Jetson Setup#

This section explains how to prepare a Jetson device before installing the DeepStream SDK.

Install Jetson SDK components#

Download NVIDIA SDK Manager from https://developer.nvidia.com/embedded/jetpack. You will use this to install JetPack 5.1 GA (corresponding to L4T 35.2.1 release)

  • NVIDIA SDK Manager is a graphical application which flashes and installs the JetPack packages.

  • The flashing procedure takes approximately 10-30 minutes, depending on the host system.

If you are using Jetson Xavier NX developer kit, you can download the SD card image from https://developer.nvidia.com/embedded/jetpack. This comes packaged with CUDA, TensorRT and cuDNN.

Install Dependencies#

Enter the following commands to install the prerequisite packages:

$ sudo apt install \
libssl1.1 \
libgstreamer1.0-0 \
gstreamer1.0-tools \
gstreamer1.0-plugins-good \
gstreamer1.0-plugins-bad \
gstreamer1.0-plugins-ugly \
gstreamer1.0-libav \
libgstreamer-plugins-base1.0-dev \
libgstrtspserver-1.0-0 \
libjansson4 \
libyaml-cpp-dev

Install librdkafka (to enable Kafka protocol adaptor for message broker)#

  1. Clone the librdkafka repository from GitHub:

    $ git clone https://github.com/edenhill/librdkafka.git

  2. Configure and build the library:

    $ cd librdkafka
$ git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a
./configure
$ make
$ sudo make install

  3. Copy the generated libraries to the deepstream directory:

    $ sudo mkdir -p /opt/nvidia/deepstream/deepstream-6.2/lib
$ sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-6.2/lib

Install latest NVIDIA BSP packages#

Installation of JetPack 5.1 GA will ensure that latest NVIDIA BSP packages are installed.

Install the DeepStream SDK#

  • Method 1: Using SDK Manager

    Select DeepStreamSDK from the Additional SDKs section along with JP 5.1 GA software components for installation.

  • Method 2: Using the DeepStream tar package: https://developer.nvidia.com/downloads/deepstream-sdk-v620-jetson-tbz2

    1. Download the DeepStream 6.2 Jetson tar package deepstream_sdk_v6.2.0_jetson.tbz2 to the Jetson device.

    2. Enter the following commands to extract and install the DeepStream SDK:

      $  sudo tar -xvf deepstream_sdk_v6.2.0_jetson.tbz2 -C /
$ cd /opt/nvidia/deepstream/deepstream-6.2
$ sudo ./install.sh
$ sudo ldconfig

  • Method 3: Using the DeepStream Debian package: https://developer.nvidia.com/downloads/deepstream-62-620-1-arm64-deb

    Download the DeepStream 6.2 Jetson Debian package deepstream-6.2_6.2.0-1_arm64.deb to the Jetson device. Enter the following command:

    $ sudo apt-get install ./deepstream-6.2_6.2.0-1_arm64.deb

  • Method 4: Use Docker containers

    DeepStream docker containers are available on NGC. See the Docker Containers section to learn about developing and deploying DeepStream using docker containers.

Run deepstream-app (the reference application)#

  1. Navigate to the samples directory on the development kit.

  2. Enter the following command to run the reference application:

    $ deepstream-app -c <path_to_config_file>

    Where <path_to_config_file> is the pathname of one of the reference application’s configuration files, found in configs/deepstream-app/. See Package Contents for a list of the available files.

    Config files that can be run with deepstream-app:

    1. source30_1080p_dec_infer-resnet_tiled_display_int8.txt

    2. source30_1080p_dec_preprocess_infer-resnet_tiled_display_int8.txt

    3. source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt

    4. source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8_gpu1.txt (dGPU only)

    5. source1_usb_dec_infer_resnet_int8.txt

    6. source1_csi_dec_infer_resnet_int8.txt (Jetson only)

    7. source2_csi_usb_dec_infer_resnet_int8.txt (Jetson only)

    8. source6_csi_dec_infer_resnet_int8.txt (Jetson only)

    9. source2_1080p_dec_infer-resnet_demux_int8.txt

    10. source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.yml

    11. source30_1080p_dec_infer-resnet_tiled_display_int8.yml

    12. source4_1080p_dec_preprocess_infer-resnet_preprocess_sgie_tiled_display_int8.txt

    • You can find sample configuration files under /opt/nvidia/deepstream/deepstream-6.2/samples directory. Enter this command to see application usage:

      $ deepstream-app --help

    • To save TensorRT Engine/Plan file, run the following command:

      $ sudo deepstream-app -c <path_to_config_file>

  1. To show labels in 2D Tiled display view, expand the source of interest with mouse left-click on the source. To return to the tiled display, right-click anywhere in the window.

  2. Keyboard selection of source is also supported. On the console where application is running, press the z key followed by the desired row index (0 to 9), then the column index (0 to 9) to expand the source. To restore 2D Tiled display view, press z again.

Boost the clocks#

After you have installed DeepStream SDK, run these commands on the Jetson device to boost the clocks:

$ sudo nvpmodel -m 0
$ sudo jetson_clocks

For Jetson Xavier NX, run sudo nvpmodel -m 8 instead of 0.

Run precompiled sample applications#

  1. Navigate to the chosen application directory inside sources/apps/sample_apps.

  2. Follow the directory’s README file to run the application.

    If the application encounters errors and cannot create Gst elements, remove the GStreamer cache, then try again. To remove the GStreamer cache, enter this command: $ rm ${HOME}/.cache/gstreamer-1.0/registry.aarch64.bin

    When the application is run for a model which does not have an existing engine file, it may take up to a few minutes (depending on the platform and the model) for the file generation and the application launch. For later runs, these generated engine files can be reused for faster loading.

Installing the reference graphs#

Download reference graphs:

https://developer.nvidia.com/deepstream-getting-started

Install reference graphs:

sudo dpkg -i deepstream-reference-graphs-6.2.deb

Graphs are installed to:

/opt/nvidia/deepstream/deepstream/reference_graphs

deepstream-test1#

Simplest example of using DeepStream for object detection. Demonstrates decoding video from a file, performing object detection and overlaying bounding boxes on the frames.

Graph Files

deepstream-test1.yaml – The main graph file parameters.yaml – File containing parameters for the various components in the graph README - Contains detailed graph description and execution instructions ds_test1_container_builder_dgpu.yaml - Configuration file for building application specific container for dGPU platform ds_test1_container_builder_jetson.yaml - Configuration file for building application specific container for Jetson platform

Path - /opt/nvidia/deepstream/deepstream/reference_graphs/deepstream-test1

Sample Commands:

On x86:

$ /opt/nvidia/graph-composer/execute_graph.sh deepstream-test1.yaml \
      parameters.yaml -d ../common/target_x86_64.yaml

On Jetson:

$ /opt/nvidia/graph-composer/execute_graph.sh deepstream-test1.yaml \
      parameters.yaml -d ../common/target_aarch64.yaml