![How distributed training works in Pytorch: distributed data-parallel and mixed-precision training | AI Summer How distributed training works in Pytorch: distributed data-parallel and mixed-precision training | AI Summer](https://theaisummer.com/static/3363b26fbd689769fcc26a48fabf22c9/ee604/distributed-training-pytorch.png)
How distributed training works in Pytorch: distributed data-parallel and mixed-precision training | AI Summer
![PyTorch on X: "For torch <= 1.9.1, AMP was limited to CUDA tensors using ` torch.cuda.amp. autocast()` v1.10 onwards, PyTorch has a generic API `torch. autocast()` that automatically casts * CUDA tensors to PyTorch on X: "For torch <= 1.9.1, AMP was limited to CUDA tensors using ` torch.cuda.amp. autocast()` v1.10 onwards, PyTorch has a generic API `torch. autocast()` that automatically casts * CUDA tensors to](https://pbs.twimg.com/media/FCCdDKKXEAMP0i6.png)
PyTorch on X: "For torch <= 1.9.1, AMP was limited to CUDA tensors using ` torch.cuda.amp. autocast()` v1.10 onwards, PyTorch has a generic API `torch. autocast()` that automatically casts * CUDA tensors to
torch.cuda.amp, example with 20% memory increase compared to apex/amp · Issue #49653 · pytorch/pytorch · GitHub
AttributeError: module 'torch.cuda.amp' has no attribute 'autocast' · Issue #776 · ultralytics/yolov5 · GitHub
![My first training epoch takes about 1 hour where after that every epoch takes about 25 minutes.Im using amp, gradient accum, grad clipping, torch.backends.cudnn.benchmark=True,Adam optimizer,Scheduler with warmup, resnet+arcface.Is putting benchmark ... My first training epoch takes about 1 hour where after that every epoch takes about 25 minutes.Im using amp, gradient accum, grad clipping, torch.backends.cudnn.benchmark=True,Adam optimizer,Scheduler with warmup, resnet+arcface.Is putting benchmark ...](https://i.redd.it/jz2pbenfw7d81.png)
My first training epoch takes about 1 hour where after that every epoch takes about 25 minutes.Im using amp, gradient accum, grad clipping, torch.backends.cudnn.benchmark=True,Adam optimizer,Scheduler with warmup, resnet+arcface.Is putting benchmark ...
![PyTorch on X: "Running Resnet101 on a Tesla T4 GPU shows AMP to be faster than explicit half-casting: 7/11 https://t.co/XsUIAhy6qU" / X PyTorch on X: "Running Resnet101 on a Tesla T4 GPU shows AMP to be faster than explicit half-casting: 7/11 https://t.co/XsUIAhy6qU" / X](https://pbs.twimg.com/media/FCCdKxXXEAA0XDf.png)