A Guide to using TensorRT on the Nvidia Jetson Nano


  • Note This guide assumes that you are using Ubuntu 18.04. If you are using Windows refer to these instructions on how to setup your computer to use TensorRT.

Step 1: Setup TensorRT on Ubuntu Machine

Follow the instructions here. Make sure you use the tar file instructions unless you have previously installed CUDA using .deb files.

Step 2: Setup TensorRT on your Jetson Nano

  • Setup some environment variables so nvcc is on $PATH. Add the following lines to your ~/.bashrc file.
# Add this to your .bashrc file
export CUDA_HOME=/usr/local/cuda
# Adds the CUDA compiler to the PATH
export PATH=$CUDA_HOME/bin:$PATH
# Adds the libraries
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
  • Test the changes to your .bashrc.
source ~/.bashrc
nvcc --version

You should see something like:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on ...
Cuda compilation tools, release 10.0, Vxxxxx
  • Switch to your virtualenv and install PyCUDA.
# This takes a a while.`
pip install pycuda
  • After this you will also need to setup PYTHONPATH such that your dist-packages are included as part of your virtualenv. Add this to your .bashrc. This needs to be done because the python bindings to tensorrt are available in dist-packages and this folder is usually not visible to your virtualenv. To make them visible we add it to PYTHONPATH.
export PYTHONPATH=/usr/lib/python3.6/dist-packages:$PYTHONPATH
  • Test this change by switching to your virtualenv and importing tensorrt.
> import tensorrt as trt
> # This import should succeed

Step 3: Train, Freeze and Export your model to TensorRT format (uff)

After you train the linear model you end up with a file with a .h5 extension.

# You end up with a Linear.h5 in the models folder
python manage.py train --model=./models/Linear.h5 --tub=./data/tub_1_19-06-29,...
# Freeze model using freeze_model.py in donkeycar/scripts
# The frozen model is stored as protocol buffers.
# This command also exports some metadata about the model which is saved in ./models/Linear.metadata
python freeze_model.py --model=./models/Linear.h5 --output=./models/Linear.pb
# Convert the frozen model to UFF. The command below creates a file ./models/Linear.uff
convert-to-uff ./models/Linear.pb

Now copy the converted uff model and the metadata to your Jetson Nano.

Step 4

  • In config.py pick the model type as tensorrt_linear.
DEFAULT_MODEL_TYPE = `tensorrt_linear`
  • Finally you can do
# After you scp your `uff` model to the Nano
python manage.py drive --model=./models/Linear.uff