1. Kernels (Functions in NNabla) are mostly implemented in Eigen.
2. Network Forward is implemented as sequential run of functions. No multi-threaded scheduling. No multi-GPU or distributed support.
3. Python binding is implemented in Cython.
4. Have some basic dynamic graph support: run functions as soon as you add them to the graph, and run backward afterwards. Somewhat similar to PyTorch.
5. No support for checkpointing and graph serialization, or I'm missing something.
I'm not sure why Sony is releasing this (yet another) deep learning framework. I don't see any new problems the project is trying to solve, compared to other frameworks like TensorFlow and PyTorch. The code is simple and clear, but nowadays people need high-performance, distributed, production-ready frameworks, not another toy-ish framework. Someone please shed some light on me?
The problem is that this library is not just a C easy-to-bind project, otherwise an high quality embeddable library that can work reasonably well with CPUs and can also benefit from commonly used GPUs in small systems, could be useful for a number of projects. Not all the problems need to have a huge dataset of complex entries (like million of images), there are many IoT problems that instead need a self contained library supporting different kinds of NNs.
Seems like out of the major libraries (TF/Caffe/Theano/pytorch), pytorch is the only one to have a core that is C (the TH*). It's not exactly a small library, though. One small library that is in C and has some state-of-the-art features is Darknet (https://pjreddie.com/darknet).
That said, seems like directly using the C++ API was a major use case here, and it looks fairly clean to me.
Does the C (or is it C++?) core of pytorch come directly from torch or do they add more functionality? Is there a way to interface with this core using C?
One thing that seems promising is built-in support for binary neural networks, which makes sense given its focus on embedded devices. No reason this couldn't have been implemented, in say, pytorch - but I'm guessing this library was started a few years back, when there were less alternatives available.
1. Kernels (Functions in NNabla) are mostly implemented in Eigen.
2. Network Forward is implemented as sequential run of functions. No multi-threaded scheduling. No multi-GPU or distributed support.
3. Python binding is implemented in Cython.
4. Have some basic dynamic graph support: run functions as soon as you add them to the graph, and run backward afterwards. Somewhat similar to PyTorch.
5. No support for checkpointing and graph serialization, or I'm missing something.
I'm not sure why Sony is releasing this (yet another) deep learning framework. I don't see any new problems the project is trying to solve, compared to other frameworks like TensorFlow and PyTorch. The code is simple and clear, but nowadays people need high-performance, distributed, production-ready frameworks, not another toy-ish framework. Someone please shed some light on me?
BTW, for newcomers to deep learning systems, [CSE 599G1](http://dlsys.cs.washington.edu/) is a good start.