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colab-distributed

Distribute machine learning training across GPUs in Google Colab using DDP, DeepSpeed ZeRO, and FSDP.

Updated Jun 4, 2026
Search Tags:#DeepSpeed#distributed training#multi-GPU#FSDP#Colab#GPU training#DDP
One-click install
npx skills add https://github.com/kngender5/hermes --skill colab-distributed
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: colab-distributed
Source: https://github.com/kngender5/hermes/tree/main/skills/mlops/colab-distributed
Command: npx skills add https://github.com/kngender5/hermes --skill colab-distributed

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires deepspeed, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps maximize Colab's distributed training capabilities, enabling efficient scaling across GPUs and optimizing memory usage.

Core Features & Use Cases

  • Distributed Data Parallel (DDP): Efficient multi-GPU training for the same-node setup.
  • DeepSpeed ZeRO: Maximize memory efficiency with single-node multi-GPU training.
  • Fully Sharded Data Parallel (FSDP): PyTorch-native distributed training for large models.
  • Accelerate and DeepSpeed Integration: Simplify distributed training with minimal code changes.
  • Colab-Specific Simulations: Simulate multi-GPU environments on single-GPU setups.
  • Gradient Accumulation: Essential for large batch training on limited VRAM.
  • Mixed Precision Training: Utilize FP16 and BF16 to optimize for T4 and A100 GPUs.
  • Memory Optimization Checklist: A guide to save VRAM with various techniques.
  • Common Issues and Solutions: Quick fixes for common Colab distributed training issues.
  • Use Case: Train complex machine learning models with minimal VRAM usage by leveraging advanced distributed strategies.

Quick Start

To train a model on Colab with DeepSpeed ZeRO-2, use the following command: !pip install deepspeed -q\nDeepSpeed ZeRO-2 Config

Frequently Asked Questions about colab-distributed

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

FAQPage Schema
How do I run multi-GPU distributed training in Google Colab?▼

Multi-GPU distributed training in Google Colab can be run using Data Distributed Parallel (DDP), DeepSpeed ZeRO, and Fully Sharded Data Parallel (FSDP) to scale models across available GPUs and optimize memory usage.

What's the best way to train large models on Colab with limited VRAM?▼

Training large models on limited Colab VRAM is best handled by DeepSpeed ZeRO and FSDP, which shard model states across GPUs. Gradient accumulation and mixed precision FP16 or BF16 further reduce the memory footprint.

Can I use DeepSpeed and FSDP together for PyTorch training on Colab?▼

DeepSpeed and FSDP are distinct distributed training strategies for PyTorch, but you can integrate Accelerate with DeepSpeed to simplify distributed training with minimal code changes on Colab.

How do I simulate multi-GPU training on a single-GPU Colab environment?▼

You can simulate multi-GPU environments on single-GPU Colab setups using provided Colab-specific simulations, allowing you to test distributed training logic before deploying to multi-GPU hardware.

Why does my distributed training fail or run out of memory on Colab?▼

Distributed training failures on Colab often stem from misconfigured memory settings. Use the memory optimization checklist and common issues guide to apply mixed precision, gradient accumulation, and quick fixes.

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