Databricks: Install Python Packages From GitHub

Hey data enthusiasts! Ever found yourself needing a specific Python package in your Databricks environment that's hosted on GitHub? Maybe it's a custom library your team built, a cutting-edge package still in development, or a fork with some sweet modifications. Well, you're in luck! This guide will walk you through the process of installing Python packages directly from GitHub within your Databricks clusters, making your data science workflows smoother and more efficient. Let's dive in!

Why Install from GitHub in Databricks?

So, why would you want to install Python packages directly from GitHub in your Databricks workspace, rather than relying solely on PyPI or other package repositories? Several compelling reasons:

• Access to the Latest Features: Some packages are under active development, with frequent updates and new features. Installing directly from GitHub ensures you have the most up-to-date version, even before it's released to PyPI. This is super helpful if you're working on cutting-edge projects or need the latest bug fixes.
• Customization and Collaboration: Perhaps you've forked a package on GitHub and made some modifications to suit your specific needs, or you have your team's internal packages. Installing from GitHub allows you to use these custom versions seamlessly within your Databricks environment. It also facilitates collaboration, as multiple team members can work on the same package hosted on GitHub.
• Private Packages: If you're dealing with private packages that aren't available publicly, GitHub provides a convenient platform for hosting and managing them. Databricks can access these private repositories, enabling you to use your proprietary code within your data pipelines.
• Experimentation and Rapid Prototyping: When experimenting with new libraries or testing different versions, installing from GitHub can speed up the process. You can quickly pull in the latest changes and iterate on your code without waiting for package releases.

The Benefits of Direct Installation

Installing directly from GitHub offers several advantages compared to other methods:

• Real-time Updates: You're always using the latest code available. No more waiting for releases or manually updating packages.
• Simplified Workflow: It streamlines the process of incorporating custom or specialized packages into your projects.
• Collaboration: Enables seamless collaboration and code sharing within your team.

Now that we've covered the why, let's jump into the how!

Methods for Installing GitHub Packages in Databricks

There are a few primary methods for installing Python packages directly from GitHub in Databricks. Each method has its own strengths and weaknesses, so the best approach depends on your specific needs and the package you're installing. We'll explore the main options:

1. Using %pip (or !pip) and Git

This is often the simplest and most straightforward method, especially for packages that are hosted publicly on GitHub. It leverages the power of pip (the Python package installer) and git (the version control system).

• How it Works: The %pip magic command (or the ! prefix) in a Databricks notebook allows you to execute shell commands. You can use it to clone the GitHub repository using git and then install the package using pip.
• Advantages:
  • Easy to implement for public repositories.
  • Works well for single-file packages or those with simple dependencies.
  • No need to modify cluster configurations.
• Disadvantages:
  • Requires the notebook to have internet access to clone the repository.
  • Can be less efficient for complex packages with numerous dependencies.
  • May require additional steps for private repositories (authentication).

# Example using %pip
%pip install git+https://github.com/YourUsername/YourPackage.git@branch_name

# Example using !pip
!pip install git+https://github.com/YourUsername/YourPackage.git@branch_name

# Or, to install a specific commit:
%pip install git+https://github.com/YourUsername/YourPackage.git@commit_hash

• Explanation:
  • git+https://github.com/YourUsername/YourPackage.git: This specifies the GitHub repository URL.
  • @branch_name: This optional part allows you to specify a branch (e.g., main, develop) to install from. If you omit this, it defaults to the main branch.
  • @commit_hash: You can use a specific commit hash for a particular version.

2. Using Setup.py and Wheel Files

This method is more suitable for packages that have a setup.py file, which is a standard way to define how a Python package should be built and installed. It involves:

1. Cloning the repository.
2. Building the package.
3. Creating a wheel file.
4. Installing the wheel file.

• How it Works: You clone the GitHub repository, navigate to the package directory, and then use python setup.py bdist_wheel to build a wheel file (a pre-built package). You can then install the wheel file using %pip install /path/to/your/wheel.whl.
• Advantages:
  • More control over the installation process.
  • Can be useful for packages with complex build requirements.
  • Allows for installing from local files.
• Disadvantages:
  • More steps are involved.
  • Requires familiarity with setup.py and wheel files.

# Clone the repository
!git clone https://github.com/YourUsername/YourPackage.git

# Navigate to the package directory
%cd YourPackage

# Build the wheel file
!python setup.py bdist_wheel

# Install the wheel file
%pip install dist/*.whl

# Go back to the original directory
%cd ..

3. Using Databricks Libraries

Databricks provides a convenient feature called