Torch-TensorRT Notebooks
Resource
Torch-TensorRT Notebooks

Collection of Jupyter Notebooks illustrating how Torch-TensorRT can optimize inference with several well-known deep learning models

Torch-TensorRT is an integration of the PyTorch deep learning framework and the TensorRT inference acceleration framework. With this toolkit, users can generate an optimized TensorRT engine from a trained PyTorch model with a single line of code. The tutorial notebooks included here illustrate the inference optimization procedure (and benchmark the results) for the following networks and applications:

  • Hugging Face BERT: Masked Language Modeling
  • EfficientNet: Image Classification
  • Resnet50: Image Classification
  • SSD: Object Detection
Publisher
Latest Versionlatest
UpdatedFebruary 27, 2024 UTC
Compressed Size1.33 MB

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking "Accept All", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on "Manage Settings." By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.