[CONTP-425] Pod level GPU tags and tag standardized name

[gabedos]

What does this PR do?

Adds Pod level GPU tags and renames the tag to gpu_vendor

Motivation

Support gpu monitoring in the k8s explorer. Expanding on the container level tags added in this previous PR.

After discussion with the gpu-monitoring-squad, the best name for the GPU tag was determined to be gpu_vendor and this PR also works to replace the name to this tag.

Describe how to test/QA your changes

Deploying the following dummy-app

apiVersion: apps/v1
kind: Deployment
metadata:
  name: dummy-nginx-app
spec:
  replicas: 1
  selector:
    matchLabels:
      app: dummy-nginx-app
  template:
    metadata:
      labels:
        app: dummy-nginx-app
    spec:
      containers:
        - name: dummy-nginx-app
          image: nginx
          resources:
            requests:
              nvidia.com/gpu: "0"
            limits:
              nvidia.com/gpu: "0"

Deploy the following agent configuration

datadog:
  kubelet:
    tlsVerify: false
  clusterName: <insert_name>
agents:
  image:
    repository: <insert_repo>
    tag: <insert_tag>
    pullPolicy: Always

The expected results are for the gpu_vendor tag showcasing nvidia for the nginx pod.

=== Entity kubernetes_pod_uid://e8ea6f84-c08d-41eb-a5f3-33c3b7c73afb ===
== Source workloadmeta-kubernetes_pod =
=Tags: [kube_deployment:dummy-nginx-app gpu_vendor:nvidia kube_namespace:default kube_ownerref_kind:replicaset kube_ownerref_name:dummy-nginx-app-7794f89cf kube_qos:Burstable kube_replica_set:dummy-nginx-app-7794f89cf pod_name:dummy-nginx-app-7794f89cf-9mqss pod_phase:running]
===

Possible Drawbacks / Trade-offs

Adds a low cardinality tag at the pod level. GPU types can only take a handful of values so not a problem.

[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=46918328 --os-family=ubuntu

Note: This applies to commit c0828fb

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 174a32b9-7557-4683-a557-c9acd18883b4 Metrics dashboard Target profiles

Baseline: 5f96339
Comparison: c0828fb

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

No significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	basic_py_check	% cpu utilization	+0.78	[-1.95, +3.52]	1	Logs
➖	idle	memory utilization	+0.15	[+0.10, +0.21]	1	Logs bounds checks dashboard
➖	file_to_blackhole_500ms_latency	egress throughput	+0.14	[-0.11, +0.38]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.11	[-0.07, +0.29]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.10, +0.11]	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	[-0.22, +0.22]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.02	[-0.34, +0.30]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.08	[-0.89, +0.73]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.13	[-0.84, +0.59]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.20	[-2.72, +2.32]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.35	[-0.40, -0.30]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.45	[-0.94, +0.03]	1	Logs
➖	file_tree	memory utilization	-1.49	[-1.64, -1.34]	1	Logs
➖	idle_all_features	memory utilization	-1.66	[-1.81, -1.51]	1	Logs bounds checks dashboard

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:

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".

[clamoriniere]

Nice work @gabedos 💯

[aliciascott]

good for docs.

[gabedos]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 24m.

Use /merge -c to cancel this operation!