fleet-profile

fleet-profile is a command for Claude Code from aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow. It costs 25 tokens per session (783 once invoked), scanned A, original, Apache-2.0.

A live profiling command that measures each model tenant alone at its full workload to find the smallest workable GPU degree. It captures a node, runs the required engine rounds, and stores the results.

In plain words
What is it for?
Use it to profile missing tenant and GPU-degree combinations, or to observe an unrecorded instance type before a real measurement run.
Why use it?
It turns a memory-based estimate into measurements of actual model behaviour, and can record a new machine type before profiling it.

Command for Claude Code ✓ vendor

Written for Claude Code: allowed-tools in frontmatter.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is IMG=$(bash plugins/colocation-pipeline/scripts/harness-image.sh --ensure) || exit 1.

Part of the colocation-pipeline plugin — 2 skills, 15 commands, 2 hooks shipped together

Good fit Use it to profile missing tenant and GPU-degree combinations, or to observe an unrecorded instance type before a real measurement run.

Compare 6 commands from other repositories ↓
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

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow
agentmods
npx agentmods add commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-profile

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.

agentmods badge for fleet-profile

README.md
[![agentmods](https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-profile/github.svg)](https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-profile)
Your own site
<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-profile"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-profile/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.

agentmods 80×15 button for fleet-profile

Your own site · 80×15
<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-profile"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-profile.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 783 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00025 $0.00783
Opus 5.5 $0.00010 $0.00313
Sonnet 5.5 $0.00005 $0.00157
Haiku 4.5 $0.00003 $0.00078

Measured 4d ago against content hash 708016b4b0d5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

fleet-profile 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/fleet-profile.md · 54 lines

What it actually says

This captures a node and deploys engines on it. From the repository's tutorials/multi-model-gpu-sharing directory:

IMG=$(bash plugins/colocation-pipeline/scripts/harness-image.sh --ensure) || exit 1
COLOCATION_HARNESS_IMAGE="$IMG" PYTHONPATH=src python3 -m colocation.agent profile --fleet $ARGUMENTS

Give the Bash call a timeout well above the default — rounds take minutes each.

On an instance type nobody has leased, it refuses without --gpu-count N, and with it the run only observes the machine: it holds one node, files its shape and device facts, gives it back to the holder and exits 0 having measured nothing. Run the same command again for the real round. That is the only way to file a new type: plan cannot do it.

One deploy-and-drive round runs per (tenant, degree) that the store does not already hold and arithmetic has not settled. --budget caps the number of rounds. Each round measures a tenant alone at its full envelope, walking degrees upward from the memory floor and stopping at the first pass.

Three outcomes take no round at all and are normal: forced by memory alone (the floor is already the widest degree the node offers), already profiled (a usable row exists — this is why profiles amortize across fleets), and a tenant with no itl_mean_ms (there is no criterion to judge a degree against, so it is skipped).

The walk widens on evidence about the degree, and only on that: a measured miss, a degree the arithmetic would not place, an engine that failed to boot twice at the same degree (the first failure is retried automatically), or a row that shows the server driven flat out and too narrow.

Anything else that comes back unusable stops the walk rather than widening it. Do not fight this. An unusable row — no ITL, delivery_passed: false, a verdict other than measured, sampling too thin — did not say the degree is too slow. It said the degree was not measured. Fix the cause and run the same command again: on the way up, a degree with no usable row is measured again without --reprofile. (Walking down from a degree that passed is the exception: there a stored unusable row stops the descent until --reprofile, and the log says so.) Widening buys the baseline a wider shard that nothing ruled the narrower one out at.

It exits 1 when any tenant's degree is still unresolved, which is what the stop above leaves.

Afterwards:

  • If sum(minimal tp) now exceeds the node's GPU count, there is nothing to search: the disjoint arrangement being scored against does not fit. Report it and go back to /fleet-sweep.
  • Otherwise run /fleet-plan again before /fleet-search. The degrees moved, so the baseline and every placement moved with them.
  • The per-shard MPS reserve a round measured needs no paste anywhere. It is filed in the profile store and merged into every later load of that model at that degree, so re-running plan is what picks it up. Report the number and which envelopes it moved.
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.

  1. 4d ago First seen · 54 lines · 25 tokens per session scan A 708016b4b0d5

Subscribe to this mod's changes

fleet-profile 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 25 tokens to every session and 783 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.

Related

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