diff --git a/demo/dask/cpu_survival.py b/demo/dask/cpu_survival.py
new file mode 100644
index 000000000..c2b815074
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+++ b/demo/dask/cpu_survival.py
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+import xgboost as xgb
+import os
+from xgboost.dask import DaskDMatrix
+import dask.dataframe as dd
+from dask.distributed import Client
+from dask.distributed import LocalCluster
+
+def main(client):
+    # Load an example survival data from CSV into a Dask data frame.
+    # 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'))
+
+    # 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)
+
+    # 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']
+
+    # you can pass output directly into `predict` too.
+    prediction = xgb.dask.predict(client, bst, dtrain)
+    print('Evaluation history: ', history)
+
+    # Uncomment the following line to save the model to the disk
+    # bst.save_model('survival_model.json')
+
+    return prediction
+
+
+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:
+            main(client)