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Shengsheng Huang 2024-02-27 13:40:51 +08:00 committed by GitHub
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docs/readthedocs/source/doc/LLM/Quickstart/install_windows_gpu.md
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@ -2,7 +2,7 @@
This guide demonstrates how to install BigDL-LLM on Windows with Intel GPUs.
This process applies to Intel Core Ultra and Core 12 - 14 gen integrated GPUs (iGPUs), as well as Intel Arc Series GPU.
It applies to Intel Core Ultra and Core 12 - 14 gen integrated GPUs (iGPUs), as well as Intel Arc Series GPU.
## Install GPU driver
@ -15,11 +15,11 @@ This process applies to Intel Core Ultra and Core 12 - 14 gen integrated GPUs (i
* Download and install the latest GPU driver from the [official Intel download page](https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html). A system reboot is necessary to apply the changes after the installation is complete.
> Note: the process could take around 10 minutes. After reboot, check for the **Intel Arc Control** application to verify the driver has been installed correctly. If the installation was successful, you should see the **Arc Control** interface similar to the figure below
> Note: The process could take around 10 minutes. After reboot, check for the **Intel Arc Control** application to verify the driver has been installed correctly. If the installation was successful, you should see the **Arc Control** interface similar to the figure below
> <img src="https://llm-assets.readthedocs.io/en/latest/_images/quickstart_windows_gpu_3.png" width=80%; />
* To monitor your GPU's performance and status, you can use either use the **Windows Task Manager** (see the left side of the figure below) or the **Arc Control** application (see the right side of the figure below) or :
* To monitor your GPU's performance and status, you can use either the **Windows Task Manager** (see the left side of the figure below) or the **Arc Control** application (see the right side of the figure below) :
> <img src="https://llm-assets.readthedocs.io/en/latest/_images/quickstart_windows_gpu_4.png" width=70%; />
## Setup Python Environment
@ -47,7 +47,7 @@ This process applies to Intel Core Ultra and Core 12 - 14 gen integrated GPUs (i
## Install `bigdl-llm`
* With the `llm` environment active, use `pip` to install `bigdl-llm` for GPU:
Choose either US or CN website for extra index url:
Choose either US or CN website for `extra-index-url`:
* US:
```bash
pip install --pre --upgrade bigdl-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
@ -56,7 +56,7 @@ This process applies to Intel Core Ultra and Core 12 - 14 gen integrated GPUs (i
```bash
pip install --pre --upgrade bigdl-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
```
> Note: If there are network issues when installing IPEX, refer to [this guide](https://bigdl.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html#install-bigdl-llm-from-wheel) for more details.
> Note: If yuu encounter network issues while installing IPEX, refer to [this guide](https://bigdl.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html#install-bigdl-llm-from-wheel) for troubleshooting advice.
* You can verfy if bigdl-llm is successfully by simply importing a few classes from the library. For example, in the Python interactive shell, execute the following import command:
```python
@ -71,7 +71,7 @@ Now let's play with a real LLM. We'll be using the [phi-1.5](https://huggingface
```bash
conda activate llm
```
* Step 2: If you're running on integrated GPU, set some environment variables by running below commands:
* Step 2: If you're running on iGPU, set some environment variables by running below commands:
> For more details about runtime configurations, refer to [this guide](https://bigdl.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html#runtime-configuration):
```bash
set SYCL_CACHE_PERSISTENT=1
@ -105,7 +105,7 @@ Now let's play with a real LLM. We'll be using the [phi-1.5](https://huggingface
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_str)
```
> Note: when running LLMs on Intel iGPUs with limited memory size, we recommend setting `cpu_embedding=True` in the from_pretrained function.
> Note: when running LLMs on Intel iGPUs with limited memory size, we recommend setting `cpu_embedding=True` in the `from_pretrained` function.
> This will allow the memory-intensive embedding layer to utilize the CPU instead of GPU.
* Step 5. Run `demo.py` within the activated Python environment using the following command: