Preface: In ten years ago, if you talk to people that your product software development use opensource products. Most likely cyber security expert will query your decision. But the trend of open-source software products usage seems change. The truth is a lot of open-source products alliances with enterprise computer vendor. So, the patch will deliver quickly when vulnerability found. As a matter of fact, in the world no software can avoid vulnerability occur. Furthermore, since open-source less portion bother by business decision. So it similar a technology booster driven the technology running more faster.

Background: In essence, a neural network accepts inputs , does some processing and produces outputs. This input-process-output mechanism is called neural network feed-forward. Understanding the feed-forward mechanism is required. To create a neural network that solves difficult practical problems such as facial recognition or voice identification.

PyTorch provides the elegantly designed modules and classes, including torch[.]nn, to help you create and train neural networks. An nn[.]Module contains layers, and a method forward(input) that returns the output.

Today’s market trends: According to news article published on Nov 2019. For autopilot, Tesla trains around 48 networks that do 1,000 different predictions and it takes 70,000 GPU hours. Moreover, this training is not a one-time affair but an iterative one and all these workflows should be automated while making sure that these 1,000 different predictions don’t regress over time.

PyTorch, especially has become the go-to framework for machine learning researchers. It is fast and efficient, allowing users to quickly iterate on experiments and build models. PyTorch supports both CUDA and OpenCL, making it easy to take advantage of powerful GPUs for faster training.

There is no doubt about the future development of artificial intelligence, so the demand for GPUs goes hand in hand with autonomous driving.