aws-samples / sagemaker-cv-preprocessing-training-performance
SageMaker training implementation for computer vision to offload JPEG decoding and augmentations on GPUs using NVIDIA DALI ??? allowing you to compare and reduce training time by addressing CPU bottlenecks caused by increasing data pre-processing load. Performance bottlenecks identified with SageMaker Debugger.
Summary
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| Main Code: 384 LOC (3 files) = PY (100%) Secondary code: Test: 0 LOC (0); Generated: 0 LOC (0); Build & Deploy: 0 LOC (0); Other: 214 LOC (4); |
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| Duplication: 5% | |||
| File Size: 0% long (>1000 LOC), 24% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 0% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 47% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (2 components) | ||
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7 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) | |||
Latest commit date: 2021-07-13
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