chore: import upstream snapshot with attribution

python/ray/serve/_private/benchmarks/handle_throughput.py

@@ -0,0 +1,62 @@
+import click
+
+from ray import serve
+from ray.serve._private.benchmarks.common import Benchmarker, Hello
+from ray.serve.handle import DeploymentHandle
+
+
+@click.command(help="Benchmark deployment handle throughput.")
+@click.option(
+    "--batch-size",
+    type=int,
+    default=100,
+    help="Number of requests to send to downstream deployment in each trial.",
+)
+@click.option(
+    "--num-replicas",
+    type=int,
+    default=1,
+    help="Number of replicas in the downstream deployment.",
+)
+@click.option(
+    "--num-trials",
+    type=int,
+    default=5,
+    help="Number of trials of the benchmark to run.",
+)
+@click.option(
+    "--trial-runtime",
+    type=int,
+    default=1,
+    help="Duration to run each trial of the benchmark for (seconds).",
+)
+def main(
+    batch_size: int,
+    num_replicas: int,
+    num_trials: int,
+    trial_runtime: float,
+):
+    app = Benchmarker.bind(
+        Hello.options(
+            num_replicas=num_replicas, ray_actor_options={"num_cpus": 0}
+        ).bind(),
+    )
+    h: DeploymentHandle = serve.run(app)
+
+    mean, stddev = h.run_throughput_benchmark.remote(
+        batch_size=batch_size,
+        num_trials=num_trials,
+        trial_runtime=trial_runtime,
+    ).result()
+
+    print(
+        "DeploymentHandle throughput {}: {} +- {} requests/s".format(
+            f"(num_replicas={num_replicas}, batch_size={batch_size})",
+            mean,
+            stddev,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()