| Sample | Language | Format | Description |
|---|---|---|---|
| sampleOnnxMNIST | C++ | ONNX | “Hello World” For TensorRT With ONNX |
| network_api_pytorch_mnist | Python | INetwork | “Hello World” For TensorRT Using Pytorch |
| Sample | Language | Format | Description |
|---|---|---|---|
| [DEPRECATED] sampleCharRNN | C++ | INetwork | Building An RNN Network Layer By Layer |
| sampleCudla | C++ | INetwork | Using The CuDLA API To Run A TensorRT Engine (aarch64 only) |
| sampleDynamicReshape | C++ | ONNX | Digit Recognition With Dynamic Shapes In TensorRT |
| sampleEditableTimingCache | C++ | INetwork | Create a deterministic build using editable timing cache |
| [DEPRECATED] sampleINT8API | C++ | ONNX | Performing Inference In INT8 Precision |
| sampleNamedDimensions | C++ | ONNX | Working with named input dimensions |
| sampleNonZeroPlugin | C++ | INetwork | Adding plugin with data-dependent output shapes |
| sampleOnnxMnistCoordConvAC | C++ | ONNX | Implementing CoordConv with a custom plugin |
| sampleIOFormats | C++ | ONNX | Specifying TensorRT I/O Formats |
| sampleProgressMonitor | C++ | ONNX | Progress Monitor API usage |
| trtexec | C++ | All | TensorRT Command-Line Wrapper: trtexec |
| engine_refit_onnx_bidaf | Python | ONNX | refitting an engine built from an ONNX model via parsers. |
| introductory_parser_samples | Python | ONNX | Introduction To Importing Models Using TensorRT Parsers |
| onnx_packnet | Python | ONNX | TensorRT Inference Of ONNX Models With Custom Layers |
| simpleProgressMonitor | Python | ONNX | Progress Monitor API usage |
| python_plugin | Python | INetwork/ONNX | Python-based TRT plugins |
| non_zero_plugin | Python | INetwork/ONNX | Python-based TRT plugin for NonZero op |
| Sample | Language | Format | Description |
|---|---|---|---|
| detectron2 | Python | ONNX | Support for Detectron 2 Mask R-CNN R50-FPN 3x model in TensorRT |
| [DEPRECATED] efficientdet | Python | ONNX | EfficientDet Object Detection with TensorRT |
| [DEPRECATED] tensorflow_object_detection_api | Python | ONNX | TensorFlow Object Detection API Models in TensorRT |
| [DEPRECATED] yolov3_onnx | Python | ONNX | Object Detection Using YOLOv3 With TensorRT ONNX Backend |
| Sample | Language | Format | Description |
|---|---|---|---|
| sampleSafeMNIST | C++ | ONNX | Build a Safety Engine for MNIST |
| sampleSafePluginV3 | C++ | ONNX | Use Safety-Supported Plugins With Safety Engines |
| trtSafeExec | C++ | ONNX | TensorRT Command-Line Wrapper With Safety Options |
Many samples require the TensorRT sample data package. If not already mounted under /usr/src/tensorrt/data (NVIDIA NGC containers), download and extract it:
-
Download the sample data from TensorRT GitHub Releases.
-
Extract and set up the data:
unzip tensorrt_sample_data_xxx.zip mkdir -p /usr/src/tensorrt/data cp -r tensorrt_sample_data_*/* /usr/src/tensorrt/data/ export TRT_DATADIR=/usr/src/tensorrt/data
After extraction, the data directory structure should be:
$TRT_DATADIR/
├── int8_api/
├── mnist/
└── resnet50/