Databricks: Easy Install Python Packages From GitHub

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Databricks: Easy Install Python Packages from GitHub

Hey there, data enthusiasts! Ever found yourself in a situation where you needed a specific Python package hosted on GitHub within your Databricks environment? Maybe it's a custom library your team built, a beta version of a popular package, or a fork with some sweet, sweet modifications. Whatever the reason, installing Python packages directly from GitHub in Databricks is a super useful skill to have. So, let's dive into how you can do this, making your data science workflow smoother and more efficient. We're going to explore different methods, from the straightforward %pip install commands to leveraging Databricks' built-in features, ensuring you have the right tools for the job. Get ready to level up your Databricks game, guys!

Understanding the Basics: Why Install from GitHub?

So, before we jump into the how-to, let's quickly touch on why you might want to install a package from GitHub in the first place. Think of GitHub as a vast library of code, where developers and teams store and share their Python packages. Sometimes, the version you need isn't available on PyPI (Python Package Index), or you want to use a specific version that's still under development. Or maybe you need to access a private repository. That's where installing directly from GitHub comes in handy.

Access to the Latest Features

Often, the latest and greatest features are available on GitHub before they're officially released on PyPI. Installing from GitHub allows you to get your hands on these new functionalities and updates without waiting for a formal release. This is especially useful if you're working on a project that requires the bleeding edge of technology.

Customization and Collaboration

Let's say you've found a package on GitHub that's almost perfect for your needs, but you need to tweak it slightly. By installing from GitHub, you can fork the repository, make your modifications, and then install your customized version directly into your Databricks environment. This is a fantastic way to tailor existing tools to fit your exact requirements.

Accessing Private Repositories

If you're working with private repositories, which is common in many organizations, installing from GitHub is often the easiest way to get access. This involves setting up authentication, which we'll cover later, but it allows you to securely access and use packages that are not publicly available.

Experimenting with Beta Versions

Developers often release beta versions or pre-release versions of their packages on GitHub. These versions allow you to test new features and provide feedback before the official release. Installing from GitHub makes it easy to experiment with these beta versions.

Version Control and Reproducibility

Installing from GitHub allows you to specify a particular commit or branch of a package. This is super important for reproducibility. By specifying a commit, you ensure that you're using the exact same code every time, making it easier to reproduce your results and collaborate with others.

Method 1: Using %pip install (The Quick and Dirty Way)

Alright, let's get into the nitty-gritty. The easiest way to install a package from GitHub is by using the %pip install magic command within your Databricks notebook. This is the quickest method, especially for public repositories. This method leverages pip, the Python package installer, directly. It's straightforward and often the first approach many people use.

Syntax

The basic syntax is as follows. You will need to replace username, repository_name, and package_name with the appropriate values. In most cases, the package_name is optional.

%pip install git+https://github.com/username/repository_name.git@branch_or_commit#egg=package_name

Here's a breakdown:

  • git+https://github.com/username/repository_name.git: Specifies that the package should be installed from a Git repository.
  • @branch_or_commit: Allows you to specify a branch or a specific commit. If you omit this, pip will install the default branch (usually main or master). Using a specific commit ensures that you're installing a specific version of the code, which is super important for reproducibility.
  • #egg=package_name: This part tells pip the name of the package. It's often the same as the repository name, but not always. If the package has a different name than the repository, you'll need to specify it here.

Example: Installing from a Public Repository

Let's say you want to install a package called my_cool_package from a public GitHub repository. Here's how you'd do it:

%pip install git+https://github.com/myusername/my_cool_package.git@main#egg=my_cool_package

Considerations and Limitations

While this method is simple, it has some limitations:

  • Authentication: This method works well for public repositories. If you're trying to install from a private repository, you'll need to use a different method involving authentication (more on that later!).
  • Dependencies: Pip will try to handle dependencies, but sometimes you might run into issues, especially if the package has complex dependencies. Be sure to check that all required dependencies are installed.
  • Notebook Scope: The package is installed within the scope of the current notebook and the associated cluster. If you want the package to be available across all notebooks, you'll need to use cluster libraries or init scripts (we'll cover that too!).

Method 2: Installing with setup.py or pyproject.toml (The More Robust Way)

If you need more control, or if the package you're installing has a setup.py or a pyproject.toml file, this method might be better. This approach is more robust because it leverages the package's build process. It's often the preferred way when the package has complex dependencies or requires custom build steps. It's the go-to if you want to make sure things are installed the way the package maintainer intended.

Cloning the Repository

The first step is to clone the repository into your Databricks environment. You can use the git clone command for this. Be sure you have Git installed on your Databricks cluster (it usually is by default, but double-check!).

%sh
git clone https://github.com/username/repository_name.git

Navigating to the Repository Directory

Next, navigate into the directory where the repository was cloned.

%sh
cd /databricks/driver/repository_name

Installing the Package

Now, you can use pip install to install the package. You'll generally run the following command. The exact command depends on the project's build system, but these are the most common scenarios.

Using setup.py

If the package uses setup.py, run:

%pip install .

This tells pip to install the package from the current directory.

Using pyproject.toml (with poetry or flit)

If the project uses pyproject.toml, the installation command will depend on the build system used. The most common are poetry and flit.

With Poetry
%pip install poetry
poetry install
With Flit
%pip install flit
flit install --symlink

Advantages of this Method

  • Build Process: This method respects the package's build process, meaning that any custom build steps or dependencies defined in the setup.py or pyproject.toml file will be properly handled.
  • Dependency Management: It usually handles dependencies more reliably than the %pip install git+... method.
  • Flexibility: It gives you more flexibility if you need to modify the package before installing it.

Important Considerations

  • Permissions: Make sure your Databricks cluster has the necessary permissions to clone the repository and install the package. You might need to adjust the cluster's settings or use a service principal.
  • Directory Structure: Be careful about the directory structure. Make sure you're in the correct directory before running the installation commands.
  • Cluster Libraries: If you want the package to be available across all notebooks, you'll still need to use cluster libraries or init scripts, after you install it.

Method 3: Using Databricks Cluster Libraries (For Production Environments)

For production or shared environments, the most reliable and scalable approach is to use Databricks Cluster Libraries. This ensures that the package is available on all nodes of your cluster, and across all notebooks that use that cluster. This is the recommended approach for any package that needs to be used consistently by multiple users or in automated workflows.

Setting Up Cluster Libraries

  1. Navigate to the Clusters UI: Go to the Databricks workspace and click on the