The tao-converter tool is provided with the TAO Toolkit to facilitate the deployment of TAO trained models on TensorRT and/or Deepstream. This section elaborates on how to generate a TensorRT engine using tao-converter.

For deployment platforms with an x86-based CPU and discrete GPUs, the tao-converter is distributed within the TAO docker. Therefore, we suggest using the docker to generate the engine. However, this requires that the user adhere to the same minor version of TensorRT as distributed with the docker. The TAO docker includes TensorRT version 8.0.

## Instructions for x86

For an x86 platform with discrete GPUs, the default TAO package includes the tao-converter built for TensorRT 8.2.5.1 with CUDA 11.4 and CUDNN 8.2. However, for any other version of CUDA and TensorRT, please refer to the overview section for download. Once the tao-converter is downloaded, follow the instructions below to generate a TensorRT engine.

1. Unzip the zip file on the target machine.

2. Install the OpenSSL package using the command:

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sudo apt-get install libssl-dev


3. Export the following environment variables:

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$export TRT_LIB_PATH=”/usr/lib/x86_64-linux-gnu”$ export TRT_INC_PATH=”/usr/include/x86_64-linux-gnu”


1. Run the tao-converter using the sample command below and generate the engine.

2. Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on x86 section above or in this GitHub repo.

Note

Make sure to follow the output node names as mentioned in the Exporting the Model section of the respective model.

## Instructions for Jetson

For the Jetson platform, the tao-converter is available to download in the NVIDIA developer zone. You may choose the version you wish to download as listed in the overview section. Once the tao-converter is downloaded, please follow the instructions below to generate a TensorRT engine.

1. Unzip the zip file on the target machine.

2. Install the OpenSSL package using the command:

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sudo apt-get install libssl-dev


3. Export the following environment variables:

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$export TRT_LIB_PATH=”/usr/lib/aarch64-linux-gnu”$ export TRT_INC_PATH=”/usr/include/aarch64-linux-gnu”


1. For Jetson devices, TensorRT comes pre-installed with Jetpack. If you are using older JetPack, upgrade to JetPack-5.0DP.

2. Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on Jetson (ARM64) section above or in this GitHub repo.

3. Run the tao-converter using the sample command below and generate the engine.

Note

Make sure to follow the output node names as mentioned in Exporting the Model section of the respective model.

## Using the tao-converter

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tao-converter [-h] -k <encryption_key>
-d <input_dimensions>
-o <comma separated output nodes>
[-c <path to calibration cache file>]
[-e <path to output engine>]
[-b <calibration batch size>]
[-m <maximum batch size of the TRT engine>]
[-t <engine datatype>]
[-w <maximum workspace size of the TRT Engine>]
[-i <input dimension ordering>]
[-p <optimization_profiles>]
[-s]
[-u <DLA_core>]
input_file


### Required Arguments

• input_file: The path to the .etlt model exported using tao mask_rcnn export.

• -k: The key used to encode the .tlt model when training.

• -d: A comma-separated list of input dimensions that should match the dimensions used for export. Unlike export, this cannot be inferred from calibration data. This parameter is not required for new models introduced in TAO Toolkit 3.21.08 (e.g., LPRNet, UNet, GazeNet, etc).

• -o: A comma-separated list of output blob names that should match the output configuration used for tao mask_rcnn export. For MaskRCNN, these should be generate_detections and mask_fcn_logits/BiasAdd.

Note

The output node names have been changed since Developer Preview. The node names in Developer Preview are generate_detections and mask_head/mask_fcn_logits/BiasAdd.

### Optional Arguments

• -e: The path to save the engine to. The default path is ./saved.engine.

• -t: The desired engine data type. The options are fp32, fp16, or int8. Selecting INT8 mode will generate a calibration cache.

• -w: The maximum workspace size for the TensorRT engine. The default value is 1073741824(1<<30).

• -i: The input-dimension ordering. All other TAO commands use NCHW. The options are nchw, nhwc, and nc. The default value is nchw, so you can omit this argument for MaskRCNN.

• -p: Optimization profiles for .etlt models with dynamic shape. The argument format is a comma-separated list of optimization profile shapes in the format <input_name>,<min_shape>,<opt_shape>,<max_shape>, where each shape has the format <n>x<c>x<h>x<w>. This argument can be specified multiple times if there are multiple input tensors for the model. This is only useful for new models introduced in TAO Toolkit v3.0.

• -s: A Boolean value specifying whether to apply TensorRT strict-type constraints when building the TensorRT engine.

• -u: (only needed if using DLA core) Specify the DLA core index when building the TensorRT engine on Jetson devices.

### INT8 Mode Arguments

• -c: The path to the calibration cache file (only used in INT8 mode). The default value is ./cal.bin.

• -b: The batch size used during the export step for INT8 calibration cache generation (default: 8)

• -m: The maximum batch size for the TensorRT engine. The default value is 16. If you encounter out-of-memory issues, decrease the batch size accordingly. This parameter is only useful for .etlt models generated with static shape.

### Sample Output Log

Here is a sample log for exporting a MaskRCNN model:

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tao-converter -d 3,576,960 \
-k nvidia_tlt \
[INFO] Detected 1 inputs and 2 output network tensors.