Source code for pipelinex.extras.decorators.memory_profiler

import logging
from functools import wraps
from importlib.util import find_spec
from typing import Callable

log = logging.getLogger(__name__)

if find_spec("memory_profiler"):
    from memory_profiler import memory_usage
    raise ImportError("Install memory_profiler.")

def _func_full_name(func: Callable):
    return getattr(func, "__qualname__", repr(func))

[docs]def mem_profile(func: Callable) -> Callable: """A function decorator which profiles the memory used when executing the function. The logged memory is collected by using the memory_profiler python module and includes memory used by children processes. The usage is collected by taking memory snapshots every 100ms. This decorator will only work with functions taking at least 0.5s to execute due to a bug in the memory_profiler python module. For more information about the bug, please see Args: func: The function to be profiled. Returns: A wrapped function, which will execute the provided function and log its max memory usage upon completion. """ @wraps(func) def with_memory(*args, **kwargs): log = logging.getLogger(__name__) mem_usage, result = memory_usage( (func, args, kwargs), interval=0.1, timeout=1, max_usage=True, retval=True, include_children=True, ) # memory_profiler < 0.56.0 returns list instead of float mem_usage = mem_usage[0] if isinstance(mem_usage, (list, tuple)) else mem_usage "Running %r consumed %2.2fMiB memory at peak time", _func_full_name(func), mem_usage, ) return result return with_memory