9adb812a0a
* [CI] Add RMM as an optional dependency * Replace caching allocator with pool allocator from RMM * Revert "Replace caching allocator with pool allocator from RMM" This reverts commit e15845d4e72e890c2babe31a988b26503a7d9038. * Use rmm::mr::get_default_resource() * Try setting default resource (doesn't work yet) * Allocate pool_mr in the heap * Prevent leaking pool_mr handle * Separate EXPECT_DEATH() in separate test suite suffixed DeathTest * Turn off death tests for RMM * Address reviewer's feedback * Prevent leaking of cuda_mr * Fix Jenkinsfile syntax * Remove unnecessary function in Jenkinsfile * [CI] Install NCCL into RMM container * Run Python tests * Try building with RMM, CUDA 10.0 * Do not use RMM for CUDA 10.0 target * Actually test for test_rmm flag * Fix TestPythonGPU * Use CNMeM allocator, since pool allocator doesn't yet support multiGPU * Use 10.0 container to build RMM-enabled XGBoost * Revert "Use 10.0 container to build RMM-enabled XGBoost" This reverts commit 789021fa31112e25b683aef39fff375403060141. * Fix Jenkinsfile * [CI] Assign larger /dev/shm to NCCL * Use 10.2 artifact to run multi-GPU Python tests * Add CUDA 10.0 -> 11.0 cross-version test; remove CUDA 10.0 target * Rename Conda env rmm_test -> gpu_test * Use env var to opt into CNMeM pool for C++ tests * Use identical CUDA version for RMM builds and tests * Use Pytest fixtures to enable RMM pool in Python tests * Move RMM to plugin/CMakeLists.txt; use PLUGIN_RMM * Use per-device MR; use command arg in gtest * Set CMake prefix path to use Conda env * Use 0.15 nightly version of RMM * Remove unnecessary header * Fix a unit test when cudf is missing * Add RMM demos * Remove print() * Use HostDeviceVector in GPU predictor * Simplify pytest setup; use LocalCUDACluster fixture * Address reviewers' commments Co-authored-by: Hyunsu Cho <chohyu01@cs.wasshington.edu>
32 lines
1.7 KiB
Markdown
32 lines
1.7 KiB
Markdown
Using XGBoost with RAPIDS Memory Manager (RMM) plugin (EXPERIMENTAL)
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====================================================================
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[RAPIDS Memory Manager (RMM)](https://github.com/rapidsai/rmm) library provides a collection of
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efficient memory allocators for NVIDIA GPUs. It is now possible to use XGBoost with memory
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allocators provided by RMM, by enabling the RMM integration plugin.
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The demos in this directory highlights one RMM allocator in particular: **the pool sub-allocator**.
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This allocator addresses the slow speed of `cudaMalloc()` by allocating a large chunk of memory
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upfront. Subsequent allocations will draw from the pool of already allocated memory and thus avoid
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the overhead of calling `cudaMalloc()` directly. See
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[this GTC talk slides](https://on-demand.gputechconf.com/gtc/2015/presentation/S5530-Stephen-Jones.pdf)
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for more details.
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Before running the demos, ensure that XGBoost is compiled with the RMM plugin enabled. To do this,
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run CMake with option `-DPLUGIN_RMM=ON` (`-DUSE_CUDA=ON` also required):
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```
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cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON
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make -j4
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```
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CMake will attempt to locate the RMM library in your build environment. You may choose to build
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RMM from the source, or install it using the Conda package manager. If CMake cannot find RMM, you
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should specify the location of RMM with the CMake prefix:
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```
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# If using Conda:
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cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON -DCMAKE_PREFIX_PATH=$CONDA_PREFIX
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# If using RMM installed with a custom location
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cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DPLUGIN_RMM=ON -DCMAKE_PREFIX_PATH=/path/to/rmm
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```
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* [Using RMM with a single GPU](./rmm_singlegpu.py)
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* [Using RMM with a local Dask cluster consisting of multiple GPUs](./rmm_mgpu_with_dask.py)
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