In order to contribute to EDITO, a few requirements need to be respected when onboarding applications, computational services and model components. These requirements ensure consistency, interoperability and operational reliability within EDITO's could-native environment.

The proposed application must be provided as a containerized component (e.g., in the form of a container image). Containerization is mandatory to guarantee compatibility with EDITO’s cloud-native infrastructure, enabling uniform deployment, scalable execution, and reproducibility across environments.

No configuration values—particularly those related to paths, credentials, endpoints, or runtime settings—may be hard-coded within the application. This approach ensures adaptability across EDITO development, testing, and production environments and supports secure and flexible orchestration.

It is recommanded that the image does not embed large datasets or unnecessary volumetric assets. Only essential code, libraries, and runtime dependencies required for execution are permitted.

EDITO's users can provide a comprehensive inventory of all external dependencies required by the project, including but not limited to:

EDITO offers default computing resource quotas, covering CPU, memory, GPU, and storage allocations for a user's space:

If the resource needs exceed these limits, users must explicitly justify such requirements through the EDITO User Support.

For services or processes that produce datasets, the output must comply with the ARCO (Analysis-Ready Cloud-Optimized) format. This ensures data interoperability, efficient storage, and optimized cloud access.

For any application, model, or dataset onboarded onto EDITO, contributors should provide clear and practical documentation enabling users to understand, use, and, where relevant, reproduce the proposed solution independently.

The information provided should be self-contained and include a short description of the purpose and intended use of the solution, detailed step-by-step instructions on how to operate it, and an explanation of how it was developed, including the data sources, models, methodologies, and validation processes used.

Relevant scientific or technical references should also be provided whenever available, such as DOIs for published methodologies or validation studies.

Finally, the documentation should identify a designated contact person who can respond to questions or provide support.

This operational, user-oriented documentation should be hosted and maintained within the EDITO Datalab or, where appropriate, hosted externally and clearly referenced and accessible from EDITO. In all cases, the documentation should remain up to date by the contributor as the solution evolves.

If you wish to explore guidance on proper deployment, you can learn how to do it using EDITO's contribution tools!

If you have any questions, problems, or suggestions, please feel free to contact us via chat using the widget available at the bottom right of the page.