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How distributed training works in Pytorch: distributed data-parallel and mixed-precision training | AI Summer
torch.cuda.amp.autocast causes CPU Memory Leak during inference · Issue #2381 · facebookresearch/detectron2 · GitHub
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When I use amp for accelarate the model, i met the problem“RuntimeError: CUDA error: device-side assert triggered”? - mixed-precision - PyTorch Forums
![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
![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)
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
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