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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
  • Supports DLPack to facilitate data exchange with popular DL frameworks like PyTorch and TensorRT.
  • Supports CUDA Array Interface to facilitate data exchange with NVIDIA’s CV-CUDA library.
• 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.

  Attention:

  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

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

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:
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   $>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 “PyNvVideCodec.zip” with the actual name of the downloaded zip file:
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   $>pip install "PyNvVideCodec.zip"
           

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 setup.py is located, run the following commands:
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   $>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.

            

            
Decode.py -g 0 -i ip_media_file_path -o op_yuv_file_path -d 1
        

            

            
DecodeAsync.py -g 0 -i ip_media_file_path -o op_yuv_file_path -s
        

            

            
DecodePerf.py -g 0 -i ip_media_file_path -d 1 -n 1
        

            

            
Encode.py -i ip_yuv444_file_path  -o op_bistream_path -s 1280x720 -if yuv444 -c hevc -json encode_config.json
        

            

            
Encode.py -i ip_yuv444_file_path  -o op_bistream_path -s 1280x720 -if yuv444 -c hevc -cb 1 -json encode_config.json
        

            

            
Encode.py -i ip_yuv444_file_path  -o op_bistream_path -s 1280x720 -if yuv444 -c hevc -json encode_config_lowlatency.json
        

            

            
Encode.py -i ip_yuv444_file_path  -o op_bistream_path -s 1280x720 -if yuv444 -c hevc -json encode_config_perf.json -n 3
        

            

            
Transcode.py -g 0 -i ip_media_file_path -o op_media_file_path -c h264
        

            

            
TranscodeWithPostProc.py -g 0 -i ip_media_file_path -o op_media_file_path
        

Notice

This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.

NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.

Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete.

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Trademarks

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