June 10, 2019
Towards Reproducible Research with PyTorch Hub
Reproducibility is an essential requirement for many fields of research including those based on machine learning techniques. However, many machine learning publications are either not reproducible or are difficult to reproduce. With the continued growth in the number of research publications, including tens of thousands of papers now hosted on arXiv and submissions to conferences at an all time high, research reproducibility is more important than ever. While many of these publications are a...
May 22, 2019
torchvision 0.3: segmentation, detection models, new datasets and more..
PyTorch domain libraries like torchvision provide convenient access to common datasets and models that can be used to quickly create a state-of-the-art baseline. Moreover, they also provide common abstractions to reduce boilerplate code that users might have to otherwise repeatedly write. The torchvision 0.3 release brings several new features including models for semantic segmentation, object detection, instance segmentation, and person keypoint detection, as well as custom C++ / CUDA ops sp...
May 08, 2019
Model Serving in PyTorch
PyTorch has seen a lot of adoption in research, but people can get confused about how well PyTorch models can be taken into production. This blog post is meant to clear up any confusion people might have about the road to production in PyTorch. Usually when people talk about taking a model “to production,” they usually mean performing inference, sometimes called model evaluation or prediction or serving. At the level of a function call, in PyTorch, inference looks something l...
