chore: import upstream snapshot with attribution

python/ray/tune/examples/logging_example.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+
+import argparse
+import time
+
+from ray import tune
+from ray.tune.logger import LoggerCallback
+
+
+class TestLoggerCallback(LoggerCallback):
+    def on_trial_result(self, iteration, trials, trial, result, **info):
+        print(f"TestLogger for trial {trial}: {result}")
+
+
+def trial_str_creator(trial):
+    return "{}_{}_123".format(trial.trainable_name, trial.trial_id)
+
+
+def evaluation_fn(step, width, height):
+    time.sleep(0.1)
+    return (0.1 + width * step / 100) ** (-1) + height * 0.1
+
+
+def easy_objective(config):
+    # Hyperparameters
+    width, height = config["width"], config["height"]
+
+    for step in range(config["steps"]):
+        # Iterative training function - can be any arbitrary training procedure
+        intermediate_score = evaluation_fn(step, width, height)
+        # Feed the score back back to Tune.
+        tune.report({"iterations": step, "mean_loss": intermediate_score})
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing"
+    )
+    args, _ = parser.parse_known_args()
+
+    tuner = tune.Tuner(
+        easy_objective,
+        run_config=tune.RunConfig(
+            name="hyperband_test",
+            callbacks=[TestLoggerCallback()],
+            stop={"training_iteration": 1 if args.smoke_test else 100},
+        ),
+        tune_config=tune.TuneConfig(
+            metric="mean_loss",
+            mode="min",
+            num_samples=5,
+            trial_name_creator=trial_str_creator,
+            trial_dirname_creator=trial_str_creator,
+        ),
+        param_space={
+            "steps": 100,
+            "width": tune.randint(10, 100),
+            "height": tune.loguniform(10, 100),
+        },
+    )
+    results = tuner.fit()
+
+    print("Best hyperparameters: ", results.get_best_result().config)