DeepSpeed is a deep learning optimization library developed by Microsoft. It is designed to make distributed training and inference more straightforward, efficient, and effective. DeepSpeed is used to power some of the world’s most potent language models, such as MT-530B and BLOOM.
Here are four key features of DeepSpeed
- Scalability: DeepSpeed can train or perform inference on dense or sparse models with billions or even trillions of parameters. It can efficiently scale to thousands of GPUs, making it suitable for large-scale deep learning tasks.
- Efficiency: DeepSpeed is designed to achieve excellent system throughput. It can also operate on resource-constrained GPU systems, making it a versatile tool for a variety of hardware setups.
- Low Latency and High Throughput for Inference: DeepSpeed can achieve unprecedented low latency and high throughput for inference tasks. This makes it a powerful tool for deploying trained models in real-world applications.
- Extreme Compression: DeepSpeed can achieve extreme compression for unparalleled inference latency and model size reduction with low costs. This feature is particularly useful when deploying large models in environments with limited resources.