If you haven’t walked through the first post covering an introduction to Jetson containers, I’d recommend looking at it first.

Compiling the CUDA samples for the Nano is really hard compared to using the Xavier as it doesn’t have nearly the resources required. We can get around this by compiling the container on the host.

Once you completed creating the dependencies image and creating the JetPack images, we can build the samples.

Building the Samples


Press Ctrl+Shift+B, select make <build samples>, select build-32.2-nano-dev-jetpack-4.2.1-samples, press Enter.


~/jetson-containers$ make build-32.2-nano-dev-jetpack-4.2.1-samples

Which runs:

docker build  --build-arg IMAGE_NAME=l4t \
              -t l4t:32.2-nano-dev-jetpack-4.2.1-samples \
              -f /home/<user>/dev/jetson-containers/docker/examples/samples/Dockerfile \

At the end we should have:

~/jetson-containers$ docker images
REPOSITORY          TAG                                    SIZE
l4t                 32.2-nano-dev-jetpack-4.2.1-samples    2.34GB

Assuming you’ve followed the device setup in the first post, we can now push this image to the device. This will save a lot of time compared to pushing to a container registry and then pulling the image down.

docker save l4t:32.2-nano-dev-jetpack-4.2.1-samples | ssh user@host 'docker load'
# Or if you have pv installed, it can be used to monitor progress.
docker save l4t:32.2-nano-dev-jetpack-4.2.1-samples | pv | ssh user@host 'docker load'

Once completed, the samples image will be available on the device. Set the DOCKER_HOST variable in the .env file to proxy the run to the device: DOCKER_HOST=ssh://<user>@<device>/<ip>. To run the image:


Press Ctrl+Shift+B, select make <run samples>, select run-32.2-nano-dev-jetpack-4.2.1-samples, press Enter.


~/jetson-containers$ make run-32.2-nano-dev-jetpack-4.2.1-samples

Which runs:

docker run  \
        --rm \
        -it \
        --device=/dev/nvhost-ctrl \
        --device=/dev/nvhost-ctrl-gpu \
        --device=/dev/nvhost-prof-gpu \
        --device=/dev/nvmap \
        --device=/dev/nvhost-gpu \
        --device=/dev/nvhost-as-gpu \
        --device=/dev/nvhost-vic \

Starting in JetPack 4.2.1, the nvidia-docker runtime is installed on the device. This isn’t available through DOCKER_HOST proxying. Open an SSH session to the device.

~/jetson-containers$ ssh user@device
user@nano-dev:~$ nvidia-docker run --rm -it l4t:32.2-nano-dev-jetpack-4.2.1-samples ./deviceQuery

./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA Tegra X1"
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    5.3
  Total amount of global memory:                 3956 MBytes (4148543488 bytes)
  ( 1) Multiprocessors, (128) CUDA Cores/MP:     128 CUDA Cores
  GPU Max Clock rate:                            922 MHz (0.92 GHz)
  Memory Clock rate:                             13 Mhz
  Memory Bus Width:                              64-bit
  L2 Cache Size:                                 262144 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 32768
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            Yes
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            No
  Supports Cooperative Kernel Launch:            No
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 0 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.0, CUDA Runtime Version = 10.0, NumDevs = 1
Result = PASS

Now we have a quick way to build images on the x86_64 host and push directly to the device.