3 个仓库
Awesome GitHub RepositoriesMulti-GPU Execution Scaling
Techniques for distributing inference tasks across multiple GPUs using independent contexts and streams to increase throughput.
Distinct from Multi-GPU Distribution: Focuses on concurrent task execution across multiple GPUs rather than sharding a single large model's parameters (distribution).
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Multi-GPU Execution Scaling. Refine with filters or upvote what's useful.
Awesome Multi-GPU Execution Scaling GitHub Repositories
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- wang-xinyu/tensorrtx
wang-xinyu/tensorrtx
7,802在 GitHub 上查看tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor acceleration. It provides a framework for optimizing deep learning models through a GPU inference optimizer, a deep learning model converter for transforming weights from frameworks like TensorFlow and PyTorch, and a custom plugin library to implement operations not natively supported by the TensorRT API. The project distinguishes itself through a comprehensive collection of pre-defined network implementations, ranging from various YOLO versions and DETR transformers for object det
Distributes model execution across multiple devices by creating independent contexts and streams for each processor.
C++arcfacecrnndetr - tensorpack/tensorpack
tensorpack/tensorpack
6,287在 GitHub 上查看Tensorpack 是一个高级 TensorFlow 神经网络框架和研究库，专为构建和训练深度学习模型而设计。它提供了一系列可复现的神经网络架构，用于计算机视觉、生成任务、强化学习和自然语言处理。 该项目通过一个专门的深度学习数据流水线脱颖而出，该流水线使用纯 Python 进行并行数据加载和流式传输。它包括一个用于通过数据并行策略分发工作负载的多 GPU 训练编排器，以及一个用于可视化模型显著性和激活图的专用可解释性工具包。 该框架涵盖了广泛的功能，包括用于目标检测和语义分割的计算机视觉流水线、用于语音和文本的序列建模，以及强化学习代理开发。它还提供用于权重量化和低位宽训练的模型优化工具，以及用于复现学术研究论文和转换遗留 Caffe 模型权重的实用程序。
Trains agents on complex environments using the Asynchronous Advantage Actor-Critic algorithm with multi-GPU support.
Python - nvidia/dali
NVIDIA/DALI
5,713在 GitHub 上查看NVIDIA DALI is a GPU-accelerated data loading and preprocessing library designed for deep learning workflows. It constructs high-performance data pipelines that offload decoding, augmentation, and normalization to the GPU, eliminating CPU bottlenecks in training and inference. The library reads data from multiple storage formats and streams it directly into GPU memory, with support for multi-GPU execution to scale throughput across large-scale workloads. DALI distinguishes itself by enabling data pipelines to be built once and executed across multiple deep learning frameworks without code cha
Distributes data pipeline execution across multiple GPUs to accelerate processing throughput.
C++audio-processingdata-augmentationdata-processing
