aws / sagemaker-huggingface-inference-toolkit
Unit Size

The distribution of size of units (measured in lines of code).

Intro
  • Unit size measurements show the distribution of size of units of code (methods, functions...).
  • Units are classified in four categories based on their size (lines of code): 1-20 (small units), 20-50 (medium size units), 51-100 (long units), 101+ (very long units).
  • You should aim at keeping units small (< 20 lines). Long units may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.
Learn more...
Unit Size Overall
  • There are 31 units with 325 lines of code in units (55.7% of code).
    • 0 very long units (0 lines of code)
    • 0 long units (0 lines of code)
    • 2 medium size units (63 lines of code)
    • 12 small units (164 lines of code)
    • 17 very small units (98 lines of code)
0% | 0% | 19% | 50% | 30%
Legend:
101+
51-100
21-50
11-20
1-10
Unit Size per Extension
101+
51-100
21-50
11-20
1-10
py0% | 0% | 19% | 50% | 30%
Unit Size per Logical Component
primary logical decomposition
101+
51-100
21-50
11-20
1-10
src/sagemaker_huggingface_inference_toolkit0% | 0% | 19% | 50% | 30%
Alternative Visuals
Longest Units
Top 20 longest units
Unit# linesMcCabe index# params
def start_model_server()
in src/sagemaker_huggingface_inference_toolkit/mms_model_server.py
 39 6 1
def validate_and_initialize_user_module()
in src/sagemaker_huggingface_inference_toolkit/handler_service.py
 24 11 1
def handle()
in src/sagemaker_huggingface_inference_toolkit/handler_service.py
 20 7 3
def _adapt_to_mms_format()
in src/sagemaker_huggingface_inference_toolkit/mms_model_server.py
 20 2 2
def wrap_conversation_pipeline()
in src/sagemaker_huggingface_inference_toolkit/transformers_utils.py
 16 1 1
def infer_task_from_model_architecture()
in src/sagemaker_huggingface_inference_toolkit/transformers_utils.py
 16 4 2
def transform_fn()
in src/sagemaker_huggingface_inference_toolkit/handler_service.py
 14 1 5
def decode_csv()
in src/sagemaker_huggingface_inference_toolkit/decoder_encoder.py
 12 6 1
def initialize()
in src/sagemaker_huggingface_inference_toolkit/handler_service.py
 11 1 2
def load()
in src/sagemaker_huggingface_inference_toolkit/handler_service.py
 11 3 2
def preprocess()
in src/sagemaker_huggingface_inference_toolkit/handler_service.py
 11 5 3
def default()
in src/sagemaker_huggingface_inference_toolkit/decoder_encoder.py
 11 5 2
def _is_gpu_available()
in src/sagemaker_huggingface_inference_toolkit/transformers_utils.py
 11 4 0
def _get_framework()
in src/sagemaker_huggingface_inference_toolkit/transformers_utils.py
 11 3 0
def __init__()
in src/sagemaker_huggingface_inference_toolkit/handler_service.py
 10 1 1
def _build_storage_path()
in src/sagemaker_huggingface_inference_toolkit/transformers_utils.py
 10 6 3
def infer_task_from_hub()
in src/sagemaker_huggingface_inference_toolkit/transformers_utils.py
 10 2 3
def encode_json()
in src/sagemaker_huggingface_inference_toolkit/decoder_encoder.py
 9 1 1
def encode_csv()
in src/sagemaker_huggingface_inference_toolkit/decoder_encoder.py
 9 2 1
def get_pipeline()
in src/sagemaker_huggingface_inference_toolkit/transformers_utils.py
 9 3 4