Horace He analyzed the 2019 state of machine learning frameworks in research and production,
following the releases of TensorFlow 2.0 and PyTorch 1.3 earlier this month (see DT #24
). He scraped papers from major AI conferences and counted how many mentioned the two frameworks by name. His conclusion is that while TensorFlow remains very popular in industry, PyTorch is increasingly dominating on the research side:
In 2018, PyTorch was a minority. Now, it is an overwhelming majority, with 69% of CVPR using PyTorch, 75+% of both NAACL and ACL, and 50+% of ICLR and ICML. While PyTorch’s dominance is strongest at vision and language conferences (outnumbering TensorFlow by 2:1 and 3:1 respectively), PyTorch is also more popular than TensorFlow at general machine learning conferences like ICLR and ICML.
He also thinks that TensorFlow’s industry position may be weakened by PyTorch’s popularity amongst researchers and graduating PhD students, and its recent focus on deployability. The full analysis is an interesting read, but definitely to be taken with a grain of salt: He interned at the PyTorch JIT team this summer, and the study did not consider Keras, an important part of modern TensorFlow. Read it at The Gradient: The State of Machine Learning Frameworks in 2019