Supporting Multi-LoRA inferencing via JetStream server#221
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Looked at it at high level, left some comments. Will take a deeper look again.
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Thanks for the PR, its a bit longish and I'd have preferred you to send the adapter_tensorstore.py and related code as a separate PR since its isolated enough along with the unittests before sending the the PR to integrate it into orchestrator.
I've some initial comments.
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Looks good for initial version
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Change to ubuntu-lastest since ubuntu-20.04 is deprecated Change to ubuntu-lastest since ubuntu-20.04 is deprecated Change to ubuntu-lastest since ubuntu-20.04 is deprecated Move prefix cache from MaxText (#239) Retry grpc async request (#240) The exception raised by asyncio task is not welled catch. If the server is not ready, it cause the benchmark serving blocked forever without noticed. Retry the connection to the server. Adding PyTests in JetStream unit test workflow for code coverage. (#242) Supporting Multi-LoRA inferencing via JetStream server (#221) Supporting Multi-LoRA inferencing via JetStream server following [LLM Inference gateway API protocols](https://github.com/kubernetes-sigs/gateway-api-inference-extension/tree/main/docs/proposals/003-model-server-protocol#inference-api-protocol). - Implemented an adapter_tensorstore to load, store, manage and unload the adapter weights - Added and exposed [required metrics](https://github.com/kubernetes-sigs/gateway-api-inference-extension/tree/main/docs/proposals/003-model-server-protocol#metrics-reporting) at prometheus endpoint - Added multi_lora_decoding service with corresponding APIs as per the [requirement](https://github.com/kubernetes-sigs/gateway-api-inference-extension/tree/main/docs/proposals/003-model-server-protocol#inference-api-protocol). - Implemented single LoRA functionality support.
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Supporting Multi-LoRA inferencing via JetStream server following LLM Inference gateway API protocols.