def main(num_replicas: Optional[int], trial_length: Optional[str], max_batch_size: Optional[int]): # Give default cluster parameter values based on smoke_test config # if user provided values explicitly, use them instead. # IS_SMOKE_TEST is set by args of releaser's e2e.py smoke_test = os.environ.get("IS_SMOKE_TEST", "1") if smoke_test == "1": num_replicas = num_replicas or DEFAULT_SMOKE_TEST_NUM_REPLICA trial_length = trial_length or DEFAULT_SMOKE_TEST_TRIAL_LENGTH logger.info( f"Running local / smoke test with {num_replicas} replicas ..\n") # Choose cluster setup based on user config. Local test uses Cluster() # to mock actors that requires # of nodes to be specified, but ray # client doesn't need to num_nodes = int(math.ceil(num_replicas / NUM_CPU_PER_NODE)) logger.info( f"Setting up local ray cluster with {num_nodes} nodes ..\n") serve_client = setup_local_single_node_cluster(num_nodes)[0] else: num_replicas = num_replicas or DEFAULT_FULL_TEST_NUM_REPLICA trial_length = trial_length or DEFAULT_FULL_TEST_TRIAL_LENGTH logger.info(f"Running full test with {num_replicas} replicas ..\n") logger.info("Setting up anyscale ray cluster .. \n") serve_client = setup_anyscale_cluster() http_host = str(serve_client._http_config.host) http_port = str(serve_client._http_config.port) logger.info(f"Ray serve http_host: {http_host}, http_port: {http_port}") logger.info(f"Deploying with {num_replicas} target replicas ....\n") deploy_replicas(num_replicas, max_batch_size) logger.info("Warming up cluster ....\n") warm_up_one_cluster.remote(10, http_host, http_port, "echo") logger.info(f"Starting wrk trial on all nodes for {trial_length} ....\n") # For detailed discussion, see https://github.com/wg/wrk/issues/205 # TODO:(jiaodong) What's the best number to use here ? all_endpoints = list(serve.list_deployments().keys()) all_metrics, all_wrk_stdout = run_wrk_on_all_nodes( trial_length, NUM_CONNECTIONS, http_host, http_port, all_endpoints=all_endpoints) aggregated_metrics = aggregate_all_metrics(all_metrics) logger.info("Wrk stdout on each node: ") for wrk_stdout in all_wrk_stdout: logger.info(wrk_stdout) logger.info("Final aggregated metrics: ") for key, val in aggregated_metrics.items(): logger.info(f"{key}: {val}") save_test_results( aggregated_metrics, default_output_file="/tmp/single_deployment_1k_noop_replica.json")
def main( min_replicas: Optional[int], max_replicas: Optional[int], num_deployments: Optional[int], trial_length: Optional[str], ): # Give default cluster parameter values based on smoke_test config # if user provided values explicitly, use them instead. # IS_SMOKE_TEST is set by args of releaser's e2e.py if is_smoke_test(): min_replicas = min_replicas or DEFAULT_SMOKE_TEST_MIN_NUM_REPLICA max_replicas = max_replicas or DEFAULT_SMOKE_TEST_MAX_NUM_REPLICA num_deployments = num_deployments or DEFAULT_SMOKE_TEST_NUM_DEPLOYMENTS trial_length = trial_length or DEFAULT_SMOKE_TEST_TRIAL_LENGTH logger.info(f"Running smoke test with min {min_replicas} and max " f"{max_replicas} replicas, {num_deployments} deployments " f".. \n") # Choose cluster setup based on user config. Local test uses Cluster() # to mock actors that requires # of nodes to be specified, but ray # client doesn't need to num_nodes = int(math.ceil(max_replicas / NUM_CPU_PER_NODE)) logger.info( f"Setting up local ray cluster with {num_nodes} nodes .. \n") serve_client = setup_local_single_node_cluster(num_nodes)[0] else: min_replicas = min_replicas or DEFAULT_FULL_TEST_MIN_NUM_REPLICA max_replicas = max_replicas or DEFAULT_FULL_TEST_MAX_NUM_REPLICA num_deployments = num_deployments or DEFAULT_FULL_TEST_NUM_DEPLOYMENTS trial_length = trial_length or DEFAULT_FULL_TEST_TRIAL_LENGTH logger.info(f"Running full test with min {min_replicas} and max " f"{max_replicas} replicas, {num_deployments} deployments " f".. \n") logger.info("Setting up anyscale ray cluster .. \n") serve_client = setup_anyscale_cluster() http_host = str(serve_client._http_config.host) http_port = str(serve_client._http_config.port) logger.info(f"Ray serve http_host: {http_host}, http_port: {http_port}") logger.info(f"Deploying with min {min_replicas} and max {max_replicas}" f"target replicas ....\n") setup_multi_deployment_replicas(min_replicas, max_replicas, num_deployments) logger.info("Warming up cluster ....\n") endpoint_refs = [] all_endpoints = list(serve.list_deployments().keys()) for endpoint in all_endpoints: endpoint_refs.append( warm_up_one_cluster.options(num_cpus=0).remote( 10, http_host, http_port, endpoint)) for endpoint in ray.get(endpoint_refs): logger.info(f"Finished warming up {endpoint}") logger.info(f"Starting wrk trial on all nodes for {trial_length} ....\n") # For detailed discussion, see https://github.com/wg/wrk/issues/205 # TODO:(jiaodong) What's the best number to use here ? all_metrics, all_wrk_stdout = run_wrk_on_all_nodes( trial_length, NUM_CONNECTIONS, http_host, http_port, all_endpoints=all_endpoints) aggregated_metrics = aggregate_all_metrics(all_metrics) logger.info("Wrk stdout on each node: ") for wrk_stdout in all_wrk_stdout: logger.info(wrk_stdout) logger.info("Final aggregated metrics: ") for key, val in aggregated_metrics.items(): logger.info(f"{key}: {val}") save_test_results( aggregated_metrics, default_output_file="/tmp/autoscaling_multi_deployment.json")