openvinotoolkit / openvino

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
https://docs.openvino.ai
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[Bug]: docker image openvino/model_server:latest-gpu does not serve the model correctly #27541

Open fedecompa opened 5 days ago

fedecompa commented 5 days ago

OpenVINO Version

2024.3

Operating System

Windows System

Device used for inference

intel UHD Graphics GPU

Framework

None

Model used

meta-llama/Llama-3.2-3B-Instruct

Issue description

I deployed the llama 3.2 -3B model using the image: openvino/model_server:latest-gpu following the documentation here:

https://docs.openvino.ai/2024/openvino-workflow/model-server/ovms_demos_continuous_batching.html

and the folder structure for the openvino IR model:

https://github.com/openvinotoolkit/model_server/blob/main/docs/models_repository.md

The command in my docker-compose is:
command: --model_path /workspace/Llama-3.2-3B-Instruct --model_name meta-llama/Llama-3.2-3B-Instruct --port 9001 --rest_port 8001 --target_device GPU

From the logs in the container I see that the server loads the model and starts correctly. Indeed if I call the API http://localhost:8001/v1/config I obtain:

{ "meta-llama/Llama-3.2-3B-Instruct" : { "model_version_status": [ { "version": "1", "state": "AVAILABLE", "status": { "error_code": "OK", "error_message": "OK" } } ] } }

However when I call the completions endpoint I get 404: { "error": "Model with requested name is not found" }

Step-by-step reproduction

No response

Relevant log output

No response

Issue submission checklist

Iffa-Intel commented 3 days ago

@fedecompa I encountered several issues too when attempting the steps in this guide (which you shared) on Windows: How to serve LLM models with Continuous Batching via OpenAI API.

Please note that this demo was officially validated on Intel® Xeon® processors Gen4 and Gen5 and Intel dGPU ARC and Flex models on Ubuntu22/24 and RedHat8/9. Other OS/hardware might work but still, issues are expected.

fedecompa commented 3 days ago

@Iffa-Intel thanks for the reply. Actually the GPU is detected correctly from the docker container running on the WSL2 Ubuntu22. And also the model is running correctly with the OVModelForCausalLM library for python on windows locally:

model_id = "Fede90/llama-3.2-3b-instruct-INT4" model = OVModelForCausalLM.from_pretrained(model_id, device="GPU.0", trust_remote_code=True)

So it is actually very strange...