awslabs / genomics-tertiary-analysis-and-machine-learning-using-amazon-sagemaker
The Genomics Tertiary Analysis and Machine Learning Using Amazon SageMaker solution creates a scalable environment in AWS to develop machine learning models using genomics data, generate predictions, and evaluate model performance.
Summary
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| Main Code: 1,661 LOC (11 files) = YML (65%) + PY (34%) Secondary code: Test: 402 LOC (6); Generated: 0 LOC (0); Build & Deploy: 165 LOC (4); Other: 726 LOC (6); |
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| Duplication: 16% | |||
| File Size: 0% long (>1000 LOC), 20% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 65% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 79% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (7 components) | ||
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1 year, 6 months old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
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Features of interest:
TODOs
1 file |
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Latest commit date: 2021-09-27
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(30 days)
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