179 lines
6.1 KiB
Markdown
179 lines
6.1 KiB
Markdown
# Install and Use IPEX-LLM on Intel Arc B-Series GPU (code-named Battlemage)
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This guide demonstrates how to install and use IPEX-LLM on the Intel Arc B-Series GPU (such as **B580**).
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> [!NOTE]
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> Ensure your GPU driver and software environment meet the prerequisites before proceeding.
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---
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## Table of Contents
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1. [Linux](#1-linux)
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1.1 [Install Prerequisites](#11-install-prerequisites)
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1.2 [Install IPEX-LLM](#for-pytorch-and-huggingface) (for PyTorch and HuggingFace)
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1.3 [Install IPEX-LLM](#for-llamacpp-and-ollama) (for llama.cpp and Ollama)
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3. [Windows](#2-windows)
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2.1 [Install Prerequisites](#21-install-prerequisites)
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2.2 [Install IPEX-LLM](#for-pytorch-and-huggingface-1) (for PyTorch and HuggingFace)
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2.3 [Install IPEX-LLM](#for-llamacpp-and-ollama-1) (for llama.cpp and Ollama)
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5. [Use Cases](#3-use-cases)
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3.1 [PyTorch](#31-pytorch)
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3.2 [Ollama](#32-ollama)
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3.3 [llama.cpp](#33-llamacpp)
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3.4 [vLLM](#34-vllm)
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---
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## 1. Linux
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### 1.1 Install Prerequisites
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We recommend using Ubuntu 24.10 and kernel version 6.11 or above, as support for Battle Mage has been backported from kernel version 6.12 to version 6.11, which is included in Ubuntu 24.10, according to the official documentation [here](https://dgpu-docs.intel.com/driver/client/overview.html#installing-client-gpus-on-ubuntu-desktop-24-10). However, since this version of Ubuntu does not include the latest compute and media-related packages, we offer the intel-graphics Personal Package Archive (PPA). The PPA provides early access to newer packages, along with additional tools and features such as EU debugging.
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Use the following commands to install the intel-graphics PPA and the necessary compute and media packages:
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```bash
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sudo apt-get update
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sudo apt-get install -y software-properties-common
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sudo add-apt-repository -y ppa:kobuk-team/intel-graphics
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sudo apt-get install -y libze-intel-gpu1 libze1 intel-ocloc intel-opencl-icd clinfo intel-gsc intel-media-va-driver-non-free libmfx1 libmfx-gen1 libvpl2 libvpl-tools libva-glx2 va-driver-all vainfo
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sudo apt-get install -y intel-level-zero-gpu-raytracing # Optional: Hardware ray tracing support
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```
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#### Setup Python Environment
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Download and install Miniforge:
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```bash
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wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
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bash Miniforge3-Linux-x86_64.sh
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source ~/.bashrc
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```
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Create and activate a Python environment:
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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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```
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---
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### 1.2 Install IPEX-LLM
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With the `llm` environment active, install the appropriate `ipex-llm` package based on your use case:
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#### For PyTorch and HuggingFace:
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Install the `ipex-llm[xpu-arc]` package. Choose either the US or CN website for `extra-index-url`:
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- For **US**:
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```bash
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pip install --pre --upgrade ipex-llm[xpu-arc] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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```
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- For **CN**:
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```bash
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pip install --pre --upgrade ipex-llm[xpu-arc] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
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```
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#### For llama.cpp and Ollama:
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Install the `ipex-llm[cpp]` package.
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```bash
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pip install --pre --upgrade ipex-llm[cpp]
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```
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> [!NOTE]
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> If you encounter network issues during installation, refer to the [troubleshooting guide](../Overview/install_gpu.md#install-ipex-llm-from-wheel-1) for alternative steps.
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---
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## 2. Windows
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### 2.1 Install Prerequisites
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#### Update GPU Driver
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If your driver version is lower than `32.0.101.6449/32.0.101.101.6256`, update it from the [Intel download page](https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html). After installation, reboot the system.
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---
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#### Setup Python Environment
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Download and install Miniforge for Windows from the [official page](https://conda-forge.org/download/). After installation, create and activate a Python environment:
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```cmd
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conda create -n llm python=3.11 libuv
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conda activate llm
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```
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---
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### 2.2 Install IPEX-LLM
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With the `llm` environment active, install the appropriate `ipex-llm` package based on your use case:
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#### For PyTorch and HuggingFace:
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Install the `ipex-llm[xpu-arc]` package. Choose either the US or CN website for `extra-index-url`:
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- For **US**:
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```cmd
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pip install --pre --upgrade ipex-llm[xpu-arc] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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```
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- For **CN**:
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```cmd
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pip install --pre --upgrade ipex-llm[xpu-arc] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
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```
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#### For llama.cpp and Ollama:
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Install the `ipex-llm[cpp]` package.
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```cmd
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pip install --pre --upgrade ipex-llm[cpp]
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```
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> [!NOTE]
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> If you encounter network issues while installing IPEX, refer to [this guide](../Overview/install_gpu.md#install-ipex-llm-from-wheel) for troubleshooting advice.
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---
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## 3. Use Cases
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### 3.1 PyTorch
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Run a Quick PyTorch Example:
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1. Activate the environment:
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```bash
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conda activate llm # On Windows, use 'cmd'
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```
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2. Run the code:
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```python
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import torch
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from ipex_llm.transformers import AutoModelForCausalLM
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tensor_1 = torch.randn(1, 1, 40, 128).to('xpu')
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tensor_2 = torch.randn(1, 1, 128, 40).to('xpu')
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print(torch.matmul(tensor_1, tensor_2).size())
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```
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3. Expected Output:
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```
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torch.Size([1, 1, 40, 40])
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```
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For benchmarks and performance measurement, refer to the [Benchmark Quickstart guide](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/benchmark_quickstart.md).
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---
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### 3.2 Ollama
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To integrate and run with **Ollama**, follow the [Ollama Quickstart guide](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_quickstart.md).
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### 3.3 llama.cpp
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For instructions on how to run **llama.cpp** with IPEX-LLM, refer to the [llama.cpp Quickstart guide](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_quickstart.md).
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### 3.4 vLLM
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To set up and run **vLLM**, follow the [vLLM Quickstart guide](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/vLLM_quickstart.md).
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