WaveShare IMX219-160 not playing nice w/ Jetson Nano 2G : Please Recommend Better Camera

Robotics & Edge Computing Jetson & Embedded Systems Jetson Nano
8

Is your WaveShare IMX219-160 camera in conjunction with your Jetson Nano 2GB freezing during execution of DLI courses? Mine did, here’s how I resolved the issue (after hours of troubleshooting):

Follow troubleshooting directions found in the DLI Course “Getting Started with AI on Jetson Nano > Setting up your Jetson Nano > Headless Device Mode.” (requires a login to view)

For those of you that don’t want to login, the following information comes verbatim from the page:

I’m using a CSI camera with the 2GB Nano and the camera freezes during training.

  • Release as much RAM as possible, increase the swap, and specify memory options during the docker run launch:
# Disable ZRAM:
sudo systemctl disable nvzramconfig

# Prevent X-Server from starting:
sudo systemctl set-default multi-user.target

# Create additional 6GB swap file (assume 4GB already for a total of 10GB)
sudo fallocate -l 6G /mnt/6GB.swap
sudo chmod 600 /mnt/6GB.swap
sudo mkswap /mnt/6GB.swap

# Append the following line to /etc/fstab
sudo su
echo "/mnt/6GB.swap swap swap defaults 0 0" >> /etc/fstab
exit

# REBOOT!

# Check your memory and swap
free -h

# Create a reusable script
echo "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 \
    --memory=500M --memory-swap=8G \
    nvcr.io/nvidia/dli/dli-nano-ai:v2.0.1-r32.6.1" > docker_dli_run.sh

# Make the script executable
chmod +x docker_dli_run.sh

# Run the script
./docker_dli_run.sh

A message to Nvidia:
As a newcomer to Python, AI, and single board computers, I purchased the Jetson Nano 2GB Developer Kit based on the following claim, found on the Jetson Nano 2GB Developer Kit website: “The NVIDIA® Jetson Nano™ 2GB Developer Kit is ideal for learning, building, and teaching AI and robotics—built for creators and priced for everyone.”
Yet, out of the box, the Jetson Nano 2GB developer kit isn’t capable of being used in Nvidia’s “Getting Started with AI on Jetson Nano.” I would happily have paid $40 more for the 4GB to avoid the hours lost trying to make the 2GB version compatible with the DLI course. If you have experience in computer science, which I don’t, I am sure that you probably feel differently. But remember, Nvidia markets this as being “ideal for learning” and compatible with their “easy-to-follow tutorials.”

That is all. I hope this post helps other newcomers like myself avoid some frustration.