pipeless VS llama-gpt

After spending some months working on the Pipeless open-source framework, today I bring something new and really cool: Pipeless Agents

pipeless init my-project --template empty # Using the empty template we avoid the interactive shell cd my-project wget -O - https://github.com/pipeless-ai/pipeless/archive/main.tar.gz | tar -xz --strip=2 "pipeless-main/examples/yolo"

wget -O - https://github.com/pipeless-ai/pipeless/archive/main.tar.gz | tar -xz --strip=2 "pipeless-main/examples/onnx-yolo"

Check the live demo in the website

The code is available in the Pipeless GitHub repository: https://github.com/pipeless-ai/pipeless/tree/main/examples/yolo

Recently, a new alternative called Pipeless has appeared. Pipeless is an open-source framework that focuses on providing a great development experience and out-of-the-box performance. It offers a really easy stream management allowing you to add, edit, and remove streams on the fly as well as processing multiple streams. Furthermore, it offers you the possibility to deploy the applications either to the cloud or directly to embedded or edge devices.

You can find the repo here: https://github.com/pipeless-ai/pipeless

Pipeless is an open-source computer vision framework that ships everything you need to build and deploy computer vision applications really fast.

wodner if you can pair with https://github.com/getumbrel/llama-gpt

I put up a draft PR to demo how to run it on a GPU: https://github.com/getumbrel/llama-gpt/pull/11

It breaks other things like model downloading, but once I got it to a working state for myself, I figured why not put it up there in case its useful. If I have time, I'll try to rework it a little bit with more parameters and less dockerfile repetition to fit the main project better.