Pytorch Environment on Apple Silicon

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Anaconda

CLICK HERE to install anaconda.

Update anaconda to the latest version via conda update anaconda.

conda -V to show your anaconda version. My version is conda 4.13.0.

Pytorch

  1. create new environment conda create -n <environment name> python=3.9

  2. change environment conda activate <environment name>

  3. install pytorch conda install -c pytorch pytorch

    PyTorch 1.12 supports GPU acceleration on Apple Silicon now.

  4. Test pytorch

    1. show pytorch version

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      $ python                          
      Python 3.9.12 (main, Jun  1 2022, 06:36:29) 
      [Clang 12.0.0 ] :: Anaconda, Inc. on darwin
      Type "help", "copyright", "credits" or "license" for more information.
      >>> import torch
      >>> torch.__version__
      '1.12.0'

    2. test code

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      import torch
      import torch.nn as nn
      import torch.nn.functional as F
      import time
      from tqdm import tqdm

      class Net(nn.Module):
          def __init__(self):
              super(Net, self).__init__()
              self.conv1 = nn.Conv2d(3, 64, 3)
              self.conv2 = nn.Conv2d(64, 128, 3)
              self.conv3 = nn.Conv2d(128, 256, 3)
              self.fc1 = nn.Linear(256, 128)
              self.fc2 = nn.Linear(128, 56)
              self.fc3 = nn.Linear(56, 10)
              self.pool = nn.MaxPool2d(2, 2, 1)
              self.global_pool = nn.AdaptiveAvgPool2d(1)
              self.act = nn.ReLU()

          def forward(self, x):
              x = self.conv1(x); x = self.act(x); x = self.pool(x)
              x = self.conv2(x); x = self.act(x); x = self.pool(x)
              x = self.conv3(x); x = self.act(x); x = self.pool(x)
              x = self.global_pool(x)
              x = x.view(x.size(0), -1)
              x = self.fc1(x); x = self.act(x)
              x = self.fc2(x); x = self.act(x)
              x = self.fc3(x)
              x = torch.softmax(x, dim=1)
              return x

      def main():
          net = Net()
          x = torch.randn(8, 3, 128, 128)
          # uncomment below to use M1 GPU
          # net = net.to('mps')
          # x = x.to('mps')

          t0 = time.time()
          for i in tqdm(range(1000)):
              y = net(x)
          t1 = time.time()
          print(t1 - t0)
          
      if __name__ == "__main__":
          main()

    3. Use Activity Monitor to see the GPU consumption.