awslabs / dynamic-training-with-apache-mxnet-on-aws

Dynamic training with Apache MXNet reduces cost and time for training deep neural networks by leveraging AWS cloud elasticity and scale. The system reduces training cost and time by dynamically updating the training cluster size during training, with minimal impact on model training accuracy.

Summary

	JL I PROTO CMAKE ST GV PYX CFG PYI G4 IN T
		Main Code: 252,286 LOC (1584 files) = H (25%) + PY (24%) + CC (18%) + PM (8%) + CU (5%) + SCALA (4%) + CUH (2%) + R (1%) + JL (1%) + I (1%) + PROTO (1%) + CLJ (<1%) + CPP (<1%) + JAVA (<1%) + HPP (<1%) + CMAKE (<1%) + ST (<1%) + GV (<1%) + M (<1%) + PYX (<1%) + PL (<1%) + YML (<1%) + CFG (<1%) + GROOVY (<1%) + PYI (<1%) + YAML (<1%) + G4 (<1%) + IN (<1%) + T (<1%) + PERL (<1%) Secondary code: Test: 55,422 LOC (298); Generated: 0 LOC (0); Build & Deploy: 3,831 LOC (109); Other: 22,992 LOC (347);
		File Size: 13% long (>1000 LOC), 34% short (<= 200 LOC)
		Unit Size: 9% long (>100 LOC), 41% short (<= 10 LOC)
		Conditional Complexity: 5% complex (McCabe index > 50), 57% simple (McCabe index <= 5)
		Logical Component Decomposition: primary (19 components)
		3 years, 2 months old 100% of code older than 365 days 100% of code not updated in the past 365 days
		0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits.
		Goals: Keep the system simple and easy to change (4)
	Features of interest: TODOs 48 files

Commits Trend

Latest commit date: 2021-04-28

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Commits	2022 2021 2020 2019 2018 1 h 7 py 1 cc 1 pm 1 cu 1 scala 1 cuh 1 r jl 1 jl i 1 i proto 1 proto 1 clj 1 cpp 1 java 1 hpp cmake 1 cmake st 1 st gv 1 gv 1 m pyx 1 pyx 1 pl 1 yml cfg 1 cfg 1 groovy pyi 1 pyi 1 yaml g4 1 g4 in 1 in t 1 t 1 perl
Contributors	2022 2021 2020 2019 2018 1 h 4 py 1 cc 1 pm 1 cu 1 scala 1 cuh 1 r jl 1 jl i 1 i proto 1 proto 1 clj 1 cpp 1 java 1 hpp cmake 1 cmake st 1 st gv 1 gv 1 m pyx 1 pyx 1 pl 1 yml cfg 1 cfg 1 groovy pyi 1 pyi 1 yaml g4 1 g4 in 1 in t 1 t 1 perl