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. Make sure to add any required additional packages (for your custom model) to the docker-compose.yml (or as environment variable to the clearml-serving-inference container), by defining for example: CLEARML_EXTRA_PYTHON_PACKAGES="vllm==0.5.4"

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" --name "test vllm" --project "serving examples"

   Or auto update

   clearml-serving --id <service_id> model auto-update --engine vllm --endpoint "test_vllm" --preprocess "examples/vllm/preprocess.py" --name "test vllm" --project "serving examples" --max-versions 2

   Or add Canary endpoint

   clearml-serving --id <service_id> model canary --endpoint "test_vllm" --weights 0.1 0.9 --input-endpoint-prefix test_vllm

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: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):

import openai
openai.api_key = "dummy"
openai.api_base = f"http://serving.apps.okd.mts.ai/clearml/v1"


r0 = await openai.ChatCompletion.acreate(
    model=vllm_endpoint,
    messages=[{"role": "system", "content": ""}, {"role": "user", "content": "Hi there, goodman!"}],
    temperature=1.0,
    max_tokens=1024,
    top_p=1.0,
    request_timeout=10000,
)

print(f"ChatCompletion: {r0['choices'][0]['message']}")

r1 = await openai.Completion.acreate(
    model=vllm_endpoint,
    prompt="Hi there, goodman!",
    temperature=1.0,
    max_tokens=256,
)

print(f"Completion: \n {r1['choices'][0]['text']}")

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