Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Handle the new device parameter in dask and demos. (#9386)

Browse Source 
* Handle the new `device` parameter in dask and demos.

- Check no ordinal is specified in the dask interface.
- Update demos.
- Update dask doc.
- Update the condition for QDM.
This commit is contained in:
Jiaming Yuan
2023-07-15 19:11:20 +08:00
committed by GitHub
parent 9da5050643
commit 16eb41936d
31 changed files with 631 additions and 450 deletions
Download Patch File Download Diff File Expand all files Collapse all files

52
demo/dask/cpu_survival.py
View File

@@ -18,43 +18,45 @@ def main(client):
# The Veterans' Administration Lung Cancer Trial
# The Statistical Analysis of Failure Time Data by Kalbfleisch J. and Prentice R (1980)
CURRENT_DIR = os.path.dirname(__file__)
df = dd.read_csv(os.path.join(CURRENT_DIR, os.pardir, 'data', 'veterans_lung_cancer.csv'))
df = dd.read_csv(
os.path.join(CURRENT_DIR, os.pardir, "data", "veterans_lung_cancer.csv")
)
# DaskDMatrix acts like normal DMatrix, works as a proxy for local
# DMatrix scatter around workers.
# For AFT survival, you'd need to extract the lower and upper bounds for the label
# and pass them as arguments to DaskDMatrix.
y_lower_bound = df['Survival_label_lower_bound']
y_upper_bound = df['Survival_label_upper_bound']
X = df.drop(['Survival_label_lower_bound',
'Survival_label_upper_bound'], axis=1)
dtrain = DaskDMatrix(client, X, label_lower_bound=y_lower_bound,
label_upper_bound=y_upper_bound)
y_lower_bound = df["Survival_label_lower_bound"]
y_upper_bound = df["Survival_label_upper_bound"]
X = df.drop(["Survival_label_lower_bound", "Survival_label_upper_bound"], axis=1)
dtrain = DaskDMatrix(
client, X, label_lower_bound=y_lower_bound, label_upper_bound=y_upper_bound
)
# Use train method from xgboost.dask instead of xgboost. This
# distributed version of train returns a dictionary containing the
# resulting booster and evaluation history obtained from
# evaluation metrics.
params = {'verbosity': 1,
'objective': 'survival:aft',
'eval_metric': 'aft-nloglik',
'learning_rate': 0.05,
'aft_loss_distribution_scale': 1.20,
'aft_loss_distribution': 'normal',
'max_depth': 6,
'lambda': 0.01,
'alpha': 0.02}
output = xgb.dask.train(client,
params,
dtrain,
num_boost_round=100,
evals=[(dtrain, 'train')])
bst = output['booster']
history = output['history']
params = {
"verbosity": 1,
"objective": "survival:aft",
"eval_metric": "aft-nloglik",
"learning_rate": 0.05,
"aft_loss_distribution_scale": 1.20,
"aft_loss_distribution": "normal",
"max_depth": 6,
"lambda": 0.01,
"alpha": 0.02,
}
output = xgb.dask.train(
client, params, dtrain, num_boost_round=100, evals=[(dtrain, "train")]
)
bst = output["booster"]
history = output["history"]
# you can pass output directly into `predict` too.
prediction = xgb.dask.predict(client, bst, dtrain)
print('Evaluation history: ', history)
print("Evaluation history: ", history)
# Uncomment the following line to save the model to the disk
# bst.save_model('survival_model.json')
@@ -62,7 +64,7 @@ def main(client):
return prediction
if __name__ == '__main__':
if __name__ == "__main__":
# or use other clusters for scaling
with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
with Client(cluster) as client:

22
demo/dask/cpu_training.py
View File

@@ -15,7 +15,7 @@ def main(client):
m = 100000
n = 100
X = da.random.random(size=(m, n), chunks=100)
y = da.random.random(size=(m, ), chunks=100)
y = da.random.random(size=(m,), chunks=100)
# DaskDMatrix acts like normal DMatrix, works as a proxy for local
# DMatrix scatter around workers.
@@ -25,21 +25,23 @@ def main(client):
# distributed version of train returns a dictionary containing the
# resulting booster and evaluation history obtained from
# evaluation metrics.
output = xgb.dask.train(client,
{'verbosity': 1,
'tree_method': 'hist'},
dtrain,
num_boost_round=4, evals=[(dtrain, 'train')])
bst = output['booster']
history = output['history']
output = xgb.dask.train(
client,
{"verbosity": 1, "tree_method": "hist"},
dtrain,
num_boost_round=4,
evals=[(dtrain, "train")],
)
bst = output["booster"]
history = output["history"]
# you can pass output directly into `predict` too.
prediction = xgb.dask.predict(client, bst, dtrain)
print('Evaluation history:', history)
print("Evaluation history:", history)
return prediction
if __name__ == '__main__':
if __name__ == "__main__":
# or use other clusters for scaling
with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
with Client(cluster) as client:

59
demo/dask/gpu_training.py
View File

@@ -13,33 +13,38 @@ from xgboost import dask as dxgb
from xgboost.dask import DaskDMatrix
def using_dask_matrix(client: Client, X, y):
# DaskDMatrix acts like normal DMatrix, works as a proxy for local
# DMatrix scatter around workers.
def using_dask_matrix(client: Client, X: da.Array, y: da.Array) -> da.Array:
# DaskDMatrix acts like normal DMatrix, works as a proxy for local DMatrix scatter
# around workers.
dtrain = DaskDMatrix(client, X, y)
# Use train method from xgboost.dask instead of xgboost. This
# distributed version of train returns a dictionary containing the
# resulting booster and evaluation history obtained from
# evaluation metrics.
output = xgb.dask.train(client,
{'verbosity': 2,
# Golden line for GPU training
'tree_method': 'gpu_hist'},
dtrain,
num_boost_round=4, evals=[(dtrain, 'train')])
bst = output['booster']
history = output['history']
# Use train method from xgboost.dask instead of xgboost. This distributed version
# of train returns a dictionary containing the resulting booster and evaluation
# history obtained from evaluation metrics.
output = xgb.dask.train(
client,
{
"verbosity": 2,
"tree_method": "hist",
# Golden line for GPU training
"device": "cuda",
},
dtrain,
num_boost_round=4,
evals=[(dtrain, "train")],
)
bst = output["booster"]
history = output["history"]
# you can pass output directly into `predict` too.
prediction = xgb.dask.predict(client, bst, dtrain)
print('Evaluation history:', history)
print("Evaluation history:", history)
return prediction
def using_quantile_device_dmatrix(client: Client, X, y):
"""`DaskQuantileDMatrix` is a data type specialized for `gpu_hist` and `hist` tree
methods for reducing memory usage.
def using_quantile_device_dmatrix(client: Client, X: da.Array, y: da.Array) -> da.Array:
"""`DaskQuantileDMatrix` is a data type specialized for `hist` tree methods for
reducing memory usage.
.. versionadded:: 1.2.0
@@ -52,26 +57,28 @@ def using_quantile_device_dmatrix(client: Client, X, y):
# the `ref` argument of `DaskQuantileDMatrix`.
dtrain = dxgb.DaskQuantileDMatrix(client, X, y)
output = xgb.dask.train(
client, {"verbosity": 2, "tree_method": "gpu_hist"}, dtrain, num_boost_round=4
client,
{"verbosity": 2, "tree_method": "hist", "device": "cuda"},
dtrain,
num_boost_round=4,
)
prediction = xgb.dask.predict(client, output, X)
return prediction
if __name__ == '__main__':
if __name__ == "__main__":
# `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.
# `n_workers` represents the number of GPUs since we use one GPU per worker process.
with LocalCUDACluster(n_workers=2, threads_per_worker=4) as cluster:
with Client(cluster) as client:
# generate some random data for demonstration
m = 100000
n = 100
X = da.random.random(size=(m, n), chunks=10000)
y = da.random.random(size=(m, ), chunks=10000)
y = da.random.random(size=(m,), chunks=10000)
print('Using DaskQuantileDMatrix')
print("Using DaskQuantileDMatrix")
from_ddqdm = using_quantile_device_dmatrix(client, X, y)
print('Using DMatrix')
print("Using DMatrix")
from_dmatrix = using_dask_matrix(client, X, y)

9
demo/dask/sklearn_gpu_training.py
View File

@@ -21,7 +21,8 @@ def main(client):
y = da.random.random(m, partition_size)
regressor = xgboost.dask.DaskXGBRegressor(verbosity=1)
regressor.set_params(tree_method='gpu_hist')
# set the device to CUDA
regressor.set_params(tree_method="hist", device="cuda")
# assigning client here is optional
regressor.client = client
@@ -31,13 +32,13 @@ def main(client):
bst = regressor.get_booster()
history = regressor.evals_result()
print('Evaluation history:', history)
print("Evaluation history:", history)
# returned prediction is always a dask array.
assert isinstance(prediction, da.Array)
return bst # returning the trained model
return bst # returning the trained model
if __name__ == '__main__':
if __name__ == "__main__":
# With dask cuda, one can scale up XGBoost to arbitrary GPU clusters.
# `LocalCUDACluster` used here is only for demonstration purpose.
with LocalCUDACluster() as cluster: