本文来自：
https://pytorch.org/docs/stable/notes/mps.html
https://pytorch.ac.cn/docs/stable/notes/mps.html
MPS 后端
mps 设备支持 在使用 Metal 编程框架的 MacOS 设备上，进行高性能 GPU 训练。
它引入了新的设备，将机器学习计算图和原语映射到 Metal Performance Shaders 图框架和 Metal Performance Shaders 框架提供的经过优化的内核上。
新的 MPS 后端扩展了 PyTorch 生态系统，并为现有脚本提供在 GPU 上设置和运行操作的功能。
要开始使用，只需将您的张量和模块移动到 mps 设备。
# Check that MPS is available
if not torch.backends.mps.is_available():if not torch.backends.mps.is_built():print("MPS not available because the current PyTorch install was not ""built with MPS enabled.")else:print("MPS not available because the current MacOS version is not 12.3+ ""and/or you do not have an MPS-enabled device on this machine.")else:mps_device = torch.device("mps")# Create a Tensor directly on the mps devicex = torch.ones(5, device=mps_device)# Orx = torch.ones(5, device="mps")# Any operation happens on the GPUy = x * 2# Move your model to mps just like any other devicemodel = YourFavoriteNet()model.to(mps_device)# Now every call runs on the GPUpred = model(x)
2024-07-16（二）
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