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APEX
Mixed precision and distributed training tools for PyTorch
BSD-3-Clause
ABOUT
Training large neural networks efficiently requires mixed precision computation and distributed training capabilities, but configuring these features manually is error-prone and requires deep CUDA expertise. APEX provides drop-in tools for automatic mixed precision (AMP), distributed data parallel (DDP) training, and optimized CUDA extensions that give PyTorch developers significant training speedups and memory savings without rewriting their model code.
INSTALL
git clone https://github.com/NVIDIA/apex
cd apex && pip install -v --no-build-isolation .
INTEGRATION GUIDE
1. Accelerate deep learning model training with automatic mixed precision (FP16) on NVIDIA GPUs
2. Scale training across multiple GPUs and nodes using distributed data parallel patterns
3. Use optimized fused CUDA kernels for faster attention, convolution, and normalization layers
4. Train larger batch sizes and models by reducing GPU memory usage through mixed precision
5. Smoothly transition from single-GPU to multi-GPU training with minimal code changes
TAGS
pytorchmixed-precisiondistributed-trainingcudagpuneural-networksoptimizationdeep-learning