DIY-ai-art VS torch2trt

Here's a generative art project I did a while back: https://github.com/maxvfischer/DIY-ai-art

It's not so much about creating the generative algorithms, but more if you wanna wrap the learning around a fun project.

Last year I built an AI-art installation from scratch and posted the DIY-documentation here on HN [0][1]. A big part of why I built the installation was to stir up this exact discussion. To me, art is about emotions and making people feel, even if that feeling is their strong opinion on art.

[0] https://github.com/maxvfischer/DIY-ai-art)

[1] https://news.ycombinator.com/item?id=28221904

DIY AI art

I have a question regarding this DIY AI Art project - https://github.com/maxvfischer/DIY-ai-art

This is amazing! I recently shared a project I've been doing, building an installation visualizing ML-generated art (https://github.com/maxvfischer/DIY-ai-art). It would be amazing to try your painting model on top of my StyleGAN.

I’ve also written an extensive guide if you want to build your own installation: https://github.com/maxvfischer/DIY-ai-art

Main thing you want for server inference is auto batching. It's a feature that's included in onnxruntime, torchserve, nvidia triton inference server and ray serve.

If you have a lot of preprocessing and post logic in your model it can be hard to export it for onnxruntime or triton so I usually recommend starting with Ray Serve (https://docs.ray.io/en/latest/serve/index.html) and using an actor that runs inference with a quantized model or optimized with tensorrt (https://github.com/NVIDIA-AI-IOT/torch2trt)

https://github.com/NVIDIA-AI-IOT/torch2trt <- pretty straightforward https://github.com/jkjung-avt/tensorrt_demos <- this helped me a lot

I find Pytorch easier to work with generally. Nvidia has a Pytorch --> TensorRT converter which yields some significant speedups and has a simple Python API. Convert the Pytorch model on the Nano.