Ray Distributed Training

Twinkle enables seamless scaling from single GPU training to multi-node Ray clusters. The same training code runs across different backends with minimal configuration changes.

Key Features

• Unified API: Same training code works with torchrun, Ray, and HTTP modes
• Flexible Parallelism: Support for FSDP, tensor parallelism, pipeline parallelism
• Model-Sampler Coordination: Efficient weight synchronization for RL training
• Dynamic Resource Management: Ray handles GPU allocation automatically

Example

import twinkle
from twinkle import DeviceMesh, DeviceGroup

device_groups = [
    DeviceGroup(name='model', ranks=4, device_type='cuda'),
    DeviceGroup(name='sampler', ranks=4, device_type='cuda'),
]

twinkle.initialize(mode='ray', nproc_per_node=8, groups=device_groups)

# Training code remains the same as single GPU!
model = TransformersModel(model_id='ms://Qwen/Qwen3.5-4B', remote_group='model')