DLI "Building Video AI Applications at the Edge on Jetson Nano" Course Environment Container

This container is used in the NVIDIA Deep Learning Institute course "Building Video AI Applications at the Edge on Jetson Nano". If you have not done so yet, we highly recommend you take the full free course, and check out other self-paced online courses and instructor-led workshops available from the NVIDIA Deep Learning Institute.

Prerequisites

The following are required to run this container.

• NVIDIA Jetson Nano Developer Kit or NVIDIA Jetson Nano 2GB Developer Kit
• microSD memory card (64GB UHS-I minimum recommended) flashed with the current Jetson Nano Developer Kit SD Card image
• USB Camera such as Logitech C270 Webcam
• USB cable (Micro-B to Type-A)
• Internet connection for Jetson Nano to download this Docker image
• Compatible Power Supply (must be 5V 4A with 2.1mm DC barrel connector if using the original 4GB Jetson Nano Developer Kit)
• 2-pin jumper (original 4GB version only)

How to Use the Container

If you've never used Docker, we recommend their Orientation and Setup.

Set the Data Directory

The applications created during the course are stored in a mounted directory on the host device. This way, the applications aren't lost when the container shuts down. The commands below assume the mounted directory is ~/my_apps, so make sure you create it first:

mkdir ~/my_apps

Run the Container

Run the container using the container tag that corresponds to the version of JetPack-L4T that you have installed on your Jetson.

The docker run command will automatically pull the container if it is not on your system already. Plug in your USB webcam prior to executing the run command.

sudo docker run --runtime nvidia -it --rm --network host \
    -v /tmp/.X11-unix/:/tmp/.X11-unix \
    -v /tmp/argus_socket:/tmp/argus_socket \
    -v ~/my_apps:/dli/task/my_apps \
    --device /dev/video0 \
    nvcr.io/nvidia/dli/dli-nano-deepstream:v2.0.0-DS6.0.1

Options Explained:

• --runtime nvidia will use the NVIDIA container runtime while running the l4t-base container
• -it means run in interactive mode
• --rm will delete the container when finished
• --network host allows the container to use your Jetson host network and ports
• -v or --volume defines a mounting directory, and is used to share the persistent data files and other assets between the Jetson host and the container
• --device allows access to the USB video device

Connect to JupyterLab

When the container is launched, the JupyterLab server will automatically start. Text similar to the following will be printed out to the user:

allow 10 sec for JupyterLab to start @ http://192.168.55.1:8888 (password dlinano)
JupterLab logging location:  /var/log/jupyter.log  (inside the container)
You can then navigate the browser on your PC to the URL shown above (http://192.168.55.1:8888) and login to JupyterLab with the password dlinano. Then proceed with the DLI course as normal.

Technical Support

If you have any questions or need help, please visit the Jetson Developer Forums

License

Copyright 2021-2022 NVIDIA

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Base Image Used for This Container

Also used in this container, and with its own licensing:

• NVIDIA DeepStream SDK on L4T

Software Installed on Top of Base Image

Also used in this container, and with its own licensing:

• DeepStream Python Apps and Bindings
• JupyterLab
• gir1.2-gst-rtsp-server-1.0