distributed-data-parallelism

What problem does it solve?

This Skill addresses the challenge of scaling deep learning training beyond a single GPU, providing strategies for efficient training across multiple GPUs.

Core Features & Use Cases

• Distributed Data Parallelism (DDP): Enables parallel training across multiple GPUs by replicating the model and splitting data.
• Fully Sharded Data Parallelism (FSDP): Suitable for models that don't fit in a single GPU, by sharding parameters, gradients, and optimizer states.
• Pipeline Parallelism (PP): Sequentially splits model layers across GPUs to handle large models.
• Tensor Parallelism (TP): Splits individual layers across GPUs for attention heads and other naturally parallelizable layers.
• Use Case: Ideal for researchers and engineers scaling their models to larger sizes and handling more complex tasks.

Quick Start

Use the distributed-data-parallelism skill to analyze and choose the appropriate distributed training strategy for your deep learning model.