ray-train SKILL.md skillA
ray-train from orchestra-research/ai-research-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add orchestra-research/ai-research-skills/ray-train@v1.0.0Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- orchestra-research/ai-research-skills
- Path
08-distributed-training/ray-train/SKILL.md- Versions recorded
- 5
- Latest version
- v1.0.0, committed Feb 2, 2026
- First version recorded
- Nov 8, 2025
- Size
- 10,696 bytes
- Repository stars
- 13,718
- License
- MIT
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Ray Train - Distributed Training Orchestration
## Quick start
Ray Train scales machine learning training from single GPU to multi-node clusters with minimal code changes.
**Installation**:
```bash
pip install -U "ray[train]"
```
**Basic PyTorch training** (single node):
```python
import ray
from ray import train
from ray.train import ScalingConfig
from ray.train.torch import TorchTrainer
import torch
import torch.nn as nn
# Define training function
def train_func(config):
# Your normal PyTorch code
model = nn.Linear(10, 1)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# Prepare for distributed (Ray handles device placement)
model = train.torch.prepare_model(model)
for epoch in range(10):
# Your training loop
output = model(torch.randn(32, 10))
loss = output.sum()
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Report metrics (logged automatically)
train.report({"loss": loss.item(), "epoch": epoch})
# Run distributed training
trainer = TorchTrainer(
train_func,
scaling_config=ScalingConfig(
num_workers=4, # 4 GPUs/workers
use_gpu=True
…Read the whole file at its exact version.
How to install
mdr add orchestra-research/ai-research-skills/ray-train@sha256:7f88c141557e3f0cThe line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_qbstd73plzhrexhj)
2 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 5 versions of this file, between Nov 8, 2025 and Feb 2, 2026, about one every 21 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0.0 latest | 2026-02-02 | 13b73c2 | 10,696 B | A | view · diff |
| v1.0.0 | 2025-11-20 | 51fc3a3 | 10,694 B | A | view · diff |
| v1.0.0 | 2025-11-20 | 81c38fe | 10,693 B | A | view · diff |
| v1.0.0 | 2025-11-19 | bd4461f | 10,692 B | A | view · diff |
| git:20251108.9c3d6bc | 2025-11-08 | 9c3d6bc | 10,443 B | A | view |
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 (10696 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
orchestra-research/ai-research-skills · 13,718 stars · license MIT · pushed 2026-10-11 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_qbstd73plzhrexhj GET https://markdownregistry.com/api/v1/resolve?ref=orchestra-research/ai-research-skills/ray-train GET https://markdownregistry.com/api/v1/blob/7f88c141557e3f0cc5b456571d1ae99bef4161a9c1765bc725847cfbd0fb1170
Your AI assistant 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.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.