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Interactive Development Environment

Overview

The Interactive Development Environment (IDE) within the APEx Project Tools primarily leverages the power of the Code Server solution (VS Code in the Cloud), as well as the JupyterLab solution, both made available through the User Workspace.

These IDE solutions allow developers to maintain a familiar environment and rich feature set while benefiting from the power and resources of server-side computing. This is particularly advantageous for those working on resource-intensive tasks or needing access to a consistent development environment from various locations and devices.

The server-based nature ensures that developers are not constrained by their local machine’s hardware capabilities, allowing them to harness the computational power of remote servers.

Tailored specifically for EO tasks, this environment furnishes developers with an array of tools and libraries fine-tuned for programming languages and productivity plugins or extensions.

Code Server

The Code Server setup encapsulates all the capabilities of Microsoft’s popular VS Code editor and extends them to be run and accessed on a remote server. Beyond the core functionality of its desktop counterpart, the Code Server IDE offers additional features tailored for remote development, such as integrated Git support, debugging tools, and a plethora of extensions (for Code Server, from the VS Code Marketplace and for JupyterLab, from the PyPI.org registry). It provides support for programming languages like Python, R, and Java. Key libraries such as SNAP and GDAL are integrated, providing robust capabilities for EO data discovery, access, processing, and analytical needs.

It seamlessly adapts to containerised environments, enabling developers to create, test, and deploy applications within isolated, replicable, and consistent environments, ensuring consistent behaviour across development, staging, and production phases.

JupyterLab

The JupyterLab setup encapsulates a web-based interactive development environment for Jupyter notebooks, code, and data. It is the user interface for Project Jupyter, offering a flexible user interface and more features than the classic notebook UI. It is a web application providing a development environment in which processing algorithms and services can be developed, tested, and debugged. JupyterLab supports execution environments (called “kernels”) in several dozen languages, including Julia, R, Haskell, Ruby, and Python (via the IPython kernel). It seamlessly adapts to containerised environments, enabling developers to create, test, and deploy applications within isolated, replicable, and consistent environments, ensuring consistent behaviour across development, staging, and production phases.

Showcase Scenarios

The Interactive Development Environment supports a variety of use cases, making it an essential tool for developers, researchers, and data scientists within the EO community. Some typical scenarios include:

  • Algorithm Development and Testing
    Researchers and developers can write, test, and debug new algorithms for processing satellite imagery or other EO data. For instance, a user might develop a script to detect deforestation using multi-temporal satellite images. To support the productivity of developers, both the Code Server solution and the JupyterLab solution use a mechanism of extensions. Code Server extensions are available from the Open VSX Registry.

  • Collaborative Projects
    Teams can work collaboratively on projects, sharing code and resources in real-time. A group of data scientists might collaboratively develop a machine-learning model to predict crop yields based on various data inputs.

  • Data Science Notebooks
    The term “Notebook” usually covers two different concepts, either the user-facing application to edit code and text (this originates from Project Jupyter‘s software product initially branded “Jupyter Notebooks”, nowadays “JupyterLab”), or more commonly the underlying file format which is interoperable across many IDE software solutions. Both solutions proposed for APEx, Code Server, and JupyterLab have multi-kernel language support (python, R, Ruby, …). Code Server supports local development of Jupyter Notebooks: the Jupyter extension for VS Code is a very popular extension in the VS Code Marketplace. JupyterLab is a very popular tool within the Open Science community for working with notebooks, with its native support for data science, data visualisation and reproducible environments.

  • Data Processing Pipelines
    Users can develop and test data processing pipelines that automate the ingestion, processing, and analysis of large EO datasets. An example use case could be to create a workflow setting up a pipeline to preprocess satellite images and extract relevant features for further analysis.

Stay Tuned

Additional information will be shared on this page as the project progresses.