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Add perf metrics for 2.36.0 (#47574)
``` REGRESSION 12.66%: single_client_get_object_containing_10k_refs (THROUGHPUT) regresses from 13.204885454613315 to 11.533423619760748 in microbenchmark.json REGRESSION 9.50%: client__1_1_actor_calls_sync (THROUGHPUT) regresses from 523.3469473257671 to 473.62862729568997 in microbenchmark.json REGRESSION 6.76%: multi_client_put_gigabytes (THROUGHPUT) regresses from 45.440179854469804 to 42.368678421213005 in microbenchmark.json REGRESSION 4.92%: 1_n_actor_calls_async (THROUGHPUT) regresses from 8803.178389859915 to 8370.014425096557 in microbenchmark.json REGRESSION 3.89%: n_n_actor_calls_with_arg_async (THROUGHPUT) regresses from 2748.863962184806 to 2641.837605625889 in microbenchmark.json REGRESSION 3.45%: client__1_1_actor_calls_async (THROUGHPUT) regresses from 1019.3028285821217 to 984.156036006501 in microbenchmark.json REGRESSION 3.06%: client__1_1_actor_calls_concurrent (THROUGHPUT) regresses from 1007.6444648899972 to 976.8103650114274 in microbenchmark.json REGRESSION 0.65%: placement_group_create/removal (THROUGHPUT) regresses from 805.1759941825478 to 799.9345402492929 in microbenchmark.json REGRESSION 0.33%: single_client_put_calls_Plasma_Store (THROUGHPUT) regresses from 5273.203424794718 to 5255.898134426729 in microbenchmark.json REGRESSION 0.02%: 1_1_actor_calls_async (THROUGHPUT) regresses from 9012.880467992636 to 9011.034048587637 in microbenchmark.json REGRESSION 0.01%: client__put_gigabytes (THROUGHPUT) regresses from 0.13947664668408546 to 0.13945791828216536 in microbenchmark.json REGRESSION 0.00%: client__put_calls (THROUGHPUT) regresses from 806.1974515278531 to 806.172478450918 in microbenchmark.json REGRESSION 70.55%: dashboard_p50_latency_ms (LATENCY) regresses from 104.211 to 177.731 in benchmarks/many_actors.json REGRESSION 13.13%: time_to_broadcast_1073741824_bytes_to_50_nodes (LATENCY) regresses from 18.961532712000007 to 21.451945214000006 in scalability/object_store.json REGRESSION 4.50%: 3000_returns_time (LATENCY) regresses from 5.680022101000006 to 5.935367576000004 in scalability/single_node.json REGRESSION 3.96%: avg_iteration_time (LATENCY) regresses from 0.9740754842758179 to 1.012664566040039 in stress_tests/stress_test_dead_actors.json REGRESSION 2.75%: stage_2_avg_iteration_time (LATENCY) regresses from 63.694758081436156 to 65.44879236221314 in stress_tests/stress_test_many_tasks.json REGRESSION 1.66%: 10000_args_time (LATENCY) regresses from 17.328640389999997 to 17.61703060299999 in scalability/single_node.json REGRESSION 1.40%: stage_4_spread (LATENCY) regresses from 0.45063567085147194 to 0.4569625792772166 in stress_tests/stress_test_many_tasks.json REGRESSION 0.69%: dashboard_p50_latency_ms (LATENCY) regresses from 3.347 to 3.37 in benchmarks/many_pgs.json REGRESSION 0.19%: 10000_get_time (LATENCY) regresses from 23.896780481999997 to 23.942006032999984 in scalability/single_node.json ``` Signed-off-by: kevin <kevin@anyscale.com>
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{ | ||
"_dashboard_memory_usage_mb": 485.167104, | ||
"_dashboard_memory_usage_mb": 429.867008, | ||
"_dashboard_test_success": true, | ||
"_peak_memory": 3.74, | ||
"_peak_process_memory": "PID\tMEM\tCOMMAND\n1215\t8.81GiB\t/app/product/go/infra/anyscaled/anyscaled_/anyscaled startv2 --control_plane_url=https://console.any\n6550\t1.74GiB\t/home/ray/anaconda3/lib/python3.9/site-packages/ray/core/src/ray/gcs/gcs_server --log_dir=/tmp/ray/s\n7868\t0.92GiB\tpython distributed/test_many_actors.py\n2138\t0.36GiB\tvector --watch-config --log-format json --config-yaml /etc/vector/vector.yaml\n6668\t0.35GiB\t/home/ray/anaconda3/bin/python /home/ray/anaconda3/lib/python3.9/site-packages/ray/dashboard/dashboa\n6842\t0.09GiB\t/home/ray/anaconda3/bin/python -u /home/ray/anaconda3/lib/python3.9/site-packages/ray/dashboard/agen\n2403\t0.07GiB\t/usr/bin/python3 /app/infra/dataplane/webterminal/webterminal_sidecar_image.binary.runfiles/product/\n6844\t0.07GiB\t/home/ray/anaconda3/bin/python -u /home/ray/anaconda3/lib/python3.9/site-packages/ray/_private/runti\n7668\t0.07GiB\tray::JobSupervisor\n6868\t0.07GiB\t/home/ray/anaconda3/bin/python /home/ray/anaconda3/bin/jupyter-lab --allow-root --ip=127.0.0.1 --no-", | ||
"actors_per_second": 603.6854672610009, | ||
"_peak_memory": 3.8, | ||
"_peak_process_memory": "PID\tMEM\tCOMMAND\n1173\t7.02GiB\t/app/product/go/infra/anyscaled/anyscaled_/anyscaled startv2 --control_plane_url=https://console.any\n3408\t1.75GiB\t/home/ray/anaconda3/lib/python3.9/site-packages/ray/core/src/ray/gcs/gcs_server --log_dir=/tmp/ray/s\n4371\t0.93GiB\tpython distributed/test_many_actors.py\n1954\t0.46GiB\tvector --watch-config --log-format json --config-yaml /etc/vector/vector.yaml\n3524\t0.37GiB\t/home/ray/anaconda3/bin/python /home/ray/anaconda3/lib/python3.9/site-packages/ray/dashboard/dashboa\n3687\t0.09GiB\t/home/ray/anaconda3/bin/python -u /home/ray/anaconda3/lib/python3.9/site-packages/ray/dashboard/agen\n2361\t0.08GiB\t/usr/bin/python3 /app/infra/dataplane/webterminal/webterminal_sidecar_image.binary.runfiles/product/\n3689\t0.07GiB\t/home/ray/anaconda3/bin/python -u /home/ray/anaconda3/lib/python3.9/site-packages/ray/_private/runti\n4167\t0.07GiB\tray::JobSupervisor\n3712\t0.07GiB\t/home/ray/anaconda3/bin/python /home/ray/anaconda3/bin/jupyter-lab --allow-root --ip=127.0.0.1 --no-", | ||
"actors_per_second": 637.7871696983945, | ||
"num_actors": 10000, | ||
"perf_metrics": [ | ||
{ | ||
"perf_metric_name": "actors_per_second", | ||
"perf_metric_type": "THROUGHPUT", | ||
"perf_metric_value": 603.6854672610009 | ||
"perf_metric_value": 637.7871696983945 | ||
}, | ||
{ | ||
"perf_metric_name": "dashboard_p50_latency_ms", | ||
"perf_metric_type": "LATENCY", | ||
"perf_metric_value": 104.211 | ||
"perf_metric_value": 177.731 | ||
}, | ||
{ | ||
"perf_metric_name": "dashboard_p95_latency_ms", | ||
"perf_metric_type": "LATENCY", | ||
"perf_metric_value": 3768.817 | ||
"perf_metric_value": 3350.577 | ||
}, | ||
{ | ||
"perf_metric_name": "dashboard_p99_latency_ms", | ||
"perf_metric_type": "LATENCY", | ||
"perf_metric_value": 3786.709 | ||
"perf_metric_value": 3361.16 | ||
} | ||
], | ||
"success": "1", | ||
"time": 16.56491756439209 | ||
"time": 15.679211616516113 | ||
} |
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@@ -1,32 +1,32 @@ | ||
{ | ||
"_dashboard_memory_usage_mb": 127.606784, | ||
"_dashboard_memory_usage_mb": 196.333568, | ||
"_dashboard_test_success": true, | ||
"_peak_memory": 2.17, | ||
"_peak_process_memory": "PID\tMEM\tCOMMAND\n1219\t7.63GiB\t/app/product/go/infra/anyscaled/anyscaled_/anyscaled startv2 --control_plane_url=https://console.any\n3423\t0.89GiB\t/home/ray/anaconda3/lib/python3.9/site-packages/ray/core/src/ray/gcs/gcs_server --log_dir=/tmp/ray/s\n4478\t0.41GiB\tpython distributed/test_many_pgs.py\n2007\t0.34GiB\tvector --watch-config --log-format json --config-yaml /etc/vector/vector.yaml\n3539\t0.11GiB\t/home/ray/anaconda3/bin/python /home/ray/anaconda3/lib/python3.9/site-packages/ray/dashboard/dashboa\n3704\t0.09GiB\t/home/ray/anaconda3/bin/python -u /home/ray/anaconda3/lib/python3.9/site-packages/ray/dashboard/agen\n3706\t0.07GiB\t/home/ray/anaconda3/bin/python -u /home/ray/anaconda3/lib/python3.9/site-packages/ray/_private/runti\n2349\t0.07GiB\t/usr/bin/python3 /app/infra/dataplane/webterminal/webterminal_sidecar_image.binary.runfiles/product/\n4272\t0.07GiB\tray::JobSupervisor\n3731\t0.07GiB\t/home/ray/anaconda3/bin/python /home/ray/anaconda3/bin/jupyter-lab --allow-root --ip=127.0.0.1 --no-", | ||
"_peak_memory": 2.13, | ||
"_peak_process_memory": "PID\tMEM\tCOMMAND\n1135\t7.03GiB\t/app/product/go/infra/anyscaled/anyscaled_/anyscaled startv2 --control_plane_url=https://console.any\n3382\t0.89GiB\t/home/ray/anaconda3/lib/python3.9/site-packages/ray/core/src/ray/gcs/gcs_server --log_dir=/tmp/ray/s\n4327\t0.41GiB\tpython distributed/test_many_pgs.py\n2463\t0.28GiB\tvector --watch-config --log-format json --config-yaml /etc/vector/vector.yaml\n3498\t0.11GiB\t/home/ray/anaconda3/bin/python /home/ray/anaconda3/lib/python3.9/site-packages/ray/dashboard/dashboa\n3663\t0.09GiB\t/home/ray/anaconda3/bin/python -u /home/ray/anaconda3/lib/python3.9/site-packages/ray/dashboard/agen\n2634\t0.08GiB\t/usr/bin/python3 /app/infra/dataplane/webterminal/webterminal_sidecar_image.binary.runfiles/product/\n3665\t0.07GiB\t/home/ray/anaconda3/bin/python -u /home/ray/anaconda3/lib/python3.9/site-packages/ray/_private/runti\n4107\t0.07GiB\tray::JobSupervisor\n3700\t0.07GiB\t/home/ray/anaconda3/bin/python /home/ray/anaconda3/bin/jupyter-lab --allow-root --ip=127.0.0.1 --no-", | ||
"num_pgs": 1000, | ||
"perf_metrics": [ | ||
{ | ||
"perf_metric_name": "pgs_per_second", | ||
"perf_metric_type": "THROUGHPUT", | ||
"perf_metric_value": 22.037557767422825 | ||
"perf_metric_value": 22.13026931060565 | ||
}, | ||
{ | ||
"perf_metric_name": "dashboard_p50_latency_ms", | ||
"perf_metric_type": "LATENCY", | ||
"perf_metric_value": 3.347 | ||
"perf_metric_value": 3.37 | ||
}, | ||
{ | ||
"perf_metric_name": "dashboard_p95_latency_ms", | ||
"perf_metric_type": "LATENCY", | ||
"perf_metric_value": 12.126 | ||
"perf_metric_value": 7.238 | ||
}, | ||
{ | ||
"perf_metric_name": "dashboard_p99_latency_ms", | ||
"perf_metric_type": "LATENCY", | ||
"perf_metric_value": 376.468 | ||
"perf_metric_value": 276.52 | ||
} | ||
], | ||
"pgs_per_second": 22.037557767422825, | ||
"pgs_per_second": 22.13026931060565, | ||
"success": "1", | ||
"time": 45.377079010009766 | ||
"time": 45.186978340148926 | ||
} |
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@@ -1 +1 @@ | ||
{"release_version": "2.35.0"} | ||
{"release_version": "2.36.0"} |
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