34 lines
1.3 KiB
Markdown
34 lines
1.3 KiB
Markdown
# Run BigDL-LLM on Multiple Intel GPUs using DeepSpeed AutoTP
|
|
|
|
This example demonstrates how to run BigDL-LLM optimized low-bit model on multiple [Intel GPUs](../README.md) by leveraging DeepSpeed AutoTP.
|
|
|
|
## 0. Requirements
|
|
To run this example with BigDL-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.
|
|
|
|
## Example:
|
|
|
|
### 1. Install
|
|
|
|
```bash
|
|
conda create -n llm python=3.9
|
|
conda activate llm
|
|
# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
|
|
# you can install specific ipex/torch version for your need
|
|
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
|
pip install oneccl_bind_pt==2.0.100 -f https://developer.intel.com/ipex-whl-stable-xpu
|
|
pip install git+https://github.com/microsoft/DeepSpeed.git@78c518e
|
|
pip install git+https://github.com/intel/intel-extension-for-deepspeed.git@ec33277
|
|
pip install mpi4py
|
|
```
|
|
|
|
### 2. Configures OneAPI environment variables
|
|
```bash
|
|
source /opt/intel/oneapi/setvars.sh
|
|
```
|
|
|
|
### 3. Run tensor parallel inference on multiple GPUs
|
|
You many want to change some of the parameters in the script such as `NUM_GPUS`` to the number of GPUs you have on your machine.
|
|
|
|
```
|
|
bash run.sh
|
|
```
|