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ray-distributed-sft

Run multi-GPU supervised fine-tuning with Ray Train and checkpointing.

1|Updated Jun 4, 2026
Search Tags:#Ray#Distributed Training#Supervised Fine-Tuning#Multi-GPU#Fault Tolerance
One-click install
npx skills add https://github.com/hung-phan/ml-skills --skill ray-distributed-sft
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ray-distributed-sft
Source: https://github.com/hung-phan/ml-skills/tree/main/skills/ml-review/references/ml-training/ray-distributed-sft
Command: npx skills add https://github.com/hung-phan/ml-skills --skill ray-distributed-sft

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ray, ray.train, deepspeed, transformers, trl, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Handles large-scale supervised fine-tuning (SFT) across multiple GPUs or nodes, addressing limitations of single-GPU setups and providing fault tolerance.

Core Features & Use Cases

  • Multi-GPU Training: Scale your SFT from a single GPU to multi-node setups.
  • Fault Tolerance: Automated restarts on worker failures and cloud-native checkpoint storage.
  • Use Case: Ideal for scenarios where your model or dataset is too large for a single GPU, or you require fault-tolerant distributed training.

Quick Start

Execute the Ray Distributed SFT script with the provided configuration file to initiate multi-GPU training.

Frequently Asked Questions about ray-distributed-sft

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run multi-GPU supervised fine-tuning when my model is too large for a single GPU?▼

Multi-GPU supervised fine-tuning scales large models across multiple nodes using Ray Train, DeepSpeed ZeRO, and FSDP, overcoming single-GPU memory limits. You execute the provided script with a configuration file to initiate distributed training.

Does Ray Train support fault tolerance for distributed training?▼

Ray Train provides fault tolerance for distributed training through automated restarts on worker failures. It ensures reliable supervised fine-tuning by utilizing cloud-native checkpoint storage on S3 or GCS.

What is the best way to scale supervised fine-tuning across multiple nodes?▼

The best way to scale supervised fine-tuning across multiple nodes is using Ray's distributed training capabilities. It integrates DeepSpeed ZeRO and FSDP to efficiently handle large datasets and models over multi-GPU setups.

Do I need DeepSpeed to use Ray for distributed training?▼

You need DeepSpeed ZeRO and FSDP to ensure efficient distributed training with Ray. These dependencies manage memory and processing for large-scale supervised fine-tuning across multiple GPUs and nodes.

Can I use cloud storage for checkpoints during multi-GPU training?▼

You can use scalable cloud storage solutions like S3 or GCS for checkpoints during multi-GPU training. This ensures fault tolerance by securely saving recovery states for automated worker restarts.

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