xgboost/tests/ci_build/Dockerfile.gpu
Jiaming Yuan 7663de956c
Run training with empty DMatrix. (#4990)
This makes GPU Hist robust in distributed environment as some workers might not
be associated with any data in either training or evaluation.

* Disable rabit mock test for now: See #5012 .

* Disable dask-cudf test at prediction for now: See #5003

* Launch dask job for all workers despite they might not have any data.
* Check 0 rows in elementwise evaluation metrics.

   Using AUC and AUC-PR still throws an error.  See #4663 for a robust fix.

* Add tests for edge cases.
* Add `LaunchKernel` wrapper handling zero sized grid.
* Move some parts of allreducer into a cu file.
* Don't validate feature names when the booster is empty.

* Sync number of columns in DMatrix.

  As num_feature is required to be the same across all workers in data split
  mode.

* Filtering in dask interface now by default syncs all booster that's not
empty, instead of using rank 0.

* Fix Jenkins' GPU tests.

* Install dask-cuda from source in Jenkins' test.

  Now all tests are actually running.

* Restore GPU Hist tree synchronization test.

* Check UUID of running devices.

  The check is only performed on CUDA version >= 10.x, as 9.x doesn't have UUID field.

* Fix CMake policy and project variables.

  Use xgboost_SOURCE_DIR uniformly, add policy for CMake >= 3.13.

* Fix copying data to CPU

* Fix race condition in cpu predictor.

* Fix duplicated DMatrix construction.

* Don't download extra nccl in CI script.
2019-11-06 16:13:13 +08:00

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1.1 KiB
Docker

ARG CUDA_VERSION
FROM nvidia/cuda:$CUDA_VERSION-runtime-ubuntu16.04
# Environment
ENV DEBIAN_FRONTEND noninteractive
# Install all basic requirements
RUN \
apt-get update && \
apt-get install -y wget unzip bzip2 libgomp1 build-essential && \
# Python
wget https://repo.continuum.io/miniconda/Miniconda3-4.5.12-Linux-x86_64.sh && \
bash Miniconda3-4.5.12-Linux-x86_64.sh -b -p /opt/python
ENV PATH=/opt/python/bin:$PATH
# Install Python packages
RUN \
pip install numpy pytest scipy scikit-learn pandas matplotlib wheel kubernetes urllib3 graphviz && \
pip install "dask[complete]" && \
conda install -c rapidsai -c nvidia -c numba -c conda-forge -c anaconda dask-cuda
ENV GOSU_VERSION 1.10
# Install lightweight sudo (not bound to TTY)
RUN set -ex; \
wget -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$GOSU_VERSION/gosu-amd64" && \
chmod +x /usr/local/bin/gosu && \
gosu nobody true
# Default entry-point to use if running locally
# It will preserve attributes of created files
COPY entrypoint.sh /scripts/
WORKDIR /workspace
ENTRYPOINT ["/scripts/entrypoint.sh"]