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.
In a Nutshell, MXNet...
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took an estimated 115 years of effort (COCOMO model)
starting with its first commit in April, 2015
ending with its most recent commit about 1 year ago
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Apache License 2.0
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These details are provided for information only. No information here is legal advice and should not be used as such.
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