patrykorwat f50a2dc1ed axelera: publish the run duty cycle through a shared Value
Replace the tmp-file duty-cycle handoff (and the FRIGATE_TMP env var
nothing else reads) with a third multiprocessing.Value alongside
avg_inference_speed and detection_start: ObjectDetectProcess creates it,
AsyncDetectorRunner passes it to the detector, and the plugin publishes
busy/wall of the blocking ModelInstance.run over a 2s window, mirroring
how memryx receives its shared state. No filesystem path, no staleness
window, no atomic rename, and stats/hardware.py no longer imports the
plugin module to find the file.

The producer also ticks the window while the queue is empty, so the duty
cycle falls to 0 when the card idles instead of sticking at its last
value.

Name it what it is: host-side occupancy of the run call, including DMA
and driver time, not an AIPU core counter (the runtime exposes no
device-side one). The NPU entry now reads temperature from the board
controller plus the detector-reported run duty cycle folded in where
the detector processes are in reach.
2026-09-05 05:44:29 +00:00
2026-03-20 07:24:34 -06:00
2021-02-25 07:01:59 -06:00
2026-07-08 08:27:38 -05:00
2026-07-25 11:22:05 -06:00
2026-05-20 08:36:49 -06:00
2026-07-25 11:22:05 -06:00
2023-01-06 07:03:16 -06:00
2026-01-01 09:56:09 -06:00
2026-08-27 20:30:35 -05:00
2023-11-18 08:04:43 -06:00
2026-07-06 12:28:02 -05:00

logo

Frigate NVR™ - Realtime Object Detection for IP Cameras

License: MIT

Translation status

[English] | 简体中文

A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.

Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported object detectors.

  • Tight integration with Home Assistant via a custom component
  • Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
  • Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
  • Uses a very low overhead motion detection to determine where to run object detection
  • Object detection with TensorFlow runs in separate processes for maximum FPS
  • Communicates over MQTT for easy integration into other systems
  • Records video with retention settings based on detected objects
  • 24/7 recording
  • Re-streaming via RTSP to reduce the number of connections to your camera
  • WebRTC & MSE support for low-latency live view

Documentation

View the documentation at https://docs.frigate.video

Donations

If you would like to make a donation to support development, please use Github Sponsors.

License

This project is licensed under the MIT License.

  • Code: The source code, configuration files, and documentation in this repository are available under the MIT License. You are free to use, modify, and distribute the code as long as you include the original copyright notice.
  • Trademarks: The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are trademarks of Frigate, Inc. and are not covered by the MIT License.

Please see our Trademark Policy for details on acceptable use of our brand assets.

Screenshots

Live dashboard

Live dashboard

Streamlined review workflow

Streamlined review workflow

Multi-camera scrubbing

Multi-camera scrubbing

Built-in mask and zone editor

Built-in mask and zone editor

Translations

We use Weblate to support language translations. Contributions are always welcome.

Translation status

Copyright © 2026 Frigate, Inc.

Languages
Python 50%
TypeScript 48.7%
Shell 0.5%
CSS 0.3%
Dockerfile 0.2%
Other 0.2%