Home / yeaight7 / agent-powerups · plugins/machine-learning-ops/skills/reproducible-training-runs/SKILL.md · GitHub

reproducible-training-runs skillA

reproducible-training-runs is agent-read markdown (skill) from yeaight7/agent-powerups: Use when reviewing or modifying ML training scripts that must produce identical results across runs or machines -- runs with the "same" config differ, or a past result must be reconstructed exactly..

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

## Purpose

Enforce seed setting, deterministic operations, and environment tracking so a training run can be reproduced exactly.

## When to Use

- Reviewing or modifying a training script that must be deterministic
- Two runs with the "same" configuration produced different results
- A past result needs to be reconstructed exactly

## Inputs

- A target Python training script

## Workflow

1. **Global seed initialization**: ensure a single function sets seeds for all relevant libraries (`random`, `numpy`, `torch`, `tensorflow`).
2. **Deterministic algorithms**: for PyTorch or TensorFlow, check that deterministic algorithms are enabled (e.g., `torch.use_deterministic_algorithms(True)`).
3. **Data loading**: verify data loaders use deterministic shuffling and that worker processes are seeded correctly to avoid identical augmentations.
4. **Environment & config tracking**: ensure the script logs the exact configuration, dependency versions, and data hashes.
5. **Review first**: point out missing reproducibility guards before rewriting the script. Provide the exact seed-initialization snippet — do not hide side effects.

## Output
…

Read the whole file at its exact version.

How to install

Latest version
mdr add yeaight7/agent-powerups/reproducible-training-runs@git:20260605.597546b
Exact content
mdr add yeaight7/agent-powerups/reproducible-training-runs@sha256:06e1c09aea6c0ca8

Pin 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.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_ofk5pig6jmcdkkta.svg)](https://markdownregistry.com/a/art_ofk5pig6jmcdkkta)

1 badge views in 30 days

Versions

versioncommittedcommitsizeaudit
git:20260605.597546b latest2026-06-05 597546b 2,226 BA view · diff
git:20260502.2ab90812026-05-02 2ab9081 1,430 BA view

Audit of the latest version

A  17 of 17 checks passed. Deterministic, no model, same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (2226 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

GitHub

yeaight7/agent-powerups · 6 stars · license Apache-2.0 · pushed 2026-09-21 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_ofk5pig6jmcdkkta
GET https://markdownregistry.com/api/v1/resolve?ref=yeaight7/agent-powerups/reproducible-training-runs
GET https://markdownregistry.com/api/v1/blob/06e1c09aea6c0ca8a522bca985ead058eeac3458671fec40af7c17b453f4ad21

Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

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