coral-pi-rest-server
HASS-coral-rest-api
coral-pi-rest-server | HASS-coral-rest-api | |
---|---|---|
44 | 4 | |
66 | 77 | |
- | - | |
0.0 | 0.0 | |
7 months ago | over 1 year ago | |
Jupyter Notebook | Dockerfile | |
MIT License | Apache License 2.0 |
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coral-pi-rest-server
- BeagleY-AI: 4 TOPS-capable $70 board from Beagleboard
- Do you recommend Orange PI for ML or LLM projects?
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Framework for machine learning?
That said, you can always look at something like https://coral.ai/products/accelerator/ to help with the performance you need.
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Mini PC for AI
Should only be ~$60 https://coral.ai/products/accelerator/
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What are some USB devices worth using in a Home Lab Environment?
The Coral USB accelerator might be of interest if you want to do some light ML with a low power budget.
- Is a PCIe x1 enough for light ML tasks
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Would I be able to run ggml models such as whisper.cpp or llama.cpp on a raspberry pi with a coral ai USB Accelerator?
However, a pi doesn't have the strength to run something like Llama.cpp, of course, so I've been considering using something like the Coral USB Accelerator (https://coral.ai/products/accelerator). As I've been learning more about it, it seems to be very geared towards TensorFlow Lite models. But whisper.cpp and Llama.cpp use ggml models.
- Looking for a Mini PC for Home Assistant and Frigate.
- AI development suite on a stick?
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Modder wires ChatGPT into Skyrim VR so NPCs can roleplay and remember past conversations
Recently found this thing, though I haven't found a use case for me.
HASS-coral-rest-api
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Is the Google Coral TPU useful for anything else other than Frigate?
I would start here: https://github.com/grinco/HASS-coral-rest-api
- Deepstack vs CodeProject.ai
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SenseAI Intel 12900T CPU vs Coral USB A AI Benchmark (~10X faster)
This was about a 10X improvement for me using Google Coral TPU REST API for HASS (coral-pi-rest-server) from Home Assistant (different physical hardware). It seems like native Blue Iris Coral support could shave off even a few more milliseconds.
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Task Manager Python Overload (Deep Stacks)
When I eventually get one my plan is to either set up a standalone machine and utilize a http rest server shim that uses the same interface as deepstack https://github.com/robmarkcole/coral-pi-rest-server or if I can get it to work in my existing home assistant server there's this addon: https://github.com/grinco/HASS-coral-rest-api. Then you essentially just point Blue Iris at this "Deepstack/SenseAI" server.
What are some alternatives?
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
docker-coral-rest-server
double-take - Unified UI and API for processing and training images for facial recognition.
rpi-urban-mobility-tracker - The easiest way to count pedestrians, cyclists, and vehicles on edge computing devices or live video feeds.
opentts - Open Text to Speech Server
os-nvr
ovos-core - OpenVoiceOS Core, the FOSS Artificial Intelligence platform.
mobilenets-ssd-pytorch - MobileNets-SSD/SSDLite on VOC/BDD100K Datasets
simple_chat_gpt_talk_back - A simple python script that allwos you to talk with chatGPT with your voice and get TTS answers.
piper - A fast, local neural text to speech system
vision-camera-realtime-object-detection - VisionCamera Frame Processor Plugin to detect objects using TensorFlow Lite Task Vision
os-nvr_assets