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.

tests/ci_build/Dockerfile.cudf

@@ -18,7 +18,7 @@ ENV PATH=/opt/python/bin:$PATH
 # Create new Conda environment with cuDF and dask
 RUN \
     conda create -n cudf_test -c rapidsai -c nvidia -c numba -c conda-forge -c anaconda \
-        cudf=0.9 python=3.7 anaconda::cudatoolkit=$CUDA_VERSION dask
+        cudf=0.9 python=3.7 anaconda::cudatoolkit=$CUDA_VERSION dask dask-cuda
 
 # Install other Python packages
 RUN \