Most deep learning applications run on TensorFlow or PyTorch. A new analysis found that they have very different audiences.
What’s new: A researcher at Cornell University compared references to TensorFlow and PyTorch in public sources over the past year. PyTorch is growing rapidly within the research community, while TensorFlow maintains an edge in industry, according to a report in The Gradient. (deeplearning.ai, which publishes The Batch, provides the TensorFlow Specialization course available on Coursera.)
Findings: Horace He used proxy data to determine whether users were from the research or business community.
- To represent the research community, he surveyed abstracts submitted to five top AI conferences in 2018. He found an average increase of 275 percent in researchers using PyTorch, and an average decrease of roughly 0.5 percent for TensorFlow, over the year.
- To track business users, he analyzed 3,000 job listings. Businesses looking for experience in TensorFlow outnumbered those asking for experience in PyTorch. He also surveyed articles on LinkedIn and found a ratio of 3,230 to 1,200 in favor of TensorFlow.
- TensorFlow also outnumbered PyTorch in terms of GitHub stars used by coders to save repositories for later use. He considers this a key metric for tracking projects in production.
- TensorFlow has a large, well established user base, and industry is typically slower to pick up on new technologies.
- TensorFlow is much more efficient than PyTorch. Even modest savings in model run times can help a company’s bottom line.
- PyTorch integrates neatly with Python, making the code simple to use and easy to debug.
- According to He, many researchers prefer PyTorch’s API, which has remained consistent since the framework’s initial release in 2016.
We’re thinking: If there is to be a reckoning between the two top frameworks, it could happen soon. The newly released TensorFlow 2.0 adds many of the benefits PyTorch users love, particularly Python integration and making Eager mode the default for execution. However, deep learning is driven largely by research, so today’s students may bring PyTorch with them as they trickle into the job market.