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>
38 lines
1.3 KiB
Docker
38 lines
1.3 KiB
Docker
ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-runtime-ubuntu16.04
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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SHELL ["/bin/bash", "-c"] # Use Bash as shell
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# Install all basic requirements
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RUN \
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apt-get update && \
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apt-get install -y wget unzip bzip2 libgomp1 build-essential && \
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# Python
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wget -O Miniconda3.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
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bash Miniconda3.sh -b -p /opt/python
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ENV PATH=/opt/python/bin:$PATH
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# Create new Conda environment with cuDF, Dask, and cuPy
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RUN \
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conda create -n gpu_test -c rapidsai-nightly -c rapidsai -c nvidia -c conda-forge -c defaults \
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python=3.7 cudf=0.15* rmm=0.15* cudatoolkit=$CUDA_VERSION dask dask-cuda dask-cudf cupy \
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numpy pytest scipy scikit-learn pandas matplotlib wheel python-kubernetes urllib3 graphviz hypothesis
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ENV GOSU_VERSION 1.10
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# Install lightweight sudo (not bound to TTY)
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RUN set -ex; \
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wget -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$GOSU_VERSION/gosu-amd64" && \
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chmod +x /usr/local/bin/gosu && \
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gosu nobody true
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# Default entry-point to use if running locally
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# It will preserve attributes of created files
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COPY entrypoint.sh /scripts/
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WORKDIR /workspace
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ENTRYPOINT ["/scripts/entrypoint.sh"]
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