That said, there's been a religious flamewar over which-is-better-TensorFlow/Keras-or-PyTorch that's dragged down a lot of productive discussion about ML/DL frameworks.
Seriously though, we're in the early days of ML and still understanding the correct patterns and abstractions.
Web dev been through this to the point we had "JS fatigue" until things started settling on the reactive model and React / Vue / Angular 2
In the early days of "differentiable programming" perhaps. Certainly not ML.
Attention is an exceedingly precious commodity, potentially the most precious and valuable commodity in the marketplace. Skilled developers must learn how to direct this so that it stops being commandeered by posers all the time.
For embedded devices you may not have access to Python. You can precompile the graph to the target device in such cases.
Note that in practice, PyTorch is as fast or faster than Tensorflow and, and newer version allow you to "post compute" the graph and export to ONNX to allow embedded inference using Caffe2
A relevant essay: "On Machine Learning and Programming Languages" https://julialang.org/blog/2017/12/ml&pl
In the maximally abstract sense, Python isn't necessarily the best choice for this, but as the closest manifestation to the correct way to do this that I know of is probably Haskell, that seems unlikely to beat out Python any time soon. There's certainly a lot of worse languages.
Ultimately, you'll want a language that has the necessary primitives built in natively, but it doesn't sound like we're to that level of maturity in the field yet.
Also Chainer is bogged down by the fact it is made and maintained by Japanese company/people or at least majority of them are Japanese.
Most of the community itself are Japanese users, and many model implementations are made by Japanese users, hence in blogged, documented in Japanese.
Chainer had similar fate like Ruby. Language barrier is real.
Can you give an example? What's not smooth?
I've been using Pytorch for quite a while, and had to use tensorflow recently. May be its my pytorch _priors_, but using tensorflow felt weird, and un-intuitive. Any good resources for pytorch folks to get into tensorflow?