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Hyperpod Slurm Debugger

Organization
awslabs
hyperpod-slurm-debugger

Diagnostic-only skill for Slurm scheduler and node-daemon issues on Amazon SageMaker HyperPod Slurm clusters. Scope mirrors the HyperPod troubleshooting guide. Invoke when the user reports a Slurm node stuck in down/drain, "Node unexpectedly rebooted" after auto-repair, slurmd not running, jobs stuck PENDING with REASON=Resources while sinfo shows idle nodes, jobs stuck COMPLETING after node replacement, GRES/GPU counts wrong, scontrol ping failing, slurmctld unresponsive, an Action:Reboot/Replace request that did not trigger HyperPod auto-recovery, or auto-resume not restarting a job. Also triggers on "drain before reboot", "diagnose a Slurm node", "investigate stuck jobs."

Overview

Publisherawslabs
Repositoryagent-plugins
Skill namehyperpod-slurm-debugger
Stars
917
Forks
162
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by awslabs on GitHub. Read the source before you install it.

Installation

Install the Hyperpod Slurm Debugger AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-slurm-debugger
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/awslabs/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/plugins/sagemaker-ai/skills/hyperpod-slurm-debugger .claude/skills/hyperpod-slurm-debugger
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hyperpod Slurm Debugger in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hyperpod Slurm Debugger on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hyperpod Slurm Debugger is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

HyperPod Slurm Debugger

Diagnostic-only. Identify and classify Slurm scheduler and node-daemon issues on HyperPod Slurm clusters. Do not run, recommend, or print any state-mutating command. For remediation, link to the official AWS or Slurm documentation.

When to invoke

Invoke when the user reports any of the symptoms in the decision table.

When NOT to invoke

  • Cluster has Orchestrator.Eks — invoke hyperpod-node-debugger or hyperpod-nccl.
  • Single-node hardware fault with healthy Slurm scheduler — invoke hyperpod-node-debugger.
  • NCCL training-hang investigation — invoke hyperpod-nccl.
  • Node unreachable via SSM — invoke hyperpod-ssm.

Constraints

  • Read-only. Do not run, recommend, or print state-mutating commands.
  • For any remediation, link to AWS or Slurm docs. The user authorizes and executes.
  • IaC-managed cluster (Terraform / CloudFormation / CDK): warn that direct mutation drifts the live state from the IaC plan.

Canonical recovery URLs: references/slurm-details.md → Authoritative recovery documentation.

Prerequisites

  • AWS CLI v2, authenticated for the target account and region with permissions:
    • sagemaker:DescribeCluster, sagemaker:ListClusterNodes
    • ssm:StartSession on the HyperPod-created SSM document
  • Session Manager plugin installed locally.
  • jq ≥ 1.6.
  • unbuffer (from the expect package). Required — without it aws ssm start-session returns empty stdout intermittently with Cannot perform start session: EOF and every check silently misreports. Install: expect package on Amazon Linux / RHEL / Debian / Ubuntu / macOS. Script exits at prerequisite check if missing.

Procedure

Step 1 — Collect inputs

Ask the user for:

  1. HyperPod cluster name (not Slurm partition name).
  2. AWS region.
  3. Optional: a specific Slurm node name.

Step 2 — Confirm orchestrator

bash
aws sagemaker describe-cluster --cluster-name <NAME/ARN> --region <REGION> \
  --query 'Orchestrator' --output json

If Orchestrator.Eks is present, stop. Route per When NOT to invoke.

Step 3 — Run the diagnostic script

bash
bash scripts/slurm-diagnose.sh --cluster <NAME> --region <REGION>
# Scope to a node:
bash scripts/slurm-diagnose.sh --cluster <NAME> --region <REGION> --node <SLURM_NODE>

Relay the script output to the user verbatim.

Step 4 — Map findings → docs

For each finding, look up the section in the decision table and link the user to the corresponding AWS / Slurm doc. Do not type out remediation commands.

Decision table

Symptom (sinfo -o "%N %T %30E" or script finding)Section
Node state = down or down*, reason other than belowA: Node Down
Node state = down*, Reason = Node unexpectedly rebootedB: Unexpected Reboot
Jobs PENDING with REASON=Resources while nodes are idleC: Controller State
Jobs stuck COMPLETING after node replacementC: Controller State
scontrol ping returns DOWN for the controllerC: Controller State
GRES (GPU) counts incorrect or not releasedC: Controller State
state=fail issued but no recovery occurredD: Action Reason Mismatch
Accounting errors or RPC errors mentioning dbdC: Controller State (slurmdbd)
slurm.conf edited; new partitions or nodes not visibleC: Controller State (config)
Job exited on a hardware failure but did not restartE: Auto-resume

Defaults

BehaviorDefaultOverride
Moderead-only — always; no remediation flag existsn/a
Region$AWS_DEFAULT_REGION, falling back to us-east-1--region <R>
Scopeall nodes in down / drain / fail / "unexpectedly rebooted"--node <SLURM_NODE_NAME>
Outputcolorized terminal--no-color
SSM target formatsagemaker-cluster:<clusterId>_<instanceGroupName>-<instanceId> (derived)n/a
Controller discovery--controller-group (if set) → SlurmConfig.NodeType=Controller → provisioning_parameters.json--controller-group <N>

Error handling

FailureSkill behaviorRequired user action
describe-cluster failsPrint AWS error; exit 1Fix credentials/region; verify cluster name
Cluster has Orchestrator.EksExit 1 with pointer to EKS-side skillsUse hyperpod-node-debugger or hyperpod-nccl
session-manager-plugin missing / SSM unreachablesinfo returns empty; exit 1Install plugin; verify node InService
Disk ≥ 95 % full on a down nodeReport finding disk-full-<node>Refer to AWS troubleshooting docs
Missing jq or awsExit 1 at prerequisite checkInstall per Prerequisites

A: Node Down

Node is down because slurmd stopped responding. Causes: slurmd crash, disk full, OOM, network partition, hardware fault.

Script checks: systemctl is-active slurmd, srun -w <NODE> hostname (RPC layer), disk, memory.

Link: https://github.com/aws/sagemaker-hyperpod-cluster-setup/blob/troubleshooting-doc-20250917/troubleshoot/index.md

If node returns to down after a manual resume → escalate to hyperpod-node-debugger.

Context: references/slurm-details.md § A.


B: Unexpected Reboot

Node is down* with Reason "Node unexpectedly rebooted" because slurmd re-registered after an out-of-band reboot. Upstream Slurm behavior, not HyperPod. Node is typically healthy.

Links:

If node reboots again within minutes → escalate to hyperpod-node-debugger.

Context: references/slurm-details.md § B.


C: Controller State

slurmctld in-memory state can desync from the on-disk state. A controller restart reloads from StateSaveLocation and clears bad caches. User decides and executes.

Restart may help:

SymptomWhy
PENDING with REASON=Resources, idle nodesRe-evaluates the queue
Jobs stuck COMPLETING after node replacementController held a reference to the old node
GRES (GPU, EFA) not released after a job endsResource accounting de-synced
Nodes stuck Unknown after reboot, slurmd is upRe-registration was not processed
scontrol ping times outController event loop is hung
Lost connection to slurmdbd / RPC errorsDBD connection wedged

Do NOT restart when:

  • HyperPod replacement (Action:Replace) in progress on any node — concurrent changes fail the replacement.
  • Only one compute node is bad — restart slurmd on that node.
  • sinfo and squeue are responsive — problem is elsewhere.
  • journalctl -u slurmctld not reviewed yet — panic / OOM will reproduce.
  • slurm.conf was just edited — try scontrol reconfigure first.

Folded triggers

Restart procedure / what's preserved:

Context: references/slurm-details.md § C.


D: Action Reason Mismatch

scontrol update state=fail reason=... was issued with a reason that does not match Action:Reboot or Action:Replace exactly. HyperPod silently ignores anything else. Script detects near-misses on nodes in fail state.

Required strings (case-sensitive, no whitespace, no punctuation):

  • Action:Reboot
  • Action:Replace

Link: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod-resiliency-slurm-replace-faulty-instance.html

Context: references/slurm-details.md § Action reason-string validation.


E: Auto-resume

--auto-resume=1 is an srun step option. It re-runs the step after HMA (the Health Monitoring Agent) flags a node and Automatic node recovery replaces it.

Why it didn't restart the job:

  • Flag on sbatch not srun — per-step; sbatch directives are silently ignored.
  • HMA did not flag the node — failure was application/transient, not hardware. Step exits as a normal Slurm failure.
  • Cluster NodeRecovery is None — faulty nodes are labeled but not replaced.
  • No checkpointing — step restarts from process zero each iteration.
  • AMI predates HMA support (released 2025-09-11) — needs AMI / cluster-software update.

Link: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod-resiliency-slurm-auto-resume.html

Context: references/slurm-details.md § HyperPod auto-resume.


Escalation

ConditionNext skill
Node returns to down shortly after a manual resumehyperpod-node-debugger (hardware)
slurmd logs contain CUDA / NVIDIA / XID errorshyperpod-node-debugger § G
Disk full or /dev/shm exhaustedhyperpod-node-debugger § I
Node unreachable via SSMhyperpod-ssm
Controller restart does not clear COMPLETING after 2 attemptshyperpod-issue-report + AWS Support

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Hyperpod Slurm Debugger AI skill do?

Diagnostic-only skill for Slurm scheduler and node-daemon issues on Amazon SageMaker HyperPod Slurm clusters. Scope mirrors the HyperPod troubleshooting guide. Invoke when the user reports a Slurm node stuck in down/drain, "Node unexpectedly rebooted" after auto-repair, slurmd not running, jobs stuck PENDING with REASON=Resources while sinfo shows idle nodes, jobs stuck COMPLETING after node replacement, GRES/GPU counts wrong, scontrol ping failing, slurmctld unresponsive, an Action:Reboot/Replace request that did not trigger HyperPod auto-recovery, or auto-resume not restarting a job. Also...

Why use Hyperpod Slurm Debugger on TypingMind?

Because you install it once and use it with any model. Hyperpod Slurm Debugger is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hyperpod Slurm Debugger in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-slurm-debugger. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hyperpod Slurm Debugger?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hyperpod Slurm Debugger?

As many as you like. As long as a model supports skills, you can use Hyperpod Slurm Debugger with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hyperpod Slurm Debugger AI skill free?

Yes. It is published on GitHub by awslabs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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