DOC: Better explanations about GPGPU driver installations (#2464)

* more details about GPGPU drivers

* advertise iGPU compat more visibly

* Update doc/sources/oneapi-gpu.rst

Co-authored-by: Ian Faust <icfaust@gmail.com>

* Update doc/sources/oneapi-gpu.rst

Co-authored-by: Ian Faust <icfaust@gmail.com>

* add link to arch wiki and more details

* reword compute-follows-data idea

* reword 'gpgpu drivers' -> 'compute runtime'

* Update doc/sources/oneapi-gpu.rst

Co-authored-by: ethanglaser <42726565+ethanglaser@users.noreply.github.com>

---------

Co-authored-by: Ian Faust <icfaust@gmail.com>
Co-authored-by: ethanglaser <42726565+ethanglaser@users.noreply.github.com>

Author: 3 people

README.md

@@ -47,7 +47,7 @@ The software acceleration is achieved with vector instructions, AI hardware-spec
 With Extension for Scikit-learn, you can:
 
 * Speed up training and inference by up to 100x with equivalent mathematical accuracy
-* Benefit from performance improvements across different CPU hardware configurations, including GPUs and multi-GPU configurations
+* Benefit from performance improvements across different hardware configurations, including [GPUs](https://uxlfoundation.github.io/scikit-learn-intelex/latest/oneapi-gpu.html) and [multi-GPU](https://uxlfoundation.github.io/scikit-learn-intelex/latest/distributed-mode.html) configurations
 * Integrate the extension into your existing Scikit-learn applications without code modifications
 * Continue to use the open-source scikit-learn API
 * Enable and disable the extension with a couple of lines of code or at the command line

doc/sources/index.rst

@@ -29,7 +29,7 @@ The software acceleration is achieved with vector instructions, AI hardware-spec
 Use |sklearnex|, to:
 
 * Speed up training and inference by up to 100x with equivalent mathematical accuracy
-* Benefit from performance improvements across different x86-64 CPUs and Intel(R) GPUs
+* Benefit from performance improvements across different x86-64 CPUs and Intel(R) GPUs (including iGPUs)
 * Integrate the extension into your existing |sklearn| applications without code modifications
 * Enable and disable the extension with a couple of lines of code or at the command line
 

doc/sources/oneapi-gpu.rst

@@ -19,18 +19,21 @@
 oneAPI and GPU support in |sklearnex|
 ##############################################################
 
-|sklearnex| can execute computations on different devices (CPUs, GPUs) through the SYCL framework in oneAPI.
+|sklearnex| can execute computations on different devices (CPUs and GPUs, including integrated GPUs from laptops and desktops) through the SYCL framework in oneAPI.
 
-The device used for computations can be easily controlled through the target offloading functionality (e.g. through ``sklearnex.config_context(target_offload="gpu")`` - see rest of this page for more details), but for finer-grained controlled (e.g. operating on arrays that are already in a given device's memory), it can also interact with objects from package |dpctl|, which offers a Python interface over SYCL concepts such as devices, queues, and USM (unified shared memory) arrays.
+The device used for computations can be easily controlled through the target offloading functionality (e.g. through ``sklearnex.config_context(target_offload="gpu")``, which moves data to GPU if it's not already there - see rest of this page for more details), but for finer-grained controlled (e.g. operating on arrays that are already in a given device's memory), it can also interact with objects from package |dpctl|, which offers a Python interface over SYCL concepts such as devices, queues, and USM (unified shared memory) arrays.
 
-While not strictly required, package |dpctl| is recommended for a better experience on GPUs.
+While not strictly required, package |dpctl| is recommended for a better experience on GPUs - for example, it can provide GPU-allocated arrays that enable compute-follows-data execution models (i.e. so that ``target_offload`` wouldn't need to move the data from CPU to GPU).
 
-.. important:: Be aware that GPU usage requires non-Python dependencies on your system, such as the `Intel(R) GPGPU Drivers <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_.
+.. important:: Be aware that GPU usage requires non-Python dependencies on your system, such as the `Intel(R) Compute Runtime <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_ (see below).
 
 Prerequisites
 -------------
 
-For execution on GPUs, DPC++ runtime and GPGPU drivers are required.
+For execution on GPUs, DPC++ runtime and Intel Compute Runtime (also referred to elsewhere as 'GPGPU drivers') are required.
+
+DPC++ Runtime
+~~~~~~~~~~~~~
 
 DPC++ compiler runtime can be installed either from PyPI or Conda:
 
@@ -46,7 +49,27 @@ DPC++ compiler runtime can be installed either from PyPI or Conda:
 
      conda install -c conda-forge dpcpp_cpp_rt
 
-For GPGPU driver installation instructions, see the general `DPC++ system requirements <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-dpcpp-system-requirements.html>`_ sections corresponding to your operating system.
+Intel Compute Runtime
+~~~~~~~~~~~~~~~~~~~~~
+
+On Windows, GPU drivers for iGPUs and dGPUs include the required Intel Compute Runtime. Drivers for windows can be downloaded from `this link <https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html>`__.
+
+For datacenters, see further instructions `here <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/oneapi-dpcpp/2025.html#inpage-nav-2-1-1>`__.
+
+
+On Linux, some distributions - namely Ubuntu Desktop 25.04 and higher, and Fedora Workstation 42 and higher - come with the compute runtime for iGPUs and dGPUs preinstalled, while others require installing them separately.
+
+Debian systems require installing package ``intel-opencl-icd`` (along with its dependencies such as ``intel-compute-runtime`` and ``intel-graphics-compiler``), which is available from Debian's ``main`` repository: ::
+
+    sudo apt-get install intel-opencl-icd
+
+.. tip:: For Debian Trixie (13), the Intel Compute Runtime is not available from the Stable repository, but can be installed by enabling the Sid (Unstable) repository.
+
+For Arch Linux, and for other distributions in general, see the `GPGPU article in the Arch wiki <https://wiki.archlinux.org/title/GPGPU>`__.
+
+Be aware that datacenter-grade devices, such as 'Flex' and 'Max', require different drivers and runtimes. For CentOS and for datacenter-grade devices, see `instructions here <https://dgpu-docs.intel.com/driver/installation.html>`__.
+
+For more details, see the `DPC++ requirements page <https://www.intel.com/content/www/us/en/developer/articles/system-requirements/oneapi-dpcpp/2025.html>`__.
 
 Device offloading
 -----------------

doc/sources/quick-start.rst

@@ -337,7 +337,7 @@ Software Requirements
    .. tab:: GPU
 
       - A Linux* or Windows* version supported by DPC++ and oneMKL
-      - Intel® GPGPU drivers
+      - Intel® Compute Runtime (see :ref:`oneapi_gpu`)
       - DPC++ runtime libraries
 
       .. important::