# accelerate
基于pytorch，更轻松地进行**分布式训练**和混合精度训练，提高模型训练效率

## Usage

```
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs

# 初始化 Accelerator
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(kwargs_handlers=[kwargs])

# 移除代码中所有的 to(device)，会自动分配gpu，原有的model交给 prepare 处理
model, optimizer, train_dataloader = accelerator.prepare(model, optimizer, train_dataloader)

# 训练循环
for epoch in range(10):
    for source, targets in train_dataloader:
        optimizer.zero_grad()
        output = model(source)
        loss = F.cross_entropy(output, targets)
        accelerator.backward(loss)
        optimizer.step()

```

分布式部署：

```
# 配置生成在 ~/.cache/huggingface/accelerate
accelerate config

accelerate launch path_to_script.py --args_for_the_script
```

保存模型：
```
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
accelerator.save(unwrapped_model.state_dict(), path)
```
加载模型：
```
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.load_state_dict(torch.load(path))
```