Hyperpod Ssm logo

Hyperpod Ssm

Organization
awslabs
hyperpod-ssm

Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). This is the primary interface for accessing HyperPod nodes — direct SSH is not available. Use when any skill, workflow, or user request needs to execute commands on cluster nodes, upload files to nodes, read/download files from nodes, run diagnostics, install packages, or perform any operation requiring shell access to HyperPod instances. Other HyperPod skills depend on this skill for all node-level operations.

Overview

Publisherawslabs
Repositoryagent-plugins
Skill namehyperpod-ssm
Stars
917
Forks
162
Bundled files
4
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.

  • 4 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 Ssm 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-ssm
  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-ssm .claude/skills/hyperpod-ssm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hyperpod Ssm 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 Ssm 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 Ssm 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 SSM Access

Prerequisites

  • aws CLI v2, authenticated for the target account/Region.
  • session-manager-plugin — installed alongside the AWS CLI.
  • jq — the scripts build JSON payloads with it.
  • unbuffer (from the expect package) — wraps aws ssm start-session with a PTY so the session-manager-plugin flushes stdout instead of racing to close. Without it, calls intermittently return empty output with Cannot perform start session: EOF even when the command ran. Install with sudo yum install expect, sudo apt install expect, or brew install expect. ssm-exec.sh detects and uses it automatically; falls back with a warning if missing.

SSM Target Format

Target: sagemaker-cluster:<CLUSTER_ID>_<GROUP_NAME>-<INSTANCE_ID>

  • CLUSTER_ID: Last segment of cluster ARN (NOT the cluster name). Extract via get-cluster-info.sh.
  • GROUP_NAME: Instance group name — retrieve via list-nodes.sh.
  • INSTANCE_ID: EC2 instance ID (e.g., i-0123456789abcdef0)

Scripts

Three scripts under scripts/. Resolve cluster info and nodes once, then execute per node.

get-cluster-info.sh — Resolve cluster name → ID (call once)

bash
scripts/get-cluster-info.sh CLUSTER_NAME [--region REGION]
# Output: {"cluster_id":"...","cluster_arn":"...","cluster_name":"...","region":"..."}

list-nodes.sh — List all nodes with pagination (call once)

bash
scripts/list-nodes.sh CLUSTER_NAME [--region REGION] [--instance-group GROUP] [--instance-id ID]
# Output: JSON array of ClusterNodeSummaries (InstanceId, InstanceGroupName, InstanceStatus, etc.)

list-cluster-nodes paginates at 100 nodes. This script handles pagination automatically.

ssm-exec.sh — Execute command on a node (call per node)

bash
# Execute — with pre-built target
scripts/ssm-exec.sh --target "sagemaker-cluster:CLUSTERID_GROUP-INSTANCEID" 'command' [--region REGION]

# Execute — with parts
scripts/ssm-exec.sh --cluster-id ID --group GROUP --instance-id INSTANCE_ID 'command' [--region REGION]

# Upload
scripts/ssm-exec.sh --target TARGET --upload LOCAL_PATH REMOTE_PATH [--region REGION]

# Read remote file
scripts/ssm-exec.sh --target TARGET --read REMOTE_PATH [--region REGION]

Running Commands Across Many Nodes

SSM start-session rate limit: 3 TPS per account. Plan batch size and delay accordingly.

aws ssm send-command does NOT support sagemaker-cluster: targets — only start-session works.

Manual SSM Commands

When the scripts aren't suitable, use aws ssm start-session directly with AWS-StartNonInteractiveCommand. Wrap every invocation in unbuffer — without it, stdout is intermittently empty (see Prerequisites).

bash
cat > /tmp/cmd.json << 'EOF'
{"command": ["bash -c 'echo hello && whoami'"]}
EOF

unbuffer aws ssm start-session \
  --target sagemaker-cluster:{CLUSTER_ID}_{GROUP_NAME}-{INSTANCE_ID} \
  --region REGION \
  --document-name AWS-StartNonInteractiveCommand \
  --parameters file:///tmp/cmd.json
  • Always use a JSON file for --parameters — inline parameters break with special characters.
  • The document's command parameter is argv, not shell input. Wrap multi-statement scripts in bash -c '...' so pipes, semicolons, and redirects evaluate.

Common Diagnostic Commands

TaskCommand
Lifecycle logscat /var/log/provision/provisioning.log
Memoryfree -h
Disk/mountsdf -h && lsblk
GPU statusnvidia-smi
GPU memorynvidia-smi --query-gpu=memory.used,memory.total --format=csv
EFA/networkfi_info -p efa
CloudWatch agentsudo systemctl status amazon-cloudwatch-agent
Top processesps aux --sort=-%mem | head -20

Key Details

  • Default SSM non-interactive user is root.
  • SSM rate limit: 3 TPS per account.
  • For interactive sessions (rare), omit --document-name to get a shell.
  • Interactive commands (vim, top) are not supported via AWS-StartNonInteractiveCommand.
  • Large outputs may be truncated by SSM.
  • For troubleshooting common errors, see references/troubleshooting.md.

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 Ssm AI skill do?

Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). This is the primary interface for accessing HyperPod nodes — direct SSH is not available. Use when any skill, workflow, or user request needs to execute commands on cluster nodes, upload files to nodes, read/download files from nodes, run diagnostics, install packages, or perform any operation requiring shell access to HyperPod instances. Other HyperPod skills depend on this skill for all node-level operations.

Why use Hyperpod Ssm on TypingMind?

Because you install it once and use it with any model. Hyperpod Ssm 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 Ssm 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-ssm. 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 Ssm?

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 Ssm?

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

Is the Hyperpod Ssm 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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