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PyNvVideoCodec (Latest Release) Download PDF

Release Notes

Key Features and Enhancements

This release of PyNvVideoCodec includes support for the following features:

  • Codec
    • H.264
    • HEVC
    • AV1
  • Surface format
    • NV12 (8 bit)
    • YUV 4:2:0 (10 bit)
    • YUV 4:4:4 (8 and 10 bit)
  • Interoperability
  • CUDA stream support for optimizing throughput.
  • Contains a collection of Python sample applications that demonstrate the usage of APIs.

Limitations and Known Issues

  • DLPack interoperability is supported only for NV12.
  • Currently, only pageable allocations are supported.
  • PyNvVideoCodec uses the FFmpeg binaries for demuxing of audio and video content.

    NVIDIA will not update the FFmpeg binaries included in our release package as these binaries are available, maintained and updated by the FFmpeg open-source community.


    NVIDIA does not provide support for FFMPEG; therefore, it is the responsibility of end users and developers, to stay informed about any vulnerabilities or quality bugs reported against FFMPEG. Users are encouraged to refer to the official FFmpeg website and community forums for the latest updates, patches, and support related to FFmpeg binaries and act as they deem necessary.

Package Contents

This package contains the following:

  1. Sample applications demonstrating usage of PyNvVideoCodec APIs for encoding, decoding and transcoding use cases.
    • [.\samples\]
  2. Python Bindings
    • [.src\PyNvVideoCodec]
  3. Video codec helper classes and utilities
    • [.src\VideoCodecSDKUtils]
  4. FFmpeg libraries and source code
    • [.external\ffmpeg]
  5. Documents
    • [.docs]

The sample applications provided in the package are for demonstration purposes only and may not be fully tuned for quality and performance. Hence the users are advised to do their independent evaluation for quality and/or performance.

System Requirements

Table 1. System Requirements
Operating System
  • Windows 10 or higher
  • Ubuntu 18.04 or higher
  • NVIDIA Windows display driver 531.61 or newer
  • NVIDIA Linux display driver 530.41.03 or newer

Get most recent NVIDIA Display Driver

Visual Studio(Windows only)
CUDA Toolkit Latest CUDA Toolkit
Python modules to run Sample applications PyCUDA and PyTorch

Windows Subsystem for Linux (WSL) Configuration Requirements

  • Add the directory /usr/lib/wsl/lib to PATH environment variable, in case it is not added by default. This is required to include path for the WSL libraries.
  • Plus all the requirements under System Requirements

Installing PyNvVideoCodec Python Module


This project will download and install additional third-party open source software projects - DLPack. Review the license terms of these open source projects before use.

The Python module can be installed using following ways.

Installing from PyPI

  1. The ready-to-use Python WHL's (Wheel) of the PyNvVideoCodec for Windows and Linux OSes are hosted on PyPI.
  2. Open the bash/shell prompt and run:

    $>pip install "PyNvVideCodec"

  3. This is the recommended way.

Building and Installing from Source on NVIDIA NGC

The package containing PyNvVideCodec Python module's source code, all dependencies, Python sample applications, and documents is hosted on NVIDIA NGC.

Follow these steps:

  1. Download the zip file of the latest package from NVIDIA NGC .
  2. Open the bash/shell prompt on the same directory where zip was downloaded and run the following command, replacing "" with the actual name of the downloaded zip file:

    $>pip install ""

  3. You can access documents and Python sample applications from the package.

Use this method if you need any customization on PyNvVideoCodec Python module e.g. enabling NVTX markers for profiling

Follow these steps to build customized version:

  1. Unzip the source package to a directory.
  2. Do the necessary modifications to the source.
  3. On the same directory where is located, run the following commands:

    $>pip install .

Running Samples

PyNvVideoCodec package contains the following Python samples in the PyNvVideoCodec/samples folder. For each of these samples, you can use the -h option to see the available command line options.

Table 2. Command Line Options per Sample Application
Sample Application Functionality Example Command Line Illustrates the demuxing and decoding of a media file.
     -g 0 -i ip_media_file_path -o op_yuv_file_path -d 1 Demonstrates how to decode media file into output surfaces allocated on non default cuda stream.Refer Stream Aware Allocations for more details.
     -g 0 -i ip_media_file_path -o op_yuv_file_path -s Measures decoding performance in FPS per process
     -g 0 -i ip_media_file_path -d 1 -n 1 Illustrates encoding of frames using CUDA device buffers as input.
     -i ip_yuv444_file_path -o op_bistream_path -s 1280x720 -if yuv444 -c hevc -json encode_config.json Illustrates encoding of frames using host memory buffers as input.
     -i ip_yuv444_file_path -o op_bistream_path -s 1280x720 -if yuv444 -c hevc -cb 1 -json encode_config.json Demonstrates bitrate change at runtime without the need to reset the encoder session.The application reduces the bitrate by half and then restores it to the original value after 100 frames.
     -i ip_yuv444_file_path -o op_bistream_path -s 1280x720 -if yuv444 -c hevc -json encode_config_lowlatency.json Measures encoding performance in FPS per process.
     -i ip_yuv444_file_path -o op_bistream_path -s 1280x720 -if yuv444 -c hevc -json encode_config_perf.json -n 3 Demonstrates transcoding of an input video stream.
     -g 0 -i ip_media_file_path -o op_media_file_path -c h264 Demonstrates zero copy data exchange with PyTorch, does the transcoding of an input video stream, runs a clamping kernel on decoded output and encodes it back.
     -g 0 -i ip_media_file_path -o op_media_file_path


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