ray-train

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• SKILL.md
• multi-node.md · references/

SKILL.md (first lines)

# 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
    )
)

result = trainer.fit()
print(f"Final loss: {result.metrics['loss']}")
```

**That's it!** Ray handles:
- Distributed coordination
- GPU allocation
- Fault tolerance
- Checkpointing
- Metric aggregation

## Common workflows

### Workflow 1: Scale existing PyTorch code

**Original single-GPU code**:
```python
model = MyModel().cuda()
optimizer = torch.optim.Adam(model.parameters())

for epoch in range(epochs):
    for batch in dataloader:
        loss = model(batch)
        loss.backward()
        optimizer.step()
```

**Ray Train version** (scales to multi-GPU/multi-node):
```python
from ray.train.torch import TorchTrainer
from ray import train

def train_func(config):
    model = MyModel()
    optimizer = torch.optim.Adam(model.parameters())

    # Prepare for distributed (automatic device placement)
    model = train.torch.prepare_model(model)
    dataloader = train.torch.prepare_data_loader(dataloader)

    for epoch in range(epochs):
        for batch in dataloader:
            loss = model(batch)
            loss.backward()
            optimizer.step()

            # Report metrics
            train.report({"loss": loss.item()})

# Scale to 8 GPUs
trainer = TorchTrainer(
    train_func,
    scaling_config=ScalingConfig(num_workers=8, use_gpu=True)
)
trainer.fit()
```

**Benefits**: Same code runs on 1 GPU or 1000 GPUs

### Workflow 2: HuggingFace Transformers integration

```python
from ray.train.huggingface import TransformersTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments

def train_func(config):
    # Load model and tokenizer
    model = AutoModelForCausalLM.from_pretrained("gpt2")
    tokenizer = AutoTokenizer.from_pretrained("gpt2")

From Orchestra-Research/AI-Research-SKILLs, MIT.

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