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1.5 – Setting Up Our Computing Environment

References

What we covered in this video lecture

In this video, we discussed the three aspects we require from our computing environment, i.e., it should be a place where we can

  1. write code
  2. install new Python libraries
  3. run and debug code

When it comes to running and debugging code, the free & open-source Visual Studio Code is a great choice. It features countless plugins to make it more powerful.

However, for prototyping machine learning code and teaching, we often prefer interactive Jupyter notebooks since they allow us to run our code incrementally and visualize results. Thus, we will spend a lot of time in JupyterLab (the most common program for running Jupyter notebooks) in this class.

For installing Python libraries, many machine learning researchers and practitioners prefer using conda, a program that lets us create virtual environments with different Python and Python package versions. It also makes it super easy to install Python libraries while automatically taking care of dependency management.

Additional resources if you want to learn more

Miniconda / Miniforge

As mentioned in the video, I recommend using conda for managing your virtual environments and Python libraries. Miniconda is the original conda distribution, whereas Miniforge is a community project around conda — the difference is that Miniforge libraries are often more up-to-date, which is why it’s often a preferred choice over Miniconda these days. If you want to learn more about the basic usage, William Falcon (CEO at Lightning AI) and I made a short video explaining the basics here.

PyCharm

There exist many other coding environments that are popular among Python users. Some of the most popular examples include PyCharm, which has many advanced code analysis and debugging features. William Falcon (CEO at Lightning AI) and I made a short video explaining the basics of using the PyCharm IDE (integrated developer environment) here. If you like to try it, we also have a video on debugging with PyCharm here. But no worries, we will revisit the topic of Debugging in Unit 2.

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Quiz: 1.5 Setting Up Our Computing Environment

JupyterLab is an

Correct. JupyterLab is a program for running Jupyter notebooks.

Incorrect. Some people convert Jupyter notebooks for certain production settings, but JupyterLab is an interactive environment for interactive analyses and workflows via Jupyter notebooks.

Please answer all questions to proceed.
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