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.. | ||
preprocess.py | ||
readme.md |
Deploy vLLM model
setting up the serving service
- Create serving Service:
clearml-serving create --name "serving example"
(write down the service ID) - 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"
- Create model endpoint:
clearml-serving --id <service_id> model add --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
- 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
- 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']}")