DeepSpeed

DeepSpeed is an open-source deep learning optimization library developed by Microsoft that dramatically reduces the computational cost and memory requirements of training and deploying large-scale AI models. It introduces groundbreaking system innovations such as ZeRO (Zero Redundancy Optimizer), 3D-Parallelism, DeepSpeed-MoE, and ZeRO-Infinity that have enabled training of models with hundreds of billions of parameters. DeepSpeed has powered some of the world's largest language models, including Megatron-Turing NLG (530B) and BLOOM (176B), and integrates seamlessly with popular frameworks like Hugging Face Transformers, PyTorch Lightning, and Accelerate.

• ZeRO Optimizer — eliminates memory redundancy across data-parallel processes, enabling training of trillion-parameter models on commodity hardware.
• 3D-Parallelism — combines data, pipeline, and tensor parallelism to scale training across thousands of GPUs efficiently.
• ZeRO-Offload & ZeRO-Infinity — offloads optimizer states, gradients, and parameters to CPU/NVMe storage, breaking the GPU memory wall for extreme-scale training.
• DeepSpeed Inference — provides highly optimized inference kernels and model parallelism for fast, cost-effective deployment of large transformer models.
• DeepSpeed-MoE — advances Mixture-of-Experts training and inference to power next-generation AI at scale.
• Model Compression — includes quantization (ZeroQuant), pruning, and knowledge distillation tools to reduce model size and accelerate inference.
• Autotuning — automatically finds the optimal DeepSpeed configuration for a given model and hardware setup.
• DeepSpeed-Chat — provides easy, fast, and affordable RLHF training for ChatGPT-like models at all scales.
• Data Efficiency — improves model quality and training efficiency via efficient data sampling and routing techniques.
• Sparse Attention — implements custom sparse attention kernels to handle long sequences efficiently.

To get started, install DeepSpeed via pip (pip install deepspeed), then wrap your PyTorch training loop using the deepspeed.initialize() API and provide a JSON configuration file specifying ZeRO stage, optimizer, and precision settings.