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Unverified Commit 9cab93a9 authored by Nikita Titov's avatar Nikita Titov Committed by GitHub
Browse files

[docs] add missed CUDA device type in docs (#4130)

parent 91f2a014
...@@ -488,7 +488,7 @@ Build GPU Version ...@@ -488,7 +488,7 @@ Build GPU Version
Linux Linux
^^^^^ ^^^^^
On Linux a GPU version of LightGBM can be built using **OpenCL**, **Boost**, **CMake** and **gcc** or **Clang**. On Linux a GPU version of LightGBM (``device_type=gpu``) can be built using **OpenCL**, **Boost**, **CMake** and **gcc** or **Clang**.
The following dependencies should be installed before compilation: The following dependencies should be installed before compilation:
...@@ -520,7 +520,7 @@ To build LightGBM GPU version, run the following commands: ...@@ -520,7 +520,7 @@ To build LightGBM GPU version, run the following commands:
Windows Windows
^^^^^^^ ^^^^^^^
On Windows a GPU version of LightGBM can be built using **OpenCL**, **Boost**, **CMake** and **VS Build Tools** or **MinGW**. On Windows a GPU version of LightGBM (``device_type=gpu``) can be built using **OpenCL**, **Boost**, **CMake** and **VS Build Tools** or **MinGW**.
If you use **MinGW**, the build procedure is similar to the build on Linux. Refer to `GPU Windows Compilation <./GPU-Windows.rst>`__ to get more details. If you use **MinGW**, the build procedure is similar to the build on Linux. Refer to `GPU Windows Compilation <./GPU-Windows.rst>`__ to get more details.
...@@ -571,9 +571,9 @@ Refer to `GPU Docker folder <https://github.com/microsoft/LightGBM/tree/master/d ...@@ -571,9 +571,9 @@ Refer to `GPU Docker folder <https://github.com/microsoft/LightGBM/tree/master/d
Build CUDA Version (Experimental) Build CUDA Version (Experimental)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The `original GPU build <#build-gpu-version>`__ of LightGBM is based on OpenCL. The `original GPU build <#build-gpu-version>`__ of LightGBM (``device_type=gpu``) is based on OpenCL.
The CUDA-based build is a separate implementation and requires an NVIDIA graphics card with compute capability 6.0 and higher. It should be considered experimental, and we suggest using it only when it is impossible to use OpenCL version (for example, on IBM POWER microprocessors). The CUDA-based build (``device_type=cuda``) is a separate implementation and requires an NVIDIA graphics card with compute capability 6.0 and higher. It should be considered experimental, and we suggest using it only when it is impossible to use OpenCL version (for example, on IBM POWER microprocessors).
**Note**: only Linux is supported, other operating systems are not supported yet. **Note**: only Linux is supported, other operating systems are not supported yet.
......
...@@ -199,7 +199,7 @@ Core Parameters ...@@ -199,7 +199,7 @@ Core Parameters
- **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors - **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors
- ``device_type`` :raw-html:`<a id="device_type" title="Permalink to this parameter" href="#device_type">&#x1F517;&#xFE0E;</a>`, default = ``cpu``, type = enum, options: ``cpu``, ``gpu``, aliases: ``device`` - ``device_type`` :raw-html:`<a id="device_type" title="Permalink to this parameter" href="#device_type">&#x1F517;&#xFE0E;</a>`, default = ``cpu``, type = enum, options: ``cpu``, ``gpu``, ``cuda``, aliases: ``device``
- device for the tree learning, you can use GPU to achieve the faster learning - device for the tree learning, you can use GPU to achieve the faster learning
......
...@@ -215,7 +215,7 @@ struct Config { ...@@ -215,7 +215,7 @@ struct Config {
// [doc-only] // [doc-only]
// type = enum // type = enum
// options = cpu, gpu // options = cpu, gpu, cuda
// alias = device // alias = device
// desc = device for the tree learning, you can use GPU to achieve the faster learning // desc = device for the tree learning, you can use GPU to achieve the faster learning
// desc = **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up // desc = **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up
......
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