chore: import upstream snapshot with attribution

python/ray/train/examples/tf/tensorflow_regression_example.py

@@ -0,0 +1,115 @@
+import os
+
+os.environ["TF_USE_LEGACY_KERAS"] = "1"
+
+import argparse
+import sys
+
+import ray
+from ray import train
+from ray.data.preprocessors import Concatenator
+from ray.train import Result, ScalingConfig
+
+if sys.version_info >= (3, 12):
+    # Skip this test in Python 3.12+ because TensorFlow is not supported.
+    sys.exit(0)
+else:
+    import tensorflow as tf
+
+    from ray.train.tensorflow import TensorflowTrainer
+    from ray.train.tensorflow.keras import ReportCheckpointCallback
+
+
+def build_model() -> tf.keras.Model:
+    model = tf.keras.Sequential(
+        [
+            tf.keras.layers.InputLayer(input_shape=(100,)),
+            tf.keras.layers.Dense(10),
+            tf.keras.layers.Dense(1),
+        ]
+    )
+    return model
+
+
+def train_func(config: dict):
+    batch_size = config.get("batch_size", 64)
+    epochs = config.get("epochs", 3)
+
+    strategy = tf.distribute.MultiWorkerMirroredStrategy()
+    with strategy.scope():
+        # Model building/compiling need to be within `strategy.scope()`.
+        multi_worker_model = build_model()
+        multi_worker_model.compile(
+            optimizer=tf.keras.optimizers.SGD(learning_rate=config.get("lr", 1e-3)),
+            loss=tf.keras.losses.mean_absolute_error,
+            metrics=[tf.keras.metrics.mean_squared_error],
+        )
+
+    dataset = train.get_dataset_shard("train")
+
+    results = []
+    for _ in range(epochs):
+        tf_dataset = dataset.to_tf(
+            feature_columns="x", label_columns="y", batch_size=batch_size
+        )
+        history = multi_worker_model.fit(
+            tf_dataset, callbacks=[ReportCheckpointCallback()]
+        )
+        results.append(history.history)
+    return results
+
+
+def train_tensorflow_regression(num_workers: int = 2, use_gpu: bool = False) -> Result:
+    dataset = ray.data.read_csv("s3://anonymous@air-example-data/regression.csv")
+    columns_to_concatenate = [f"x{i:03}" for i in range(100)]
+    preprocessor = Concatenator(columns=columns_to_concatenate, output_column_name="x")
+    dataset = preprocessor.fit_transform(dataset)
+
+    config = {"lr": 1e-3, "batch_size": 32, "epochs": 4}
+    scaling_config = ScalingConfig(num_workers=num_workers, use_gpu=use_gpu)
+    trainer = TensorflowTrainer(
+        train_loop_per_worker=train_func,
+        train_loop_config=config,
+        scaling_config=scaling_config,
+        datasets={"train": dataset},
+    )
+    results = trainer.fit()
+    print(results.metrics)
+    return results
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--address", required=False, type=str, help="the address to use for Ray"
+    )
+    parser.add_argument(
+        "--num-workers",
+        "-n",
+        type=int,
+        default=2,
+        help="Sets number of workers for training.",
+    )
+    parser.add_argument(
+        "--use-gpu", action="store_true", default=False, help="Enables GPU training"
+    )
+    parser.add_argument(
+        "--smoke-test",
+        action="store_true",
+        default=False,
+        help="Finish quickly for testing.",
+    )
+
+    args, _ = parser.parse_known_args()
+
+    if args.smoke_test:
+        # 2 workers, 1 for trainer, 1 for datasets
+        num_gpus = args.num_workers if args.use_gpu else 0
+        ray.init(num_cpus=4, num_gpus=num_gpus)
+        result = train_tensorflow_regression(num_workers=2, use_gpu=args.use_gpu)
+    else:
+        ray.init(address=args.address)
+        result = train_tensorflow_regression(
+            num_workers=args.num_workers, use_gpu=args.use_gpu
+        )
+    print(result)