Add multi-stage Pipeline-Parallel-FastAPI example --------- Co-authored-by: hzjane <a1015616934@qq.com>
33 lines
1.3 KiB
Markdown
33 lines
1.3 KiB
Markdown
# Serve IPEX-LLM on Multiple Intel GPUs in multi-stage pipeline parallel fashion
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This example demonstrates how to run IPEX-LLM serving on multiple [Intel GPUs](../README.md) with Pipeline Parallel.
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## Requirements
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To run this example with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information. For this particular example, you will need at least two GPUs on your machine.
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## Example
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### 1. Install
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```bash
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conda create -n llm python=3.11
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install oneccl_bind_pt==2.1.100 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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# configures OneAPI environment variables
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source /opt/intel/oneapi/setvars.sh
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# pip install git+https://github.com/microsoft/DeepSpeed.git@ed8aed5
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# pip install git+https://github.com/intel/intel-extension-for-deepspeed.git@0eb734b
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pip install mpi4py fastapi uvicorn
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conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
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```
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### 2. Run pipeline parallel serving on multiple GPUs
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```bash
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# Need to set MODEL_PATH in run.sh first
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bash run.sh
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```
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