Accelerate

Accelerate | findAIList | findAIList

Use Cases

Training a large language model (LLM) with billions of parameters.

Overcome GPU memory limitations and enable training on multiple GPUs.

VIEW EXECUTION STEPS

Load the LLM using Hugging Face Transformers.

Prepare the training data using Hugging Face Datasets.

Initialize the Accelerator.

Prepare the model, optimizer, and data loaders using `accelerator.prepare()`.

Train the model using the standard PyTorch training loop, replacing `loss.backward()` with `accelerator.backward(loss)`.

Fine-tuning a pre-trained transformer model for a specific NLP task.

Accelerate the fine-tuning process and reduce training time.

VIEW EXECUTION STEPS

Load the pre-trained model using Hugging Face Transformers.

Prepare the fine-tuning data.

Initialize the Accelerator.

Prepare the model, optimizer, and data loaders using `accelerator.prepare()`.

Fine-tune the model using the standard PyTorch training loop, replacing `loss.backward()` with `accelerator.backward(loss)`.

Running inference with a large model on multiple GPUs.

Distribute the inference workload across multiple devices to improve throughput.

VIEW EXECUTION STEPS

Load the model.

Initialize the Accelerator.

Prepare the model and data loaders using `accelerator.prepare()`.

Run inference using the prepared data loaders.

Experimenting with different distributed training strategies.

Easily switch between different data parallelism and model parallelism techniques.

VIEW EXECUTION STEPS

Configure the desired distributed training strategy using Accelerate's configuration options.

Initialize the Accelerator.

Prepare the model, optimizer, and data loaders using `accelerator.prepare()`.

Train the model using the standard PyTorch training loop.

Developing and debugging distributed training code.

Simplify the debugging process by providing a unified interface for distributed training.

VIEW EXECUTION STEPS

Initialize the Accelerator.

Prepare the model, optimizer, and data loaders using `accelerator.prepare()`.

Debug the training code using standard PyTorch debugging tools.

About Accelerate

Core Capabilities

Main Tasks

Fully Sharded Data Parallelism (FSDP)

Automatic Mixed Precision

Minimal Code Changes

Key Features

Fully Sharded Data Parallelism (FSDP)

DeepSpeed Integration

Mixed-Precision Training

Automatic Device Placement

Integration with Hugging Face Ecosystem

Use Cases

Training a large language model (LLM) with billions of parameters.

Fine-tuning a pre-trained transformer model for a specific NLP task.

Running inference with a large model on multiple GPUs.

Experimenting with different distributed training strategies.

Developing and debugging distributed training code.

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