7663de956c
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.
13 lines
509 B
Bash
Executable File
13 lines
509 B
Bash
Executable File
#!/bin/bash
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rm -f *.model*
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export DMLC_SUBMIT_CLUSTER=local
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submit="timeout 30 python ../../dmlc-core/tracker/dmlc-submit"
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echo -e "\n ====== 1. Basic distributed-gpu test with Python: 4 workers; 1 GPU per worker ====== \n"
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$submit --num-workers=$(nvidia-smi -L | wc -l) python distributed_gpu.py basic_1x4 || exit 1
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echo -e "\n ====== 2. RF distributed-gpu test with Python: 4 workers; 1 GPU per worker ====== \n"
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$submit --num-workers=$(nvidia-smi -L | wc -l) python distributed_gpu.py rf_1x4 || exit 1
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