feat: changes for Bare metal Ai tier release#79
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kupratyu-splunk wants to merge 14 commits intomainfrom
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feat: changes for Bare metal Ai tier release#79kupratyu-splunk wants to merge 14 commits intomainfrom
kupratyu-splunk wants to merge 14 commits intomainfrom
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Version 3.0.0 does not exist in the splunk helm repo; 3.1.0 is the latest available. Also regenerates Chart.lock with correct digest.
The splunkai_models_apps package no longer exists in ai-platform-models. The ray applications are now resolved relative to their working_dir zip, so import paths should be bare module names (main:SERVE_APP / main:create_serve_app).
…ersion into ApplicationParams Without working_dir, Ray has no zip to load main from and fails with 'No module named main'. Added WorkingDirBase and ModelVersion fields to ApplicationParams, computed from object storage path and MODEL_VERSION env var, and templated working_dir into all 13 app entries in applications.yaml.
…b_storage Two bugs causing NoSuchBucket when Ray downloads working_dir zips: 1. rayS3DownloadEnv() was missing AWS_S3_ADDRESSING_STYLE=path. Boto3 defaults to virtual-hosted style (bucket.endpoint) for custom endpoints, which fails DNS resolution with MinIO. Path-style (endpoint/bucket/key) is required for all S3-compatible stores. 2. applications.yaml used 'object_storage' as the model_loader sub-field but ModelLoader in model_definition.py defines it as 'blob_storage' (renamed in commit e62d93da). Pydantic silently ignored the unknown key, leaving blob_storage=None and causing a model validation error at startup.
…handler
Ray's s3:// protocol handler (protocol.py _handle_s3_protocol) creates a
plain boto3.Session().client('s3') with no endpoint_url, so it always hits
AWS S3 regardless of AWS_ENDPOINT_URL set on the pod. This causes NoSuchBucket
when the bucket only exists in MinIO.
Replace rayRuntimeWorkingDirScheme() with rayWorkingDirBase() which, for
S3-compatible stores with a custom endpoint, builds the working_dir as a
direct HTTP URL to MinIO (endpoint/bucket/path). Ray's https handler uses
urllib which simply fetches the URL without any S3-specific boto3 logic.
Also remove the ineffective AWS_S3_ADDRESSING_STYLE env var added in the
previous commit.
…nIO zips
Ray's s3:// protocol handler creates a bare boto3.Session().client('s3')
with no endpoint_url, so it always hits AWS S3 regardless of any custom
endpoint config. Rather than fighting Ray internals, switch to file://
working_dir pointing to app source baked into the Ray image.
- applications.yaml: replace all 'minio-zip' working_dir templates with
file:///home/ray/ray/applications/entrypoint (Entrypoint) and
file:///home/ray/ray/applications/generic_application (all other apps)
- builder.go: remove WorkingDirBase, ModelVersion fields and rayWorkingDirBase()
function — no longer needed since working_dir is a static file:// path
- builder_test.go: remove TestRayWorkingDirBase test for deleted function
…ote URL for others
PromptInjectionTfidf, PromptInjectionCrossEncoder, PromptInjectionClassifier are
baked into the Ray worker image at /home/ray/ray/applications/generic_application,
so they use file:// working_dir with no network dependency.
All other apps (UaeLarge, AllMinilmL6V2, BiEncoder, MbartTranslator, etc.) continue
to use {{.WorkingDirBase}}/AppName-{{.ModelVersion}}.zip resolved at runtime from
the configured object storage (s3, gs, azure, or s3compat/MinIO endpoint).
…cile - saia/impl.go: bump default memory request 1Gi->2Gi, limits CPU 1->2 / memory 2Gi->4Gi to prevent kubelet OOMKill during SAIA startup - reconciler.go: preserve existing AIService Resources on reconcile so user-set limits are not wiped back to defaults on every AIPlatform reconcile
Ray requires file:// working_dir URIs to point to a .zip or .whl file. Update the 3 prompt injection apps to reference generic_application.zip which is built during the Docker image build in ai-platform-models.
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