autoscaler directory. Use a rayproject/ray image for the autoscaler. Add an entry point for the kuberay autoscaler to scripts.py. Use the entry point in the example config. Slightly simplify the code that starts the autoscaler. Ray versions are updated to Ray 1.11.0, which will be officially released within the next couple of days. By default, Ray >= 1.11.0 runs without Redis. References to Redis are removed from the example config. Add the autoscaler configuration test to the CI. Update development documentation to reflect the changes in this PR. |
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|---|---|---|
| .. | ||
| autoscaler | ||
| base-deps | ||
| development | ||
| examples | ||
| ray | ||
| ray-deps | ||
| ray-ml | ||
| ray-worker-container | ||
| retag-lambda | ||
| fix-docker-latest.sh | ||
| README.md | ||
Overview of how the ray images are built:
Images without a "-cpu" or "-gpu" tag are built on ubuntu/focal. They are just an alias for -cpu (e.g. ray:latest is the same as ray:latest-cpu).
ubuntu/focal
└── base-deps:cpu
└── ray-deps:cpu
└── ray:cpu
└── ray-ml:cpu
nvidia/cuda
└── base-deps:gpu
└── ray-deps:gpu
└── ray:gpu
└── ray-ml:gpu