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
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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
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LanguagePython
LicenseMIT
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distributed-training-ray-train.SKILL.md
---name: ray-train
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Ray Train, Distributed Training, Orchestration, Ray, Hyperparameter Tuning, Fault Tolerance, Elastic Scaling, Multi-Node, PyTorch, TensorFlow]
dependencies: [ray[train], torch, transformers]
---# 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
- **DeepSpeed**: Maximum performance, complex setup
- **Raw DDP**: Maximum control, minimal overhead
## Common issues
**Issue: Ray cluster not connecting**
Check ray status:
```bash
ray status
# Should show:
# - Nodes: 4
# - GPUs: 32
# - Workers: Ready
```
If not connected:
```bash
# Restart head node
ray stop
ray start --head --port=6379 --dashboard-host=0.0.0.0
# Restart worker nodes
ray stop
ray start --address=<head-ip>:6379
```
**Issue: Out of memory**
Reduce workers or use gradient accumulation:
```python
scaling_config=ScalingConfig(
num_workers=4, # Reduce from 8
use_gpu=True
)
# In train_func, accumulate gradients
for i, batch in enumerate(dataloader):
loss = model(batch) / accumulation_steps
loss.backward()
if (i + 1) % accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
```
**Issue: Slow training**
Check if data loading is bottleneck:
```python
import time
def train_func(config):
for epoch in range(epochs):
start = time.time()
for batch in dataloader:
data_time = time.time() - start
# Train...
start = time.time()
print(f"Data loading: {data_time:.3f}s")
```
If data loading is slow, increase workers:
```python
dataloader = DataLoader(dataset, num_workers=8)
```
## Advanced topics
**Multi-node setup**: See [references/multi-node.md](references/multi-node.md) for Ray cluster deployment on AWS, GCP, Kubernetes, and SLURM.
**Hyperparameter tuning**: See [references/hyperparameter-tuning.md](references/hyperparameter-tuning.md) for Ray Tune integration, search algorithms (Optuna, HyperOpt), and population-based training.
**Custom training loops**: See [references/custom-loops.md](references/custom-loops.md) for advanced Ray Train usage, custom backends, and integration with other frameworks.
## Hardware requirements
- **Single node**: 1+ GPUs (or CPUs)
- **Multi-node**: 2+ machines with network connectivity
