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PROJECT REFERENCE

Development of a platform for the development and publication of scalable, agile and 'devops ready' services

CS GROUP (FINANCEMENT BPI)/
PIA


Customer: CS GROUP France (Financement Bpifrance) (CS GROUP (FINANCEMENT BPI))

Programme: PIA

Supply Chain: CS GROUP (FINANCEMENT BPI) > CS Group SPACE

Context

CS Group responsabilities for Development of a platform for the development and publication of scalable, agile and “devops ready” services “ are as follows:

  • Design, development and deployment on CNES HPC, Cloud, DIAS. Integration of partner contributions. Help Desk, Documentation, User Support

Main Picture

The features are as follows:

  • Provide an artificial intelligence oriented interactive development environment for Earth Observation (EO) data
  • Allow execution of processing on large amounts of data Scaling different AI technologies in EO on Cloud and HPC environments Facilitate the integration of existing AI processing chains Standardize interfaces for calling processing chains (porting, standards, API, etc.)
  • Make image processing chains more flexible in terms of evolution (DevOps).

Project implementation

The project objectives are as follows:

  • Study, design and publish a ** turnkey platform ** Massive treatment, ** optimized ** for 2D/3D analysis and artificial intelligence in the field of earth observation (OT).
  • Used as ** base platform of the AI4GEO ** project, carried out in partnership with CNES, Onera, IGN, Airbus, CLS, Geosat, QuantCube

The processes for carrying out the project are:

  • Agile method

Technical characteristics

The solution key points are as follows:

  • Environment ** in-line , accessible through a browser, ** without plugin
  • Simplified access to data, regardless of the Data Provider
  • Transparent management of the CNES Datalake
  • Batteries included: from scientific bookstore to heavy applications
  • Dynamic and interactive visualization of results in 1D, 2D and 3D
  • Centralized and secure access

Archi Picture

The main technologies used in this project are:

Domain Technology(ies)
Hardware environment(s) Native, CNES HPC Cluster, Docker, Singularity, Kubernetes
Operating System(s) Multi OS Servers (containers); Multi OS Clients (interfaces WEB - no plugins)
Programming language(s) Python, C/C++, R, Java, …
Interoperability (protocols, format, APIs) WMS, WPS, CWL
Production software (IDE, DEVOPS etc.) Jupyter, JupyterHub, Keras, TensorFlow, PyTorch, Spark, Dask, Scipy, Numpy, EODAG, Pangeo, QGIS…