Enables running PyTorch code across any distributed configuration with minimal code changes.

Accelerate is a PyTorch library designed to simplify distributed training and inference. It abstracts away the complexities of different distributed configurations (e.g., multi-GPU, TPU) by requiring only a few lines of code to adapt existing PyTorch models. It leverages `torch_xla` and `torch.distributed` to handle the underlying distributed communication and synchronization. Accelerate allows users to easily convert existing codebases to utilize DeepSpeed for memory optimization, perform fully sharded data parallelism (FSDP) for large models, and automatically supports mixed-precision training for faster execution. The library provides a command-line interface for launching training scripts on various systems, and includes comprehensive tutorials and guides to facilitate adoption.
Accelerate is a PyTorch library designed to simplify distributed training and inference.
Explore all tools that specialize in fully sharded data parallelism (fsdp). This domain focus ensures Accelerate delivers optimized results for this specific requirement.
Explore all tools that specialize in automatic mixed precision. This domain focus ensures Accelerate delivers optimized results for this specific requirement.
Explore all tools that specialize in minimal code changes. This domain focus ensures Accelerate delivers optimized results for this specific requirement.
Splits the model parameters across multiple devices to overcome memory limitations when training large models.
Integrates with Microsoft's DeepSpeed library to further optimize memory usage during training, including ZeRO optimization techniques.
Leverages FP16 or BF16 precision to reduce memory usage and accelerate training.
Automatically manages the placement of model and data on the appropriate devices (GPU, TPU, CPU).
Seamlessly integrates with other Hugging Face libraries, such as Transformers and Datasets.
Install the Accelerate library: `pip install accelerate`
Import the Accelerator class: `from accelerate import Accelerator`
Create an Accelerator instance: `accelerator = Accelerator()`
Prepare your model, optimizer, data loaders, and scheduler using `accelerator.prepare()`
Replace `loss.backward()` with `accelerator.backward(loss)`
Launch your training script with `accelerate launch {my_script.py}`
All Set
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"Highly praised for simplifying distributed training and enabling large model experimentation."
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