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Analyzed 3 days ago. based on code collected 3 days ago.

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 cloud_computing cluster cpu CUDA deep_learning deep_neural_networks distributed efficiency flow_graphs framework gpu gpu_computing julia machine_learning multiple_gpu multiple_machines python R symbolic_programming

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Project Security

Vulnerabilities per Version ( last 10 releases )

There are no reported vulnerabilities

Project Vulnerability Report

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About Project Security

Languages

Languages?height=75&width=75
C++
49%
Python
41%
CUDA
5%
14 Other
5%

30 Day Summary

Mar 18 2021 — Apr 17 2021

12 Month Summary

Apr 17 2020 — Apr 17 2021
  • 675 Commits
    Down -713 (51%) from previous 12 months
  • 108 Contributors
    Down -91 (45%) from previous 12 months

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