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Test federated plugin using GitHub action. (#10336)

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Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Jiaming Yuan
2024-05-29 02:28:14 +08:00
committed by GitHub
parent 7ae5c972f9
commit 7354955cbb
4 changed files with 27 additions and 27 deletions
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doc/build.rst
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@@ -134,7 +134,7 @@ From the command line on Linux starting from the XGBoost directory:
.. note:: Specifying compute capability
To speed up compilation, the compute version specific to your GPU could be passed to cmake as, e.g., ``-DGPU_COMPUTE_VER=50``. A quick explanation and numbers for some architectures can be found `in this page <https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/>`_.
To speed up compilation, the compute version specific to your GPU could be passed to cmake as, e.g., ``-DCMAKE_CUDA_ARCHITECTURES=75``. A quick explanation and numbers for some architectures can be found `in this page <https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/>`_.
.. note:: Faster distributed GPU training with NCCL
@@ -147,6 +147,8 @@ From the command line on Linux starting from the XGBoost directory:
cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DNCCL_ROOT=/path/to/nccl2
make -j4
Some additional flags are available for NCCL, ``BUILD_WITH_SHARED_NCCL`` enables building XGBoost with NCCL as a shared library, while ``USE_DLOPEN_NCCL`` enables XGBoost to load NCCL at runtime using ``dlopen``.
On Windows, run CMake as follows:
.. code-block:: bash
@@ -165,6 +167,17 @@ The above cmake configuration run will create an ``xgboost.sln`` solution file i
To speed up compilation, run multiple jobs in parallel by appending option ``-- /MP``.
Federated Learning
==================
The federated learning plugin requires ``grpc`` and ``protobuf``. To install grpc, refer
to the `installation guide from the gRPC website
<https://grpc.io/docs/languages/cpp/quickstart/>`_. Alternatively, one can use the
``libgrpc`` and the ``protobuf`` package from conda forge if conda is available. After
obtaining the required dependencies, enable the flag: `-DPLUGIN_FEDERATED=ON` when running
CMake. Please note that only Linux is supported for the federated plugin.
.. _build_python:
***********************************
@@ -228,11 +241,12 @@ There are several ways to build and install the package from source:
3. Editable installation
To further enable rapid development and iteration, we provide an **editable installation**.
In an editable installation, the installed package is simply a symbolic link to your
working copy of the XGBoost source code. So every changes you make to your source
directory will be immediately visible to the Python interpreter. Here is how to
install XGBoost as editable installation:
To further enable rapid development and iteration, we provide an **editable
installation**. In an editable installation, the installed package is simply a symbolic
link to your working copy of the XGBoost source code. So every changes you make to your
source directory will be immediately visible to the Python interpreter. To install
XGBoost as editable installation, first build the shared library as previously
described, then install the Python package:
.. code-block:: bash