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Release Notes

Release 2.6 Beta

  • You can now select, at install time, which modules you wish to have initially installed
  • Some modules (Coral, Yolov8) now allow you to download individual models at runtime via the dashboard.
  • A new generative AI module (Llama LLM Chatbot)
  • A standardised way to handle (in code) modules that run long processes such as generative AI
  • Debian support has been improved
  • Small UI improvements to the dashboard
  • Some simplification of the modulesettings files
  • The inclusion, in the source code, of template .NET and Python modules (both simple and long process demos)
  • Improvements to the Coral and ALPR modules (thanks to Seth and Mike)
  • Docker CUDA 12.2 image now includes cuDNN
  • Install script fixes
  • Added Object Segmentation to the YOLOv8 module

Release 2.5 Beta

  • Dynamic Explorer UI: Each module now supplies its own UI for the explorer
  • More information returned by each module's response as standard
  • Support for sound audition modules in the explorer
  • Improvements to, and a more responsive module status on the dashboard
  • Updated module settings schema that includes module author and original project acknowledgement
  • A separate status update from each module that decouples the stats for a module. This just cleans things up a little on the backend
  • Installer fixes
  • Minor modulesettings.json schema update, which introduces the concept of model requirements.
  • Updated ALPR, OCR (thanks to Mike Lud) and Coral Object Detection (Thanks to Seth Price) modules
  • Improved Jetson support
  • Pre-installed modules in Docker can now be uninstalled / reinstalled

Release 2.4 Beta

  • Mesh support Automatically offload inference work to other servers on your network based on inference speed. Zero config, and dashboard support to enable/disable.
  • CUDA detection fixed
  • Support for CUDA 10.2
  • Module self-test performed on installation
  • YOLOv8 module added
  • YOLOv5 .NET module fixes for GPU, and YOLOv5 3.1 GPU support fixed
  • Python package and .NET installation issues fixed
  • Better prompts for admin-only installs
  • Fixes for Python package installs
  • Issues installing .NET
  • More logging output to help diagnose issues
  • VC Redist hash error fixed
  • General bug fixes.
  • Breaking: modulesettings.json schema changed

Release 2.3 Beta

  • A focus on improving the installation of modules at runtime. More error checks, faster re-install, better reporting, and manual fallbacks in situations where admin rights are needed
  • A revamped SDK that removes much (or all, in some cases) of the boilerplate code needed in install scripts
  • Fine grained support for different CUDA versions as well as systems such as Raspberry Pi, Orange Pi and Jetson
  • Support for CUDA 12.2
  • GPU support for PaddlePaddle (OCR and license plate readers benefit)
  • CUDA 12.2 Docker image
  • Lots of bug fixes in install scripts
  • UI tweaks
  • ALPR now using GPU in Windows
  • Corrections to Linux/macOS installers

Release 2.2 Beta

  • An entirely new Windows installer offering more installation options and a smoother upgrade experience from here on.
  • New macOS and Ubuntu native installers, for x64 and arm64 (including Raspberry Pi)
  • A new installation SDK for making module installers far easier
  • Improved installation feedback and self-checks
  • Coral.AI support for Linux, macOS (version 11 and 12 only) and Windows

Release 2.1 Beta

  • Improved Raspberry Pi support. A new, fast object detection module with support for the Coral.AI TPU, all within an Arm64 Docker image
  • All modules can now be installed / uninstalled (rather than having some modules fixed and uninstallble).
  • Installer is streamlined: Only the server is installed at installation time, and on first run we install Object Detection (Python and .NET) and Face Processing (which can be uninstalled).
  • Reworking of the Python module SDK. Modules are new child classes, not aggregators of our module runner.
  • Reworking of the modulesettings file to make it simpler and have less replication
  • Improved logging: quantity, quality, filtering and better information
  • Addition of 2 modules: ObjectDetectionTFLite for Object Detection on a on Raspberry Pi using Coral, and Cartoonise for some fun
  • Improvements to half-precision support checks on CUDA cards
  • Modules are now versioned and our module registry will now only show modules that fit your current server version.
  • Various bug fixes
  • Shared Python runtimes now in runtimes.
  • All modules moved from the AnalysisLayer folder to the modules folder
  • Tested on CUDA 12 (Note: ALPR and OCR do not run on CUDA 12)

Release 2.0 Beta

  • New Downloadable module system
  • Re-introduction of PyTorch 1.7 YOLO module for older GPUs
  • .NET 7

Release Beta

  • Optimised RAM use
  • Ability to enable / disable modules and GPU support via the dashboard
  • REST settings API for updating settings on the fly
  • Apple M1/M2 GPU support
  • Async processes and logging for a performance boost
  • Breaking: the CustomObjectDetection is now part of ObjectDetectionYolo

Release Beta

  • Docker NVIDIA GPU support
  • Further performance improvements
  • cuDNN install script to help with NVIDIA driver and toolkit installation
  • Bug fixes

Release 1.5.6 Beta

  • NVIDIA GPU support for Windows
  • Perf improvements to Python modules
  • Work on the Python SDK to make creating modules easier
  • Dev installers now drastically simplified for those creating new modules
  • Added SuperResolution as a demo module

Release 1.5 Beta

  • Support for custom models

Release 1.3.x Beta

  • Refactored and improved setup and module addition system
  • Introduction of modulesettings.json files
  • New analysis modules

Release 1.2.x Beta

  • Support for Apple Silicon for development mode
  • Native Windows installer
  • Runs as Windows Service
  • Run in a Docker Container
  • Installs and Builds using VSCode in Linux (Ubuntu), macOS and Windows, as well as Visual Studio on Windows
  • General optimisation of the download payload sizes


  • We started with a proof of concept on Windows 10+ only. Installs we via a simple BAT script, and the code has is full of exciting sharp edges. A simple dashboard and playground are included. Analysis is currently Python code only
  • Version checks are enabled to alert users to new versions
  • A new .NET implementation scene detection using the YOLO model to ensure the codebase is platform and tech stack agnostic
  • Blue Iris integration completed