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Training / NCP-AII

Control Plane Installation and Configuration

Module study guide

Priority 3 of 5 · Domain 3 in exam order

Scope

Exam study content

This module contains expanded study notes, practical drills, and an exam-style question set.

Exam weight: 19%
Priority tier: Tier 2
Why this domain: Critical integration domain for OS, scheduler stack, drivers, containers, and tooling.

Exam Framework

How to reason under pressure

1. Stabilize Before Optimizing

Verify hardware and management-plane integrity first.
Confirm firmware/software baseline consistency.
Only then run performance tuning decisions.

2. Single-Variable Changes

Change one parameter at a time when investigating regressions.
Use before/after evidence with constant workload input.
Discard changes without reproducible benefit.

Exam Scope Coverage

What this module now covers

Domain 3 focuses on control-plane assembly: BCM/HA setup, OS and cluster stack deployment, drivers (GPU + DOCA), container runtime integration, and host-level NGC tooling.

Track 1: Control-plane architecture and dependency order

Installation order mistakes create hard-to-debug cascading failures across scheduler and runtime layers.

Sequence OS, driver, runtime, and scheduler installation with clear dependency gates.
Define management-plane and workload-plane interfaces before cluster bootstrap.
Validate each layer independently before proceeding to next stage.

Drill: Create a dependency-aware install order from bare host to workload-capable node.

Track 2: BCM installation and HA verification

Blueprint explicitly calls out Base Command Manager and high availability checks.

Install BCM in a controlled environment with version-pinned components.
Validate HA behavior with failover/restore checks.
Capture operational runbook for day-2 management tasks.

Drill: Design a BCM HA validation test with expected outcomes for failover and recovery.

Track 3: Cluster stack (Slurm, Enroot, Pyxis)

Scheduler and container integration are central to production AI workload orchestration.

Configure cluster categories, interfaces, and scheduler policies before workload onboarding.
Use Enroot/Pyxis integration patterns for container-native job execution with Slurm.
Verify job submission path from scheduler to containerized GPU workload.

Drill: Submit one Slurm job through Enroot/Pyxis and document end-to-end execution path.

Track 4: Driver lifecycle (GPU + DOCA)

Driver install/update/remove flows are explicit exam objectives and operational risk points.

Maintain compatibility matrix for kernel, GPU driver, and DOCA stack.
Perform controlled upgrades with post-change validation.
Avoid mixed-driver state across cluster nodes.

Drill: Build a driver lifecycle checklist covering install, update, rollback, and verification.

Track 5: Container runtime integration

GPU workload execution depends on correct container toolkit and runtime wiring.

Install and configure NVIDIA Container Toolkit for Docker-based GPU access.
Validate container runtime GPU visibility with deterministic checks.
Separate runtime setup issues from scheduler issues during debugging.

Drill: Run a GPU-enabled Docker validation flow and capture each verification checkpoint.

Track 6: Host tooling and NGC CLI operations

Host-level tooling readiness affects artifact access and operational speed.

Install and verify NGC CLI on all target hosts or management nodes.
Standardize authentication and artifact retrieval workflows.
Document tooling baseline as part of cluster compliance checks.

Drill: Create a host onboarding checklist that includes NGC CLI validation and access policy steps.

Concept Explanations

Deep-dive concept library

Exam Decision Hierarchy

Prioritize decisions in this order: safety and hardware integrity, baseline consistency, controlled validation, then optimization.

If integrity checks fail, stop optimization and remediate first.
Compare against known-good baseline before changing multiple variables.
Document rationale for each decision to support incident replay.

Operational Evidence Standard

Treat every key action as evidence-producing: command, output, timestamp, and expected vs observed behavior.

Evidence should be reproducible by another engineer.
Use stable command templates for repeated environments.
Keep concise but complete validation artifacts for exam-style reasoning.

Dependency-aware control-plane bring-up

Control-plane setup is a layered dependency system where each layer must validate before the next one begins.

OS and interface baseline precede cluster and scheduler configuration.
Driver/runtime compatibility should be validated before containerized job tests.
HA and failover behavior should be tested before production promotion.

Scheduler-container integration model

Slurm + Enroot + Pyxis defines the path from scheduling policy to GPU container execution.

Scheduler queues and resource policy must map to real node capabilities.
Container runtime checks are separate from scheduler policy checks.
End-to-end validation should include job submission, launch, and runtime telemetry.

Driver and runtime lifecycle governance

GPU/DOCA drivers and container toolkit updates can destabilize clusters if changed without staged controls.

Use staged rollout with compatibility matrix and rollback criteria.
Avoid mixed driver state across production nodes.
Preserve evidence from pre-change and post-change validation.

Scenario Playbooks

Exam-style scenario explanations

Scenario A: Fresh 16-node control-plane deployment

You need to bring a new 16-node environment to workload-ready status with BCM HA, scheduler stack, and GPU container runtime.

Architecture Diagram

[BCM HA Pair]
     |
[Management Network]---[Head/Control Nodes]---[Compute Nodes x16]
                                   |
                          [Slurm + Enroot/Pyxis]
                                   |
                          [Docker + NVIDIA Toolkit]

Response Flow

Install OS and interface baselines on all nodes.
Deploy BCM and validate failover/restore behavior.
Install scheduler and container integration stack.
Validate GPU runtime in container and scheduler-submitted path.

Success Signals

BCM failover behaves as expected with no management outage.
Scheduler runs containerized GPU jobs successfully.
All nodes report consistent driver/runtime state.

Docker GPU runtime validation

docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi

Expected output (example)

NVIDIA-SMI 550.xx ...\nGPU  Name   Persistence-M|...

Scenario B: Driver update introduces scheduler launch failures

After a planned driver update, scheduled container jobs fail to launch on a subset of compute nodes.

Architecture Diagram

[Scheduler]
   |
[Node Group A: updated + validated]  [Node Group B: updated + failing]
                    \                 /
                   [Container Runtime + Driver Stack]

Response Flow

Diff node-level driver/runtime versions between working and failing groups.
Validate container toolkit compatibility with updated driver.
Rollback failing group or patch runtime mismatch.
Run canary Slurm jobs before full group re-entry.

Success Signals

Launch failures no longer occur on patched/rolled-back nodes.
Version matrix is consistent across target node group.
Canary scheduler jobs pass with expected runtime behavior.

Node driver version check

nvidia-smi --query-gpu=driver_version --format=csv,noheader

Expected output (example)

550.90.07

CLI and Commands

High-yield command runbooks

CLI Execution Pattern

1. Capture baseline state before running any intrusive command.
2. Execute command with explicit scope (node, interface, GPU set).
3. Compare output against expected baseline signature.
4. Record timestamp and decision (pass, investigate, remediate).

Control-plane post-install validation runbook

Use this sequence to confirm host, driver, runtime, and scheduler path health after installation.

Host GPU driver check

nvidia-smi

Expected output (example)

NVIDIA-SMI 550.xx ...\nGPU  Name  Persistence-M|...

Container runtime check

docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi -L

Expected output (example)

GPU 0: NVIDIA H100\nGPU 1: NVIDIA H100

Slurm path smoke test

srun --nodes=1 --gpus-per-node=1 --container-image=nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi -L

Expected output (example)

GPU 0: NVIDIA H100

Run checks on representative nodes from each role group.
Any mismatch between Docker and Slurm path is a configuration issue to isolate.

NGC CLI and artifact access runbook

Validate host-level tooling readiness for NVIDIA artifact workflows used in operations and testing.

NGC CLI version check

ngc --version

Expected output (example)

NGC CLI Version 3.x.x

Authenticated catalog test

ngc registry image list nvidia

Expected output (example)

NAMESPACE\tIMAGE\n nvidia\tcuda\n nvidia\tpytorch ...

Use service account policy for non-interactive environments.
Standardize auth and proxy settings across control and compute nodes.

Common Problems

Failure patterns and fixes

Scheduler jobs fail while Docker GPU test passes

Symptoms

Direct Docker command sees GPUs successfully.
Slurm/Enroot/Pyxis job launch fails or cannot map GPU resource.

Likely Cause

Scheduler-container integration misconfiguration (resource mapping or plugin/runtime wiring).

Remediation

Verify Slurm GPU resource definitions and plugin configuration.
Validate Enroot/Pyxis integration state on failing nodes.
Run minimal canary scheduler job after each config correction.

Prevention: Include Docker-path and scheduler-path checks in every post-change validation set.

Post-update node subset diverges from cluster behavior

Symptoms

Only some nodes fail job launches after driver/runtime change.
Error signatures differ by node group.

Likely Cause

Driver/runtime version drift or incomplete update across node groups.

Remediation

Build version diff report for all nodes.
Normalize runtime stack or rollback to known-good baseline.
Re-admit nodes only after canary validation.

Prevention: Enforce staged rollout with node-group compliance checks before promotion.

BCM appears healthy but failover is unproven

Symptoms

Normal operation is stable.
No recent tested evidence of HA behavior.

Likely Cause

HA configured but not validated under realistic failover conditions.

Remediation

Run planned failover simulation and verify recovery behavior.
Capture RTO/RPO-style operational metrics if applicable.
Update runbook with tested failover steps.

Prevention: Schedule recurring HA drills with evidence retention.

Lab Walkthroughs

Step-by-step execution guides

Walkthrough: End-to-end control-plane validation lab

Validate OS baseline, driver/runtime readiness, and scheduler-container execution path on a representative node set.

Prerequisites

Cluster nodes provisioned with base OS and network connectivity.
Scheduler and container stack installed.
Access to test container image and basic Slurm queue.

Verify host GPU driver health on each role group.
```
nvidia-smi
```
Expected: All target nodes report expected driver and visible GPUs.
Validate Docker GPU runtime path.
```
docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi -L
```
Expected: Containerized GPU visibility works on target nodes.
Run scheduler-managed containerized GPU test.
```
srun --nodes=1 --gpus-per-node=1 --container-image=nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi -L
```
Expected: Scheduler path launches and maps GPU successfully.
Verify NGC CLI availability on management hosts.
```
ngc --version
```
Expected: CLI returns expected version and is usable.
Record evidence bundle for promotion gate.

Expected: All checks archived with pass/fail status and timestamps.

Success Criteria

Host, runtime, and scheduler paths all pass.
No node-group drift detected in driver/runtime inventory.
Control-plane stack marked workload-ready.

Study Sprint

10-day execution plan

Day	Focus	Output
1	Dependency graph and installation order planning.	Control-plane install sequence blueprint.
2	BCM initial setup and baseline validation.	BCM install evidence log.
3	BCM HA test scenario and failover validation.	HA verification report.
4	OS + interface configuration for cluster nodes.	Node baseline configuration checklist.
5	Slurm/Enroot/Pyxis integration and job-path checks.	Scheduler-container integration report.
6	GPU and DOCA driver lifecycle rehearsal.	Driver lifecycle runbook.
7	NVIDIA Container Toolkit install + Docker GPU validation.	Container runtime readiness checklist.
8	NGC CLI install and host-level tooling checks.	Tooling compliance checklist.
9	Timed end-to-end cluster setup simulation.	Execution trace and gap list.
10	Final objective revision and command-level recall.	Control-plane exam cheat sheet.

Hands-on Labs

Practical module work

Each lab includes a collapsed execution sample with representative CLI usage and expected output.

Lab A: BCM + HA operational drill

Install BCM and validate high-availability behavior.

Install BCM components in controlled sequence.
Simulate failover and verify service continuity.
Capture rollback and recovery timing notes.

Execution Sample (Collapsed)

Capture baseline state for the target node/group before changes.
Run scoped validation command for this lab objective.
Compare observed output against expected signature.

Sample Command (Control-plane post-install validation runbook)

nvidia-smi

Expected output (example)

NVIDIA-SMI 550.xx ...\nGPU  Name  Persistence-M|...

Lab B: Scheduler and container stack bring-up

Validate Slurm + Enroot + Pyxis execution path.

Install and configure scheduler components.
Run containerized GPU job via scheduler.
Collect logs proving successful end-to-end path.

Execution Sample (Collapsed)

Capture baseline state for the target node/group before changes.
Run scoped validation command for this lab objective.
Compare observed output against expected signature.

Sample Command (Control-plane post-install validation runbook)

docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi -L

Expected output (example)

GPU 0: NVIDIA H100\nGPU 1: NVIDIA H100

Lab C: Driver lifecycle control

Practice safe install/update/remove workflows for GPU and DOCA drivers.

Record pre-change driver state and compatibility matrix.
Execute controlled update workflow in staging.
Verify node health and runtime compatibility post-change.

Execution Sample (Collapsed)

Capture baseline state for the target node/group before changes.
Run scoped validation command for this lab objective.
Compare observed output against expected signature.

Sample Command (Control-plane post-install validation runbook)

srun --nodes=1 --gpus-per-node=1 --container-image=nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi -L

Expected output (example)

GPU 0: NVIDIA H100

Lab D: Docker GPU runtime and NGC tooling

Confirm container and artifact tooling readiness on host baseline.

Install NVIDIA Container Toolkit and validate Docker GPU access.
Install NGC CLI and test authenticated artifact retrieval.
Document host onboarding acceptance criteria.

Execution Sample (Collapsed)

Capture baseline state for the target node/group before changes.
Run scoped validation command for this lab objective.
Compare observed output against expected signature.

Sample Command (NGC CLI and artifact access runbook)

ngc --version

Expected output (example)

NGC CLI Version 3.x.x

Exam Pitfalls

Common failure patterns

Installing stack components out of dependency order.
Declaring BCM installation complete without HA validation.
Configuring scheduler without validating container runtime integration.
Upgrading drivers cluster-wide without staged compatibility checks.
Assuming Docker GPU visibility implies scheduler readiness.
Skipping NGC CLI/tooling standardization across hosts.

Practice Set

Domain checkpoint questions

Attempt each question first, then open the answer and explanation.

Q1. Why is dependency-aware install order critical in control-plane setup?

A. It reduces log volume
B. It prevents cascading failures across stack layers
C. It removes the need for validation
D. It only matters for UI tools

Answer: B

Layered infrastructure depends on correct sequencing to avoid compounded misconfigurations.

Q2. What is the primary operational purpose of BCM HA verification?

A. UI branding checks
B. Validate failover behavior and continuity under controller failure
C. Increase benchmark throughput
D. Replace backups

Answer: B

HA testing confirms management continuity when a component fails.

Q3. Which stack combination is explicitly in NCP-AII scope for cluster setup?

A. Slurm, Enroot, Pyxis
B. Spark, Hive, Kafka
C. Kubernetes only
D. Terraform only

Answer: A

The blueprint explicitly names Slurm, Enroot, and Pyxis in cluster installation scope.

Q4. What is a safe driver lifecycle approach?

A. Upgrade all nodes at once without staging
B. Stage changes, validate compatibility, then roll out
C. Keep mixed versions indefinitely
D. Skip post-change validation

Answer: B

Staged rollout with compatibility validation reduces outage risk.

Q5. What does successful Docker GPU test prove?

A. Scheduler is fully configured
B. GPU runtime path is working for container execution
C. Network fabric is validated
D. BCM HA is verified

Answer: B

Runtime validation confirms container GPU access but does not prove scheduler or HA readiness.

Q6. Why include NGC CLI installation in host onboarding?

A. It is optional and unrelated
B. It standardizes access to NVIDIA artifacts and tooling workflows
C. It replaces all package managers
D. It disables security controls

Answer: B

NGC CLI provides consistent access to NVIDIA resources used in operations and validation.

Q7. What is a common false assumption during control-plane setup?

A. Each layer needs independent validation
B. If drivers load, entire control plane is ready
C. HA must be tested
D. Scheduler runtime path should be tested

Answer: B

Driver readiness is necessary but not sufficient for complete control-plane readiness.

Q8. Which artifact best supports repeatable control-plane rollout?

A. Untracked shell history
B. Versioned runbook with validation checkpoints
C. One-time screenshots only
D. Verbal notes

Answer: B

Versioned runbooks make installations reproducible and auditable.

Q9. Why avoid mixed driver state across nodes?

A. Mixed state always improves performance
B. It can cause inconsistent behavior and hard-to-debug failures
C. It only affects login nodes
D. It is required for HA

Answer: B

Version drift across nodes introduces unpredictable runtime and scheduler behavior.

Q10. In exam scope, what does control-plane success include?

A. Only OS installation
B. BCM/HA, scheduler stack, drivers, container runtime, and host tooling readiness
C. Only GPU benchmark results
D. Only storage tuning

Answer: B

The domain spans coordinated installation and validation across all these control-plane layers.

Primary References

Curated from the NCP-AII blueprint/study-guide sources and official documentation.

Objectives

3.1 Install Base Command Manager (BCM), configure and verify HA.
3.2 Install OS.
3.3 Install Cluster (configure category, configure interfaces, install Slurm/Enroot/Pyxis).
3.4 Install/update/remove NVIDIA GPU and DOCA drivers.
3.5 Install the NVIDIA container toolkit.
3.6 Demonstrate how to use NVIDIA GPUs with Docker.
3.7 Install NGC CLI on hosts.

Navigation

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