feat(templates): declarative K8S compute configs for 3 pilot templates - #940
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Aydin-ab wants to merge 3 commits into
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feat(templates): declarative K8S compute configs for 3 pilot templates#940Aydin-ab wants to merge 3 commits into
Aydin-ab wants to merge 3 commits into
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The AWS/GCP compute keys name VM instance types, which K8s-stack clouds (AKS/EKS/GKE) cannot honor — the backend currently flattens them to a head-only CPU pod at launch. A declarative k8s.yaml (required_resources) instead resolves server-side into free pods, needing no registered instance types, and one config covers every K8s stack — hence a single K8S key rather than per-provider ones. Pilots span the shape space: single node + auto-select (workspace-intro), multi-node CPU (getting-started), multi-node GPU (pytorch-fsdp, 2x T4 — chosen because Azure GPU pools have no L4/A10G). The validator keeps k8s.yaml declarative (instance_type is an error) so the file class cannot drift back into per-cluster registration dependencies. Claude-Session: https://claude.ai/code/session_013BsS5dSk6s6xsLx2FbBy7g Signed-off-by: Aydin Abiar <aydin@anyscale.com>
A non-dict node (e.g. a bare string in worker_nodes) crashed the hook
with a traceback before collected errors printed — pydantic already
reports those, so skip them here. And required_resources: {} slipped
past the is-None guard while the SDK rejects it at deploy time; treat
empty as missing.
Claude-Session: https://claude.ai/code/session_013BsS5dSk6s6xsLx2FbBy7g
Signed-off-by: Aydin Abiar <aydin@anyscale.com>
Two authoring errors vs the declarative contract (docs.anyscale.com/configuration/compute/declarative + product compute_templates.py check_gpu_accelerator_consistency): auto_select_worker_config is not compatible with declarative configs, and the GPU type must ride in required_labels (ray.io/accelerator-type) — the launch-time GPU validation never reads required_resources.accelerator, so the previous form would have been rejected at launch. basic-single-node becomes an explicit single cpu_worker group, heads slim to coordinator sizing (4CPU/8Gi, they are CPU: 0), the FSDP GPU workers keep the AWS-proven g4dn.xlarge shape (4CPU/16Gi/1xT4). The hook now enforces all three rules so the next k8s.yaml can't repeat this. Claude-Session: https://claude.ai/code/session_013BsS5dSk6s6xsLx2FbBy7g Signed-off-by: Aydin Abiar <aydin@anyscale.com>
elliot-barn
reviewed
Aug 14, 2026
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can we rename them to azure.yaml to match the convention of the other templates?
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Adds an optional
K8S:compute-config key pointing at a declarativek8s.yaml—required_resourcesshapes resolved server-side into free pods, no registered instance types, one config covering AKS/EKS/GKE. Three pilots span the shape space:workspace-intro(single node),getting-started(multi-node CPU),pytorch-fsdp(multi-node GPU, 2×T4 viarequired_labels: {ray.io/accelerator-type: T4}— Azure GPU pools have no L4/A10G). The validator accepts the new key and enforces the declarative contract: noinstance_type, noauto_select_worker_config(incompatible with declarative configs), and GPU counts paired with the accelerator-type label (launch validation reads the label, not arequired_resources.acceleratorkey).Groundwork for the Azure/K8s template path:
rayapp buildalready carries the key into bundles as-is; the product launch path selecting + honoring it lands separately (today it force-selects AWS and flattens on K8s clouds), so the key is inert in prod until then.Testing
pre-commit run --all-filesgreen (BUILD.yaml validator gate included).rayapp build: all 56 bundles build; the three pilots'ray-app.jsoncarry theK8Sconfig withrequired_resources+required_labelspreserved verbatim.anyscale.compute_config.models.ComputeConfig(instance_type→custom).check_gpu_accelerator_consistency(GPU count must pair with theray.io/accelerator-typelabel; auto-select unsupported with declarative).instance_type,auto_select_worker_config, GPU without label,acceleratorinsiderequired_resources, emptyrequired_resources, non-dict nodes, cross-dir K8S path, wrong filename — each fails with a targeted error.https://claude.ai/code/session_013BsS5dSk6s6xsLx2FbBy7g