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DeepStream python bindings

SDK version supported: 7.1

The latest prebuilt release package complete with python bindings and sample applications can be downloaded from the release section for x86, Jetson and SBSA platforms.

This readme describes how to compile and install DeepStream python bindings (henceforth referred as bindings). This process is mainly useful for making customizations in the bindings and compiling it yourself instead of using the prebuilt versions provided in the release section.

The readme is divided into three main parts:

1 Prerequisites

The following dependencies need to be met in order to compile bindings:

1.1 Deepstream SDK

Go to https://developer.nvidia.com/deepstream-sdk, download and install Deepstream SDK and its dependencies

1.2 Base dependencies

To compile bindings on Ubuntu - 22.04 [use python-3.10, python-3.8 will not work] :

apt install python3-gi python3-dev python3-gst-1.0 python-gi-dev git meson \
    python3 python3-pip python3.10-dev cmake g++ build-essential libglib2.0-dev \
    libglib2.0-dev-bin libgstreamer1.0-dev libtool m4 autoconf automake libgirepository1.0-dev libcairo2-dev

NEW Additionally, install PyPA :

pip3 install build

1.3 Initialization of submodules

Make sure you clone the deepstream_python_apps repo under <DeepStream ROOT>/sources: git clone https://github.com/NVIDIA-AI-IOT/deepstream_python_apps

This will create the following directory:

<DeepStream ROOT>/sources/deepstream_python_apps

The repository utilizes gst-python and pybind11 submodules. To initializes them, run the following command:

cd /opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/
git submodule update --init
python3 bindings/3rdparty/git-partial-submodule/git-partial-submodule.py restore-sparse

1.4 Installing Gst-python

Build and install gst-python:

cd bindings/3rdparty/gstreamer/subprojects/gst-python/
meson setup build
cd build
ninja
ninja install

2 Compiling the bindings

Python bindings are compiled using PyPA with a CMake extension. The compiled bindings wheel will be found inside directory dist/. Following commands provide instructions for common compilation options:

2.1 Quick build 1 (x86-ubuntu-22.04 | python 3.10 | Deepstream 7.1)

cd deepstream_python_apps/bindings
export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)
python3 -m build

2.2 Quick build 2 (Jetson-ubuntu-22.04 | python 3.10 | Deepstream 7.1)

The following commands will work for a native build on Jetson platform.

cd deepstream_python_apps/bindings
export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)
python3 -m build

2.3 Quick build 3 (SBSA-ubuntu-22.04 | python 3.10 | Deepstream 7.1)

The following commands will work for a native build on SBSA platform.

cd deepstream_python_apps/bindings
export CMAKE_ARGS="-DIS_SBSA=on"
export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)
python3 -m build

2.4 Advanced build

2.4.1 Using Cmake options

Multiple options can be used with cmake as follows:

export CMAKE_ARGS="-D<var>=<value> -D<var>=<value> -D<var>=<value> ..."

2.4.2 Available cmake options

Var Default value Purpose Available values
DS_VERSION 7.1 Used to determine default deepstream library path should match to the deepstream version installed on your computer
PYTHON_MAJOR_VERSION 3 Used to set the python version used for the bindings 3
PYTHON_MINOR_VERSION 10 Used to set the python version used for the bindings 10
DS_PATH /opt/nvidia/deepstream/deepstream-${DS_VERSION} Path where deepstream libraries are available Should match the existing deepstream library folder
IS_SBSA (Optional) Indicate whether the build is for SBSA platform 1

2.4.3 Example

Following commands can be used to compile the bindings natively on Jetson devices.

cd deepstream_python_apps/bindings
export CMAKE_ARGS="-DDS_PATH=/opt/nvidia/deepstream/deepstream/"
export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)
python3 -m build

Following commands can be used to compile the bindings natively on SBSA platform.

cd deepstream_python_apps/bindings
export CMAKE_ARGS="-DIS_SBSA=on"
export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)
python3 -m build

2.5 Cross-Compilation for aarch64 on x86

This section outlines how to enable cross-compiling of the DeepStreamSDK python bindings for aarch64 using Docker on x86 host.

NOTE: This will only emulate the CPU, so any calls requiring embedded hardware, such as using CUDA or inference are not supported.

2.5.1 Build Pre-requisites

We use qemu processor emulator to achieve cross-compilation. Qemu can be installed on the x86 Ubuntu host machine as shown below:

# Install qemu packages
sudo apt-get install qemu binfmt-support qemu-user-static

# Execute the registering scripts
docker run --rm --privileged dockerhub.nvidia.com/multiarch/qemu-user-static --reset -p yes

# Verify qemu installation
docker run --platform linux/aarch64 --rm -t  nvcr.io/nvidia/deepstream:7.1-samples-multiarch uname -m
#aarch64

2.5.2 Download the JetPack SDK 6.1

Cross-compilation for Jetson on x86 host requires some low level libraries which can be downloaded using SDK Manager. Follow these steps to obtain these libraries, which are utilized by the docker build later.

  1. Download and install the NVIDIA SDK manager
  2. Launch the SDK Manager and login with your NVIDIA developer account.
  3. Select the platform and target OS (example: Jetson AGX Xavier, Linux Jetpack 6.1) and click Continue.
  4. Under Download & Install Options change the download folder and select Download now, Install later. Agree to the license terms and click Continue.
  5. Go to the download folder, and run:
# create directories as follows:
mkdir -p deepstream_python_apps/bindings/docker/jetpack_files
# <path/to> would be /opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/
# where you downloaded the deepstream_python_apps repository
mv ~/Downloads/nvidia/sdkm_downloads/* <path/to>/deepstream_python_apps/bindings/docker/jetpack_files

2.5.3 Generate the cross-compile build container

Below command generates the build container

# cd to bindings dir
cd deepstream_python_apps/bindings
# Make sure you are in deepstream_python_apps/bindings directory
# This command builds the cross-compile docker and adds the mentioned tag
docker build --platform linux/aarch64 --tag=deepstream-7.1-ubuntu22.04-python-l4t -f qemu_docker/ubuntu-cross-aarch64.Dockerfile .

2.5.4 Launch the cross-compile build container

# Create a directory to mount to the container and store your pyds wheel package in
mkdir export_pyds

# Run the container. Make sure the tag matches the one from Generate step above
docker run --platform linux/aarch64 -it --entrypoint bash -v $PWD/export_pyds:/export_pyds deepstream-7.1-ubuntu22.04-python-l4t

2.5.5 Build DeepStreamSDK python bindings

After the container launches successfully, while inside the cross-compile docker, run following commands:

# cd to /opt/nvidia/deepstream/deepstream/sources/ dir
cd /opt/nvidia/deepstream/deepstream/sources/

# Sync deepstream_python_apps repo from github
git clone https://github.com/NVIDIA-AI-IOT/deepstream_python_apps.git

cd deepstream_python_apps/bindings/

# Initialize submodules
git submodule update --init

# Set parallelization level
export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)

# Build pybind wheel
python3 -m build

# Copy the pyds wheel package to the export dir
cp dist/pyds-*.whl /export_pyds

Build output (pip wheel) is copied to the previously created export_pyds directory (deepstream_python_apps/bindings/export_pyds) on the host machine.

3 Installing the bindings

Following commands can be used to install the generated pip wheel.

3.1 Installing the pip wheel

cd dist/
pip3 install ./pyds-1.2.0-*.whl

3.1.1 pip wheel troubleshooting

If the wheel installation fails, upgrade the pip using the following command:

python3 -m pip install --upgrade pip

3.2 Launching test-1 app

cd apps/deepstream-test1
python3 deepstream_test_1.py <input .h264 file>