Scaffolding projects


This is a guide on ploomber scaffold. For API docs see Create new project.

You can quickly create new projects using the scaffold command:

ploomber scaffold

After running it, type a name for your project and press enter. The command will create a pre-configured project with a sample pipeline.

New in 0.16: ploomber scaffold now takes a positional argument. For example, ploomber example my-project.

By adding the --empty flag to scaffold, you can create a project with an empty pipeline.yaml:

ploomber scaffold --empty

Scaffolding tasks

Once you have a pipeline.yaml file, ploomber scaffold behaves differently, allowing you to create new task files quickly. For example, say you add the following task to your YAML file:

    # some existing tasks....

    # new task
    - source: tasks/
      product: output/my-new-task.ipynb


ploomber scaffold

Will create a base task at tasks/ This command works with Python scripts, functions, Jupyter notebooks, R Markdown files, R scripts, and SQL scripts.

ploomber scaffold works as long as your pipeline.yaml file is in a standard location (Default locations); hence, you can use it even if you didn’t create your project with an initial call to ploomber scaffold.

By adding the --entry-point/ -e, you can specify a custom entry point. For example, if your spec is named pipeline.serve.yaml:

ploomber scaffold --entry-point pipeline.serve.yaml

Packaging projects

When working on larger projects, it’s a good idea to configure them as a Python package. Packaged projects have more structure and require more configuration, but they allow you to organize your work better.

For example, if you have Python functions that you re-use in several files, you must modify your PYTHONPATH or sys.path to ensure that such functions are importable wherever you want to use them. If you package your project, this is no longer necessary since you can install your project using pip:

pip install --editable path/to/myproject

Installing with pip tells Python to treat your project as any other package, allowing you to import modules anywhere (in a Python session, notebook, or other modules inside your project).

You can scaffold a packaged project with:

ploomber scaffold --package

Note that the layout is different. At the root of your project, you’ll see a file, which tells Python that this directory contains a package. The pipeline.yaml file is located at src/{package-name}/pipeline.yaml. All your pipeline’s source code must be inside the src/{package-name} directory. Other files such as exploratory notebooks or documentation must be outside the src directory.

For example, say you have a process_data function defined at src/my_awesome_package/, you may start a Python session and run:

from my_awesome_package import processors


Such import statement works independently of the current working directory; you no longer have to modify the PYTHONPATH or sys.path. Everything under src/{package-name} is importable.

Managing development and production dependencies

ploomber scaffold generates two dependencies files:

  • pip: requirements.txt (production) and (development)

  • conda: environment.yml (production) and (development)

While not required, separating development from production dependencies is highly recommended. During development, we usually need more dependencies than we do in production. A typical example is plotting libraries (e.g., matplotlib or seaborn); we need them for model evaluation but not for serving predictions. Fewer production dependencies make the project faster to install, but more importantly, it reduces dependency resolution errors. The more dependencies you have, the higher the chance of running into installation issues.

After executing ploomber scaffold command, and editing your dependency files, you can run:

ploomber install

To install dependencies. Furthermore, it configures your project if it’s a package (i.e., you created it with ploomber scaffold --package).

During deployment, only install production dependencies and ignore development ones.

If you want to learn more about the ploomber install command, check out the CLI documentation: install.

If you want to know more about dependency management, check out this post in our blog.

Locking dependencies

Changes in your dependencies may break your project at any moment if you don’t pin versions. For example, if you train a model using scikit-learn version 0.24 but only set scikit-learn as a dependency (without the version number). As soon as scikit-learn introduces breaking API changes, your project will fail. Therefore, it is essential to record specific versions to prevent broken projects.

You can do so with:

ploomber install

Such command detects whether to use pip/conda and creates lock files for development and production dependencies; lock files contain an exhaustive list of dependencies with a specific version.