ray-train
仓库创建 2025年11月3日最近提交 1 个月前SkillHot 收录 20 天前
▸ 精选理由
便于横向扩展、容错与超参搜索，适合跨节点大规模训练场景。
▸ 风险提示
需要集群端口与远程资源权限，部署与网络配置可能复杂
这个 Skill 做什么
使用 Ray Train 在单机到数千节点集群上编排分布式训练与超参调优。
在单机到数千节点的集群上编排分布式训练，支持 PyTorch、TensorFlow 和 HuggingFace，并内建 Ray Tune 做超参调优，具备容错与弹性扩缩能力。适合训练大模型或运行大规模超参搜索时用，只需做少量改动就能横向扩展到多机环境。特点是从笔电到大集群无缝放大并自动管理失败与伸缩。
▸ 展开 SKILL.md 英文原文
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.
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安装 / 使用
给你的 Agent 一句话（通用）
帮我安装这个 skill：https://raw.githubusercontent.com/Orchestra-Research/AI-Research-SKILLs/main/08-distributed-training/ray-train/SKILL.md或 curl 直取 SKILL.md
curl -fsSL "https://raw.githubusercontent.com/Orchestra-Research/AI-Research-SKILLs/main/08-distributed-training/ray-train/SKILL.md"SKILL.MD 节选查看完整文件 ↗
# 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_via SKILL·HOT · 数据来自 GitHub 公开信息 · 原文版权归作者所有