aws / sagemaker-tensorflow-training-toolkit

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Learn more...

Conditional Complexity Overall

There are 84 units with 1,135 lines of code in units (61.0% of code).

0 very complex units (0 lines of code)
1 complex units (179 lines of code)
1 medium complex units (118 lines of code)
8 simple units (210 lines of code)
74 very simple units (628 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 50 most complex units

Unit	# lines	McCabe index	# params
def main() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	179	37	0
def cnn_model_function() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	118	12	4
def apply_gradients() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	19	10	4
def train() in src/sagemaker_tensorflow_container/training.py	44	10	2
def _build_tf_config_for_ps() in src/sagemaker_tensorflow_container/training.py	26	9	3
def _log_model_missing_warning() in src/sagemaker_tensorflow_container/training.py	32	9	1
def residual2d() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	18	8	6
def inference_resnet_v1_impl() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	14	8	4
def generate_report() in benchmarks/horovod-resnet/execute_horovod_training.py	40	7	0
def compute_gradients() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	17	6	5
def pad2d() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	16	5	4
def rank0log() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	6	4	3
def _conv2d() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	14	4	5
def resnet_bottleneck_v1() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	21	4	6
def add_bool_argument() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	18	4	5
def _s3_region() in src/sagemaker_tensorflow_container/s3_utils.py	9	4	2
def activate() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	3	3	3
def spatial_average2d() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	9	3	2
def flatten2d() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	9	3	2
def crop_and_resize_image() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	29	3	6
def stage() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	9	3	1
def after_run() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	14	3	3
def get_with_default() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	2	3	3
def sort_and_load_ckpts() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	16	3	1
def create_hyperparameters() in benchmarks/tf_benchmarks/execute_tensorflow_training.py	9	3	2
def _wait_until_master_is_down() in src/sagemaker_tensorflow_container/training.py	11	3	1
def _model_dir_with_training_job() in src/sagemaker_tensorflow_container/training.py	5	3	2
def main() in src/sagemaker_tensorflow_container/training.py	11	3	0
def _retrieve_instance_region() in src/sagemaker_tensorflow_container/deep_learning_container.py	27	3	0
def query_bucket() in src/sagemaker_tensorflow_container/deep_learning_container.py	11	3	0
def main() in src/sagemaker_tensorflow_container/deep_learning_container.py	4	3	0
def _retrieve_instance_region() in docker/build_artifacts/deep_learning_container.py	13	3	0
def query_bucket() in docker/build_artifacts/deep_learning_container.py	10	3	0
def main() in docker/build_artifacts/deep_learning_container.py	4	3	0
def conv2d_linear_last_bn() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	21	2	4
def batch_norm() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	7	2	3
def get_model_func() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	6	2	1
def deserialize_image_record() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	20	2	1
def after_create_session() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	4	2	3
def __init__() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	4	2	5
def get_num_records() in benchmarks/horovod-resnet/train_imagenet_resnet_hvd.py	8	2	1
def main() in benchmarks/tf_benchmarks/execute_tensorflow_training.py	21	2	2
def _build_tf_config_for_mwms() in src/sagemaker_tensorflow_container/training.py	8	2	2
def _validate_instance_id() in src/sagemaker_tensorflow_container/deep_learning_container.py	6	2	1
def _retrieve_instance_id() in src/sagemaker_tensorflow_container/deep_learning_container.py	7	2	0
def requests_helper() in src/sagemaker_tensorflow_container/deep_learning_container.py	7	2	2
def _validate_instance_id() in docker/build_artifacts/deep_learning_container.py	7	2	1
def _retrieve_instance_id() in docker/build_artifacts/deep_learning_container.py	7	2	0
def requests_helper() in docker/build_artifacts/deep_learning_container.py	7	2	2
def _parse_args() in scripts/build_all.py	11	1	0