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hyperloom-workload-optimizer

Autonomously optimizes end-to-end LLM inference throughput on AMD Instinct GPUs and reports a validated gain, using the Hyperloom multi-agent optimizer. Given a

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Prix non confirmé★ 395 Stars GitHubRegistre mis à jour · 9 oct. 2026agent-skill

Vue d’ensemble

Autonomously optimizes end-to-end LLM inference throughput on AMD Instinct GPUs and reports a validated gain, using the Hyperloom multi-agent optimizer. Given a model, framework, workload (TP/EP, concurrency, ISL/OSL, precision), an objective and a time budget, it explores per-workload which levers to pull (serving/config parameters and env, framework enablement and source patches, and hot GPU-kernel rewrites), benchmarks each candidate, and returns the optimization stack that produced the gain. Use when the user wants to make a model serve faster, raise tokens/sec or throughput, optimize or tune vLLM or SGLang on MI300X/MI325X/MI355X, run Hyperloom, run the kernel-agent, quantize-then-optimize with Quark, set up Hyperloom from scratch, or resume a Hyperloom session. Do not use to stand up a server for plain serving, diagnose a broken ROCm install, or run a one-off kernel/benchmark or trace analysis without the optimization loop.

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

Hyperloom Workload Optimizer

You are the entry point for Hyperloom optimization on AMD Instinct GPUs. Your job is the bootstrap: confirm the workspace, install the Hyperloom wheel, run /hyperloom-setup, then hand the run to the skill that owns it.

The wheel installs the skills that own everything after setup: hyperloom-setup for credentials and run mode, the demo skills for a workload preset, and inference_optimizer for the launcher gates and monitoring. They ship with the runtime, so they always match the installed version.

Out of scope for this skill

Do not run python -m hyperloom.inference_optimizer.cli optimize yourself.
Do not implement a GPU preflight, launcher gate, or background launch here. The installed skills own those, including the Iron Rules and the resume path.
Do not ask for workload values (model, TP/EP, concurrency, ISL/OSL, precision, objective, budget) while installing or while setup is running. They belong to the run skill, after setup finishes.
Do not optimize by hand in chat.

Prerequisites

AMD Instinct GPU host (MI300X / MI325X / MI355X) with ROCm, /dev/kfd and /dev/dri present, and amd-smi or rocm-smi working.
Python 3.10+ and pip on the machine that runs the install.
LLM credentials: Anthropic API access, or the AMD LLM gateway.
A dedicated empty directory, opened in the agent as the workspace.

Confirm the shell you are in is on the GPU host before installing. Setup may later point Docker at a different target host; until it does, everything here runs where the agent is.

Step 1: Confirm the workspace

The current directory is both the install target and the agent workspace. Confirm with the user that it is a dedicated directory before installing: setup creates or updates .env in it. Do not switch to another directory on your own, and do not install into an existing project unless the user accepts the .env change.

Step 2: Install the Hyperloom wheel

pip install hyperloom-inference-optimizer --target .

Install the current release unless the user asks for a specific version. It is normal for the directory to hold many Python package folders afterwards; the user does not need to inspect them.

Step 3: Run `/hyperloom-setup`

The wheel installs hyperloom-setup into the agent's skill directories (.agents/skills/, .claude/skills/, .cursor/skills/). Run it:

/hyperloom-setup

It is interactive and owns credentials, USER_DATA_PATH, the run mode (docker recommended, or baremetal), the Docker target host, and the bare-metal framework install. It writes .env and stops before any optimization. Run it once per workspace; the run skills reuse those values.

Let setup ask its own questions. Do not preempt them, do not restate its option lists, and never ask the user to paste an API key into chat.

If the agent does not list hyperloom-setup after the install, the skill directories were written after the agent scanned them. Tell the user to restart the agent, then run it again. Do not substitute your own setup steps.

Step 4: Hand off to a run skill

Setup ends by offering a run and loading the matching skill, so normally you just follow it. When the user asks for a run directly, load the skill by name and follow its instructions instead of this one:

hyperloom-qwen3-8b-3h — short no-kernel Qwen3-8B run; best first end-to-end check.
hyperloom-qwen3-14b-fp8-12h — medium-length Qwen3-14B-FP8 run.
hyperloom-qwen3-14b-fp8-12h-forge — the same run on the KernelForge kernel backend.
hyperloom-custom-advanced — explicit model, framework, workload, budget, and phase toggles.

A preset keeps its workload even if the user supplies their own MODEL_PATH; tensor parallelism, concurrency, sequence lengths, precision, and budget are not retuned for that model. When those need to change, use hyperloom-custom-advanced.

To resume a stopped session, follow the optimizer skill at .env HYPERLOOM_SKILL_PATH; it owns the resume path and the gates a relaunch still has to clear.

What to expect during a run

Optimization runs for hours in the background. Do not stream the log.

Before launch the run skill shows a plan: resolved model path, run mode, framework, TP, concurrency, ISL/OSL, precision, budget, and USER_DATA_PATH. Get the user's go-ahead on that plan before the optimizer starts — it then owns the GPU for hours. After launch it reports the optimizer PID, run log, launch-info JSON, session directory, state.json, and the first health check.

During the run, report a short status about every 300 seconds: process alive, current phase, stop_reason, baseline and current best throughput, cumulative gain, the latest benchmark or candidate decision, and the most relevant log lines. Never print API keys, tokens, or custom headers.

Troubleshooting

Many package folders in the workspace after pip install --target . is expected.
/hyperloom-setup not listed: the install landed after the agent scanned for skills. Restart the agent and check .claude/skills/hyperloom-setup/ exists.
ImportError: libamdhip64.so.7 or libhipblas.so.3: the framework torch wheel wants different ROCm user-space libraries; align ROCM_PATH and LD_LIBRARY_PATH.
hipDeviceAttributePciChipId missing during an AITER build: hipcc is using older ROCm headers; put the matching ROCm bin first on PATH.
Anything past setup (preflight failures, launch, phases, gains) belongs to the installed run and optimizer skills. Read those rather than reproducing their checks here.

Voir la source ↗

Métadonnées du fichier

name: hyperloom-workload-optimizer
description: >-
  Autonomously optimizes end-to-end LLM inference throughput on AMD Instinct GPUs
  and reports a validated gain, using the Hyperloom multi-agent optimizer. Given a
  model, framework, workload (TP/EP, concurrency, ISL/OSL, precision), an objective
  and a time budget, it explores per-workload which levers to pull (serving/config
  parameters and env, framework enablement and source patches, and hot GPU-kernel
  rewrites), benchmarks each candidate, and returns the optimization stack that
  produced the gain. Use when the user wants to make a model serve faster, raise
  tokens/sec or throughput, optimize or tune vLLM or SGLang on MI300X/MI325X/MI355X,
  run Hyperloom, run the kernel-agent, quantize-then-optimize with Quark, set up
  Hyperloom from scratch, or resume a Hyperloom session. Do not use to stand up a
  server for plain serving, diagnose a broken ROCm install, or run a one-off
  kernel/benchmark or trace analysis without the optimization loop.

Voir le texte original

---
name: hyperloom-workload-optimizer
description: >-
Autonomously optimizes end-to-end LLM inference throughput on AMD Instinct GPUs
and reports a validated gain, using the Hyperloom multi-agent optimizer. Given a
model, framework, workload (TP/EP, concurrency, ISL/OSL, precision), an objective
and a time budget, it explores per-workload which levers to pull (serving/config
parameters and env, framework enablement and source patches, and hot GPU-kernel
rewrites), benchmarks each candidate, and returns the optimization stack that
produced the gain. Use when the user wants to make a model serve faster, raise
tokens/sec or throughput, optimize or tune vLLM or SGLang on MI300X/MI325X/MI355X,
run Hyperloom, run the kernel-agent, quantize-then-optimize with Quark, set up
Hyperloom from scratch, or resume a Hyperloom session. Do not use to stand up a
server for plain serving, diagnose a broken ROCm install, or run a one-off
kernel/benchmark or trace analysis without the optimization loop.
---

<!--
Copyright (c) 2026 Advanced Micro Devices, Inc. All rights reserved.

See LICENSE for license information.
-->

# Hyperloom Workload Optimizer

You are the entry point for Hyperloom optimization on AMD Instinct GPUs. Your job
is the bootstrap: confirm the workspace, install the Hyperloom wheel, run
`/hyperloom-setup`, then hand the run to the skill that owns it.

The wheel installs the skills that own everything after setup: `hyperloom-setup`
for credentials and run mode, the demo skills for a workload preset, and
`inference_optimizer` for the launcher gates and monitoring. They ship with the
runtime, so they always match the installed version.

## Out of scope for this skill

- Do not run `python -m hyperloom.inference_optimizer.cli optimize` yourself.
- Do not implement a GPU preflight, launcher gate, or background launch here. The
installed skills own those, including the Iron Rules and the resume path.
- Do not ask for workload values (model, TP/EP, concurrency, ISL/OSL, precision,
objective, budget) while installing or while setup is running. They belong to
the run skill, after setup finishes.
- Do not optimize by hand in chat.

## Prerequisites

- AMD Instinct GPU host (MI300X / MI325X / MI355X) with ROCm, `/dev/kfd` and
`/dev/dri` present, and `amd-smi` or `rocm-smi` working.
- Python 3.10+ and `pip` on the machine that runs the install.
- LLM credentials: Anthropic API access, or the AMD LLM gateway.
- A dedicated empty directory, opened in the agent as the workspace.

Confirm the shell you are in is on the GPU host before installing. Setup may later
point Docker at a different target host; until it does, everything here runs where
the agent is.

## Step 1: Confirm the workspace

The current directory is both the install target and the agent workspace. Confirm
with the user that it is a dedicated directory before installing: setup creates or
updates `.env` in it. Do not switch to another directory on your own, and do not
install into an existing project unless the user accepts the `.env` change.

## Step 2: Install the Hyperloom wheel

```bash
pip install hyperloom-inference-optimizer --target .
```

Install the current release unless the user asks for a specific version. It is
normal for the directory to hold many Python package folders afterwards; the user
does not need to inspect them.

## Step 3: Run `/hyperloom-setup`

The wheel installs `hyperloom-setup` into the agent's skill directories
(`.agents/skills/`, `.claude/skills/`, `.cursor/skills/`). Run it:

```text
/hyperloom-setup
```

It is interactive and owns credentials, `USER_DATA_PATH`, the run mode
(`docker` recommended, or `baremetal`), the Docker target host, and the bare-metal
framework install. It writes `.env` and stops before any optimization. Run it once
per workspace; the run skills reuse those values.

Let setup ask its own questions. Do not preempt them, do not restate its option
lists, and never ask the user to paste an API key into chat.

If the agent does not list `hyperloom-setup` after the install, the skill
directories were written after the agent scanned them. Tell the user to restart
the agent, then run it again. Do not substitute your own setup steps.

## Step 4: Hand off to a run skill

Setup ends by offering a run and loading the matching skill, so normally you just
follow it. When the user asks for a run directly, load the skill by name and follow
its instructions instead of this one:

- `hyperloom-qwen3-8b-3h` — short no-kernel Qwen3-8B run; best first end-to-end check.
- `hyperloom-qwen3-14b-fp8-12h` — medium-length Qwen3-14B-FP8 run.
- `hyperloom-qwen3-14b-fp8-12h-forge` — the same run on the KernelForge kernel backend.
- `hyperloom-custom-advanced` — explicit model, framework, workload, budget, and phase toggles.

A preset keeps its workload even if the user supplies their own `MODEL_PATH`;
tensor parallelism, concurrency, sequence lengths, precision, and budget are not
retuned for that model. When those need to change, use `hyperloom-custom-advanced`.

To resume a stopped session, follow the optimizer skill at `.env`
`HYPERLOOM_SKILL_PATH`; it owns the resume path and the gates a relaunch still has
to clear.

## What to expect during a run

Optimization runs for hours in the background. Do not stream the log.

Before launch the run skill shows a plan: resolved model path, run mode, framework,
TP, concurrency, ISL/OSL, precision, budget, and `USER_DATA_PATH`. Get the user's
go-ahead on that plan before the optimizer starts — it then owns the GPU for hours.
After launch it reports the optimizer PID, run log, launch-info JSON, session
directory, `state.json`, and the first health check.

During the run, report a short status about every 300 seconds: process alive,
current phase, `stop_reason`, baseline and current best throughput, cumulative
gain, the latest benchmark or candidate decision, and the most relevant log lines.
Never print API keys, tokens, or custom headers.

## Troubleshooting

- Many package folders in the workspace after `pip install --target .` is expected.
- `/hyperloom-setup` not listed: the install landed after the agent scanned for
skills. Restart the agent and check `.claude/skills/hyperloom-setup/` exists.
- `ImportError: libamdhip64.so.7` or `libhipblas.so.3`: the framework torch wheel
wants different ROCm user-space libraries; align `ROCM_PATH` and
`LD_LIBRARY_PATH`.
- `hipDeviceAttributePciChipId` missing during an AITER build: `hipcc` is using
older ROCm headers; put the matching ROCm `bin` first on `PATH`.
- Anything past setup (preflight failures, launch, phases, gains) belongs to the
installed run and optimizer skills. Read those rather than reproducing their
checks here.

Examiner la source

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Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
L’approbation de revue IA est absente
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 395 stars, 39 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
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3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

RépertoriéContrôle statique

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: amd/skills
Licence: MIT
Version: Unknown
Dernier push GitHub: 7 oct. 2026
Registre mis à jour: 9 oct. 2026
Chemin des instructions: skills/hyperloom-workload-optimizer/SKILL.md @ 6c92b41304c7

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

67/100

Prometteur

Confiance

62/100

Sandbox uniquement

Audit

75/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
L’approbation de revue IA est absente
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 395 stars, 39 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Résultats: —

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    "label": "Manual review",
    "version": "trust-score-v4",
    "install_policy": "block",
    "evidence": {
      "stars": "395 GitHub stars",
      "repoActivity": "395 stars, 39 forks",
      "lastPushed": "4d since push",
      "license": "MIT",
      "repository": "https://github.com/amd/skills/tree/main/skills/hyperloom-workload-optimizer",
      "install": "npx skills add amd/skills --skill hyperloom-workload-optimizer",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "other",
      "agent-skill"
    ],
    "known_risks": [
      "AI review approval is missing",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 395 stars, 39 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution",
      "Review status: AI review approval is missing"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "AI review approval is missing",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 395 stars, 39 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 67,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "4d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "orchestra-research-peft-fine-tuning",
      "name": "peft-fine-tuning",
      "url": "https://www.openagentskill.com/skills/orchestra-research-peft-fine-tuning",
      "stars": 13443,
      "install_command": "npx skills add Orchestra-Research/AI-Research-SKILLs --skill peft-fine-tuning",
      "trust_score": 80,
      "audit_score": 85
    },
    {
      "slug": "orchestra-research-distributed-llm-pretraining-torchtitan",
      "name": "distributed-llm-pretraining-torchtitan",
      "url": "https://www.openagentskill.com/skills/orchestra-research-distributed-llm-pretraining-torchtitan",
      "stars": 13443,
      "install_command": "npx skills add Orchestra-Research/AI-Research-SKILLs --skill distributed-llm-pretraining-torchtitan",
      "trust_score": 79,
      "audit_score": 84
    },
    {
      "slug": "google-ai-edge-litert-lm",
      "name": "litert-lm",
      "url": "https://www.openagentskill.com/skills/google-ai-edge-litert-lm",
      "stars": 459,
      "install_command": "",
      "trust_score": 75,
      "audit_score": 78
    },
    {
      "slug": "amd-quark-torch-llm-ptq",
      "name": "quark-torch-llm-ptq",
      "url": "https://www.openagentskill.com/skills/amd-quark-torch-llm-ptq",
      "stars": 395,
      "install_command": "npx skills add amd/skills --skill quark-torch-llm-ptq",
      "trust_score": 73,
      "audit_score": 77
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "AI review approval is missing"
  ],
  "agent_contract": {
    "task_input": "Use hyperloom-workload-optimizer in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 70/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "amd-hyperloom-workload-optimizer (hyperloom-workload-optimizer)",
      "install_command": "npx skills add amd/skills --skill hyperloom-workload-optimizer",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "amd-hyperloom-workload-optimizer",
      "task": "Use hyperloom-workload-optimizer in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/amd-hyperloom-workload-optimizer",
    "api": "https://www.openagentskill.com/api/agent/skills/amd-hyperloom-workload-optimizer",
    "audit": "https://www.openagentskill.com/skills/amd-hyperloom-workload-optimizer/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=amd-hyperloom-workload-optimizer&task=Use%20hyperloom-workload-optimizer%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20hyperloom-workload-optimizer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20hyperloom-workload-optimizer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/amd-hyperloom-workload-optimizer/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/amd-hyperloom-workload-optimizer"
  }
}

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Créateur: amd
Source: amd/skills
Indexé par: Index communautaire OpenAgentSkill

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