We're excited to introduce Heroku-Jupyter, an open-source, production-ready solution for running Jupyter Notebooks on Heroku with persistent storage, seamless deployment, and built-in security. Whether you're a data scientist, educator, or developer, you can now spin up a cloud-based Jupyter environment in minutes.
Why Jupyter on Heroku?
Jupyter Notebooks provide an interactive computing environment ideal for data analysis, visualization, and machine learning. However, cloud-based Jupyter deployments often face challenges like ephemeral storage and complex server configurations. Heroku-Jupyter solves these issues by providing a streamlined cloud-based experience.
Persistent Storage for reliable, up-to-date models
- Reliable Data Management: Your notebooks are safely stored in PostgreSQL, ensuring they remain accessible and secure across sessions.
- Data Integrity: With persistent storage, you can trust that your data and models are always up-to-date and backed up, reducing the risk of data loss.
Security First Dev Environment
- Built-in Password Protection: Protect your work with robust password authentication, ensuring that only authorized users can access your notebooks.
- Compliance and Privacy: Heroku’s security features help you meet compliance requirements and maintain data privacy, making it suitable for enterprise-level applications.
Scalability and Flexibility
- One-Click Deployment: No manual setup or infrastructure management—just deploy and start coding.
- Auto-Scaling: Heroku automatically scales your Jupyter environment to handle increased loads, ensuring your applications perform optimally under varying conditions.
- Customizable Environment: While Heroku-Jupyter comes with smart defaults, you can easily override settings to tailor the environment to your specific needs, whether you’re working on a small project or a large-scale application.
Advance AI Capabilities
By leveraging Heroku’s developer-friendly platform, Jupyter users can focus on innovation without worrying about infrastructure.
You can now supercharge your Retrieval-Augmented Generation (RAG) applications on Heroku by combining Heroku-Jupyter, pgvector, and Heroku MIA (Managed Inference and Agents)—now in pilot!
You can use an embedding model in MIA (cohere-embed-multilingual) to convert text into vector representations stored in pgvector for fast retrieval. Then, leverage an inference model in MIA (claude-3-5-sonnet) to generate intelligent responses using the retrieved context.
With Heroku-Jupyter, you can easily experiment, fine-tune, and optimize your pipeline—all within Heroku’s ecosystem
A Production-Ready Jupyter Notebook Environment
A delightful developer experience is at the heart of what we do at Heroku. Heroku-Jupyter enhances your workflow through One-Click Deployment buttons.This makes it easy to get started in minutes.No need for complex configuration—just deploy and start working instantly. Once logged into your Heroku account, after clicking the Deploy to Heroku button your Jupiter Notebook will be live in minutes.
Conclusion
We’re committed to bringing Heroku, the beloved developer platform into the AI era by integrating with tools like pgvector and Heroku MIA. Whether you’re a data scientist, educator, or developer, Heroku-Jupyter is designed to meet your needs and help you achieve your goals with a production-ready Jupyter Notebook environment.
We’d love your feedback! Join the open-source community on GitHub, contribute to the project, and help shape the future of Heroku-Jupyter.