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DLI Getting Started with AI on Jetson Nano

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Course environment for the Deep Learning Institute (DLI) course, "Getting Started with AI on Jetson Nano".



Latest Tag



July 1, 2022

Compressed Size

1.52 GB

Multinode Support


Multi-Arch Support


v2.0.2-r32.7.1 (Latest) Scan Results

Linux / arm64

DLI "Getting Started with AI on Jetson Nano" Course Environment Container

This container is used in the NVIDIA Deep Learning Institute course Getting Started with AI on Jetson Nano and should be run on an NVIDIA Jetson Nano. This course is also an element of the Jetson AI Fundamentals and the Jetson AI Certification Program. 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.


The following are required to run this container.

How to Use the Container

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

Set the Data Directory

The data collected during the course is stored in a mounted directory on the host device. This way, the data and trained models aren't lost when the container shuts down. The commands below assume the mounted directory is ~/nvdli-data, so make sure you create it first:

mkdir ~/nvdli-data

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.

JetPack Release Container Version Tag Language
4.4 v2.0.0-r32.4.3 en-US
4.4.1 v2.0.1-r32.4.4 en-US
4.4.1 v2.0.1-r32.4.4zh zh-CN
4.5 v2.0.1-r32.5.0 en-US
4.5 v2.0.1-r32.5.0zh zh-CN
4.6 v2.0.1-r32.6.1 en-US
4.6 v2.0.1-r32.6.1zh zh-CN
4.6 v2.0.1-r32.6.1tw zh-TW
4.6 v2.0.1-r32.6.1ja JA
4.6 v2.0.2-r32.6.1kr KR
4.6.1 v2.0.2-r32.7.1 en-US
4.6.1 v2.0.2-r32.7.1zh zh-CN
4.6.1 v2.0.2-r32.7.1tw zh-TW
4.6.1 v2.0.2-r32.7.1ja JA
4.6.1 v2.0.2-r32.7.1kr KR

The docker run command will automatically pull the container if it is not on your system already.

USB Camera option:
sudo docker run --runtime nvidia -it --rm --network host \
 --volume ~/nvdli-data:/nvdli-nano/data \
 --device /dev/video0 \
CSI camera option:
sudo docker run --runtime nvidia -it --rm --network host \
 --volume ~/nvdli-data:/nvdli-nano/data \
 --volume /tmp/argus_socket:/tmp/argus_socket \
 --device /dev/video0 \

note: if you have both CSI and USB cameras plugged in (or multiple USB cameras), also add --device /dev/video1 above. Then in the DLI notebooks, you will need to set the capture_device number to 1 (the CSI camera will be /dev/video0 and the USB camera will be /dev/video1 - don't use the CSI camera through V4L2)

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
  • --volume defines a mounting directory, and is used to share the persistent data files and the CSI camera socket between the Jetson host and the container
  • --device allows access to the video device

Connect to JupyterLab

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

allow 10 sec for JupyterLab to start @ (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 ( 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


Copyright 2020-2022 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

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:

Software Installed on Top of Base Image

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