Swift for TensorFlow, a Google-led project to integrate the TensorFlow machine learning library and Apple's Swift language, is no longer in active development. Nevertheless, parts of the effort live on, including language-differentiated programming for Swift. The GitHub repo for the project notes it is now in archive mode and will not receive further updates. The project, the repo notes, was positioned as a new way to develop machine learning models. "Swift for TensorFlow was an experiment in the next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond."
How has the landscape changed for the leading deep learning frameworks in the past six months? To answer that question, I looked at the number of job listings on Indeed, Monster, LinkedIn, and SimplyHired. I also evaluated changes in Google search volume, GitHub activity, Medium articles, ArXiv articles, and Quora topic followers. Overall, these sources paint a comprehensive picture of growth in demand, usage, and interest. We've recently seen several important developments in the TensorFlow and PyTorch frameworks.
In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity in this article. TensorFlow was the undisputed heavyweight champion of deep learning frameworks. PyTorch was the young rookie with lots of buzz. How has the landscape changed for the leading deep learning frameworks in the past six months? To answer that question, I looked at the number of job listings on Indeed, Monster, LinkedIn, and SimplyHired.
While the majority of us are'wow'ing the early applications of machine learning, it continues to evolve at quite a promising pace, introducing us to more advanced algorithms like Deep Learning. This branch, by the way, is attracting even more attention than all other ML-algorithms combined. Of course, I don't have to declare it. It is simply great in terms of accuracy when trained with a huge amount of data. Also, it plays a significant role to fill the gap when a scenario is challenging for the human brain.
On Thursday the developers of PyTorch announced PyTorch Mobile, which they say will allow for "end-to-end workflow from Python to deployment on iOS and Android." PyTorch Mobile is part of PyTorch 1.3, which currently is an "experimental release" that the organization will be "building on over the next couple of months." PyTorch 1.2 was released in August. New features coming will include preprocessing and integration APIs, support for ARM CPUs and QNNPACK (a quantized neural network package designed for PyTorch), build-level optimization, and performance enhancements for mobile CPUs/GPUs. Android builds will use the Maven plug-in and iOS will use CocoaPods with Swift.