The State of Machine Learning Frameworks in 2019

Summary

Since deep learning regained prominence in 2012, many machine learning frameworks have clamored to become the new favorite among researchers and industry practitioners. From the early academic outputs Caffe and Theano to the massive industry-backed PyTorch and TensorFlow, this deluge of options makes it difficult to keep track of what the most popular frameworks actually are. If you only browsed Reddit, you might assume that everyone’s switching to PyTorch. Judging instead by Francois Chollet’s Twitter, TensorFlow/Keras may appear as the dominant framework while PyTorch’s momentum is stalling. In 2019, the war for ML frameworks has two remaining main contenders: PyTorch and TensorFlow. My analysis suggests that researchers are abandoning TensorFlow and flocking to PyTorch in droves. Meanwhile in industry, Tensorflow is currently the platform of choice, but that may not be true for long. PyTorch’s increasing dominance in research Let’s examine the data. The graph below shows the ratio between PyTorch papers and papers that use either Tensorflow or PyTorch at each of the top research conferences over time. All the lines slope upward, and every major conference in 2019 has had a majority of papers implemented in PyTorch. Conference legend CVPR, ICCV, ECCV - computer vision conferences NAACL, ACL, EMNLP - NLP conferences ICML, ICLR, NeurIPS - general ML conferences Details on the data collection process This graph was generated by scraping every paper published in a major ML conference over the last few years. Papers were categorized based on whether they mention PyTorch or TensorFlow, excluding papers with authors affiliated with either Google or Facebook, as well as those that mention both Tensorflow and PyTorch. An ablation of these factors can be found in the Appendix Interactive versions of these figures can be found here. If you need more evidence of how fast PyTorch has gained traction in the research community, here's a graph of the raw counts of PyTorch vs. Ten...