Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

After choosing a repository, it will list the available notebooks and corresponding
links to open them in a new browser tab.
Figure S.1 - Google Colab’s special URL
You also get access to a GPU, which is very useful to train deep learning models
faster. More important, if you make changes to the notebook, Google Colab will
keep them. The whole setup is very convenient; the only cons I can think of are:
• You need to be logged in to a Google account.
• You need to (re)install Python packages that are not part of Google Colab’s
default configuration.
Binder
Binder "allows you to create custom computing environments that can be shared and
used by many remote users." [19]
You can also load notebooks directly from GitHub, but the process is slightly
different. Binder will create something like a virtual machine (technically, it is a
container, but let’s leave it at that), clone the repository, and start Jupyter. This
allows you to have access to Jupyter’s home page in your browser, just like you
would if you were running it locally, but everything is running in a JupyterHub
server on their end.
Just go to Binder’s site (https://mybinder.org/) and type in the URL to the GitHub
repository you want to explore (for instance,
https://github.com/dvgodoy/PyTorchStepByStep) and click on Launch. It will take
a couple of minutes to build the image and open Jupyter’s home page.
You can also launch Binder for this book’s repository directly using the following
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