PyTorch gets smarter on mobile devices in its pursuit to dethrone TensorFlow
PyTorch is rapidly closing the functionality gap with TensorFlow.
While TensorFlow gained an early lead as the most robust and feature-rich machine learning framework, PyTorch’s recent announcement of new mobile capabilities makes PyTorch a growing contender for software developers.
PyTorch mobile helps developers build powerful machine learning features on less powerful devices. With an all new end-to-end development workflow from Python to deployment, developers can bring advanced AI algorithms—like computer vision and natural language processing—to Android and iOS to supercharge their apps.
Using machine learning on edge devices improves security and speed while minimizing data traffic to the cloud or on networks. PyTorch needs these mobile capabilities to be a serious option for many engineering teams.
PyTorch often lags behind TensorFlow. Backed by Google, TensorFlow grew quickly and amassed unmatched popularity in the development community. AWS and Azure quickly integrated TensorFlow as a core part of their cloud offerings.
Google’s reputation as an AI juggernaut grew and TensorFlow came to dominate developer mindshare. TensorFlow became the default tool for production-grade machine learning implementations.
Still, PyTorch is growing rapidly and recovering lost ground.
First, PyTorch is growing in academics and research. PyTorch citations in research papers grew 194% in the first half of 2019 alone. Second, its cloud presence is expanding. Alibaba Cloud recently joined Amazon Web Services, Microsoft Azure, and Google Cloud as supported cloud platforms for PyTorch developers.
The next battle is for the edge developer.
While Microsoft pushes its IoT cloud platform and Google tests its Raspberry Pi-like Coral board, PyTorch might be able to hook more developers with its revamped mobile tools.
Edge computing might be a new frontier, but it could also PyTorch’s ticket to widespread adoption.
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