colab-distributed

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