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Project Summary

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, and more

MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of symbolic programming and imperative programming together to maximize the efficiency and your productivity. In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer is build on top, which makes symbolic execution fast and memory efficient. The library is portable and lightweight, and is ready scales to multiple GPUs, and multiple machines.

Tags

bigdata blueprints cloud_computing cluster cpu deep_learning distributed efficiency flexibility flow_graphs framework gpu gpu_computing julia machine_learning multiple_gpu multiple_machines python R symbolic_programming

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Languages

Languages?height=75&width=75
C++
32%
Python
32%
Scala
10%
14 Other
26%

30 Day Summary

Feb 8 2017 — Mar 10 2017

12 Month Summary

Mar 10 2016 — Mar 10 2017
  • 3519 Commits
    Down -2743 (43%) from previous 12 months
  • 254 Contributors
    Up + 102 (67%) from previous 12 months