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Debugging, Profiling, Logging and Boilerplate

Slides

  • Learn how to use the debugger in your editor to find bugs in your code.

    M11: Debugging

  • Learn how to use a profiler to identify bottlenecks in your code and from those profiles optimize the runtime of your programs.

    M12: Profiling

  • Learn how to systematically log experiments and hyperparameters to make your code reproducible.

    M13: Logging

  • Learn how to use pytorch-lightning framework to minimize boilerplate code and structure deep learning models.

    M14: Boilerplate

Today we are initially going to go over three different topics that are all fundamentally necessary for any data scientist or DevOps engineer:

  • Debugging
  • Profiling
  • Logging

All three topics can be characterized by something you probably already are familiar with. Since you started programming, you have done debugging as nobody can write perfect code on the first try. Similarly, while you have not directly profiled your code, I bet that you at some point have had some very slow code and optimized it to run faster. Identifying and improving are the fundamentals of profiling code. Finally, logging is a very broad term and refers to any kind of output from your applications that helps you at a later point identify the "performance" of you application.

However, while we expect you to already be familiar with these topics, we do not expect all of you to be experts as it is very rare that these topics are focused on. Today we are going to introduce some best practices and tools to help you overcome every one of these three important topics. As the final topic for today, we are going to learn about how we can minimize boilerplate and focus on coding what matters for our project instead of all the boilerplate to get it working.

Learning objectives

The learning objectives of this session are:

  • Understand the basics of debugging and how to use a debugger to find bugs in your code
  • Can use a profiler to identify bottlenecks in your code and from those profiles optimize the runtime of your programs
  • Familiar with an experiment logging framework for tracking experiments and hyperparameters of your code to make it reproducible
  • Be able to use pytorch-lightning framework to minimize boilerplate code and structure deep learning models