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Deprecate single node multi-gpu mode (#4579)

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* deprecate multi-gpu training

* add single node

* add warning
This commit is contained in:
Rong Ou 2019-06-18 20:51:38 -07:00 committed by Rory Mitchell
parent 6757654337
commit e94f85f0e4
5 changed files with 16 additions and 6 deletions
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2
CMakeLists.txt
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@ -28,7 +28,7 @@ option(USE_NVTX "Build with cuda profiling annotations. Developers only." OFF)
set(NVTX_HEADER_DIR "" CACHE PATH "Path to the stand-alone nvtx header")
## CUDA
option(USE_CUDA "Build with GPU acceleration" OFF)
option(USE_NCCL "Build with NCCL to enable multi-GPU support." OFF)
option(USE_NCCL "Build with NCCL to enable distributed GPU support." OFF)
option(BUILD_WITH_SHARED_NCCL "Build with shared NCCL library." OFF)
set(GPU_COMPUTE_VER "" CACHE STRING
"Semicolon separated list of compute versions to be built against, e.g. '35;61'")

4
doc/build.rst
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@ -196,9 +196,9 @@ From the command line on Linux starting from the XGBoost directory:
cmake .. -DUSE_CUDA=ON
make -j4
.. note:: Enabling multi-GPU training
.. note:: Enabling distributed GPU training
By default, multi-GPU training is disabled and only a single GPU will be used. To enable multi-GPU training, set the option ``USE_NCCL=ON``. Multi-GPU training depends on NCCL2, available at `this link <https://developer.nvidia.com/nccl>`_. Since NCCL2 is only available for Linux machines, **multi-GPU training is available only for Linux**.
By default, distributed GPU training is disabled and only a single GPU will be used. To enable distributed GPU training, set the option ``USE_NCCL=ON``. Distributed GPU training depends on NCCL2, available at `this link <https://developer.nvidia.com/nccl>`_. Since NCCL2 is only available for Linux machines, **distributed GPU training is available only for Linux**.
.. code-block:: bash

2
doc/gpu/index.rst
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@ -80,6 +80,8 @@ The GPU algorithms currently work with CLI, Python and R packages. See :doc:`/bu
Single Node Multi-GPU
=====================
.. note:: Single node multi-GPU training is deprecated. Please use distributed GPU training with one process per GPU.
Multiple GPUs can be used with the ``gpu_hist`` tree method using the ``n_gpus`` parameter. which defaults to 1. If this is set to -1 all available GPUs will be used. If ``gpu_id`` is specified as non-zero, the selected gpu devices will be from ``gpu_id`` to ``gpu_id+n_gpus``, please note that ``gpu_id+n_gpus`` must be less than or equal to the number of available GPUs on your system. As with GPU vs. CPU, multi-GPU will not always be faster than a single GPU due to PCI bus bandwidth that can limit performance.
.. note:: Enabling multi-GPU training

4
include/xgboost/generic_parameters.h
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@ -66,7 +66,9 @@ struct LearnerTrainParam : public dmlc::Parameter<LearnerTrainParam> {
DMLC_DECLARE_FIELD(n_gpus)
.set_default(0)
.set_lower_bound(-1)
.describe("Number of GPUs to use for multi-gpu algorithms.");
.describe("Deprecated, please use distributed training with one "
"process per GPU. "
"Number of GPUs to use for multi-gpu algorithms.");
DMLC_DECLARE_FIELD(booster)
.set_default("gbtree")
.describe("Gradient booster used for training.");

8
src/learner.cc
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@ -200,9 +200,15 @@ class LearnerImpl : public Learner {
<< " Internal Error: Always call InitModel or Load before any evaluation.";
this->ValidateDMatrix(dmat);
CHECK(this->gbm_) << " Internal: GBM is not set";
if (this->gbm_->UseGPU() && cfg_.find("n_gpus") == cfg_.cend()) {
if (this->gbm_->UseGPU()) {
if (cfg_.find("n_gpus") == cfg_.cend()) {
tparam_.n_gpus = 1;
}
if (tparam_.n_gpus != 1) {
LOG(WARNING) << "Multi-GPU training is deprecated. "
"Please use distributed GPU training with one process per GPU.";
}
}
}
void Load(dmlc::Stream* fi) override {