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Google Colab

Managing local Python installations can be troublesome with a large class. Different versions, dependencies, and system setups could mean it runs on your computer but not elsewhere. To minimize these issues, we will be using Google Colab to write, run, and distribute Python code in Jupyter Notebooks.

Features

  • Google Colab runs in the cloud on Google's servers. This means you don't need to install any software on your local machine, and you can access it from anywhere with an internet connection.
  • One of the notable features of Google Colab is that it provides free access to Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). This can be especially beneficial for machine learning tasks that require significant computational power.
  • Multiple users can collaborate in real-time on a Colab Notebook. This makes it easy for teams to work together on coding projects, share insights, and provide feedback.
  • Colab integrates seamlessly with Google Drive. You can save your Colab Notebooks directly to your Google Drive, share them, and access them from any device.
  • Colab Notebooks run on virtual machines in the cloud, and your session state persists as long as the virtual machine is active. However, if there is inactivity for too long, the session may be disconnected.