torch.cuda.amp, example with 20% memory increase compared to apex/amp · Issue #49653 · pytorch/pytorch · GitHub
![Rohan Paul on X: "📌 The `with torch.cuda.amp.autocast():` context manager in PyTorch plays a crucial role in mixed precision training 📌 Mixed precision training involves using both 32-bit (float32) and 16-bit (float16) Rohan Paul on X: "📌 The `with torch.cuda.amp.autocast():` context manager in PyTorch plays a crucial role in mixed precision training 📌 Mixed precision training involves using both 32-bit (float32) and 16-bit (float16)](https://pbs.twimg.com/media/GAnFpsvWsAAvBtB.jpg:large)
Rohan Paul on X: "📌 The `with torch.cuda.amp.autocast():` context manager in PyTorch plays a crucial role in mixed precision training 📌 Mixed precision training involves using both 32-bit (float32) and 16-bit (float16)
![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
![Faster and Memory-Efficient PyTorch models using AMP and Tensor Cores | by Rahul Agarwal | Towards Data Science Faster and Memory-Efficient PyTorch models using AMP and Tensor Cores | by Rahul Agarwal | Towards Data Science](https://miro.medium.com/v2/resize:fit:1400/1*kGLDPqIEOxMd4B5ziUdjCg.png)
Faster and Memory-Efficient PyTorch models using AMP and Tensor Cores | by Rahul Agarwal | Towards Data Science
AttributeError: module 'torch.cuda.amp' has no attribute 'autocast' · Issue #776 · ultralytics/yolov5 · GitHub
![When I use amp for accelarate the model, i met the problem“RuntimeError: CUDA error: device-side assert triggered”? - mixed-precision - PyTorch Forums When I use amp for accelarate the model, i met the problem“RuntimeError: CUDA error: device-side assert triggered”? - mixed-precision - PyTorch Forums](https://discuss.pytorch.org/uploads/default/original/3X/7/2/725104aa64c721d24e9ee63bf92dffbbb832ce94.png)
When I use amp for accelarate the model, i met the problem“RuntimeError: CUDA error: device-side assert triggered”? - mixed-precision - PyTorch Forums
torch.cuda.amp.autocast causes CPU Memory Leak during inference · Issue #2381 · facebookresearch/detectron2 · 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)