An end-to-end command for preparing and measuring a fleet of models that share GPUs, then producing an export file. It runs planning, demand checks, profiling, placement search, inspection, and export in the required order.

Use it to take a fleet file from initial setup through measured GPU placement and an export ready for serving.

It removes the need to remember the pipeline order and its feedback steps, while keeping deployment to KAI as a separate action.

run-fleet — Command for Claude Code by aws-samples

About the project

Amazon EKS Machine Learning with Terraform and Kubeflow is a modular platform for running machine-learning operations on Amazon EKS, the managed Kubernetes service, including training pipelines and model-serving systems. It is intended for teams deploying distributed training and inference with tools such as Kubeflow, MLflow, Airflow, and KServe, using configurable AWS infrastructure. The catalogue entries provide commands, skills, hooks, and a plugin for working with this deployment setup.

aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow · 132 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo

git clone --depth 1 https://github.com/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow

Made for: Claude Code.

Or install colocation-pipeline, the plugin that ships this one along with the rest of its 2 skills, 15 commands, 2 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/run-fleet/github.svg)](https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/run-fleet)

Your own site

<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/run-fleet"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/run-fleet/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/run-fleet"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/run-fleet.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 27 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 350 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00027	$0.00350
Opus 5.5	$0.00011	$0.00140
Sonnet 5.5	$0.00005	$0.00070
Haiku 4.5	$0.00003	$0.00035

Measured 4d ago against content hash 277b61830f05, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

run-fleet scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

tutorials/multi-model-gpu-sharing/plugins/colocation-pipeline/commands/run-fleet.md · 31 lines

What it actually says

Run the co-location pipeline on: $ARGUMENTS

Use the run-fleet skill in this plugin. It owns the sequence, the back-edges, and how to read every refusal. Nothing in the code sequences these steps, so follow it in order rather than from memory:

harness image -> plan -> demand sweep -> profile -> plan again -> search -> read -> export

The individual steps are also available on their own, if part of this is already done: /fleet-preflight, /fleet-image, /fleet-plan, /fleet-sweep, /fleet-profile, /fleet-search, /fleet-calibrate, /fleet-inspect, /fleet-export.

A fleet with hardware.max_nodes above 1 takes the same sequence, but search measures it one group per node, and its export is the split's export.<stamp>.yaml that search writes. The export mode refuses such a fleet.

The pipeline ends at the export. Serving it on KAI (/fleet-kai-deploy) is a separate step the user starts; do not run it from here.

If no fleet file was named, list the fleet files under fleets/ (the shipped ones are under fleets/examples/<instance type>/) with one line each from the file's own header comment, and ask which one.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

4d ago First seen · 31 lines · 27 tokens per session scan A 277b61830f05

Subscribe to this mod's changes

run-fleet is a command published in the GitHub repository aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow (132 stars, last pushed 4d ago), licensed Apache-2.0. It adds 27 tokens to every session and 350 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-11-09.

Cloud DevOps and cloud Planning

run-fleet next to 6 commands from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
run-fleetaws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow	Command	132	4d ago	Apache-2.0	A	27	Claude Code
cost-optimizewshobson/agents	Command	42k	5d ago	MIT	A	26	Claude Code
deploybutterbase-ai/butterbase-skills	Command	536	14d ago	MIT	A	20	Claude Code
envvercel/vercel-plugin	Command	336	yesterday	none	A	35	Claude Code
firebase-publishPierry/harness-kit	Command	4	1mo ago	MIT	A	20	Claude Code
scanprajapatimehul/claude-aws-cost-saver	Command	24	4mo ago	MIT	A	6	Claude Code
migrationdatabayt/hogwarts	Command	18	3d ago	none	A	4	Claude Code