chore: import upstream snapshot with attribution

doc/source/tune/doc_code/tune.py

@@ -0,0 +1,60 @@
+# flake8: noqa
+
+# fmt: off
+# __step1_begin__
+from ray import tune
+import ray
+import os
+
+NUM_MODELS = 100
+
+def train_model(config):
+    score = config["model_id"]
+
+    # Import model libraries, etc...
+    # Load data and train model code here...
+
+    # Return final stats. You can also return intermediate progress
+    # using ray.tune.report() if needed.
+    # To return your model, you could write it to storage and return its
+    # URI in this dict, or return it as a Tune Checkpoint:
+    # https://docs.ray.io/en/latest/tune/tutorials/tune-checkpoints.html
+    return {"score": score, "other_data": ...}
+# __step1_end__
+
+# __step2_begin__
+# Define trial parameters as a single grid sweep.
+trial_space = {
+    # This is an example parameter. You could replace it with filesystem paths,
+    # model types, or even full nested Python dicts of model configurations, etc.,
+    # that enumerate the set of trials to run.
+    "model_id": tune.grid_search([
+        "model_{}".format(i)
+        for i in range(NUM_MODELS)
+    ])
+}
+# __step2_end__
+
+# __step3_begin__
+# Can customize resources per trial, here we set 1 CPU each.
+train_model = tune.with_resources(train_model, {"cpu": 1})
+# __step3_end__
+
+# __step4_begin__
+# Start a Tune run and print the best result.
+tuner = tune.Tuner(train_model, param_space=trial_space)
+results = tuner.fit()
+
+# Access individual results.
+print(results[0])
+print(results[1])
+print(results[2])
+# __step4_end__
+
+# __tasks_begin__
+remote_train = ray.remote(train_model)
+futures = [remote_train.remote({"model_id": i}) for i in range(NUM_MODELS)]
+print("Submitting tasks...")
+results = ray.get(futures)
+print("Trial results", results)
+# __tasks_end__