aws / sagemaker-training-toolkit - Conditional Complexity

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 203 units with 2,097 lines of code in units (67.2% of code).

1 very complex units (142 lines of code)
0 complex units (0 lines of code)
5 medium complex units (281 lines of code)
22 simple units (577 lines of code)
175 very simple units (1,097 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
int jsmn_parse() in src/sagemaker_training/c/jsmn.c	142	73	5
static int jsmn_parse_string() in src/sagemaker_training/c/jsmn.c	51	23	5
def _watch() in src/sagemaker_training/intermediate_output.py	23	22	4
static int jsmn_parse_primitive() in src/sagemaker_training/c/jsmn.c	40	13	5
def _create_command() in src/sagemaker_training/mpi.py	78	12	1
def __init__() in src/sagemaker_training/environment.py	89	12	4
def get_modelparallel_exception_classes() in src/sagemaker_training/mpi.py	17	10	0
def _decode() in src/sagemaker_training/mapping.py	11	10	1
def train() in src/sagemaker_training/trainer.py	56	10	0
def _write_spmatrix_to_sparse_tensor() in src/sagemaker_training/recordio.py	28	10	3
def run() in src/sagemaker_training/mpi.py	47	9	3
def _write_numpy_to_dense_tensor() in src/sagemaker_training/recordio.py	21	9	3
def run() in src/sagemaker_training/smdataparallel.py	44	8	3
def to_cmd_args() in src/sagemaker_training/mapping.py	17	8	1
def get_distribution_instance_groups_from_resource_config() in src/sagemaker_training/environment.py	26	8	1
def download_and_extract() in src/sagemaker_training/files.py	21	8	2
def get_debugger_exception_classes() in src/sagemaker_training/process.py	13	7	0
def to_env_vars() in src/sagemaker_training/mapping.py	17	7	1
def num_neurons() in src/sagemaker_training/environment.py	31	7	1
def _create_command() in src/sagemaker_training/torch_distributed.py	36	7	1
def install() in src/sagemaker_training/entry_point.py	10	6	3
def __str__() in src/sagemaker_training/errors.py	26	6	1
def _modelparallel_environment_command() in src/sagemaker_training/mpi.py	18	6	1
def get_tensorflow_exception_classes() in src/sagemaker_training/process.py	11	6	0
def check_error() in src/sagemaker_training/process.py	60	6	6
int gethostname() in src/sagemaker_training/c/gethostname.c	39	6	2
def get_dataparallel_exception_classes() in src/sagemaker_training/smdataparallel.py	14	6	0
def _wait_for_workers() in src/sagemaker_training/smdataparallel.py	14	6	1
def _orted_process() in src/sagemaker_training/mpi.py	7	5	0
def _wait_for_workers() in src/sagemaker_training/mpi.py	8	5	1
def process_error_classes() in src/sagemaker_training/process.py	6	5	1
def _upload_to_s3() in src/sagemaker_training/intermediate_output.py	11	5	4
def _setup() in src/sagemaker_training/torch_distributed.py	9	5	1
def _wait_for_status_file() in src/sagemaker_training/mpi.py	12	4	2
def _create_command() in src/sagemaker_training/process.py	17	4	1
static int jsoneq() in src/sagemaker_training/c/gethostname.c	9	4	3
def _copy_file() in src/sagemaker_training/intermediate_output.py	10	4	4
def num_gpus() in src/sagemaker_training/environment.py	8	4	0
def get_distribution_hosts() in src/sagemaker_training/environment.py	10	4	1
def get_instance_groups() in src/sagemaker_training/environment.py	8	4	1
def _resolve_type() in src/sagemaker_training/recordio.py	8	4	1
def _write_feature_tensor() in src/sagemaker_training/recordio.py	7	4	3
def _write_label_tensor() in src/sagemaker_training/recordio.py	7	4	3
def _write_keys_tensor() in src/sagemaker_training/recordio.py	7	4	3
def _write_shape() in src/sagemaker_training/recordio.py	7	4	3
def _read_recordio() in src/sagemaker_training/recordio.py	12	4	1
def get() in src/sagemaker_training/_entry_point_type.py	9	4	2
def _setup() in src/sagemaker_training/pytorch_xla.py	19	4	1
def _create_command() in src/sagemaker_training/smdataparallel.py	27	3	1
def properties() in src/sagemaker_training/mapping.py	3	3	1