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1.7 Google Colab: The Cloud-Based Jupyter Notebook

Python can be a pain in the butt to install locally on your own machine, which means jupyter notebook, which can be installed with a simple pip install notebook or pip install jupyterlab (official documentation found here for installation) can still be difficult dispite it being a simple command to execute in your termal. As an example, depending on if you are on a Mac (M-chip or intel) or PC...there are many difficulities that can arise and error messages that would need to be addressed during the installation process.

In order to avoid these infastructure difficulties and focus on learning data science, I believe the best way forward is utilizing cloud vendors that manage the infastructure required to deploy services like Jupyter Notebooks. This way we can get a seamless and collaborative notebook experience.

One such solution is Google Colab (short for Colaboratory), a free-to-use platform that provides a cloud-hosted environment for creating and running Jupyter notebooks.

Other Notebook Options​

As previously stated, running notebooks on your local machine requires installing Python and Jupyter, which can be difficult, but made easier by tools like Anaconda.

Other platforms, such as Amazon SageMaker or IDEs like Visual Studio Code with notebook plugins, offer notebook capabilities with additional features and integrations.

In addition to these, their are many other companies that are emerging that allow for real-time collaboration and additional advanced functionalities with their notebooks:

  • Deepnote: Explore data with Python and SQL, work together with your team, and share insights that lead to action all from the comfort of your browse
  • Azure Machine Learning Studio: Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models, and manage MLOps
  • GCP Vertex AI Workbench: Vertex AI Workbench is a Jupyter notebook-based development environment for the entire data science workflow. You can interact with Vertex AI and other Google Cloud services from within a Vertex AI Workbench instance's Jupyter notebook.
  • Kaggle Kernels: Kaggle provides a platform for data science competitions, and part of their platform includes Kernels. Kernels are Jupyter notebooks that you can run on their infrastructure, and they often contain solutions and analyses from various Kaggle competitions.
  • Cocalc: Cocalc is a cloud-based platform that supports collaborative Jupyter notebooks as well as other coding environments. It's designed for both teaching and research.
  • Binder: Binder is an open-source tool that allows you to create custom computing environments for your projects. You can specify a GitHub repository that contains Jupyter notebooks, and Binder will create a Docker image and deploy it, allowing others to interact with your notebooks.

Introducing Google Colab​

Google Colab offers several advantages, including pre-installed popular Python packages such as numpy, pandas, and matplotlib, saving you the effort of manual installations. Colab provides access to free GPU resources for accelerating computations, which is particularly beneficial for training machine learning models.

Additionally, Google Colab integrates seamlessly with Google Drive, allowing you to save and load notebooks directly from your Drive, ensuring version control and easy sharing with collaborators.

Sharing and Collaboration with Google Colab​

Google Colab offers several ways to share and collaborate on your notebooks with others:

Downloading and Exporting Notebooks​

Google Colab allows you to download your notebooks in their native format, which is the .ipynb file extension. This format preserves the interactive code cells, visualizations, and Markdown content. You can share these files with colleagues or collaborators who can then open and run them in their own Colab environment.

Additionally, you can export your Colab notebooks as .py files, which are Python script files. This can be useful if you want to share your code without the interactive aspects of a notebook. To export to a .py file, go to "File" > "Download .py" in the Colab interface.

Integration with GitHub​

Google Colab seamlessly integrates with GitHub, making it easy to share, collaborate, and version control your notebooks using the popular Git (github) platform:

  1. Save to GitHub: You can save your Colab notebooks directly to your GitHub repository. This allows you to version control your notebooks and collaborate with others on the same repository.

  2. Open in Colab: You can open any .ipynb file hosted on GitHub directly in Colab. Just click the "Open in Colab" button when viewing the notebook on GitHub.

  3. Sync with GitHub: You can connect Colab with your GitHub account to sync notebooks between your local machine and Colab. This enables a seamless workflow between your local development environment and the cloud-based Colab environment.

This integration makes it easy to maintain a central repository of your notebooks, collaborate with teammates, and track changes over time. It also ensures that your notebooks are accessible and shareable with others who may not have Colab accounts.

Integration with Google Drive​

Google Colab integrates seamlessly with Google Drive, allowing you to save and load notebooks directly from your Drive. When you create a new Colab notebook, it is automatically saved to a 'Colab' folder in your personal Google Drive. This ensures that your notebooks are easily accessible and organized. Moreover, you can load notebooks from your Drive into Colab by selecting "File" > "Open notebook" > "Google Drive" and navigating to the 'Colab' folder.

Note of Caution​

It's important to note that Google Colab is a public cloud-based service. While convenient, it is not recommended for handling sensitive or private healthcare data. Healthcare data is subject to strict regulations and security requirements. Google Colab notebooks are hosted on Google servers, and the data you upload could be accessible by others if not properly managed.

If you are dealing with real healthcare data, consider more secure alternatives. For instance, you can:

  • Local Jupyter Notebook: Install Jupyter Notebook on your local machine and work with your data locally.
  • Cloud Solutions with BAA: Use cloud-hosted solutions that offer Business Associate Agreements (BAAs), such as Microsoft Azure or AWS with proper access controls and security measures.
  • Private JupyterHub: Set up a private JupyterHub instance protected by VPN, enabling secure collaboration within your organization.

Remember that safeguarding healthcare data is paramount, and using an appropriate platform with the right security measures is essential to comply with regulations and ensure data integrity and privacy.