Full Stack Deep Learning
  • Full Stack Deep Learning
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    • Data Management
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  • Guest Lectures
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    • Chip Huyen (Snorkel)
    • Lukas Biewald (Weights & Biases)
    • Jeremy Howard (Fast.ai)
    • Richard Socher (Salesforce)
    • Raquel Urtasun (Uber ATG)
    • Yangqing Jia (Alibaba)
    • Andrej Karpathy (Tesla)
    • Jai Ranganathan (KeepTruckin)
    • Franziska Bell (Toyota Research)
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  1. Course Content
  2. Testing and Deployment

Hardware/Mobile

How to deploy your models to hardware and mobile devices?

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Last updated 4 years ago

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Summary

  • Embedded and mobile devices have low-processor with little memory, which makes the process slow and expensive to compute. Often, we can try some tricks such as reducing network size, quantizing the weights, and distilling knowledge.

    • Both pruning and quantization are model compression techniques that make the model physically smaller to save disk space and make the model require less memory during computation to run faster.

    • Knowledge distillation is a compression technique in which a small “student” model is trained to reproduce the behavior of a large “teacher” model.

  • Embedded and mobile PyTorch/TensorFlow frameworks are less fully featured than the full PyTorch/TensorFlow frameworks. Therefore, we have to be careful with the model architecture. An alternative option is using the interchange format.

    • Mobile machine learning frameworks are regularly in flux: Tensorflow Lite, PyTorch Mobile, CoreML, MLKit, FritzAI.

    • The best solution in the industry for embedded devices is NVIDIA.

    • The Open Neural Network Exchange (ONNX for short) is designed to allow framework interoperability.

Hardware and Mobile - Testing and Deployment