# IPEX-LLM Installation: GPU
## Windows
### Prerequisites
IPEX-LLM on Windows supports Intel iGPU and dGPU.
> [!IMPORTANT]
> IPEX-LLM on Windows only supports PyTorch 2.1.
To apply Intel GPU acceleration, please first verify your GPU driver version.
> [!NOTE]
> The GPU driver version of your device can be checked in the "Task Manager" -> GPU 0 (or GPU 1, etc.) -> Driver version.
If you have driver version lower than `31.0.101.5122`, it is recommended to [**update your GPU driver to the latest**](https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html).
### Install IPEX-LLM
#### Install IPEX-LLM From PyPI
We recommend using [Miniforge](https://conda-forge.org/download/) to create a python 3.11 enviroment.
> [!IMPORTANT]
> ``ipex-llm`` is tested with Python 3.9, 3.10 and 3.11. Python 3.11 is recommended for best practices.
The easiest ways to install `ipex-llm` is the following commands, choosing either US or CN website for `extra-index-url`:
- For **US**:
```cmd
conda create -n llm python=3.11 libuv
conda activate llm
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
```
- For **CN**:
```cmd
conda create -n llm python=3.11 libuv
conda activate llm
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
```
#### Install IPEX-LLM From Wheel
If you encounter network issues when installing IPEX, you can also install IPEX-LLM dependencies for Intel XPU from source archives. First you need to download and install torch/torchvision/ipex from wheels listed below before installing `ipex-llm`.
Download the wheels on Windows system:
```
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torch-2.1.0a0%2Bcxx11.abi-cp311-cp311-win_amd64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.16.0a0%2Bcxx11.abi-cp311-cp311-win_amd64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.1.10%2Bxpu-cp311-cp311-win_amd64.whl
```
You may install dependencies directly from the wheel archives and then install `ipex-llm` using following commands:
```
pip install torch-2.1.0a0+cxx11.abi-cp311-cp311-win_amd64.whl
pip install torchvision-0.16.0a0+cxx11.abi-cp311-cp311-win_amd64.whl
pip install intel_extension_for_pytorch-2.1.10+xpu-cp311-cp311-win_amd64.whl
pip install --pre --upgrade ipex-llm[xpu]
```
> [!NOTE]
> All the wheel packages mentioned here are for Python 3.11. If you would like to use Python 3.9 or 3.10, you should modify the wheel names for ``torch``, ``torchvision``, and ``intel_extension_for_pytorch`` by replacing ``cp11`` with ``cp39`` or ``cp310``, respectively.
### Runtime Configuration
To use GPU acceleration on Windows, several environment variables are required before running a GPU example:
- For **Intel iGPU**:
```cmd
set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1
```
- For **Intel Arc™ A-Series Graphics**:
```cmd
set SYCL_CACHE_PERSISTENT=1
```
> [!NOTE]
> For **the first time** that **each model** runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
### Troubleshooting
#### 1. Error loading `intel_extension_for_pytorch`
If you met error when importing `intel_extension_for_pytorch`, please ensure that you have completed the following steps:
* Ensure that you have installed Visual Studio with "Desktop development with C++" workload.
* Make sure that the correct version of oneAPI, specifically 2024.0, is installed.
* Ensure that `libuv` is installed in your conda environment. This can be done during the creation of the environment with the command:
```cmd
conda create -n llm python=3.11 libuv
```
If you missed `libuv`, you can add it to your existing environment through
```cmd
conda install libuv
```
## Linux
### Prerequisites
IPEX-LLM GPU support on Linux has been verified on:
* Intel Arc™ A-Series Graphics
* Intel Data Center GPU Flex Series
* Intel Data Center GPU Max Series
> [!IMPORTANT]
> IPEX-LLM on Linux supports PyTorch 2.0 and PyTorch 2.1.
>
> **Warning**
>
> IPEX-LLM support for Pytorch 2.0 is deprecated as of ``ipex-llm >= 2.1.0b20240511``.
> [!IMPORTANT]
> We currently support the Ubuntu 20.04 operating system and later.
- For **PyTorch 2.1**:
To enable IPEX-LLM for Intel GPUs with PyTorch 2.1, here are several prerequisite steps for tools installation and environment preparation:
- Step 1: Install Intel GPU Driver version >= stable_775_20_20231219. We highly recommend installing the latest version of intel-i915-dkms using apt.
> **Tip**:
>
> Please refer to our [driver installation](https://dgpu-docs.intel.com/driver/installation.html) for general purpose GPU capabilities.
>
> See [release page](https://dgpu-docs.intel.com/releases/index.html) for latest version.
> **Note**:
>
> For Intel Core™ Ultra integrated GPU, please make sure level_zero version >= 1.3.28717. The level_zero version can be checked with ``sycl-ls``, and verison will be tagged be ``[ext_oneapi_level_zero:gpu]``.
> ```
> [opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix]
> [opencl:cpu:1] Intel(R) OpenCL, Intel(R) Core(TM) Ultra 5 125H OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
> [opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) Graphics OpenCL 3.0 NEO [24.09.28717.12]
> [ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) Graphics 1.3 [1.3.28717]
> ```
>
> If you have level_zero version < 1.3.28717, you could update as follows:
>
> ```bash
> wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.16238.4/intel-igc-core_1.0.16238.4_amd64.deb
> wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.16238.4/intel-igc-opencl_1.0.16238.4_amd64.deb
> wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-level-zero-gpu-dbgsym_1.3.28717.12_amd64.ddeb
> wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-level-zero-gpu_1.3.28717.12_amd64.deb
> wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-opencl-icd-dbgsym_24.09.28717.12_amd64.ddeb
> wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/intel-opencl-icd_24.09.28717.12_amd64.deb
> wget https://github.com/intel/compute-runtime/releases/download/24.09.28717.12/libigdgmm12_22.3.17_amd64.deb
> sudo dpkg -i *.deb
> ```
- Step 2: Download and install [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) with version 2024.0. OneDNN, OneMKL and DPC++ compiler are needed, others are optional.
Intel® oneAPI Base Toolkit 2024.0 installation methods:
For APT installer
- Step 1: Set up repository
```bash
wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
sudo apt update
```
- Step 2: Install the package
```bash
sudo apt install intel-oneapi-common-vars=2024.0.0-49406 \
intel-oneapi-common-oneapi-vars=2024.0.0-49406 \
intel-oneapi-diagnostics-utility=2024.0.0-49093 \
intel-oneapi-compiler-dpcpp-cpp=2024.0.2-49895 \
intel-oneapi-dpcpp-ct=2024.0.0-49381 \
intel-oneapi-mkl=2024.0.0-49656 \
intel-oneapi-mkl-devel=2024.0.0-49656 \
intel-oneapi-mpi=2021.11.0-49493 \
intel-oneapi-mpi-devel=2021.11.0-49493 \
intel-oneapi-dal=2024.0.1-25 \
intel-oneapi-dal-devel=2024.0.1-25 \
intel-oneapi-ippcp=2021.9.1-5 \
intel-oneapi-ippcp-devel=2021.9.1-5 \
intel-oneapi-ipp=2021.10.1-13 \
intel-oneapi-ipp-devel=2021.10.1-13 \
intel-oneapi-tlt=2024.0.0-352 \
intel-oneapi-ccl=2021.11.2-5 \
intel-oneapi-ccl-devel=2021.11.2-5 \
intel-oneapi-dnnl-devel=2024.0.0-49521 \
intel-oneapi-dnnl=2024.0.0-49521 \
intel-oneapi-tcm-1.0=1.0.0-435
```
> **Note**:
>
> You can uninstall the package by running the following command:
>
> ```bash
> sudo apt autoremove intel-oneapi-common-vars
> ```
For PIP installer
- Step 1: Install oneAPI in a user-defined folder, e.g., ``~/intel/oneapi``.
```bash
export PYTHONUSERBASE=~/intel/oneapi
pip install dpcpp-cpp-rt==2024.0.2 mkl-dpcpp==2024.0.0 onednn==2024.0.0 --user
```
> **Note**:
>
> The oneAPI packages are visible in ``pip list`` only if ``PYTHONUSERBASE`` is properly set.
- Step 2: Configure your working conda environment (e.g. with name ``llm``) to append oneAPI path (e.g. ``~/intel/oneapi/lib``) to the environment variable ``LD_LIBRARY_PATH``.
```bash
conda env config vars set LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/intel/oneapi/lib -n llm
```
> **Note**:
>
> You can view the configured environment variables for your environment (e.g. with name ``llm``) by running ``conda env config vars list -n llm``.
> You can continue with your working conda environment and install ``ipex-llm`` as guided in the next section.
> **Note**:
>
> You are recommended not to install other pip packages in the user-defined folder for oneAPI (e.g. ``~/intel/oneapi``).
> You can uninstall the oneAPI package by simply deleting the package folder, and unsetting the configuration of your working conda environment (e.g., with name ``llm``).
>
> ```bash
> rm -r ~/intel/oneapi
> conda env config vars unset LD_LIBRARY_PATH -n llm
> ```
For Offline installer
Using the offline installer allows you to customize the installation path.
```bash
wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/20f4e6a1-6b0b-4752-b8c1-e5eacba10e01/l_BaseKit_p_2024.0.0.49564_offline.sh
sudo sh ./l_BaseKit_p_2024.0.0.49564_offline.sh
```
> **Note**:
>
> You can also modify the installation or uninstall the package by running the following commands:
>
> ```bash
> cd /opt/intel/oneapi/installer
> sudo ./installer
> ```
- For **PyTorch 2.0** (deprecated for versions ``ipex-llm >= 2.1.0b20240511``):
To enable IPEX-LLM for Intel GPUs with PyTorch 2.0, here're several prerequisite steps for tools installation and environment preparation:
- Step 1: Install Intel GPU Driver version >= stable_775_20_20231219. Highly recommend installing the latest version of intel-i915-dkms using apt.
> **Tip**:
>
> Please refer to our [driver installation](https://dgpu-docs.intel.com/driver/installation.html) for general purpose GPU capabilities.
>
> See [release page](https://dgpu-docs.intel.com/releases/index.html) for latest version.
- Step 2: Download and install [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) with version 2023.2. OneDNN, OneMKL and DPC++ compiler are needed, others are optional.
Intel® oneAPI Base Toolkit 2023.2 installation methods:
For APT installer
- Step 1: Set up repository
```bash
wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
sudo apt update
```
- Step 2: Install the packages
```bash
sudo apt install -y intel-oneapi-common-vars=2023.2.0-49462 \
intel-oneapi-compiler-cpp-eclipse-cfg=2023.2.0-49495 intel-oneapi-compiler-dpcpp-eclipse-cfg=2023.2.0-49495 \
intel-oneapi-diagnostics-utility=2022.4.0-49091 \
intel-oneapi-compiler-dpcpp-cpp=2023.2.0-49495 \
intel-oneapi-mkl=2023.2.0-49495 intel-oneapi-mkl-devel=2023.2.0-49495 \
intel-oneapi-mpi=2021.10.0-49371 intel-oneapi-mpi-devel=2021.10.0-49371 \
intel-oneapi-tbb=2021.10.0-49541 intel-oneapi-tbb-devel=2021.10.0-49541\
intel-oneapi-ccl=2021.10.0-49084 intel-oneapi-ccl-devel=2021.10.0-49084\
intel-oneapi-dnnl-devel=2023.2.0-49516 intel-oneapi-dnnl=2023.2.0-49516
```
> **Note**:
>
> You can uninstall the package by running the following command:
>
> ```bash
> sudo apt autoremove intel-oneapi-common-vars
> ```
For PIP installer
- Step 1: Install oneAPI in a user-defined folder, e.g., ``~/intel/oneapi``
```bash
export PYTHONUSERBASE=~/intel/oneapi
pip install dpcpp-cpp-rt==2023.2.0 mkl-dpcpp==2023.2.0 onednn-cpu-dpcpp-gpu-dpcpp==2023.2.0 --user
```
> **Note**:
>
> The oneAPI packages are visible in ``pip list`` only if ``PYTHONUSERBASE`` is properly set.
- Step 2: Configure your working conda environment (e.g. with name ``llm``) to append oneAPI path (e.g. ``~/intel/oneapi/lib``) to the environment variable ``LD_LIBRARY_PATH``.
```bash
conda env config vars set LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/intel/oneapi/lib -n llm
```
> **Note**:
>
> You can view the configured environment variables for your environment (e.g. with name ``llm``) by running ``conda env config vars list -n llm``.
> You can continue with your working conda environment and install ``ipex-llm`` as guided in the next section.
> **Note**:
>
> You are recommended not to install other pip packages in the user-defined folder for oneAPI (e.g. ``~/intel/oneapi``).
> You can uninstall the oneAPI package by simply deleting the package folder, and unsetting the configuration of your working conda environment (e.g., with name ``llm``).
>
> ```bash
> rm -r ~/intel/oneapi
> conda env config vars unset LD_LIBRARY_PATH -n llm
> ```
For Offline installer
Using the offline installer allows you to customize the installation path.
```bash
wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/992857b9-624c-45de-9701-f6445d845359/l_BaseKit_p_2023.2.0.49397_offline.sh
sudo sh ./l_BaseKit_p_2023.2.0.49397_offline.sh
```
> **Note**:
>
> You can also modify the installation or uninstall the package by running the following commands:
>
> ```bash
> cd /opt/intel/oneapi/installer
> sudo ./installer
> ```
### Install IPEX-LLM
#### Install IPEX-LLM From PyPI
We recommend using [Miniforge](https://conda-forge.org/download/) to create a python 3.11 enviroment:
> [!IMPORTANT]
> ``ipex-llm`` is tested with Python 3.9, 3.10 and 3.11. Python 3.11 is recommended for best practices.
> [!IMPORTANT]
> Make sure you install matching versions of ipex-llm/pytorch/IPEX and oneAPI Base Toolkit. IPEX-LLM with Pytorch 2.1 should be used with oneAPI Base Toolkit version 2024.0. IPEX-LLM with Pytorch 2.0 should be used with oneAPI Base Toolkit version 2023.2.
- For **PyTorch 2.1**:
Choose either US or CN website for ``extra-index-url``:
- For **US**:
```bash
conda create -n llm python=3.11
conda activate llm
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
```
> **Note**:
>
> The ``xpu`` option will install IPEX-LLM with PyTorch 2.1 by default, which is equivalent to
>
> ```bash
> pip install --pre --upgrade ipex-llm[xpu_2.1] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/> xpu/us/
> ```
- For **CN**:
```bash
conda create -n llm python=3.11
conda activate llm
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
```
> **Note**:
>
> The ``xpu`` option will install IPEX-LLM with PyTorch 2.1 by default, which is equivalent to
>
> ```bash
> pip install --pre --upgrade ipex-llm[xpu_2.1] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/> xpu/cn/
> ```
- For **PyTorch 2.0** (deprecated for versions ``ipex-llm >= 2.1.0b20240511``):
Choose either US or CN website for ``extra-index-url``:
- For **US**:
```bash
conda create -n llm python=3.11
conda activate llm
pip install --pre --upgrade ipex-llm[xpu_2.0]==2.1.0b20240510 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
```
- For **CN**:
```bash
conda create -n llm python=3.11
conda activate llm
pip install --pre --upgrade ipex-llm[xpu_2.0]==2.1.0b20240510 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
```
#### Install IPEX-LLM From Wheel
If you encounter network issues when installing IPEX, you can also install IPEX-LLM dependencies for Intel XPU from source archives. First you need to download and install torch/torchvision/ipex from wheels listed below before installing `ipex-llm`.
- For **PyTorch 2.1**:
```bash
# get the wheels on Linux system for IPEX 2.1.10+xpu
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torch-2.1.0a0%2Bcxx11.abi-cp311-cp311-linux_x86_64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.16.0a0%2Bcxx11.abi-cp311-cp311-linux_x86_64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.1.10%2Bxpu-cp311-cp311-linux_x86_64.whl
```
Then you may install directly from the wheel archives using following commands:
```bash
# install the packages from the wheels
pip install torch-2.1.0a0+cxx11.abi-cp311-cp311-linux_x86_64.whl
pip install torchvision-0.16.0a0+cxx11.abi-cp311-cp311-linux_x86_64.whl
pip install intel_extension_for_pytorch-2.1.10+xpu-cp311-cp311-linux_x86_64.whl
# install ipex-llm for Intel GPU
pip install --pre --upgrade ipex-llm[xpu]
```
- For **PyTorch 2.0** (deprecated for versions ``ipex-llm >= 2.1.0b20240511``):
```bash
# get the wheels on Linux system for IPEX 2.0.110+xpu
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torch-2.0.1a0%2Bcxx11.abi-cp311-cp311-linux_x86_64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/torchvision-0.15.2a0%2Bcxx11.abi-cp311-cp311-linux_x86_64.whl
wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.0.110%2Bxpu-cp311-cp311-linux_x86_64.whl
```
Then you may install directly from the wheel archives using following commands:
```bash
# install the packages from the wheels
pip install torch-2.0.1a0+cxx11.abi-cp311-cp311-linux_x86_64.whl
pip install torchvision-0.15.2a0+cxx11.abi-cp311-cp311-linux_x86_64.whl
pip install intel_extension_for_pytorch-2.0.110+xpu-cp311-cp311-linux_x86_64.whl
# install ipex-llm for Intel GPU
pip install --pre --upgrade ipex-llm[xpu_2.0]==2.1.0b20240510
```
> [!NOTE]
> All the wheel packages mentioned here are for Python 3.11. If you would like to use Python 3.9 or 3.10, you should modify the wheel names for ``torch``, ``torchvision``, and ``intel_extension_for_pytorch`` by replacing ``cp11`` with ``cp39`` or ``cp310``, respectively.
### Runtime Configuration
To use GPU acceleration on Linux, several environment variables are required or recommended before running a GPU example.
- For **Intel Arc™ A-Series and Intel Data Center GPU Flex**:
For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series, we recommend:
```bash
# Configure oneAPI environment variables. Required step for APT or offline installed oneAPI.
# Skip this step for PIP-installed oneAPI since the environment has already been configured in LD_LIBRARY_PATH.
source /opt/intel/oneapi/setvars.sh
# Recommended Environment Variables for optimal performance
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
```
- For **Intel Data Center GPU Max**:
For Intel Data Center GPU Max Series, we recommend:
```bash
# Configure oneAPI environment variables. Required step for APT or offline installed oneAPI.
# Skip this step for PIP-installed oneAPI since the environment has already been configured in LD_LIBRARY_PATH.
source /opt/intel/oneapi/setvars.sh
# Recommended Environment Variables for optimal performance
export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
export ENABLE_SDP_FUSION=1
```
Please note that ``libtcmalloc.so`` can be installed by ``conda install -c conda-forge -y gperftools=2.10``
- For **Intel iGPU**:
```bash
# Configure oneAPI environment variables. Required step for APT or offline installed oneAPI.
# Skip this step for PIP-installed oneAPI since the environment has already been configured in LD_LIBRARY_PATH.
source /opt/intel/oneapi/setvars.sh
export SYCL_CACHE_PERSISTENT=1
export BIGDL_LLM_XMX_DISABLED=1
```
> [!NOTE]
> For **the first time** that **each model** runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
### Known issues
#### 1. Potential suboptimal performance with Linux kernel 6.2.0
For Ubuntu 22.04 and driver version < stable_775_20_20231219, the performance on Linux kernel 6.2.0 is worse than Linux kernel 5.19.0. You can use `sudo apt update && sudo apt install -y intel-i915-dkms intel-fw-gpu` to install the latest driver to solve this issue (need to reboot OS).
Tips: You can use `sudo apt list --installed | grep intel-i915-dkms` to check your intel-i915-dkms's version, the version should be latest and >= `1.23.9.11.231003.15+i19-1`.
#### 2. Driver installation unmet dependencies error: intel-i915-dkms
The last apt install command of the driver installation may produce the following error:
```
The following packages have unmet dependencies:
intel-i915-dkms : Conflicts: intel-platform-cse-dkms
Conflicts: intel-platform-vsec-dkms
```
You can use `sudo apt install -y intel-i915-dkms intel-fw-gpu` to install instead. As the intel-platform-cse-dkms and intel-platform-vsec-dkms are already provided by intel-i915-dkms.
### Troubleshooting
#### 1. Cannot open shared object file: No such file or directory
Error where libmkl file is not found, for example,
```
OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory
```
```
Error: libmkl_sycl_blas.so.4: cannot open shared object file: No such file or directory
```
The reason for such errors is that oneAPI has not been initialized properly before running IPEX-LLM code or before importing IPEX package.
* For oneAPI installed using APT or Offline Installer, make sure you execute `setvars.sh` of oneAPI Base Toolkit before running IPEX-LLM.
* For PIP-installed oneAPI, activate your working environment and run ``echo $LD_LIBRARY_PATH`` to check if the installation path is properly configured for the environment. If the output does not contain oneAPI path (e.g. ``~/intel/oneapi/lib``), check [Prerequisites](#prerequisites-1) to re-install oneAPI with PIP installer.
* Make sure you install matching versions of ipex-llm/pytorch/IPEX and oneAPI Base Toolkit. IPEX-LLM with PyTorch 2.1 should be used with oneAPI Base Toolkit version 2024.0. IPEX-LLM with PyTorch 2.0 should be used with oneAPI Base Toolkit version 2023.2.