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.
npxskills add orchestra-research/ai-research-skills--skill ray-trainLoading…
Ray Train scales machine learning training from single GPU to multi-node clusters with minimal code changes.
Installation:
pip install -U "ray[train]"
Basic PyTorch training (single node):
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:
Original single-GPU code:
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):
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
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")
# Training arguments (HuggingFace API)
training_args = TrainingArguments(
output_dir="./output",
num_train_epochs=3,
per_device_train_batch_size=8,
learning_rate=2e-5,
)
# Ray automatically handles distributed training
from transformers import Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
)
trainer.train()
# Scale to multi-node (2 nodes × 8 GPUs = 16 workers)
trainer = TransformersTrainer(
train_func,
scaling_config=ScalingConfig(
num_workers=16,
use_gpu=True,
resources_per_worker={"GPU": 1}
)
)
result = trainer.fit()
from ray import tune
from ray.train.torch import TorchTrainer
from ray.tune.schedulers import ASHAScheduler
def train_func(config):
# Use hyperparameters from config
lr = config["lr"]
batch_size = config["batch_size"]
model = MyModel()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
model = train.torch.prepare_model(model)
for epoch in range(10):
# Training loop
loss = train_epoch(model, optimizer, batch_size)
train.report({"loss": loss, "epoch": epoch})
# Define search space
param_space = {
"lr": tune.loguniform(1e-5, 1e-2),
"batch_size": tune.choice([16, 32, 64, 128])
}
# Run 20 trials with early stopping
tuner = tune.Tuner(
TorchTrainer(
train_func,
scaling_config=ScalingConfig(num_workers=4, use_gpu=True)
),
param_space=param_space,
tune_config=tune.TuneConfig(
num_samples=20,
scheduler=ASHAScheduler(metric="loss", mode="min")
)
)
results = tuner.fit()
best = results.get_best_result(metric="loss", mode="min")
print(f"Best hyperparameters: {best.config}")
Result: Distributed hyperparameter search across cluster
from ray import train
from ray.train import Checkpoint
def train_func(config):
model = MyModel()
optimizer = torch.optim.Adam(model.parameters())
# Try to resume from checkpoint
checkpoint = train.get_checkpoint()
if checkpoint:
with checkpoint.as_directory() as checkpoint_dir:
state = torch.load(f"{checkpoint_dir}/model.pt")
model.load_state_dict(state["model"])
optimizer.load_state_dict(state["optimizer"])
start_epoch = state["epoch"]
else:
start_epoch = 0
model = train.torch.prepare_model(model)
for epoch in range(start_epoch, 100):
loss = train_epoch(model, optimizer)
# Save checkpoint every 10 epochs
if epoch % 10 == 0:
checkpoint = Checkpoint.from_directory(
train.get_context().get_trial_dir()
)
torch.save({
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"epoch": epoch
}, checkpoint.path / "model.pt")
train.report({"loss": loss}, checkpoint=checkpoint)
trainer = TorchTrainer(
train_func,
scaling_config=ScalingConfig(num_workers=8, use_gpu=True)
)
# Automatically resumes from checkpoint if training fails
result = trainer.fit()
from ray.train import ScalingConfig
# Connect to Ray cluster
ray.init(address="auto") # Or ray.init("ray://head-node:10001")
# Train across 4 nodes × 8 GPUs = 32 workers
trainer = TorchTrainer(
train_func,
scaling_config=ScalingConfig(
num_workers=32,
use_gpu=True,
resources_per_worker={"GPU": 1, "CPU": 4},
placement_strategy="SPREAD" # Spread across nodes
)
)
result = trainer.fit()
Launch Ray cluster:
# On head node
ray start --head --port=6379
# On worker nodes
ray start --address=<head-node-ip>:6379
Use Ray Train when:
Key advantages:
Use alternatives instead:
Issue: Ray cluster not connecting
Check ray status:
ray status
# Should show:
# - Nodes: 4
# - GPUs: 32
# - Workers: Ready
If not connected:
# 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:
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:
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:
dataloader = DataLoader(dataset, num_workers=8)
Multi-node setup: See references/multi-node.md for Ray cluster deployment on AWS, GCP, Kubernetes, and SLURM.
Hyperparameter tuning: See references/hyperparameter-tuning.md for Ray Tune integration, search algorithms (Optuna, HyperOpt), and population-based training.
Custom training loops: See references/custom-loops.md for advanced Ray Train usage, custom backends, and integration with other frameworks.
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).