chore: import upstream snapshot with attribution

python/ray/tune/examples/pb2_example.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python
+
+import argparse
+
+import ray
+from ray import tune
+from ray.tune.examples.pbt_function import pbt_function
+from ray.tune.schedulers.pb2 import PB2
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing"
+    )
+    args, _ = parser.parse_known_args()
+
+    if args.smoke_test:
+        ray.init(num_cpus=2)  # force pausing to happen for test
+
+    perturbation_interval = 5
+    pbt = PB2(
+        time_attr="training_iteration",
+        perturbation_interval=perturbation_interval,
+        hyperparam_bounds={
+            # hyperparameter bounds.
+            "lr": [0.0001, 0.02],
+        },
+    )
+
+    tuner = tune.Tuner(
+        pbt_function,
+        run_config=tune.RunConfig(
+            name="pbt_test",
+            verbose=False,
+            stop={
+                "training_iteration": 30,
+            },
+            failure_config=tune.FailureConfig(
+                fail_fast=True,
+            ),
+        ),
+        tune_config=tune.TuneConfig(
+            scheduler=pbt,
+            metric="mean_accuracy",
+            mode="max",
+            num_samples=8,
+            reuse_actors=True,
+        ),
+        param_space={
+            "lr": 0.0001,
+            # note: this parameter is perturbed but has no effect on
+            # the model training in this example
+            "some_other_factor": 1,
+            # This parameter is not perturbed and is used to determine
+            # checkpoint frequency. We set checkpoints and perturbations
+            # to happen at the same frequency.
+            "checkpoint_interval": perturbation_interval,
+        },
+    )
+    results = tuner.fit()
+
+    print("Best hyperparameters found were: ", results.get_best_result().config)