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Analyzed 4 months ago. based on code collected 7 months 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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These details are provided for information only. No information here is legal advice and should not be used as such.

Project Security

Vulnerabilities per Version ( last 10 releases )

There are no reported vulnerabilities

Project Vulnerability Report

Security Confidence Index

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Vulnerability Exposure Index

Many reported vulnerabilities
Few reported vulnerabilities

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About Project Security

Languages

Languages?height=75&width=75
C++
35%
Python
37%
Perl
8%
17 Other
20%

30 Day Summary

Aug 20 2019 — Sep 19 2019

12 Month Summary

Sep 19 2018 — Sep 19 2019
  • 1505 Commits
    Down -475 (23%) from previous 12 months
  • 218 Contributors
    Down -36 (14%) from previous 12 months