“Before this, we experimented with other frameworks that were “Pythonic,” but PyTorch was the clear winner for us here.” said Yashal Kanungo, Applied Scientist. “Using PyTorch was easy because the structure felt native to Python programming, which the data scientists were very familiar with”.
Training pipeline
Today, we build our text models entirely in PyTorch. To save time and money, we often skip the early stages of training by fine-tuning a pre-trained NLP model for language analysis. If we need a new model to evaluate images or video, we start by browsing PyTorch’s torchvision library, which offers pretrained options for image and video classification, object detection, instance segmentation, and pose estimation. For specialized tasks, we build a custom model from the ground up. PyTorch is perfect for this, because eager mode and the user-friendly front end make it easy to experiment with different architectures.