What It Does
DeepSpeed is Microsoft’s deep learning optimization library that enables training of extremely large models by distributing memory efficiently across GPUs and CPUs. Its ZeRO (Zero Redundancy Optimizer) family of techniques dramatically reduce memory requirements.
Key Features
DeepSpeed offers ZeRO stages 1-3 for progressive memory reduction, ZeRO-Offload to CPU/NVMe, ZeRO-Infinity for trillion-parameter models, mixed precision training, gradient compression, pipeline parallelism, and DeepSpeed-Chat for RLHF training with one click.
When To Use It
Use DeepSpeed when training models that do not fit in single-GPU memory, need to maximize training efficiency on limited hardware, or want turnkey solutions for RLHF training. It excels at making large model training accessible on moderate hardware.
Alternatives
PyTorch FSDP provides native distributed training without external dependencies. Megatron-LM offers model parallelism for very large models. Colossal-AI provides similar features with different optimization strategies.