wmeta: Add GPU entity #32019
wmeta: Add GPU entity #32019
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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=51806239 --os-family=ubuntuNote: This applies to commit fb4504a |
Package size comparisonComparison with ancestor Diff per package
Decision |
Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: f8e543c Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +0.96 | [+0.89, +1.04] | 1 | Logs |
| ➖ | quality_gate_logs | % cpu utilization | +0.67 | [-2.61, +3.96] | 1 | Logs |
| ➖ | quality_gate_idle_all_features | memory utilization | +0.64 | [+0.56, +0.72] | 1 | Logs bounds checks dashboard |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.23 | [-0.55, +1.01] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.08 | [-0.60, +0.76] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | +0.04 | [-0.42, +0.51] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | +0.02 | [-0.73, +0.77] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | -0.01 | [-0.11, +0.10] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | -0.01 | [-0.87, +0.85] | 1 | Logs |
| ➖ | file_to_blackhole_300ms_latency | egress throughput | -0.01 | [-0.65, +0.63] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | -0.03 | [-0.92, +0.87] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.07 | [-0.85, +0.72] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.07 | [-0.97, +0.83] | 1 | Logs |
| ➖ | file_tree | memory utilization | -0.33 | [-0.45, -0.22] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | -0.84 | [-0.88, -0.81] | 1 | Logs bounds checks dashboard |
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 | memory_usage | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_idle_all_features | memory_usage | 10/10 | bounds checks dashboard |
| ✅ | 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:
-
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_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
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Decision |
comp/core/workloadmeta/def/types.go
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| EntityID | ||
| EntityMeta | ||
| Vendor string | ||
| Model string |
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what is model in this case? is it device (as per the agreed tags names)?
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+1 What is the difference between model and device? Could we leave some more comments alongside the field names in the struct to help guide us and reduce ambiguity. Thanks!
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We investigated a bit to see the exact terminology that NVIDIA uses and the tags we're having for GPUs. I've changed this to Device, which is the commercial name of the hardware. Added also a comment clarifying.
What does this PR do?
This PR adds definitions of GPU devices to workloadmeta. This data will be filled by the GPU monitoring module and used by the tagger in other PRs.
Motivation
Share the GPU devices detected to allow further integration with other parts of the agent.
Describe how you validated your changes
Added unit tests for the functional parts of the code.
Possible Drawbacks / Trade-offs
Additional Notes