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[EBPF] GPU-monitoring: added nvml lib path config knob #30263
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- switched the process scan interval to a config flag with default value
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LGTM, only one comment for clarification.
Test changes on VMUse this command from test-infra-definitions to manually test this PR changes on a VM: inv create-vm --pipeline-id=46916792 --os-family=ubuntu Note: This applies to commit d09c912 |
/merge |
🚂 MergeQueue: pull request added to the queue The median merge time in Use |
Regression DetectorRegression Detector ResultsRun ID: 513b98c8-2075-4920-b8f0-ebbe14c7f355 Metrics dashboard Target profiles Baseline: 7583542 Performance changes are noted in the perf column of each table:
No significant changes in experiment optimization goalsConfidence level: 90.00% There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.
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perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
---|---|---|---|---|---|---|
➖ | file_tree | memory utilization | +2.48 | [+2.33, +2.64] | 1 | Logs |
➖ | basic_py_check | % cpu utilization | +1.11 | [-1.70, +3.92] | 1 | Logs |
➖ | tcp_syslog_to_blackhole | ingress throughput | +0.41 | [+0.36, +0.47] | 1 | Logs |
➖ | otel_to_otel_logs | ingress throughput | +0.22 | [-0.59, +1.03] | 1 | Logs |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.18 | [-0.54, +0.90] | 1 | Logs |
➖ | file_to_blackhole_500ms_latency | egress throughput | +0.12 | [-0.12, +0.37] | 1 | Logs |
➖ | uds_dogstatsd_to_api | ingress throughput | +0.01 | [-0.09, +0.12] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.01 | [-0.48, +0.50] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency | egress throughput | +0.00 | [-0.33, +0.34] | 1 | Logs |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.01, +0.01] | 1 | Logs |
➖ | file_to_blackhole_100ms_latency | egress throughput | -0.00 | [-0.23, +0.22] | 1 | Logs |
➖ | file_to_blackhole_300ms_latency | egress throughput | -0.07 | [-0.25, +0.11] | 1 | Logs |
➖ | idle_all_features | memory utilization | -0.45 | [-0.60, -0.30] | 1 | Logs bounds checks dashboard |
➖ | idle | memory utilization | -0.67 | [-0.73, -0.61] | 1 | Logs bounds checks dashboard |
➖ | pycheck_lots_of_tags | % cpu utilization | -0.97 | [-3.48, +1.54] | 1 | Logs |
Bounds Checks
perf | experiment | bounds_check_name | replicates_passed |
---|---|---|---|
✅ | file_to_blackhole_0ms_latency | memory_usage | 10/10 |
✅ | file_to_blackhole_1000ms_latency | memory_usage | 10/10 |
✅ | file_to_blackhole_100ms_latency | memory_usage | 10/10 |
✅ | file_to_blackhole_300ms_latency | memory_usage | 10/10 |
✅ | file_to_blackhole_500ms_latency | memory_usage | 10/10 |
✅ | idle | memory_usage | 10/10 |
✅ | idle_all_features | memory_usage | 10/10 |
Explanation
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".
What does this PR do?
allows to provide a custom path to libnvml-so native lib path
Motivation
can simplify the deployment in containerized environments and allow side-by-side libnvml-so libs
Describe how to test/QA your changes
Possible Drawbacks / Trade-offs
Additional Notes
Jira ticket