2554ba0913
* add usage of vllm * add usage of vllm * add usage of vllm * add usage of vllm * add usage of vllm * add usage of vllm
97 lines
No EOL
3.4 KiB
Markdown
97 lines
No EOL
3.4 KiB
Markdown
## Build/Use BigDL-LLM-serving cpu image
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### Build Image
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```bash
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docker build \
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--build-arg http_proxy=.. \
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--build-arg https_proxy=.. \
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--build-arg no_proxy=.. \
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--rm --no-cache -t intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT .
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```
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### Use the image for doing cpu serving
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You could use the following bash script to start the container. Please be noted that the CPU config is specified for Xeon CPUs, change it accordingly if you are not using a Xeon CPU.
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```bash
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#/bin/bash
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export DOCKER_IMAGE=intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT
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sudo docker run -itd \
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--net=host \
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--cpuset-cpus="0-47" \
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--cpuset-mems="0" \
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--memory="32G" \
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--name=CONTAINER_NAME \
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--shm-size="16g" \
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$DOCKER_IMAGE
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```
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After the container is booted, you could get into the container through `docker exec`.
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To run model-serving using `BigDL-LLM` as backend, you can refer to this [document](https://github.com/intel-analytics/BigDL/tree/main/python/llm/src/bigdl/llm/serving).
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Also you can set environment variables and start arguments while running a container to get serving started initially. You may need to boot several containers to support. One controller container and at least one worker container are needed. The api server address(host and port) and controller address are set in controller container, and you need to set the same controller address as above, model path on your machine and worker address in worker container.
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To start a controller container:
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```bash
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#/bin/bash
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export DOCKER_IMAGE=intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT
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controller_host=localhost
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controller_port=23000
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api_host=localhost
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api_port=8000
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sudo docker run -itd \
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--net=host \
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--privileged \
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--cpuset-cpus="0-47" \
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--cpuset-mems="0" \
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--memory="64G" \
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--name=serving-cpu-controller \
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--shm-size="16g" \
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-e ENABLE_PERF_OUTPUT="true" \
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-e CONTROLLER_HOST=$controller_host \
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-e CONTROLLER_PORT=$controller_port \
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-e API_HOST=$api_host \
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-e API_PORT=$api_port \
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$DOCKER_IMAGE -m controller
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```
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To start a worker container:
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```bash
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#/bin/bash
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export DOCKER_IMAGE=intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT
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export MODEL_PATH=YOUR_MODEL_PATH
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controller_host=localhost
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controller_port=23000
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worker_host=localhost
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worker_port=23001
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sudo docker run -itd \
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--net=host \
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--privileged \
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--cpuset-cpus="0-47" \
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--cpuset-mems="0" \
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--memory="64G" \
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--name="serving-cpu-worker" \
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--shm-size="16g" \
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-e ENABLE_PERF_OUTPUT="true" \
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-e CONTROLLER_HOST=$controller_host \
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-e CONTROLLER_PORT=$controller_port \
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-e WORKER_HOST=$worker_host \
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-e WORKER_PORT=$worker_port \
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-e OMP_NUM_THREADS=48 \
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-e MODEL_PATH=/llm/models/Llama-2-7b-chat-hf \
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-v $MODEL_PATH:/llm/models/ \
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$DOCKER_IMAGE -m worker -w vllm_worker # use -w model_worker if vllm worker is not needed
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```
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Then you can use `curl` for testing, an example could be:
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```bash
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curl -X POST -H "Content-Type: application/json" -d '{
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"model": "YOUR_MODEL_NAME",
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"prompt": "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun",
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"n": 1,
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"best_of": 1,
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"use_beam_search": false,
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"stream": false
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}' http://localhost:8000/v1/completions
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``` |