[DJM] Enable GPU integration for Databricks cluster if env. var is present

[RickyMarou]

What does this PR do?

• Jira

This PR adds the GPU agent integration configuration to the databricks install scripts if the environment variable GPU_MONITORING_ENABLED is present.

Motivation

This is mainly for one customer, T-Mobile. They spin up 15K+ databricks clusters every day (!!), and most, if not all, are GPU-enabled instances.

They are certain that they are wasting a lot of resources on provisioning GPU-enabled instances that don't need to be there, or that don't need to be GPU enabled. They are currently trialing Data Jobs Monitoring, and if the deals goes through (which is looking like it will), GPU monitoring is a must for them.

We made a demo of GPU Monitoring on Databricks clusters using a duct-taped init bash script on one of our own Databricks workspace, after seeing it, they expressed interest in trying this out themselves.

To simplify the configuration for them we are doing in the installer. However we don't want all customers to always have the integration enabled. It's a similar case to #37089, while the work is direct result of this customer's interest, it is also useful for other customers.

Describe how you validated your changes

In a similar fashion that we allow customers to opt-in to Databricks logs collection, we want to allow them to opt-in to GPU monitoring.

This is done through the addition of an environment variable. While customers using the "Manual" version of the databricks integration can modify init-script(s) themselves, we can also do follow-up work to make the GPU integration configurable via the Integration tile UI for customers on the "Managed" version of the databricks integration (see how we can dynamically do that here)

Possible Drawbacks / Trade-offs

The only drawback is that this might end up unused, however considering trends around running ML workloads on databricks, that's very unlikely.

Additional Notes

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: f5e675d8-d3c4-4f7f-88fd-9d169ffc13c1

Baseline: 0b19806
Comparison: 892fa09
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.67	[+0.82, +2.51]	1	Logs
➖	docker_containers_cpu	% cpu utilization	+1.63	[-1.40, +4.67]	1	Logs
➖	ddot_logs	memory utilization	+0.84	[+0.70, +0.99]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.62	[+0.50, +0.74]	1	Logs bounds checks dashboard
➖	file_to_blackhole_100ms_latency	egress throughput	+0.07	[-0.49, +0.63]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.06	[-0.55, +0.67]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.02	[-0.55, +0.58]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.01	[-0.22, +0.25]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.25, +0.26]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	-0.01	[-0.56, +0.55]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.01	[-0.03, +0.01]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.02	[-0.58, +0.54]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.03	[-0.65, +0.59]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	-0.09	[-0.67, +0.49]	1	Logs
➖	quality_gate_idle	memory utilization	-0.17	[-0.23, -0.11]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.17	[-0.23, -0.11]	1	Logs
➖	otlp_ingest_metrics	memory utilization	-0.31	[-0.47, -0.15]	1	Logs
➖	ddot_metrics	memory utilization	-0.60	[-0.72, -0.48]	1	Logs
➖	docker_containers_memory	memory utilization	-0.60	[-0.68, -0.53]	1	Logs
➖	file_tree	memory utilization	-0.98	[-1.11, -0.84]	1	Logs
➖	otlp_ingest_logs	memory utilization	-1.29	[-1.42, -1.16]	1	Logs
➖	quality_gate_logs	% cpu utilization	-1.55	[-4.26, +1.15]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	-1.61	[-1.67, -1.56]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	docker_containers_cpu	simple_check_run	10/10
✅	docker_containers_memory	memory_usage	10/10
✅	docker_containers_memory	simple_check_run	10/10
✅	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	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10	bounds checks dashboard
✅	quality_gate_logs	memory_usage	10/10	bounds checks dashboard

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_all_features, bounds check intake_connections: 10/10 replicas passed. Gate 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 intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.

[agent-platform-auto-pr]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor 0b19806

Successful checks

Info

	Quality gate	Delta	On disk size (MiB)	Delta	On wire size (MiB)
✅	agent_deb_amd64	$${+0.01}$$	$${696.05}$$ < $${752.99}$$	$${+0.04}$$	$${176.06}$$ < $${187.44}$$
✅	agent_deb_amd64_fips	$${+0.01}$$	$${694.28}$$ < $${751.36}$$	$${+0.03}$$	$${175.51}$$ < $${187.06}$$
✅	agent_heroku_amd64	$${+0}$$	$${358.56}$$ < $${369.68}$$	$${+0.01}$$	$${96.5}$$ < $${99.55}$$
✅	agent_msi	$${+0}$$	$${959.88}$$ < $${987.01}$$	$${+0.02}$$	$${146.45}$$ < $${150.72}$$
✅	agent_rpm_amd64	$${+0.01}$$	$${696.04}$$ < $${752.98}$$	$${+0}$$	$${177.54}$$ < $${190.03}$$
✅	agent_rpm_amd64_fips	$${+0.01}$$	$${694.27}$$ < $${751.35}$$	$${+0}$$	$${177.41}$$ < $${189.81}$$
✅	agent_rpm_arm64	$${+0}$$	$${685.98}$$ < $${739.42}$$	$${-0.01}$$	$${161.0}$$ < $${171.23}$$
✅	agent_rpm_arm64_fips	$${+0}$$	$${684.33}$$ < $${737.91}$$	$${-0.02}$$	$${160.07}$$ < $${170.22}$$
✅	agent_suse_amd64	$${+0.01}$$	$${696.04}$$ < $${752.98}$$	$${+0}$$	$${177.54}$$ < $${190.03}$$
✅	agent_suse_amd64_fips	$${+0.01}$$	$${694.27}$$ < $${751.35}$$	$${+0}$$	$${177.41}$$ < $${189.81}$$
✅	agent_suse_arm64	$${+0}$$	$${685.98}$$ < $${739.42}$$	$${-0.01}$$	$${161.0}$$ < $${171.23}$$
✅	agent_suse_arm64_fips	$${+0}$$	$${684.33}$$ < $${737.91}$$	$${-0.02}$$	$${160.07}$$ < $${170.22}$$
✅	docker_agent_amd64	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_agent_arm64	$${+0}$$	$${793.24}$$ < $${858.97}$$	$${+0.04}$$	$${255.99}$$ < $${274.36}$$
✅	docker_agent_jmx_amd64	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_agent_jmx_arm64	$${+0}$$	$${793.24}$$ < $${858.97}$$	$${+0.04}$$	$${255.99}$$ < $${274.36}$$
✅	docker_agent_windows1809	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_agent_windows1809_core	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_agent_windows1809_core_jmx	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_agent_windows1809_jmx	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_agent_windows2022	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_agent_windows2022_core	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_agent_windows2022_core_jmx	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_agent_windows2022_jmx	$${+0.01}$$	$${779.84}$$ < $${849.39}$$	$${-0}$$	$${268.59}$$ < $${288.34}$$
✅	docker_cluster_agent_amd64	$${-0}$$	$${259.2}$$ < $${259.73}$$	$${+0}$$	$${102.88}$$ < $${103.68}$$
✅	docker_cluster_agent_arm64	$${-0}$$	$${273.58}$$ < $${274.24}$$	$${+0}$$	$${97.61}$$ < $${98.45}$$
✅	docker_cws_instrumentation_amd64	$${-0}$$	$${7.08}$$ < $${7.12}$$	$${-0}$$	$${2.95}$$ < $${3.29}$$
✅	docker_cws_instrumentation_arm64	$${0}$$	$${6.69}$$ < $${6.92}$$	$${+0}$$	$${2.7}$$ < $${3.07}$$
✅	docker_dogstatsd_amd64	$${+0}$$	$${38.93}$$ < $${39.57}$$	$${+0}$$	$${14.95}$$ < $${15.76}$$
✅	docker_dogstatsd_arm64	$${-0}$$	$${37.52}$$ < $${38.2}$$	$${-0}$$	$${13.96}$$ < $${14.83}$$
✅	dogstatsd_deb_amd64	$${0}$$	$${30.61}$$ < $${31.52}$$	$${+0}$$	$${8.04}$$ < $${8.97}$$
✅	dogstatsd_deb_arm64	$${0}$$	$${29.16}$$ < $${30.08}$$	$${+0}$$	$${6.98}$$ < $${7.92}$$
✅	dogstatsd_rpm_amd64	$${0}$$	$${30.61}$$ < $${31.52}$$	$${-0}$$	$${8.04}$$ < $${8.98}$$
✅	dogstatsd_suse_amd64	$${0}$$	$${30.61}$$ < $${31.52}$$	$${-0}$$	$${8.04}$$ < $${8.98}$$
✅	iot_agent_deb_amd64	$${0}$$	$${50.49}$$ < $${60.17}$$	$${+0}$$	$${12.85}$$ < $${15.82}$$
✅	iot_agent_deb_arm64	$${0}$$	$${47.94}$$ < $${56.94}$$	$${+0}$$	$${11.15}$$ < $${13.86}$$
✅	iot_agent_deb_armhf	$${0}$$	$${47.52}$$ < $${56.41}$$	$${-0.01}$$	$${11.2}$$ < $${13.86}$$
✅	iot_agent_rpm_amd64	$${0}$$	$${50.49}$$ < $${60.18}$$	$${+0}$$	$${12.87}$$ < $${15.84}$$
✅	iot_agent_rpm_arm64	$${0}$$	$${47.94}$$ < $${56.94}$$	$${-0}$$	$${11.16}$$ < $${13.76}$$
✅	iot_agent_suse_amd64	$${0}$$	$${50.49}$$ < $${60.18}$$	$${+0}$$	$${12.87}$$ < $${15.84}$$