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* NVIDIA Corporation and its licensors retain all intellectual property
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================================================================================
DeepStream SDK
================================================================================
Setup pre-requisites:
- Ubuntu 18.04
- Gstreamer 1.14.1
- NVIDIA driver 418+
- CUDA 10.1
- TensorRT 5.1+

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Package Contents
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The DeepStream packages include:
1. binaries.tbz2 - Core binaries
2. sources - Sources for sample application and plugin
3. samples - Config files, Models, streams and tools to run the sample app

Note for running with docker
-----------------------------
While running DeepStream with docker, necessary packages are already pre-installed.
Hence please skip the installation steps and proceed to "Running the samples" section of this document.

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Installing Pre-requisites:
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Packages to be installed:
$ sudo apt-get install \
    libssl1.0.0 \
    libgstreamer1.0-0 \
    gstreamer1.0-tools \
    gstreamer1.0-plugins-good \
    gstreamer1.0-plugins-bad \
    gstreamer1.0-plugins-ugly \
    gstreamer1.0-libav \
    libgstrtspserver-1.0-0 \
    libjansson4

CUDA 10.1
--------
Download and install Cuda-10.1 from nvidia website
https://developer.nvidia.com/cuda-toolkit-archive

TensorRT 5.1.5
-------------
Download and install TensorRT 5.1.5 from nvidia website
https://developer.nvidia.com/nvidia-tensorrt-download

Create symlink for libnvcuvid.so:
---------------------------------
sudo ln -s /usr/lib/nvidia-<driver-version>/libnvcuvid.so \
    /usr/lib/x86_64-linux-gnu/libnvcuvid.so

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librdkafka: to enable kafka protocol adaptor for message broker
-------------------------------------------------------------------------------
git clone https://github.com/edenhill/librdkafka.git
cd librdkafka
git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a
./configure
make
sudo make install
sudo mkdir -p /usr/local/deepstream
sudo cp /usr/local/lib/librdkafka* /usr/local/deepstream

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Using azure MQTT protocol adaptor with message broker
-----------------------------------------------------------------------
Refer README files availabe under sources/libs/azure_protocol_adaptor for
detailed documentation on prerequisites and usages of azure MQTT protocol
adaptor with message broker plugin for sending messages to cloud.

Refer source code and README of deepstream-test4 available under
sources/apps/sample_apps/deepstream-test4/ to send messages to the cloud.

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[Optional] Uninstall DeepStream 3.0
---------------------------------------------------------------------------
To uninstall any previously installed DeepStream 3.0 libraries, run the following command.

sudo rm -rf /usr/local/deepstream /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libnvdsgst_* \
            /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstnv* \
            /usr/bin/deepstream*

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Extract and Install DeepStream SDK
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1. This package will have binaries.tbz2 and samples directory.
2. Untar binaries.tbz2
   sudo tar -xvf binaries.tbz2 -C /
3. Run the install.sh script as follows:
   sudo ./install.sh

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Running the samples
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1. Go to samples directory and run
   deepstream-app -c <path to config.txt>
2. Application config files included in `configs/deepstream-app/`
   a. source30_1080p_dec_infer-resnet_tiled_display_int8.txt (30 Decode + Infer)
   b. source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt
      (4 Decode + Infer + SGIE + Tracker)
   c. source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8_gpu1.txt
      (4 Decode + Infer + SGIE + Tracker executed on gpu1)
3. Configuration files for "nvinfer" element in `configs/deepstream-app/`
   a. config_infer_primary.txt (Primary Object Detector)
   b. config_infer_secondary_carcolor.txt (Secondary Car Color Classifier)
   c. config_infer_secondary_carmake.txt (Secondary Car Make Classifier)
   d. config_infer_secondary_vehicletypes.txt (Secondary Vehicle Type Classifier)

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Notes:
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1. If the application runs into errors, cannot create gst elements, try again
after removing gstreamer cache
   rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin