DeepSpeed vs Keras: What are the differences?

# Introduction

Key differences between DeepSpeed and Keras:

1. **Framework Type**: DeepSpeed is a deep learning optimization library, specifically designed for large-scale distributed training, while Keras is a high-level neural networks API that can run on top of other deep learning frameworks like TensorFlow and Theano.
2. **Model Parallelism Support**: DeepSpeed provides native support for model parallelism, allowing for efficient training of models with large numbers of parameters across multiple GPUs, whereas Keras focuses more on ease of use and rapid prototyping for smaller models on a single GPU.
3. **Optimization Techniques**: DeepSpeed offers advanced optimization techniques like ZeRO-Offload, which significantly reduces memory usage during training by offloading optimizer states, enabling the training of larger models, whereas Keras provides a simplified interface for common optimization algorithms but may lack some of the more cutting-edge optimization methods.
4. **Training Efficiency**: DeepSpeed is known for its ability to scale training to thousands of GPUs efficiently, making it suitable for training very large models on massive datasets, while Keras is better suited for smaller-scale projects or research prototyping where quick iteration and model development are the primary focus.
5. **Community Support**: Keras benefits from being a widely-used and well-supported framework, with a large community of developers and resources available for troubleshooting and learning, whereas DeepSpeed, being a more specialized library, may have a smaller but highly focused user base and community support.
6. **Integration with Existing Frameworks**: Keras seamlessly integrates with TensorFlow, allowing users to take advantage of both the high-level API and the lower-level functionalities of TensorFlow, while DeepSpeed offers more limited integration options with other frameworks, primarily focusing on enhancing the capabilities of PyTorch for large-scale distributed training.

In summary, DeepSpeed is specialized for large-scale distributed training with advanced optimization techniques and model parallelism support, while Keras is a high-level API focused on ease of use and rapid prototyping for smaller-scale projects.