Colab or Colaboratory is a Google Research tool that allows developers to create and run Python code directly from their browser. For deep learning tasks, Google Colab is a pretty decent tool. It’s a hosted Jupyter notebook that doesn’t require any setup and includes an amazing free edition that offers you free access to Google computing resources like GPUs and TPUs.
There are various reasons to choose Google Colab over a standard Jupyter Notebook instance:
- Collaboration feature
- Pre-Installed Libraries
- Saved in the Cloud
- Use of a GPU and a TPU for free
The collaboration tool is another fantastic feature provided by Google Colab. When working on a project with numerous developers, Google Colab notebook is ideal. You can co-code with many developers using a Google Colab notebook, just like you can with a Google Docs page. Furthermore, you may share your completed work with other developers.
The Anaconda installation of Jupyter Notebook came with various data libraries pre-installed, including Pandas, NumPy, and Matplotlib, which is fantastic. Google Colab, on the other hand, comes with even more machine learning libraries pre-installed, including Keras, TensorFlow, and PyTorch.
Everything is stored on your local PC when you use a standard Jupyter notebook as your development environment. If you are concerned about your privacy, this may be a useful tool for you. However, if you want to be able to access your notebooks from any device with a simple Google log-in, Google Colab is the way to go. All of your Google Colab notebooks, like your Google Docs and Google Sheets files, are kept under your Google Drive account.
GPU and a TPU for free
I think it’s a no-brainer to use Google Colab instead of a local Jupyter notebook. You may use Google Research’s dedicated GPUs and TPUs for your personal machine learning projects. Speaking from experience, GPU and TPU acceleration may make a significant impact in some applications, even tiny ones. This is one of the primary reasons I use Google Colab to code all of my instructional projects. Furthermore, because it uses Google resources, the neural network optimization procedures do not interfere with my CPUs, and my cooling fan does not overheat.
Despite all of these benefits, Google Colab has some drawbacks and limits that limit a machine learning practitioner’s coding capacity to run without any speed bumps. Let’s take a look at these Google Colab features that might ruin machine learning sessions.
Google Colab allows anybody to develop and run arbitrary Python code in the browser. However, it remains a very limited environment, since machine learning practitioners may only use the python package that has already been pre-installed on the Colab. There is no method to install one’s own Python package and begin running the code. As a result, while the platform can provide common tools, it is not ideal for specialization.
Also, you may collaborate by writing code and sharing it with your partner or team. However, with Google Colab, the option for live editing is totally absent, limiting two persons to create or modify scripts at the same time. As a result, there is a lot of back and forth re-sharing.
Additionally, Google Colab does not support permanent storage, uploaded files are erased when the session is reopened. As a result, if the device is switched off, the data may be destroyed, which is a nightmare for many. Furthermore, when using the current session in Google Storage, a downloaded file that needs to be utilized later must be stored before the session expires. Furthermore, because all Colaboratory notebooks are saved on Google Drive, one must always be signed in to their Google account.
So I can think of two practical situations in which you should use a local Jupyter Notebook instance:
- If you value privacy and want to keep your code concealed, avoid Google Colab; or
- If you have a very powerful local computer with access to GPUs and TPUs, a local Jupyter Notebook is also a viable option.
However, for practically all other circumstances, I would choose Google Colab.