[dchichkov]

Complexity of installing TensorFlow, even with the inclusion of custom compilation and hacking Bazel (to make it work under CUDA version that it doesn't officially support) is low, compared to releasing a model that works in production.

Because of that, it doesn't make much sense to judge a "differential programming language" like TensorFlow or PyTorch by the ease of installation. It'd be like saying "I prefer C# over C++" because it is easier to install.

[rtkaratekid]

I did say I had a fantastic model with Tensorflow. I gave up after a while because I didn't have time to hack on that stuff. I wouldn't mind learning to and trying it out, but the nature of the small company meant I needed to find a solution sooner. Now I have comparable results with Pytorch and it's easier to work with. That's a win/win in my book.

[dchichkov]

Your original comment stated that you've explicitly had difficulty in installation: "So I get building that environment and install the latest CUDA, cuDNN, nvidia driver and use tensorflow 2.0 aaaaaand it wouldn't work. I actually spent a long time hacking on it till on a forum I read that it was just a bug that hadn't been fixed yet.".

I don't want to say anything encouraging or discouraging about TensorFlow. Just that it doesn't make much sence to make a judgement based on installation experience. Installing TensorFlow or PyTorch is a very small percentage of man-hours, compared to releasing a DNN to production.