PyNvVideoCodec

PyNvVideoCodec is NVIDIA's Python-based library that provides simple yet powerful Python APIs for hardware-accelerated video encoding and decoding on NVIDIA GPUs.

PyNvVideoCodec is built on top of the Video Codec SDK and offers encode, decode, and transcode performance on par with it.

The library is distributed under MIT license and is officially supported by NVIDIA.

Features

PyNvVideoCodec supports following features:

Decode Features

Seek and frame sampling: Provides efficient and flexible methods for fetching video frames in various modes, including sequential, random, periodic, indexed, batched, and sliced, as well as at a specified target frame rate.
Decoder caching: Optimizes decoding of short video clips through decoder caching and reconfiguration.
Threaded decoder: Supports decoding on separate threads, delivering pre-decoded frames with near-zero latency, enabling high-performance video processing pipelines.
Decode statistics extraction: Enables extraction of low-level decode statistics—such as QP values, coding-unit types, and motion vectors—from H.264 and H.265 streams. These statistics provide valuable insights into video quality, content complexity, and encoder behaviour.
Video processing from buffer: Supports video processing from memory buffers, reducing I/O overhead, enabling streaming applications.
Low latency decode: Offers zero-latency decoding for video sequences that do not contain B-frames.
SEI extraction: Supports the extraction of Supplemental Enhancement Information (SEI) messages, allowing access to additional information such as HDR information, timecodes, and custom user data.
Stream metadata access: Enables access to stream metadata, including frame width, height, bit depth, and keyframe indices, to enhance content management.
GIL handling: Improved multithreaded performance through better handling of Global Interpreter Lock (GIL) in C++ layer.
Multi-GPU decode: Enables multi-GPU decoding to efficiently handle larger workloads.
Extended codec support: Supports codecs H.264, HEVC, AV1, VP8, VP9, VC1, MPEG4, MPEG2, and MPEG1
4:2:2 decode: Supports 4:2:2 decoding for both H.264 and HEVC formats on Blackwell GPUs (NV16, P210 and P216 surface formats).
Extended output formats: Decode to various output formats including NV12, YUV420, YUV444, NV16, P010, P016 and RGB24(interleaved and planar)

Encode Features

Encoder reconfiguration: Supports encoder reconfiguration, enabling dynamic updating of encoding parameters without recreating encoder instances.
SEI insertion: Allows insertion of SEI messages during encoding.
GIL handling: Improved multithreaded performance through better handling of Global Interpreter Lock (GIL) in C++ layer.
Multi-GPU encode: Enables multi-GPU encoding to efficiently handle larger workloads.
Codec support: Support encoding to codec H.264, HEVC, and AV1.
4:2:2 encode: Supports 4:2:2 encoding for both H.264 and HEVC formats on Blackwell GPUs (NV16 and P210 surface formats).
Extended input formats: Encode from various input formats including NV12, YV12, IYUV, YUV444, YUV420_10BIT, YUV444_10BIT, NV16, P210, ARGB, ABGR, ARGB10, and ABGR10.

Transcode Features

Segment-based transcode: Enables transcoding of video segments based on timestamp ranges, ideal for content editing and partial processing.

What's New

v 2.2 (Latest)

NVIDIA Jetson support: Brings easy-to-use, GPU-accelerated Python video APIs to Jetson, making it easy to build video and AI pipelines for robotics and other edge devices.
NVIDIA DGX Spark support: Unlocks high-throughput, Python-based video decode and encode on DGX Spark, accelerating video curation, experimentation, prototyping, training, and deployment of video and AI pipelines.
Access to in-memory video data: Reads video directly from Python byte data and streams, allowing video input from network buffers or cloud storage (such as S3) without writing to temporary files, while still supporting random frame access and batch retrieval.
New samples:\n
- Face protection: Jupyter notebook that demonstrates an important privacy use case on Jetson—detecting faces in video with a TensorRT-optimized RetinaFace model and blurring them using GPU-accelerated Gaussian blur to protect identities in video.
- Video decode from memory: Illustrates decoding video directly from in-memory Python byte data and seekable binary streams (such as bytes, bytearray, memoryview, BytesIO, or file handles).
- Video transcode: Shows end-to-end GPU video transcoding—demuxing, decoding, encoding, and muxing—using hardware-accelerated video decode and encode.
- Video encode with muxing: The encode sample now shows how to write encoded video directly into container formats such as MP4 and MOV.
\n

Distribution

PyNvVideoCodec library is distributed in two formats: binary distribution via PyPI and source code distribution via NVIDIA NGC. In both cases, the library and its dependencies can be installed using a single pip install command.

This package on NGC contains source code, benchmark scripts, all dependencies, Python sample applications, and documents.

To install these please download the latest zip package from NGC, open the bash/shell prompt from the same directory where zip was downloaded and run the following command, replacing \"PyNvVideoCodec.zip\" with the actual name of the downloaded zip file:

$pip install \"PyNvVideoCodec.zip\"\n

Sample Applications and Documents

A package containing PyNvVideoCodec source code, Python sample applications and documentation can be downloaded from NVIDIA NGC.
For your convenience, the documents are also accessible online at PyNvVideoCodec Online Documentation.

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These statistics provide valuable insights into video quality, content complexity, and encoder behaviour.\n* **Video processing from buffer:** Supports video processing from memory buffers, reducing I/O overhead, enabling streaming applications.\n* **Low latency decode:** Offers zero-latency decoding for video sequences that do not contain B-frames.\n* **SEI extraction:** Supports the extraction of Supplemental Enhancement Information (SEI) messages, allowing access to additional information such as HDR information, timecodes, and custom user data.\n* **Stream metadata access:** Enables access to stream metadata, including frame width, height, bit depth, and keyframe indices, to enhance content management.\n* **GIL handling:** Improved multithreaded performance through better handling of Global Interpreter Lock (GIL) in C++ layer.\n* **Multi-GPU decode:** Enables multi-GPU decoding to efficiently handle larger workloads.\n* **Extended codec support:** Supports codecs H.264, HEVC, AV1, VP8, VP9, VC1, MPEG4, MPEG2, and MPEG1\n* **4:2:2 decode:** Supports 4:2:2 decoding for both H.264 and HEVC formats on Blackwell GPUs (NV16, P210 and P216 surface formats).\n* **Extended output formats:** Decode to various output formats including NV12, YUV420, YUV444, NV16, P010, P016 and RGB24(interleaved and planar)\n\n### **Encode Features**\n\n* **Encoder reconfiguration:** Supports encoder reconfiguration, enabling dynamic updating of encoding parameters without recreating encoder instances.\n* **SEI insertion:** Allows insertion of SEI messages during encoding.\n* **GIL handling:** Improved multithreaded performance through better handling of Global Interpreter Lock (GIL) in C++ layer.\n* **Multi-GPU encode:** Enables multi-GPU encoding to efficiently handle larger workloads.\n* **Codec support:** Support encoding to codec H.264, HEVC, and AV1.\n* **4:2:2 encode:** Supports 4:2:2 encoding for both H.264 and HEVC formats on Blackwell GPUs (NV16 and P210 surface formats).\n* **Extended input formats:** Encode from various input formats including NV12, YV12, IYUV, YUV444, YUV420_10BIT, YUV444_10BIT, NV16, P210, ARGB, ABGR, ARGB10, and ABGR10.\n\n### **Transcode Features**\n\n* **Segment-based transcode:** Enables transcoding of video segments based on timestamp ranges, ideal for content editing and partial processing.\n\n## **What's New**\n\n### **v 2.2 (Latest)**\n\n* **NVIDIA Jetson support:** Brings easy-to-use, GPU-accelerated Python video APIs to Jetson, making it easy to build video and AI pipelines for robotics and other edge devices.\n* **NVIDIA DGX Spark support:** Unlocks high-throughput, Python-based video decode and encode on DGX Spark, accelerating video curation, experimentation, prototyping, training, and deployment of video and AI pipelines.\n* **Access to in-memory video data:** Reads video directly from Python byte data and streams, allowing video input from network buffers or cloud storage (such as S3) without writing to temporary files, while still supporting random frame access and batch retrieval.\n* **New samples:**\n * **Face protection:** Jupyter notebook that demonstrates an important privacy use case on Jetson—detecting faces in video with a TensorRT-optimized RetinaFace model and blurring them using GPU-accelerated Gaussian blur to protect identities in video.\n * **Video decode from memory:** Illustrates decoding video directly from in-memory Python byte data and seekable binary streams (such as bytes, bytearray, memoryview, BytesIO, or file handles).\n * **Video transcode:** Shows end-to-end GPU video transcoding—demuxing, decoding, encoding, and muxing—using hardware-accelerated video decode and encode.\n * **Video encode with muxing:** The encode sample now shows how to write encoded video directly into container formats such as MP4 and MOV.\n\n## **Distribution**\n\nPyNvVideoCodec library is distributed in two formats: binary distribution via [PyPI](https://pypi.org/project/pynvvideocodec/) and source code distribution via [NVIDIA NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/pynvvideocodec). In both cases, the library and its dependencies can be installed using a single pip install command.\n\nThis package on NGC contains source code, benchmark scripts, all dependencies, Python sample applications, and documents.\n\nTo install these please download the latest zip package from NGC, open the bash/shell prompt from the same directory where zip was downloaded and run the following command, replacing \"PyNvVideoCodec.zip\" with the actual name of the downloaded zip file:\n\n```py\n$pip install \"PyNvVideoCodec.zip\"\n```\n\n## **Sample Applications and Documents**\n\n* A package containing PyNvVideoCodec source code, Python sample applications and documentation can be downloaded from [NVIDIA NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/pynvvideocodec).\n* For your convenience, the documents are also accessible online at [PyNvVideoCodec Online 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715],[662,663,664,665,666],{"_500":575,"_273":575,"_234":152,"_473":686},{"_500":549,"_273":549,"_234":152,"_473":681},{"_500":676,"_273":676,"_234":152,"_473":677},{"_500":671,"_273":671,"_234":152,"_473":672},{"_500":667,"_273":667,"_234":152,"_473":668},"nvidia.partners",[669],{"_118":670,"_506":667,"_507":35,"_234":543},"edge","app.hushly.com",[673],{"_118":674,"_506":671,"_507":35,"_234":675},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[678],{"_118":679,"_506":676,"_507":35,"_234":680},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. 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	Files	Compressed Size
2.2.0SelectedSignedScanned	1	31.51 MB	July 28, 2026 UTC
2.1.0SignedScanned	1	105.44 MB	January 30, 2026 UTC
2.0.2Signed	1	87.65 MB	July 18, 2025 UTC
2.0.0Signed	1	87.77 MB	May 29, 2025 UTC
1.0.2	1	77.84 MB	April 17, 2024 UTC
1.0.0	1	76.71 MB	April 11, 2024 UTC