1. Scaffold
Use ploomber scaffold to create a ready-to-use base structure for your project.
Try It Out!
Terminal
# install pip install ploomber # get example ploomber examples -n templates/ml-basic -o ml-basic cd ml-basic # install dependencies pip install -r requirements.txt # run pipeline ploomber build
Terminal
# install conda install ploomber -c conda-forge # get example ploomber examples -n templates/ml-basic -o ml-basic cd ml-basic # install dependencies conda env create --file environment.yml conda activate ml-basic # run pipeline ploomber build
Click here to learn more about the integration with Jupyter.
Note: Open JupyterLab will launch Jupyter in a hosted environment, it may take a few minutes to load.
How It Works
Why Ploomber?
Enables best practices
Follow development best practices while using the Jupyter interface you love.
Read post on the Jupyter blogGentle learning curve
Get started quickly. Learn advanced features as you progress.
Show me some codeUnlocks fast iterations
Data Science is an iterative process. Iterate faster by skipping redundant computations.
Explain me howEases transition to production
Deploy without code changes to a single server, Kubernetes, AWS Batch or Airflow.
See deployment guideSelected Examples
Selected Presentations
Recent Posts
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read moreExisting experiment trackers come with a high setup cost. To get one working, you usually have to spin up a database and run a web application. After trying multiple options, I thought that using Jupyter notebooks could be an excellent choice to store experiment results and retrieve them for comparison. This post explains how I use .ipynb files to track experiments without any extra …
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