chore: import upstream snapshot with attribution

python/ray/tune/examples/hyperband_function_example.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python
+
+import argparse
+import json
+import os
+import tempfile
+
+import numpy as np
+
+import ray
+from ray import tune
+from ray.tune import Checkpoint
+from ray.tune.schedulers import HyperBandScheduler
+
+
+def train_func(config):
+    step = 0
+    checkpoint = tune.get_checkpoint()
+    if checkpoint:
+        with checkpoint.as_directory() as checkpoint_dir:
+            with open(os.path.join(checkpoint_dir, "checkpoint.json")) as f:
+                step = json.load(f)["timestep"] + 1
+
+    for timestep in range(step, 100):
+        v = np.tanh(float(timestep) / config.get("width", 1))
+        v *= config.get("height", 1)
+
+        # Checkpoint the state of the training every 3 steps
+        # Note that this is only required for certain schedulers
+        with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
+            checkpoint = None
+            if timestep % 3 == 0:
+                with open(
+                    os.path.join(temp_checkpoint_dir, "checkpoint.json"), "w"
+                ) as f:
+                    json.dump({"timestep": timestep}, f)
+                checkpoint = Checkpoint.from_directory(temp_checkpoint_dir)
+
+            # Here we use `episode_reward_mean`, but you can also report other
+            # objectives such as loss or accuracy.
+            tune.report({"episode_reward_mean": v}, checkpoint=checkpoint)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing"
+    )
+    args, _ = parser.parse_known_args()
+
+    ray.init(num_cpus=4 if args.smoke_test else None)
+
+    # Hyperband early stopping, configured with `episode_reward_mean` as the
+    # objective and `training_iteration` as the time unit,
+    # which is automatically filled by Tune.
+    hyperband = HyperBandScheduler(max_t=200)
+
+    tuner = tune.Tuner(
+        train_func,
+        run_config=tune.RunConfig(
+            name="hyperband_test",
+            stop={"training_iteration": 10 if args.smoke_test else 99999},
+            failure_config=tune.FailureConfig(
+                fail_fast=True,
+            ),
+        ),
+        tune_config=tune.TuneConfig(
+            num_samples=20,
+            metric="episode_reward_mean",
+            mode="max",
+            scheduler=hyperband,
+        ),
+        param_space={"height": tune.uniform(0, 100)},
+    )
+    results = tuner.fit()
+    print("Best hyperparameters found were: ", results.get_best_result().config)