Document for device ordinal. (#9398)

- Rewrite GPU demos. notebook is converted to script to avoid committing additional png plots.
- Add GPU demos into the sphinx gallery.
- Add RMM demos into the sphinx gallery.
- Test for firing threads with different device ordinals.

demo/gpu_acceleration/README.md

@@ -1,5 +0,0 @@
-# GPU Acceleration Demo
-
-`cover_type.py` shows how to train a model on the [forest cover type](https://archive.ics.uci.edu/ml/datasets/covertype) dataset using GPU acceleration. The forest cover type dataset has 581,012 rows and 54 features, making it time consuming to process. We compare the run-time and accuracy of the GPU and CPU histogram algorithms.
-
-`shap.ipynb` demonstrates using GPU acceleration to compute SHAP values for feature importance.

demo/gpu_acceleration/README.rst

@@ -0,0 +1,8 @@
+:orphan:
+
+GPU Acceleration Demo
+=====================
+
+This is a collection of demonstration scripts to showcase the basic usage of GPU. Please
+see :doc:`/gpu/index` for more info. There are other demonstrations for distributed GPU
+training using dask or spark.

demo/gpu_acceleration/cover_type.py

@@ -1,41 +1,49 @@
+"""
+Using xgboost on GPU devices
+============================
+
+Shows how to train a model on the `forest cover type
+<https://archive.ics.uci.edu/ml/datasets/covertype>`_ dataset using GPU
+acceleration. The forest cover type dataset has 581,012 rows and 54 features, making it
+time consuming to process. We compare the run-time and accuracy of the GPU and CPU
+histogram algorithms.
+
+In addition, The demo showcases using GPU with other GPU-related libraries including
+cupy and cuml. These libraries are not strictly required.
+
+"""
 import time
 
+import cupy as cp
+from cuml.model_selection import train_test_split
 from sklearn.datasets import fetch_covtype
-from sklearn.model_selection import train_test_split
 
 import xgboost as xgb
 
 # Fetch dataset using sklearn
-cov = fetch_covtype()
-X = cov.data
-y = cov.target
+X, y = fetch_covtype(return_X_y=True)
+X = cp.array(X)
+y = cp.array(y)
+y -= y.min()
 
 # Create 0.75/0.25 train/test split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, train_size=0.75,
-                                                    random_state=42)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.25, train_size=0.75, random_state=42
+)
 
 # Specify sufficient boosting iterations to reach a minimum
 num_round = 3000
 
 # Leave most parameters as default
-param = {'objective': 'multi:softmax', # Specify multiclass classification
-         'num_class': 8, # Number of possible output classes
-         'tree_method': 'gpu_hist' # Use GPU accelerated algorithm
-         }
-
-# Convert input data from numpy to XGBoost format
-dtrain = xgb.DMatrix(X_train, label=y_train)
-dtest = xgb.DMatrix(X_test, label=y_test)
-
-gpu_res = {} # Store accuracy result
-tmp = time.time()
+clf = xgb.XGBClassifier(device="cuda", n_estimators=num_round)
 # Train model
-xgb.train(param, dtrain, num_round, evals=[(dtest, 'test')], evals_result=gpu_res)
-print("GPU Training Time: %s seconds" % (str(time.time() - tmp)))
+start = time.time()
+clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
+gpu_res = clf.evals_result()
+print("GPU Training Time: %s seconds" % (str(time.time() - start)))
 
 # Repeat for CPU algorithm
-tmp = time.time()
-param['tree_method'] = 'hist'
-cpu_res = {}
-xgb.train(param, dtrain, num_round, evals=[(dtest, 'test')], evals_result=cpu_res)
-print("CPU Training Time: %s seconds" % (str(time.time() - tmp)))
+clf = xgb.XGBClassifier(device="cpu", n_estimators=num_round)
+start = time.time()
+cpu_res = clf.evals_result()
+print("CPU Training Time: %s seconds" % (str(time.time() - start)))

demo/gpu_acceleration/tree_shap.py

@@ -0,0 +1,55 @@
+"""
+Use GPU to speedup SHAP value computation
+=========================================
+
+Demonstrates using GPU acceleration to compute SHAP values for feature importance.
+
+"""
+import shap
+from sklearn.datasets import fetch_california_housing
+
+import xgboost as xgb
+
+# Fetch dataset using sklearn
+data = fetch_california_housing()
+print(data.DESCR)
+X = data.data
+y = data.target
+
+num_round = 500
+
+param = {
+    "eta": 0.05,
+    "max_depth": 10,
+    "tree_method": "hist",
+    "device": "cuda",
+}
+
+# GPU accelerated training
+dtrain = xgb.DMatrix(X, label=y, feature_names=data.feature_names)
+model = xgb.train(param, dtrain, num_round)
+
+# Compute shap values using GPU with xgboost
+model.set_param({"device": "cuda"})
+shap_values = model.predict(dtrain, pred_contribs=True)
+
+# Compute shap interaction values using GPU
+shap_interaction_values = model.predict(dtrain, pred_interactions=True)
+
+
+# shap will call the GPU accelerated version as long as the device parameter is set to
+# "cuda"
+explainer = shap.TreeExplainer(model)
+shap_values = explainer.shap_values(X)
+
+# visualize the first prediction's explanation
+shap.force_plot(
+    explainer.expected_value,
+    shap_values[0, :],
+    X[0, :],
+    feature_names=data.feature_names,
+    matplotlib=True,
+)
+
+# Show a summary of feature importance
+shap.summary_plot(shap_values, X, plot_type="bar", feature_names=data.feature_names)