clearml-serving/examples/vllm

Deploy vLLM model

setting up the serving service

1. Create serving Service: clearml-serving create --name "serving example" (write down the service ID)

2. Add vLLM engine parameters in VLLM_ENGINE_ARGS variable as it was done in this file. Make sure to add any required additional packages (for your custom model) to the requirements.txt or docker-compose.yml (or as environment variable to the clearml-serving-inference container), by defining for example: CLEARML_EXTRA_PYTHON_PACKAGES="vllm==0.7.3 prometheus_client==0.21.1"

3. Create model endpoint:

   clearml-serving --id <service_id> model add --model-id <model_id> --engine vllm --endpoint "test_vllm" --preprocess "examples/vllm/preprocess.py"
   

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.

   Or you can run the clearml-serving container independently:

   docker run -v ~/clearml.conf:/root/clearml.conf -p 8080:8080 -e CLEARML_SERVING_TASK_ID=<service_id> clearml-serving-inference:latest
   

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):

   python examples/vllm/test_openai_api.py
   

   Available routes:

   • /v1/completions
   • /v1/chat/completions
   • /v1/models

   see test_openai_api.py for more information.

NOTE!

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:

prompt = "Hi there, goodman!"
result = self.send_request(endpoint="chat/completions", version=None, data={"model": "test_vllm", "messages": [{"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": prompt}]})
answer = result.choises[0].message.content

OR If you want to use send_request method, use openai client instead