Nvvl

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nvvl is an Open Source Coding & Development tool that uses hardware acceleration to load sequences of video frames for machine learning training. It leverages NVIDIA GPUs to offload and accelerate video decoding.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is nvvl?

NVVL (NVIDIA Video Loader) is an open-source library designed to accelerate machine learning training by efficiently loading sequences of video frames from compressed video files. It utilizes FFmpeg's libraries for parsing and reading compressed packets and offloads video decoding to NVIDIA GPUs, providing ready-for-training tensors in GPU device memory. The library also supports data augmentation during loading, including scaling, cropping, and horizontal flipping, using the GPUs' dedicated texture mapping units. While NVVL itself is no longer maintained, its functionality has been incorporated into the NVIDIA DALI library, which is recommended for new projects. It significantly reduces storage and I/O demands compared to using individual image frames.

Best used for

Ideal for developers who need to efficiently load video data for machine learning training, accelerate video decoding using NVIDIA GPUs, and perform on-the-fly data augmentation. Especially valuable for projects dealing with large video datasets where I/O and CPU bottlenecks are a concern.

Common actions

accelerate video decoding

load video frames

process video data

train machine learning models

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Capabilities

Key features

Hardware-accelerated video decoding
Load video frame sequences
GPU-based data augmentation
Supports H.264/HEVC codecs
PyTorch wrapper available
Reduces storage/I/O demands

Target Audience

developer

Integrations

pytorch

Pricing & Plans

Open Source

Free

FAQs

Is NVVL still actively maintained?

No, NVVL is no longer actively maintained. Its functionality has been incorporated into the NVIDIA DALI (Data Loading Library), which is recommended for new projects. All future issues and requests should be submitted to the DALI repository.

What are the primary benefits of using NVVL (or DALI)?

NVVL significantly reduces storage and I/O demands by using compressed video files instead of individual frames. It also offloads video decoding to NVIDIA GPUs, freeing up CPU resources and accelerating the data loading process for machine learning training.

What are the system requirements for NVVL?

NVVL requires the CUDA Toolkit (version 8.0 or later, with better performance on 9.0+), FFmpeg's libavformat, libavcodec, libavfilter, and libavutil libraries, and CMake version 3.8 or above for building. Dockerfiles are provided for easier setup.

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