Latex test

Created on January 15|Last edited on January 15
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TLDRWe ran a sweep of 8 different configurations of our training script and show that the Apple M1 offers impressive performance within reach of much more expensive and less energy efficient accelerators such as the Nvidia V100 for smaller architectures and datasets.
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Average Runtime Seconds
Average Runtime Seconds
hardware: Nvidiahardware: Apple M1hardware: Apple Intel02,0004,0006,0008,00010,000
Run set24
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MethodologyWe trained a computer vision model using the MobileNetV2 architecture on Cifar 10.  We trained one in this colab on an Nvidia V100 and an identical model using the tensorflow_macos fork on a 16GB M1 Mac Mini.  We varied the following hyper-parameters using W&B Sweeps:
  batch_size:
    - 32
    - 64
  img_dim:
    - 96
    - 128
  trainable:
    - true
    - false
When trainable is false we only train the final layer in the network.  When trainable is true we update all weights in MobileNetV2.
We can see better performance gains with the m1 when there are fewer weights to train likely due to the superior memory architecture of the M1.
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