hyperloom-workload-optimizer skillA
hyperloom-workload-optimizer is agent-read markdown (skill) from amd-agi/hyperloom: 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..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
<!-- 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. This is the agent-facing form of the human quickstart in [examples/README.md](../../README.md). ## 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 …
Read the whole file at its exact version.
How to install
mdr add amd-agi/hyperloom/hyperloom-workload-optimizer@git:20260921.1213b1bmdr add amd-agi/hyperloom/hyperloom-workload-optimizer@sha256:5d9c6e74514c37abPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_vpjtx3gdhdggbkf7)
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Versions
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (6862 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
amd-agi/hyperloom · 199 stars · license NOASSERTION · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_vpjtx3gdhdggbkf7 GET https://markdownregistry.com/api/v1/resolve?ref=amd-agi/hyperloom/hyperloom-workload-optimizer GET https://markdownregistry.com/api/v1/blob/5d9c6e74514c37ab56968b902fa9dce7db2b2c88049f4d2759b86b69e281b313
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