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Time the CPU tests on Jenkins. (#6257)

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* Time the CPU tests on Jenkins.
* Reduce thread contention.
* Add doc.
* Skip heavy tests on ARM.
This commit is contained in:
Jiaming Yuan
2020-10-21 08:19:07 +08:00
committed by GitHub
parent d1254808d5
commit 81c37c28d5
9 changed files with 29 additions and 18 deletions
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12
demo/guide-python/sklearn_examples.py
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@@ -19,7 +19,7 @@ y = digits['target']
X = digits['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])
xgb_model = xgb.XGBClassifier(n_jobs=1).fit(X[train_index], y[train_index])
predictions = xgb_model.predict(X[test_index])
actuals = y[test_index]
print(confusion_matrix(actuals, predictions))
@@ -30,7 +30,7 @@ y = iris['target']
X = iris['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])
xgb_model = xgb.XGBClassifier(n_jobs=1).fit(X[train_index], y[train_index])
predictions = xgb_model.predict(X[test_index])
actuals = y[test_index]
print(confusion_matrix(actuals, predictions))
@@ -41,7 +41,7 @@ y = boston['target']
X = boston['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBRegressor().fit(X[train_index], y[train_index])
xgb_model = xgb.XGBRegressor(n_jobs=1).fit(X[train_index], y[train_index])
predictions = xgb_model.predict(X[test_index])
actuals = y[test_index]
print(mean_squared_error(actuals, predictions))
@@ -49,10 +49,10 @@ for train_index, test_index in kf.split(X):
print("Parameter optimization")
y = boston['target']
X = boston['data']
xgb_model = xgb.XGBRegressor()
xgb_model = xgb.XGBRegressor(n_jobs=1)
clf = GridSearchCV(xgb_model,
{'max_depth': [2, 4, 6],
'n_estimators': [50, 100, 200]}, verbose=1)
'n_estimators': [50, 100, 200]}, verbose=1, n_jobs=1)
clf.fit(X, y)
print(clf.best_score_)
print(clf.best_params_)
@@ -69,6 +69,6 @@ print(np.allclose(clf.predict(X), clf2.predict(X)))
X = digits['data']
y = digits['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = xgb.XGBClassifier()
clf = xgb.XGBClassifier(n_jobs=1)
clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
eval_set=[(X_test, y_test)])

3
demo/guide-python/sklearn_parallel.py
View File

@@ -1,6 +1,7 @@
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
import xgboost as xgb
import multiprocessing
if __name__ == "__main__":
print("Parallel Parameter optimization")
@@ -8,7 +9,7 @@ if __name__ == "__main__":
y = boston['target']
X = boston['data']
xgb_model = xgb.XGBRegressor()
xgb_model = xgb.XGBRegressor(n_jobs=multiprocessing.cpu_count() // 2)
clf = GridSearchCV(xgb_model, {'max_depth': [2, 4, 6],
'n_estimators': [50, 100, 200]}, verbose=1,
n_jobs=2)