Innovative Web Frontends for a secure access to High-Performance Computing resources via DASF

The open-source Data Analytics Software Framework (DASF) available at https://dasf.readthedocs.io is an advanced remote procedure call (RPC) framework designed to abstract Python code and make it securely callable over the internet. This framework ensures that computing resources remain protected from direct exposure to the internet. The latest innovation within DASF is the development of a flexible framework to automatically generate a web frontend for computing resources. 

The basic concept of DASF lies in the transformation of original Python code into a JSON schema. This schema serves a dual purpose: it is used to generate the web frontend and to validate the input received via the web interface. By converting Python code into a JSON schema, DASF leverages the underlying code to create a dynamic and interactive web frontend. This approach simplifies the deployment of web interfaces and additionally ensures that the input data conforms to the expected format, thereby reducing the likelihood of errors and enhancing the overall user experience. The web interface includes forms, input fields, and other interactive elements necessary for user interaction. This automated generation process eliminates the need for manual coding of the web frontend, saving time and reducing the potential for human error, and makes it especially useful for scientists without background in web development. 

One of the key benefits of this approach is the seamless integration of the web frontend with the underlying computing resources. Users can interact with the web interface to submit data and trigger computations, all while the JSON schema ensures that the input data is correctly formatted and validated. This validation step is crucial, as it prevents invalid data from being processed, which could otherwise lead to errors or security vulnerabilities. This security feature is particularly important in a web-based environment, where the potential for unauthorized access and data breaches is higher. 

Our framework is especially useful for digital twins in the earth system sciences, where considerable amounts of data and computing resources are often required. Digital twins are virtual representations of physical systems, and they rely on such resources to function effectively. DASF is particularly well-suited for these applications because it allows the code to run on high-performance computing (HPC) resources without exposing them to the internet. This ensures that sensitive infrastructure remains protected while still providing the necessary computational power. 

In our presentation we show how DASF works and provide live examples of the frontend implementation.