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WMS: GPU entity fixes #34490
WMS: GPU entity fixes #34490
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removed SMCount (the API is available only for MIG devices) added TotalCores instead changed TotalMemory compute to use a different nvml API that is available for all devices
Uncompressed package size comparisonComparison with ancestor Diff per package
Decision✅ Passed |
Test changes on VMUse this command from test-infra-definitions to manually test this PR changes on a VM: inv aws.create-vm --pipeline-id=57088704 --os-family=ubuntu Note: This applies to commit 5161d96 |
Static quality checks ✅Please find below the results from static quality gates Successful checksInfo
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Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: e3dc9b4 Optimization Goals: ✅ No significant changes detected
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perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
---|---|---|---|---|---|---|
➖ | quality_gate_logs | % cpu utilization | +0.34 | [-2.53, +3.22] | 1 | Logs |
➖ | file_tree | memory utilization | +0.20 | [+0.15, +0.26] | 1 | Logs |
➖ | file_to_blackhole_500ms_latency | egress throughput | +0.13 | [-0.66, +0.92] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | +0.13 | [-0.34, +0.60] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency | egress throughput | +0.01 | [-0.83, +0.86] | 1 | Logs |
➖ | file_to_blackhole_300ms_latency | egress throughput | +0.01 | [-0.62, +0.64] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | +0.01 | [-0.83, +0.84] | 1 | Logs |
➖ | file_to_blackhole_100ms_latency | egress throughput | +0.00 | [-0.62, +0.63] | 1 | Logs |
➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.28, +0.28] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | -0.00 | [-0.80, +0.80] | 1 | Logs |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.01 | [-0.02, +0.01] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.07 | [-0.84, +0.71] | 1 | Logs |
➖ | tcp_syslog_to_blackhole | ingress throughput | -0.17 | [-0.25, -0.09] | 1 | Logs |
➖ | quality_gate_idle_all_features | memory utilization | -0.53 | [-0.58, -0.48] | 1 | Logs bounds checks dashboard |
➖ | quality_gate_idle | memory utilization | -0.63 | [-0.68, -0.59] | 1 | Logs bounds checks dashboard |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -2.28 | [-3.14, -1.43] | 1 | Logs |
Bounds Checks: ✅ Passed
perf | experiment | bounds_check_name | replicates_passed | links |
---|---|---|---|---|
✅ | file_to_blackhole_0ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_0ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http1 | lost_bytes | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http1 | memory_usage | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http2 | lost_bytes | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http2 | memory_usage | 10/10 | |
✅ | file_to_blackhole_1000ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_1000ms_latency_linear_load | memory_usage | 10/10 | |
✅ | file_to_blackhole_100ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_100ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_300ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_300ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_500ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_500ms_latency | memory_usage | 10/10 | |
✅ | quality_gate_idle | intake_connections | 10/10 | bounds checks dashboard |
✅ | quality_gate_idle | memory_usage | 10/10 | bounds checks dashboard |
✅ | quality_gate_idle_all_features | intake_connections | 10/10 | bounds checks dashboard |
✅ | quality_gate_idle_all_features | memory_usage | 10/10 | bounds checks dashboard |
✅ | quality_gate_logs | intake_connections | 10/10 | |
✅ | quality_gate_logs | lost_bytes | 10/10 | |
✅ | quality_gate_logs | memory_usage | 10/10 |
Explanation
Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%
Performance changes are noted in the perf column of each table:
- ✅ = significantly better comparison variant performance
- ❌ = significantly worse comparison variant performance
- ➖ = no significant change in performance
A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".
For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.
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Its configuration does not mark it "erratic".
CI Pass/Fail Decision
✅ Passed. All Quality Gates passed.
- quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
/merge |
View all feedbacks in Devflow UI.
The median merge time in
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This reverts commit 8a9ff47.
What does this PR do?
This PR fixes GPUEntity collection:
SMCount
(the previously usedGetAttributes
nvml API is available only for MIG devices)TotalCores
via GetNumCores API (SMCount can be calculated using pre-defined table of device architecture to number of cores for SM)GetAttributes
API)Motivation
Wider support for GPU devices data
Describe how you validated your changes
Updated nvml mocks and UTs to test the new fields
Possible Drawbacks / Trade-offs
Additional Notes