A flexible and efficient library for deep learning.
Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
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Automatically parallelizes both symbolic and imperative operations on the fly for maximum efficiency.
Enhances symbolic execution speed and memory efficiency through advanced graph optimization techniques.
Lightweight and scalable, MXNet can effectively scale to multiple GPUs and machines.
Supports both symbolic and imperative programming to maximize productivity and efficiency.
Efficiently scales across multiple GPUs to accelerate deep learning tasks.
Offers APIs in multiple programming languages, making it accessible to a wide range of developers.
Comes with a comprehensive library of pre-built models and algorithms for various deep learning tasks.
Backed by a vibrant community of developers and researchers contributing to its continuous improvement.
Apache License 2.0
https://github.com/apache/incubator-mxnet
Comprehensive documentation available at https://mxnet.apache.org/versions/1.8.0/
MXNet has a strong community support system including forums, mailing lists, and chat rooms for real-time discussions.
MXNet welcomes contributions from the community. Guidelines for contributing can be found in the project's repository.
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