pith. sign in

arxiv: 1512.01274 · v1 · pith:23I7M26Rnew · submitted 2015-12-03 · 💻 cs.DC · cs.LG· cs.MS· cs.NE

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Tianqi Chen , Mu Li , Yutian Li , Min Lin , Naiyan Wang , Minjie Wang , Tianjun Xiao , Bing Xu
show 2 more authors
Chiyuan Zhang Zheng Zhang
This is my paper
classification 💻 cs.DC cs.LGcs.MScs.NE
keywords mxnetcomputationdeepdistributedefficientexpressionheterogeneouslearning
0
0 comments X
read the original abstract

MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MONAI: An open-source framework for deep learning in healthcare

    cs.LG 2022-11 accept novelty 6.0

    MONAI is a community-supported PyTorch framework that extends deep learning to medical data with domain-specific architectures, transforms, and deployment tools.

  2. MediaPipe: A Framework for Building Perception Pipelines

    cs.DC 2019-06 accept novelty 5.0

    MediaPipe is a new open-source framework that lets developers assemble, prototype, and deploy ML-based perception pipelines across platforms with reproducible performance measurements.

  3. A Survey of Large Language Models

    cs.CL 2023-03 accept novelty 3.0

    This survey reviews the background, key techniques, and evaluation methods for large language models, emphasizing emergent abilities that appear at large scales.

  4. A Comprehensive Overview of Large Language Models

    cs.CL 2023-07 unverdicted novelty 2.0

    A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.

  5. Array Programming with NumPy

    cs.MS 2020-06 unverdicted novelty 2.0

    NumPy provides array programming tools that form the foundation of the scientific Python ecosystem and enable data analysis across many disciplines.