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System Setup

Running the Docker Container

Pre-built Docker container images for this project are hosted on DockerHub. Alternatively, you can Build the Project from Source.

Below are the currently available container tags:

Container Tag L4T version JetPack version
dustynv/jetson-inference:r32.6.1 L4T R32.6.0 JetPack 4.6
dustynv/jetson-inference:r32.5.0 L4T R32.5.0 JetPack 4.5
dustynv/jetson-inference:r32.4.4 L4T R32.4.4 JetPack 4.4.1
dustynv/jetson-inference:r32.4.3 L4T R32.4.3 JetPack 4.4

note: the version of JetPack-L4T that you have installed on your Jetson needs to match the tag above. If you have a different version of JetPack-L4T installed, either upgrade to the latest JetPack or Build the Project from Source to compile the project directly.

These containers use the l4t-pytorch base container, so support for transfer learning / re-training is already included.

Launching the Container

Due to various mounts and devices needed to run the container, it's recommended to use the docker/run.sh script to run the container:

$ git clone --recursive https://github.com/dusty-nv/jetson-inference
$ cd jetson-inference
$ docker/run.sh

note: because of the Docker scripts used and the data directory structure that gets mounted into the container, you should still clone the project on your host device (i.e. even if not intending to build/install the project natively)

docker/run.sh will automatically pull the correct container tag from DockerHub based on your currently-installed version of JetPack-L4T, and mount the appropriate data directories and devices so that you can use cameras/display/ect from within the container. It will also prompt you to download DNN models if you haven't already done so, which get mounted into the container to load. This initial setup is only done once.

Mounted Data Volumes

For reference, the following paths automatically get mounted from your host device into the container:

  • jetson-inference/data (stores the network models, serialized TensorRT engines, and test images)
  • jetson-inference/python/training/classification/data (stores classification training datasets)
  • jetson-inference/python/training/classification/models (stores classification models trained by PyTorch)
  • jetson-inference/python/training/detection/ssd/data (stores detection training datasets)
  • jetson-inference/python/training/detection/ssd/models (stores detection models trained by PyTorch)

These mounted volumes assure that the models and datasets are stored outside the container, and aren't lost when the container is shut down.

If you wish to mount your own directory into the container, you can use the --volume HOST_DIR:MOUNT_DIR argument to docker/run.sh:

$ docker/run.sh --volume /my/host/path:/my/container/path    # these should be absolute paths

For more info, run docker/run.sh --help or see the help text inside docker/run.sh

Running Applications

Once the container is up and running, you can then run example programs from the tutorial like normal inside the container:

# cd build/aarch64/bin
# ./video-viewer /dev/video0
# ./imagenet images/jellyfish.jpg images/test/jellyfish.jpg
# ./detectnet images/peds_0.jpg images/test/peds_0.jpg
# (press Ctrl+D to exit the container)

note: when you are saving images from one of the sample programs (like imagenet or detectnet), it's recommended to save them to images/test. These images will then be easily viewable from your host device in the jetson-inference/data/images/test directory.

Building the Container

If you are following the Hello AI World tutorial, you can ignore this section and skip ahead to the next step. But if you wish to re-build the container or build your own, you can use the docker/build.sh script which builds the project's Dockerfile:

$ docker/build.sh

note: you should first set your default docker-runtime to nvidia, see here for the details.

You can also base your own container on this one by using the line FROM dustynv/jetson-inference:r32.4.3 in your own Dockerfile.

Getting Started

If you have chosen to run the project inside the Docker container, you can proceed to Classifying Images with ImageNet.

However, if you would prefer to install the project directly on your Jetson (outside of container), go to Building the Project from Source.

Next | Building the Project from Source
Back | Setting up Jetson with JetPack

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