4.0-devel-jp5.1.2 (stereolabs/zed:4.0-devel-jetson-jp5.1.2ā )
R35.2.1-yolov5-MAVROS-FastPlanner-stable (nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3ā )
R35.2.1-yolov5-MAVROS (nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3ā )
R35.2.1-yolov5-ROS-stable (nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3ā )
R35.2.1-yolov7-ROS-stable (nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3ā )
R35.2.1-yolov7 (dustynv/jetson-inference:r35.2.1)
yolov5_noetic_ros (dustynv/ros:noetic-pytorch-l4t-r35.2.1)
dusty-nx/jetson-inference : hereā
š dustynv/jetson-inference:r35.2.1
Jetpack 5.1 (L4T R35.2.1)
CUDA 11.4, python 3.8, OpenCV 4.5.0
torch 2.0.0a0+ec3941ad.nv23.2
torchaudio 0.13.1+b90d798
torchvision 0.14.1a0+5e8e2f1
git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inference
jetson-inference/docker/run.sh
# run the container
if [ $ARCH = "aarch64" ]; then
# /proc or /sys files aren't mountable into docker
cat /proc/device-tree/model > /tmp/nv_jetson_model
sudo docker run --runtime nvidia -it \
--network host \
-v /tmp/argus_socket:/tmp/argus_socket \
-v /etc/enctune.conf:/etc/enctune.conf \
-v /etc/nv_tegra_release:/etc/nv_tegra_release \
-v /tmp/nv_jetson_model:/tmp/nv_jetson_model \
-v /dev:/dev \
--privileged \
-v ~/docker_share:/root/share:rw \
-h noetic \
--name noetic \
$DISPLAY_DEVICE $V4L2_DEVICES \
$DATA_VOLUME $USER_VOLUME $DEV_VOLUME \
$CONTAINER_IMAGE $USER_COMMAND
cd jetson-inference
docker/run.sh --ros=noetic
Content type
Image
Digest
sha256:52138a76cā¦
Size
6.1 GB
Last updated
almost 3 years ago
docker pull lakms123456/orin:zed-4.0-devel-jetson-jp5.1.2-FenceAvoidance