[EBPF] fix gpu-monitoring flaky test

[val06]

What does this PR do?

fixes the flaky test (TestDetectsContainer) by switching to use pkg/util/testutil/docker (see this PR) and making the cudasample process to wait until killed explicitly (avoiding potential race conditions when trying to get docker's main process PID)

Motivation

• fix flakiness
• move to use common test infrastructure

Describe how to test/QA your changes

All gpu UTs should pass, need to monitor on main the aforementioned test is not flaky

Possible Drawbacks / Trade-offs

Additional Notes

removed the flaky marker

Jira ticket

[val06]

I could move the RunSample (and specifically internal func runCommandAndPipeOutput) logic into the global pkg/util/testutil/docker package (as it shares similar code with docker execution), but i preferred not to do it in this PR, since it will require to rename that package

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 50097572 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

[agent-platform-auto-pr]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=50097572 --os-family=ubuntu

Note: This applies to commit ca4af26

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 115c9c5c-260b-4466-bd60-0f01bcef0ca7

Baseline: 462437b
Comparison: ca4af26
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	pycheck_lots_of_tags	% cpu utilization	+3.10	[-0.41, +6.61]	1	Logs
➖	file_tree	memory utilization	+0.65	[+0.51, +0.78]	1	Logs
➖	basic_py_check	% cpu utilization	+0.35	[-3.42, +4.12]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.30	[-0.17, +0.77]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.16	[+0.10, +0.22]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.07	[-0.66, +0.80]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.07	[-0.73, +0.86]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.11, +0.10]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.02	[-0.65, +0.61]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.03	[-0.81, +0.75]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.04	[-0.80, +0.72]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.07	[-0.97, +0.83]	1	Logs
➖	quality_gate_idle	memory utilization	-0.12	[-0.17, -0.07]	1	Logs bounds checks dashboard
➖	quality_gate_idle_all_features	memory utilization	-1.10	[-1.21, -1.00]	1	Logs bounds checks dashboard
➖	otel_to_otel_logs	ingress throughput	-1.59	[-2.28, -0.90]	1	Logs
➖	quality_gate_logs	% cpu utilization	-2.12	[-4.99, +0.75]	1	Logs

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	file_to_blackhole_500ms_latency	lost_bytes	9/10
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	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	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:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. 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.

3. Its configuration does not mark it "erratic".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_idle_all_features, 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_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.