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

Current PyNvVideoCodec version supports following features:

Decode Features

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.
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.
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 2.1 (Latest)

\n
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.
\n
\n
Jupyter notebooks: Added two step-by-step, interactive Jupyter notebook tutorials—one demonstrating the SimpleDecoder API for easy video decoding and flexible frame sampling, and another showcasing the use of ThreadedDecoder in a real-world deep-learning object detection workflow.
\n
\n
Enhanced sample applications: Simplified, restructured and enhanced sample applications along with documentation to help developers learn, experiment, and build faster.
\n
\n
Enhanced Documentation: Comprehensive API reference and programming guide, enriched with practical code snippets to help developers quickly understand and use the library.
\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 PyPI contains Python WHLs of the PyNvVideoCodec library and sample applications that demonstrate the use of the PyNvVideoCodec API. To install these please open the shell prompt, and run the following command.

$ pip install pynvvideocodec\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* **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. \n* **Decoder caching:** Optimizes decoding of short video clips through decoder caching and reconfiguration. \n* **Threaded decoder:** Supports decoding on separate threads, delivering pre-decoded frames with near-zero latency, enabling high-performance video processing pipelines. \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 \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 \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 2.1 (Latest)**\n\n* **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.\n\n* **Jupyter notebooks:** Added two step-by-step, interactive Jupyter notebook tutorials—one demonstrating the SimpleDecoder API for easy video decoding and flexible frame sampling, and another showcasing the use of ThreadedDecoder in a real-world deep-learning object detection workflow.\n\n* **Enhanced sample applications:** Simplified, restructured and enhanced sample applications along with documentation to help developers learn, experiment, and build faster.\n\n* **Enhanced Documentation:** Comprehensive API reference and programming guide, enriched with practical code snippets to help developers quickly understand and use the library.\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 PyPI contains Python WHLs of the PyNvVideoCodec library and sample applications that demonstrate the use of the PyNvVideoCodec API. To install these please open the shell prompt, and run the following command.\n\n```py\n$ pip install pynvvideocodec\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 Documentation](https://docs.nvidia.com/video-technologies/pynvvideocodec/index.html).","displayName","PyNvVideoCodec",["D",1769793383279],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/resources/pynvvideocodec","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_262":368,"_311":369,"_313":370,"_315":87},{"_262":365,"_311":366,"_313":367,"_315":87},{"_262":362,"_311":363,"_313":364,"_315":87},{"_262":359,"_311":360,"_313":361,"_315":87},{"_262":356,"_311":357,"_313":358,"_315":87},{"_262":353,"_311":354,"_313":355,"_315":87},{"_262":350,"_311":351,"_313":352,"_315":87},{"_262":347,"_311":348,"_313":349,"_315":87},{"_262":344,"_311":345,"_313":346,"_315":87},{"_262":341,"_311":342,"_313":343,"_315":87},{"_262":337,"_311":338,"_313":339,"_315":87},{"_262":335,"_311":335,"_313":336,"_315":87},{"_262":332,"_311":333,"_313":334,"_315":87},{"_262":329,"_311":329,"_313":330,"_315":87},{"_262":325,"_311":326,"_313":327,"_315":87},{"_262":322,"_311":323,"_313":324,"_315":87},{"_262":318,"_311":319,"_313":320,"_315":87},{"_262":310,"_311":312,"_313":314,"_315":87},"Vision 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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	Files	Compressed Size
2.1.0SignedScanned	1	105.44 MB	January 30, 2026 UTC
2.0.2SelectedSigned	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