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update readme and fix docker-compose-gpu.yml
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@ -75,7 +75,7 @@ services:
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clearml-serving-inference:
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image: clearml-serving-inference:latest
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image: allegroai/clearml-serving-inference:latest
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container_name: clearml-serving-inference
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restart: unless-stopped
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# optimize perforamnce
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@ -11,12 +11,7 @@
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clearml-serving --id <service_id> model add --model-id <model_id> --engine vllm --endpoint "test_vllm" --preprocess "examples/vllm/preprocess.py"
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```
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4. If you already have the `clearml-serving` docker-compose running, it might take it a minute or two to sync with the new endpoint.
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Or you can run the clearml-serving container independently:
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```
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docker run -v ~/clearml.conf:/root/clearml.conf -p 8080:8080 -e CLEARML_SERVING_TASK_ID=<service_id> clearml-serving-inference:latest
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```
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4. If you already have the `clearml-serving` docker-compose running, it might take it a minute or two to sync with the new endpoint. To run docker-compose, see [docker-compose instructions](/README.md#nail_care-initial-setup), p. 8 (and use [docker-compose-gpu.yml](/docker/docker-compose-gpu.yml) file for vllm on gpu and [docker-compose.yml](/docker/docker-compose.yml) otherwise)
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5. Test new endpoint (do notice the first call will trigger the model pulling, so it might take longer, from here on, it's all in memory):
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@ -32,6 +27,8 @@
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see [test_openai_api.py](test_openai_api.py) for more information.
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6. Check metrics using grafana (You have to select Prometheus as data source, all of vLLM metrics have "vllm:" prefix). For more information, see [Model monitoring and performance metrics](/README.md#bar_chart-model-monitoring-and-performance-metrics-bell)
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NOTE!
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If you want to use send_request method, keep in mind that you have to pass "completions" or "chat/completions" in entrypoint (and pass model as a part of "data" parameter) and use it for non-streaming models:
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