Work in progress

To-do List

  • GeoServer LDAP integration

  • Dataverse LDAP integration

  • Check high memory allocation issue with CUDA/CuPy

    • The reason is identified: pre-allocation of GPU memory to speed-up processing.

    • Unfortunately this doesn’t work nicely with unified CPU-GPU memory architecture

    • Framework specific solutions exists and will be implemented.

  • Spark usage example

Waiting List

  • Application: Paraview - 3D data analysis and visualization application

  • Framework: Geonode - Open Source Geospatial Content Management System

  • Application: QGIS Server - WMS, WFS and WCS implementation that implements advanced cartographic features for thematic mapping

  • Application: Spyder - Scientific Python development environment

Finished

  • ODC installation

  • Application: ODK - Open Data Kit

  • Application : SNAP - SNAP and the Sentinel Toolboxes

  • Application: Firefox - Safe and easy web browser from Mozilla

  • Application: PyCharm - Python Integrated Development Environment (IDE)

  • Application: FragStats - calculates landscape metrics (can be installed through Wine)

  • Application: GMTSAR - InSAR processing system based on GMT

  • Application: Glueviz - Multi-dimensional linked-data exploration

  • Framework: Horovod - Framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet.

  • Framework: TensorFlow Addons - Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

  • Framework: fast.ai - Making neural nets uncool again

  • Framework: SpaCy - Industrial-strength NLP

  • Language: Rust - Fast and memory-efficient programming language

  • Application: NetLogo - Multi-agent programmable modeling environment

  • Application: GRASS - Geographic Resources Analysis Support System

  • Framework: MLflow - Open source platform for the machine learning lifecycle

  • System Upgrade: Python 3.6 -> Python 3.8

    • Python 3.6 currently receives only security updates and has end-of-life of 2021/12.

    • NVIDIA L4T is based on Ubuntu 18.04, which has Python 3.6. Some packages are only available for Python 3.6 (e.g. tensorrt)

    • Alternative Docker files for Ubuntu 20.04 + Python 3.8 are in production.

    • All packages are migrated, except the following heavy ones:

      • TensorFlow: Build commands ready, to be tested.

      • MXNet: Build commands ready, to be tested.

      • ONNX Runtime: Build commands ready, to be tested.

      • Keras: Pending TensorFlow

  • Bug Fix: JAX CPU-backend

    • This was a difficult bug to identify, but it is fixed now (https://github.com/google/jax/issues/5679)

    • Will be available with Python 3.8

  • Application: Orange3 - Interactive data analysis

    • Built and tested, ready to deploy.

  • Application: RStudio - Integrated development environment for R

    • Built, but QT GLX fails at runtime.

    • It looks like we need to change VNC server again, possibly to TurboVNC

    • Tried TurboVNC, but solution also works with TigerVNC.

  • PostgreSQL LDAP integration