aws / amazon-sagemaker-examples
Example ???? Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using ???? Amazon SageMaker.
Summary
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| Main Code: 188,951 LOC (1501 files) = PY (93%) + YAML (2%) + YML (1%) + ORG (<1%) + JSONL (<1%) + HTML (<1%) + R (<1%) + CFG (<1%) + RMD (<1%) + JAVA (<1%) + C (<1%) + DOCKERFILE (<1%) + JQ (<1%) + JS (<1%) Secondary code: Test: 2,298 LOC (102); Generated: 0 LOC (0); Build & Deploy: 3,473 LOC (101); Other: 292,140 LOC (675); |
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| File Size: 17% long (>1000 LOC), 45% short (<= 200 LOC) | |||
| Unit Size: 7% long (>100 LOC), 45% short (<= 10 LOC) | |||
| Conditional Complexity: 5% complex (McCabe index > 50), 61% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (41 components) | ||
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4 years, 3 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
120 files |
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Latest commit date: 2022-01-28
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32
commits
(30 days)
10
contributors
(30 days) |
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