chore: import upstream snapshot with attribution

python/ray/tune/examples/mlflow_example.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python
+"""Examples using MLfowLoggerCallback and setup_mlflow.
+"""
+import os
+import tempfile
+import time
+
+import mlflow
+
+from ray import tune
+from ray.air.integrations.mlflow import MLflowLoggerCallback, setup_mlflow
+
+
+def evaluation_fn(step, width, height):
+    return (0.1 + width * step / 100) ** (-1) + height * 0.1
+
+
+def train_function(config):
+    # Hyperparameters
+    width, height = config["width"], config["height"]
+
+    for step in range(config.get("steps", 100)):
+        # Iterative training function - can be any arbitrary training procedure
+        intermediate_score = evaluation_fn(step, width, height)
+        # Feed the score back to Tune.
+        tune.report({"iterations": step, "mean_loss": intermediate_score})
+        time.sleep(0.1)
+
+
+def tune_with_callback(mlflow_tracking_uri, finish_fast=False):
+
+    tuner = tune.Tuner(
+        train_function,
+        run_config=tune.RunConfig(
+            name="mlflow",
+            callbacks=[
+                MLflowLoggerCallback(
+                    tracking_uri=mlflow_tracking_uri,
+                    experiment_name="example",
+                    save_artifact=True,
+                )
+            ],
+        ),
+        tune_config=tune.TuneConfig(
+            num_samples=5,
+        ),
+        param_space={
+            "width": tune.randint(10, 100),
+            "height": tune.randint(0, 100),
+            "steps": 5 if finish_fast else 100,
+        },
+    )
+    tuner.fit()
+
+
+def train_function_mlflow(config):
+    setup_mlflow(config)
+
+    # Hyperparameters
+    width, height = config["width"], config["height"]
+
+    for step in range(config.get("steps", 100)):
+        # Iterative training function - can be any arbitrary training procedure
+        intermediate_score = evaluation_fn(step, width, height)
+        # Log the metrics to mlflow
+        mlflow.log_metrics(dict(mean_loss=intermediate_score), step=step)
+        # Feed the score back to Tune.
+        tune.report({"iterations": step, "mean_loss": intermediate_score})
+        time.sleep(0.1)
+
+
+def tune_with_setup(mlflow_tracking_uri, finish_fast=False):
+    # Set the experiment, or create a new one if does not exist yet.
+    mlflow.set_tracking_uri(mlflow_tracking_uri)
+    mlflow.set_experiment(experiment_name="mixin_example")
+    tuner = tune.Tuner(
+        train_function_mlflow,
+        run_config=tune.RunConfig(
+            name="mlflow",
+        ),
+        tune_config=tune.TuneConfig(
+            num_samples=5,
+        ),
+        param_space={
+            "width": tune.randint(10, 100),
+            "height": tune.randint(0, 100),
+            "steps": 5 if finish_fast else 100,
+            "mlflow": {
+                "experiment_name": "mixin_example",
+                "tracking_uri": mlflow.get_tracking_uri(),
+            },
+        },
+    )
+    tuner.fit()
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing"
+    )
+    parser.add_argument(
+        "--tracking-uri",
+        type=str,
+        help="The tracking URI for the MLflow tracking server.",
+    )
+    args, _ = parser.parse_known_args()
+
+    if args.smoke_test:
+        mlflow_tracking_uri = os.path.join(tempfile.gettempdir(), "mlruns")
+    else:
+        mlflow_tracking_uri = args.tracking_uri
+
+    tune_with_callback(mlflow_tracking_uri, finish_fast=args.smoke_test)
+    if not args.smoke_test:
+        df = mlflow.search_runs(
+            [mlflow.get_experiment_by_name("example").experiment_id]
+        )
+        print(df)
+
+    tune_with_setup(mlflow_tracking_uri, finish_fast=args.smoke_test)
+    if not args.smoke_test:
+        df = mlflow.search_runs(
+            [mlflow.get_experiment_by_name("mixin_example").experiment_id]
+        )
+        print(df)