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.
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.
git clone --depth 1 https://github.com/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflownpx agentmods add commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-sweepWrote 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.
[](https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-sweep)<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-sweep"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-sweep/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.
<a href="https://agentmods.dev/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-sweep"><img src="https://agentmods.dev/badge/commands/aws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow/fleet-sweep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00022 | $0.00737 |
| Opus 5.5 | $0.00009 | $0.00295 |
| Sonnet 5.5 | $0.00004 | $0.00147 |
| Haiku 4.5 | $0.00002 | $0.00074 |
Grade A, and why
fleet-sweep 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.
What it actually says
From the repository's tutorials/multi-model-gpu-sharing directory:
python3 tools/demand_sweep.py --fleet $ARGUMENTS
By default it sweeps the rate of every tenant whose prediction misses at its memory floor, at the
multipliers 1.0, 0.7, 0.6, 0.5 and 0.4. --tenant (repeatable) picks the tenants to sweep, --rates
the multipliers, and --set chat.rate_hz=1.5 fixes an override on every row. On an instance type
nobody has leased it refuses exactly as plan does, with the same remedy.
Run it whenever a rate is new or a target looks tight — that is, before every first /fleet-profile
of a fleet. It answers the question plan does not: is this demand serveable at all? Skipping it
means learning arithmetic from a measurement round.
Its memory half is exactly as trustworthy as plan's — the same solver/ functions. Its latency
half is a prediction, marked ~ in every row and fitted per model in the FIT table. Treat it as
a ranking of candidate demands, never as a result.
THE BASELINE DOES NOT FIT— the disjoint arrangement needs more GPUs than the node has. Co-location cannot rescue this. Stop and report: demand comes down, or a tenant leaves.no placement solved, or a tenant listed as unpredicted — an MoE off its measured degree, or a model absent fromFIT. Report it as unpredictable, not infeasible: the sweep is declining to guess.cap N vs M decoding at target— the batch cap against the decoders the target implies. The cap is derived from the rate, the SLO and the engine's own default, sized on a whole slot with the prefill queue included, so it cannot come out below this number. There is nothing to set: if a row shows the cap losing, the derivation is wrong. Report it rather than editing the fleet.- A row that lands only without the co-residency factor (1.27× anchor, 2.04× stacked rider) is a predicted co-resident miss. Say so.
When demand has to change, change the rate — never the target. Relaxing an ITL ceiling moves the goalposts and raises the KV floor, so it changes the memory geometry at the same time. Lowering the rate lowers both honestly. A target that genuinely has to move is the user's call.
If you do change a fleet file, read the comments around the change before moving on: a rate, a shape
or a target that moves usually makes the sentence next to it wrong. A fleet file's comments are the
author's reasons, not a cache of arithmetic — report the geometry plan prints, do not write it back
into the file. Then run /fleet-plan again.
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 · 46 lines · 22 tokens per session scan A b4d69b891fc3
fleet-sweep 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 22 tokens to every session and 737 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.
fleet-sweep compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fleet-sweepaws-samples/amazon-eks-machine-learning-with-terraform-and-kubeflow | Command | 132 | 4d ago | Apache-2.0 | A | 22 | Claude Code |
| checklistgithub/spec-kit | Command | 150k | yesterday | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 150k | yesterday | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 150k | yesterday | MIT | A | 13 | Claude Code |
| convergegithub/spec-kit | Command | 150k | yesterday | MIT | A | 35 | Claude Code |
| implementgithub/spec-kit | Command | 150k | yesterday | MIT | A | 14 | Claude Code |
| speckit.bug.fixgithub/spec-kit | Command | 150k | yesterday | MIT | A | 12 | Claude Code |