eprofiler 0.0.9

A lightweight tool to monitor execution of functions.

eprofiler

A lightweight, zero-dependency toolkit to monitor execution of functions or code blocks.

eprofiler provides decorators and context managers to observe execution time, cpu time, peak memory and arguments used for a function.

for a function or code block you can monitor and log;

• execution time
• peak memory usage
• CPU time
• parameters passed to a function

Installation

pip install eprofiler

Core Tools

• @audit: Execution logging with SUCCESS/FAIL status and error capturing.
• @timeit: Execution timing (microsecond precision).
• @memit: Simple peak memory tracking using tracemalloc.
• @profile / @profile_cpu: Combined time, memory, and CPU profiling.
• Timer: Context manager and/or decorator for granular blocks.

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Usage

1. Function Auditing (@audit)

Good for monitoring what parameters are passed to a function/method. where you need to know if a function finished, how long it took, and why it failed and with which parameters.

⚠ Warning:
if parameters passed to function are not printable like str, int or a python object without __str__ or __repr__ that can be used in fstrings its better to use callback to handle them in logging

from eprofiler import audit

@audit(label="PAYMENT_GATEWAY", include_args=True)
def process_payment(user_id, amount):
    # Logic here...
    return True

process_payment(123, 50.0)

Output (Standard Logging):

INFO: {'timestamp': '2026-03-02T10:00:00.123', 'function': 'process_payment', 'label': 'PAYMENT_GATEWAY', 'args': (123, 50.0), 'status': 'SUCCESS', 'elapsed_seconds': '0.045200'}

2. Basic Timing (@timeit)

For quick performance checks during development. By default, results are printed to the console.

from eprofiler import timeit

@timeit(label="Computation")
def my_func():
    return sum(i**2 for i in range(100000))

my_func()

Output:

{'label': 'Computation', 'function': 'my_func', 'duration': 0.008421}

3. Comprehensive Profiling (@profile)

Track time and memory (current and peak) simultaneously.

from eprofiler import profile

@profile(label="Heavy Task")
def memory_intensive():
    return [x for x in range(1000000)]

memory_intensive()

Output:

{'label': 'Heavy Task', 'function': 'memory_intensive', 'duration': 0.041200, 'peak': 324502, 'current': 1204}

4. Advanced: Callbacks & Custom Capture

Instead of printing to the console, you can capture results programmatically.

from eprofiler import audit

def my_db_callback(payload):
    # Send payload to Datadog, Slack, or a Database
    print(f"Captured: {payload['status']} in {payload['elapsed_seconds']}s")

@audit(callback=my_db_callback)
def sync_data():
    pass

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Links

• PyPI: https://pypi.org/project/eprofiler/
• GitHub: https://github.com/eyukselen/eprofiler
• Docs: https://eprofiler.readthedocs.io

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