“Hello World” For TensorRT Using PyTorch And Python

Table Of Contents

• Description
• How does this sample work?
  • TensorRT API layers and ops
• Prerequisites
• Running the sample
  • Sample --help options
• Additional resources
• License
• Changelog
• Known issues

Description

This sample, network_api_pytorch_mnist, trains a convolutional model on the MNIST dataset and runs inference with a TensorRT engine.

How does this sample work?

This sample is an end-to-end sample that trains a model in PyTorch, recreates the network in TensorRT, imports weights from the trained model, and finally runs inference with a TensorRT engine. For more information, see Creating A Network Definition In Python.

The sample.py script imports the functions from the mnist.py script for training the PyTorch model, as well as retrieving test cases from the PyTorch Data Loader.

TensorRT API layers and ops

In this sample, the following layers are used. For more information about these layers, see the TensorRT Developer Guide: Layers documentation.

Activation layer The Activation layer implements element-wise activation functions. Specifically, this sample uses the Activation layer with the type RELU.

Convolution layer The Convolution layer computes a 2D (channel, height, and width) convolution, with or without bias.

FullyConnected layer The FullyConnected layer implements a matrix-vector product, with or without bias.

Pooling layer The Pooling layer implements pooling within a channel. Supported pooling types are maximum, average and maximum-average blend.

Prerequisites

1. Install the dependencies for Python. python3 -m pip install -r requirements.txt

   • NOTE: On PowerPC systems, you will need to manually install PyTorch using IBM's PowerAI.

Running the sample

1. Run the sample to create a TensorRT inference engine and run inference:

   python sample.py

2. Verify that the sample ran successfully. If the sample runs successfully you should see a match between the test case and the prediction.

   Test Case: 0
   Prediction: 0
   

Sample --help options

To see the full list of available options and their descriptions, use the -h or --help command line option.

Additional resources

The following resources provide a deeper understanding about getting started with TensorRT using Python:

Model

• MNIST model

Dataset

• MNIST database

Documentation

• Introduction To NVIDIA’s TensorRT Samples
• Working With TensorRT Using The Python API
• NVIDIA’s TensorRT Documentation Library

License

For terms and conditions for use, reproduction, and distribution, see the TensorRT Software License Agreement documentation.

Changelog

February 2019 This README.md file was recreated, updated and reviewed.

Known issues

There are no known issues in this sample.