StackUp BMT (Build More Things) Workshop - Power up your Raspberry Pi with PyTorch & Deep Learning

StackUp BMT (Build More Things) Workshop - Power up your Raspberry Pi with PyTorch & Deep Learning

With millions sold, the Raspberry Pi is one of the most popular single-board computers for IoT applications. Its key strengths are affordability, availability of hardware and broad community support.

Developed by Facebook, PyTorch is an open source machine learning library in Python that is growing in popularity with the deep learning community due to its flexibility, ease of programming and strong GPU support compared to other machine learning libraries.

Running Deep Learning models locally on the Raspberry Pi can enable IoT scenarios such as predictive maintenance, robotics, and supply chain monitoring to run without cloud connectivity.

In this one-day workshop, you will learn how to train a deep learning recurrent neural network (RNN) using PyTorch and deploy the trained model on a Raspberry Pi. The model will be used to predict equipment failure from IoT sensor data. By the end of the workshop, you will have a Raspberry Pi 3 with source code and a working RNN model to take home for your own projects.

If you have been thinking about transitioning to a tech-related career, let us help you get started. For more on NUS-ISS StackUp's course offerings and tracks, click here.

Key Takeaways
By the end of the workshop, you should be able to:
  • Describe the machine learning workflow
  • Describe the application of deep learning on sequential data
  • Describe the overview and application of deep neural network layers such as fully-connected and Long Short Term Memory (LSTM)
  • Understand how the machine learning workflow is performed using Python libraries such as Pandas, Scikit-Learn, and PyTorch
  • Use PyTorch to train an RNN model using multivariate sensor data to predict equipment failure
  • Deploy the trained RNN model on a Raspberry Pi to get predictions

Who Should Attend
  • Developers with basic knowledge of Python and would like to learn more about using Python for machine learning
  • Developers with basic knowledge of machine learning and would like to learn more about PyTorch
  • Developers who would like a crash course on deploying deep learning models on a Raspberry Pi

  • Pre-Requisite
    The workshop is targeted at participants who are comfortable with at least 1 programming language (preferably Python)

    What to Bring

    Notebook running either Windows, Mac or Linux (Ubuntu preferred). NVidia GPU is not required.

    Note: you cannot use iPad or Android tablets

    The following will be provided (included in workshop fees):
  • Raspberry Pi 3 Model B
  • 8GB SD card with Raspbian Stretch
  • Raspberry Pi Power Adapter
  • Date / Time / Venue
    • 24 January 2019
    • 9.00 am - 5:00 pm (Registration starts from 8.30 am)
    • JTC Launchpad
      NUS-ISS StackUp Studio 1, #02-09
      Singapore 139957
    Workshop Fees: S$321
    (Fee is inclusive of GST)
    Individual Registration   For corporate registrations (company sponsored), please download the registration form HERE and returned the completed form to isstraining@nus.edu.sg.
    Registration ends
    on Monday, 14 January 2019 

     

    Pre-Workshop Set-up
    Due to time limitation, it is important that you perform the following installation on your notebook before coming to the workshop

    Windows

    1. Download and install Git for Windows: git-scm.com/download/win

    2. Download and install the Putty SSH client: www.chiark.greenend.org.uk/~sgtatham/putty/latest.html

    3. Download and install WinSCP: winscp.net/eng/download.php

    4. Download and install latest Anaconda for Python 3.7: www.anaconda.com/download/

    5. Install PyTorch:
      1. Start Menu -> Anaconda Prompt:
      2. conda create -n torch python=3
        conda activate torch
        conda install pytorch -c pytorch
        conda install pandas scikit-learn

    6. Download and install a code editor such as Visual Studio Code: code.visualstudio.com/download

    Mac
    1. Download and install Git for Mac: git-scm.com/download/mac

    2. Download and install latest Anaconda for Python 3.7: www.anaconda.com/download/

    3. Install PyTorch:
      1. Launch a new Terminal:
      2. conda create -n torch python=3
        conda activate torch
        conda install pytorch -c pytorch
        conda install pandas scikit-learn

    4. Download and install a code editor such as Visual Studio Code: code.visualstudio.com/download

     


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