DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU:

• Extreme scale: Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters.
• Extremely memory efficient: With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of arts, democratizing multi-billion-parameter model training such that many deep learning scientists can explore bigger and better models.
• Extremely long sequence length: Sparse attention of DeepSpeed powers an order-of-magnitude longer input sequence and obtains up to 6x faster execution comparing with dense transformers.
• Extremely communication efficient: 3D parallelism improves communication efficiency allows users to train multi-billion-parameter models 2–7x faster on clusters with limited network bandwidth. 1-bit Adam/1-bit LAMB reduce communication volume by up to 5x while achieving similar convergence efficiency to Adam/LAMB, allowing for scaling to different types of GPU clusters and networks.

Below we provide a brief feature list, see our detailed feature overview for descriptions and usage.

• Distributed Training with Mixed Precision
  • 16-bit mixed precision
  • Single-GPU/Multi-GPU/Multi-Node
• Model Parallelism
  • Support for Custom Model Parallelism
  • Integration with Megatron-LM
• Pipeline Parallelism
  • 3D Parallelism
• The Zero Redundancy Optimizer (ZeRO)
  • Optimizer State and Gradient Partitioning
  • Activation Partitioning
  • Constant Buffer Optimization
  • Contiguous Memory Optimization
• ZeRO-Offload
  • Leverage both CPU/GPU memory for model training
  • Support 10B model training on a single GPU
• Ultra-fast dense transformer kernels
• Sparse attention
  • Memory- and compute-efficient sparse kernels
  • Support 10x longer sequences than dense
  • Flexible support to different sparse structures
• 1-bit Adam and 1-bit LAMB
  • Custom communication collective
  • Up to 5x communication volume saving
• Additional Memory and Bandwidth Optimizations
  • Smart Gradient Accumulation
  • Communication/Computation Overlap
• Training Features
  • Simplified training API
  • Gradient Clipping
  • Automatic loss scaling with mixed precision
• Training Optimizers
  • Fused Adam optimizer and arbitrary torch.optim.Optimizer
  • Memory bandwidth optimized FP16 Optimizer
  • Large Batch Training with LAMB Optimizer
  • Memory efficient Training with ZeRO Optimizer
  • CPU-Adam
• Training Agnostic Checkpointing
• Advanced Parameter Search
  • Learning Rate Range Test
  • 1Cycle Learning Rate Schedule
• Simplified Data Loader
• Curriculum Learning
  • A curriculum learning-based data pipeline that presents easier or simpler examples earlier during training
  • Stable and 3.3x faster GPT-2 pre-training with 8x/4x larger batch size/learning rate while maintaining token-wise convergence speed
  • Complementary to many other DeepSpeed features
• Performance Analysis and Debugging
• Mixture of Experts (MoE)

https://github.com/microsoft/DeepSpeed 

https://www.deepspeed.ai/