What problem does it solve?
This Skill simplifies the process of implementing distributed training in PyTorch, making it easy to scale your models across multiple GPUs or machines without extensive code changes.
Core Features & Use Cases
- Unified API: Supports DeepSpeed/FSDP/Megatron/DDP with a single, easy-to-use API.
- Automatic Device Placement and Mixed Precision: Automatically handles device placement and mixed precision training, including FP16/BF16/FP8 for efficiency.
- Interactive Configuration: Offers an interactive configuration for setting up training environments.
- Quick Prototyping: Enables quick experimentation and prototyping without complex setup.
- Use Cases: Ideal for scaling models for tasks like language translation, image recognition, and generative models.
Quick Start
To get started, install the Accelerate library using pip:
pip install accelerate
Then, convert your PyTorch script to use Accelerate's API:
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()
