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[dask] Add seed to demos. (#10009)

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Jiaming Yuan 2024-01-26 02:09:38 +08:00 committed by GitHub
parent c8f5d190c6
commit d3f2dbe64f
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2 changed files with 8 additions and 4 deletions
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5
demo/dask/cpu_training.py
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@ -14,8 +14,9 @@ def main(client):
# generate some random data for demonstration # generate some random data for demonstration
m = 100000 m = 100000
n = 100 n = 100
X = da.random.random(size=(m, n), chunks=100) rng = da.random.default_rng(1)
y = da.random.random(size=(m,), chunks=100) X = rng.normal(size=(m, n))
y = X.sum(axis=1)
# DaskDMatrix acts like normal DMatrix, works as a proxy for local # DaskDMatrix acts like normal DMatrix, works as a proxy for local
# DMatrix scatter around workers. # DMatrix scatter around workers.

7
demo/dask/gpu_training.py
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@ -2,6 +2,7 @@
Example of training with Dask on GPU Example of training with Dask on GPU
==================================== ====================================
""" """
import cupy as cp
import dask_cudf import dask_cudf
from dask import array as da from dask import array as da
from dask import dataframe as dd from dask import dataframe as dd
@ -72,10 +73,12 @@ if __name__ == "__main__":
with LocalCUDACluster(n_workers=2, threads_per_worker=4) as cluster: with LocalCUDACluster(n_workers=2, threads_per_worker=4) as cluster:
with Client(cluster) as client: with Client(cluster) as client:
# generate some random data for demonstration # generate some random data for demonstration
rng = da.random.default_rng(1)
m = 100000 m = 100000
n = 100 n = 100
X = da.random.random(size=(m, n), chunks=10000) X = rng.normal(size=(m, n))
y = da.random.random(size=(m,), chunks=10000) y = X.sum(axis=1)
print("Using DaskQuantileDMatrix") print("Using DaskQuantileDMatrix")
from_ddqdm = using_quantile_device_dmatrix(client, X, y) from_ddqdm = using_quantile_device_dmatrix(client, X, y)