Deepspeed

Deepspeed is an open-source deep learning optimization library designed to simplify and accelerate distributed training of large-scale models. Its primary purpose is to enable training and inference of models with billions or even trillions of parameters on commodity GPU clusters, while improving throughput, memory efficiency, and cost-effectiveness. Deepspeed integrates with popular frameworks such as PyTorch, providing a scalable engine that abstracts away many low-level details of distributed systems and parallelization strategies.

Key capabilities include ZeRO (Zero Redundancy Optimizer), which partitions model states across devices to drastically reduce memory usage and enable training of models that would not fit on a single GPU. Deepspeed supports mixed-precision training, gradient accumulation, and advanced parallelism strategies including data, model, and pipeline parallelism. It also offers DeepSpeed-Inference for optimized low-latency, high-throughput inference, as well as features like automatic loss scaling, activation checkpointing, and communication optimizations. Configuration is managed via simple JSON files, allowing fine-grained control over optimization, memory, and parallelism settings.