1.. _MPS-Backend: 2 3MPS backend 4=========== 5 6:mod:`mps` device enables high-performance 7training on GPU for MacOS devices with Metal programming framework. It 8introduces a new device to map Machine Learning computational graphs and 9primitives on highly efficient Metal Performance Shaders Graph framework and 10tuned kernels provided by Metal Performance Shaders framework respectively. 11 12The new MPS backend extends the PyTorch ecosystem and provides existing scripts 13capabilities to setup and run operations on GPU. 14 15To get started, simply move your Tensor and Module to the ``mps`` device: 16 17.. code:: python 18 19 # Check that MPS is available 20 if not torch.backends.mps.is_available(): 21 if not torch.backends.mps.is_built(): 22 print("MPS not available because the current PyTorch install was not " 23 "built with MPS enabled.") 24 else: 25 print("MPS not available because the current MacOS version is not 12.3+ " 26 "and/or you do not have an MPS-enabled device on this machine.") 27 28 else: 29 mps_device = torch.device("mps") 30 31 # Create a Tensor directly on the mps device 32 x = torch.ones(5, device=mps_device) 33 # Or 34 x = torch.ones(5, device="mps") 35 36 # Any operation happens on the GPU 37 y = x * 2 38 39 # Move your model to mps just like any other device 40 model = YourFavoriteNet() 41 model.to(mps_device) 42 43 # Now every call runs on the GPU 44 pred = model(x) 45