speedopy 0.3.0

Zero-dependency performance measurement decorators for Python

⚡ speedopy

Zero-dependency performance measurement decorators + CLI profiler for Python.

Just import a decorator, slap it on your function, and instantly see beautiful, organized metrics in the terminal. No config, no setup, no external packages.

Install

pip install speedopy

🚀 CLI Runner — Profile Any Script Instantly

Run any Python script and get a full performance report — no decorators needed:

python -m speedopy your_script.py

This collects all 9 metrics at once and prints a single organized, color-coded report:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  ⚡  SPEEDOPY — Performance Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

┌──────────────────── ⏱  TIMING ───────────────────────┐
│ Real (wall-clock) time                       69.15 ms │
│ CPU time                                     62.50 ms │
│ Thread time                                  62.50 ms │
│ I/O wait (estimated)                          6.65 ms │
└────────────────────────────────────────────────────────┘

┌──────────────────── 🧠  MEMORY ──────────────────────┐
│ Current memory                              87.96 KB  │
│ Peak memory                                274.82 KB  │
└────────────────────────────────────────────────────────┘

┌─────────────────── ♻  GC & OBJECTS ──────────────────┐
│ GC objects collected                              11  │
│ GC runs during execution                          2  │
│ Net new objects                                 +81  │
└────────────────────────────────────────────────────────┘

🎯 Decorators

Each decorator shows its own neat box in the terminal:

Decorator	What It Measures
cpu_time	CPU processing time (excludes I/O)
real_time	Wall-clock time
memory_usage	Current + peak memory (auto-formats B/KB/MB)
thread_time	CPU time for the current thread only
call_count	Number of times a function has been called
function_profile	Full cProfile report (top 10 calls)
io_time	Real time, CPU time, and I/O wait
gc_stats	Garbage collections triggered
object_count	Net new Python objects created

Usage

from speedopy import cpu_time, memory_usage, io_time

@cpu_time
@memory_usage
@io_time
def process_data():
    data = [i ** 2 for i in range(1_000_000)]
    return sum(data)

process_data()

Output:

┌─────────── 💾  I/O TIME › process_data ───────────┐
│ Real time                              182.35 ms   │
├────────────────────────────────────────────────────┤
│ CPU time                               171.88 ms   │
├────────────────────────────────────────────────────┤
│ I/O wait                                10.47 ms   │
└────────────────────────────────────────────────────┘

┌──────────── 🧠  MEMORY › process_data ────────────┐
│ Current                                    472 B   │
├────────────────────────────────────────────────────┤
│ Peak                                      8.06 MB  │
└────────────────────────────────────────────────────┘

┌────────── ⏱  CPU TIME › process_data ─────────────┐
│ CPU time                               183.29 ms   │
└────────────────────────────────────────────────────┘

Stack Multiple Decorators

from speedopy import cpu_time, memory_usage, call_count

@call_count
@cpu_time
@memory_usage
def my_func():
    return sum(range(500_000))

my_func()
my_func()

Requirements

• Python 3.8+
• No external dependencies

License

MIT