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Reduce time for some multi-gpu tests (#8288)

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* Faster dask tests

* Reuse AllReducer objects in tests.

* Faster boost from prediction tests.

* Use rmm dask fixture.

* Speed up dask demo.

* mypy

* Format with black.

* mypy

* Clang-tidy

Co-authored-by: Hyunsu Philip Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Rory Mitchell
2022-10-04 12:49:33 +02:00
committed by GitHub
parent ca0547bb65
commit d686bf52a6
8 changed files with 337 additions and 336 deletions
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25
demo/dask/gpu_training.py
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@@ -4,13 +4,12 @@ Example of training with Dask on GPU
"""
from dask_cuda import LocalCUDACluster
import dask_cudf
from dask.distributed import Client, wait
from dask.distributed import Client
from dask import array as da
from dask import dataframe as dd
import xgboost as xgb
from xgboost import dask as dxgb
from xgboost.dask import DaskDMatrix
import argparse
def using_dask_matrix(client: Client, X, y):
@@ -51,7 +50,7 @@ def using_quantile_device_dmatrix(client: Client, X, y):
# `DaskDeviceQuantileDMatrix` is used instead of `DaskDMatrix`, be careful
# that it can not be used for anything else other than training.
dtrain = dxgb.DaskDeviceQuantileDMatrix(client, X, y)
dtrain = dxgb.DaskQuantileDMatrix(client, X, y)
output = xgb.dask.train(client,
{'verbosity': 2,
'tree_method': 'gpu_hist'},
@@ -63,12 +62,6 @@ def using_quantile_device_dmatrix(client: Client, X, y):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--ddqdm', choices=[0, 1], type=int, default=1,
help='''Whether should we use `DaskDeviceQuantileDMatrix`''')
args = parser.parse_args()
# `LocalCUDACluster` is used for assigning GPU to XGBoost processes. Here
# `n_workers` represents the number of GPUs since we use one GPU per worker
# process.
@@ -77,12 +70,10 @@ if __name__ == '__main__':
# generate some random data for demonstration
m = 100000
n = 100
X = da.random.random(size=(m, n), chunks=100)
y = da.random.random(size=(m, ), chunks=100)
X = da.random.random(size=(m, n), chunks=10000)
y = da.random.random(size=(m, ), chunks=10000)
if args.ddqdm == 1:
print('Using DaskDeviceQuantileDMatrix')
from_ddqdm = using_quantile_device_dmatrix(client, X, y)
else:
print('Using DMatrix')
from_dmatrix = using_dask_matrix(client, X, y)
print('Using DaskQuantileDMatrix')
from_ddqdm = using_quantile_device_dmatrix(client, X, y)
print('Using DMatrix')
from_dmatrix = using_dask_matrix(client, X, y)