aws / deep-learning-containers
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
Summary
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| Main Code: 9,353 LOC (146 files) = YML (47%) + PY (44%) + YAML (3%) + JS (3%) + TOML (<1%) + C (<1%) Secondary code: Test: 33,243 LOC (494); Generated: 0 LOC (0); Build & Deploy: 579 LOC (17); Other: 1,241 LOC (32); |
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| Duplication: 54% | |||
| File Size: 0% long (>1000 LOC), 79% short (<= 200 LOC) | |||
| Unit Size: 3% long (>100 LOC), 54% short (<= 10 LOC) | |||
| Conditional Complexity: 4% complex (McCabe index > 50), 58% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (11 components) | ||
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2 years old
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1% 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
9 files |
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Latest commit date: 2022-01-28
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18
commits
(30 days)
10
contributors
(30 days) |
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