What problem does it solve?
This Skill addresses the complexity and diversity of distributed training frameworks, offering a straightforward, unified interface for training machine learning models across various hardware configurations and parallelism strategies.
Core Features & Use Cases
- Unified API: Simplifies integration with DDP, DeepSpeed, FSDP, and Megatron frameworks using a single codebase.
- Hardware Support: Supports single GPU, multi-GPU, multi-node setups, and TPU environments.
- Mixed Precision: Enables mixed precision training with FP16/BF16/FP8 for optimized performance and reduced memory usage.
- Use Case: When you need to efficiently train a large Transformer model on multiple GPUs, Accelerate allows you to manage distributed training with minimal changes to your existing code.
Quick Start
To get started with Accelerate, install the package:
pip install accelerate
Then, modify your PyTorch script to integrate Accelerate:
import torch
from accelerate import Accelerator
accelerator = Accelerator()
model = torch.nn.Transformer()
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset)
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
for batch in dataloader:
optimizer.zero_grad()
loss = model(batch)
accelerator.backward(loss)
optimizer.step()
