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0.1.5

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agibot-videodataset

A GPU-accelerated library that enables random frame access and efficient video decoding for data loading.

Published8 Jan 2026

Direct dependencies2

Transitive dependenciesNot calculated

README

Version 0.1.5 documentation

VideoDataset

A GPU-accelerated library that enables random frame access and efficient video decoding for data loading.

[!WARNING] VideoDataset is in the Alpha phase. Frequent changes and instability should be anticipated. Any feedback, comments, suggestions and contributions are welcome!

Overview

VideoDataset is a high-performance video decoding multi-framework supporting library. It aims to provide framework-integrated solutions for working with video decoding tasks.

Key Features:

GPU-accelerated video decoding using NvCodec library
Support for common video formats (H.264, H.265, etc.)
Easy integration with multi-frameworks and multi-formats.

Installation

Prerequisites

NVIDIA GPU with CUDA support and CUDA Toolkit 12.0+ installed
Python 3.10 or later

Install from PyPI

pip install agibot-videodataset

Building from Source

pip install git+https://github.com/AgiBot-World/VideoDataset.git

Quick Start

The complete example can be found in the quickstart documentation.

Documentation

Please refer to full documentation here.

Also, a sphinx-based documentation can be generated by running the following command:

make dev-doc doc

It will generate the documentation in the docs/_build/html directory and serve it on http://localhost:8000.

Performance

VideoDataset is optimized for high-throughput video processing. Benchmark results show:

GPU Decoding: A decoding throughput of 20,000 FPS is achieved in a multiprocessing scenario.
Random Access: Minimal overhead for non-sequential frame access.
GPU Decoder Utilization: Over 90% GPU decoder utilization is achieved in a multiprocessing scenario.

See the benchmark documentation for detailed performance analysis.

Comparison with other CPU decoding solutions

In addition, we conducted a comprehensive benchmark comparing it against mainstream CPU software decoding solutions, including OpenCV, Torchvision (PyAV), Torchvision (VideoReader), and TorchCodec (CPU).The results demonstrate that VideoDataset achieves a 3 to 4 times improvement in decoding throughput.

CPU Throughput

Furthermore, it also demonstrates outstanding performance in reducing CPU utilization.

CPU Utilization

Development Status

License

MIT License, for more details, see the LICENSE file.

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Declared dependencies 2

Native requirements

2 direct targetsNot calculated transitive count

Package / target	Requirement	Relationship
numpy Environment { "extras": "", "marker": null, "requires_python": ">=3.10", "evaluation": "NOT_EVALUATED" }	`numpy`	runtime
torch Environment { "extras": "", "marker": null, "requires_python": ">=3.10", "evaluation": "NOT_EVALUATED" }	`torch>=2.2.1`	runtime