[komuher]

Ok my fault i was writing from perspective of ML engineer not reasercher (I'm using Julia for 1.5 year now and my bois reaserchers prefer pure julia solutions cause its easier to write u can use symbols and not using OOP etc.)

But for production ready models PyTorch and TF is miles ahead first of all: NLP, audio and vision based packages building frameworks, (attention layers, vocoders etc.) then u have option to compile models using XLA and use TPU (about 2/3 times cheaper then gpu for most of our models [audio and nlp])

Next inference performance (dunno about now maybe this change but about ~8 months ago flux was about 15-20% time slower [tested on VGG and Resnet's]then pytorch 1.0 without XLA)

Time to make it to production: Sure maybe writing model from scratch can take a bit longer on PyTorch then Flux (if u not using build in torch layers) but getting in into production is a lot faster, first of all u can compile model (something not possible in Flux) and u can just use it anywhere from Azure and AWS to GCP and Alibaba Cloud make a rest api using Flask/Fast-api etc. or just using ONNX.

Dont get me wrong i love Julia and Flux but there is still a LONG way before most people can even consider using Flux on production enviroment not for reasearch or some MVP stuff.

[FabHK]

I have no special insight into ML or Julia (though I love it), but one thing I can confirm from experience is that there is a huge difference between getting a model work once in an academic or research setting, and having something reliably and scalable work in production day after day. Mind boggling, totally different challenges.