Profiling the Python Baseline
Find the slow path worth porting.
Profiling the Python Baseline is a free Mojo Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Mojo Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Start With a Baseline
Before you port anything, measure the Python app as it is today. This baseline is the number every later speedup is compared against. 📊
Why Profile First
Most of a program's time hides in a tiny slice of code. Profiling shows you exactly where, so you port the slow part and skip the rest.
Time the Whole Run
First, time the full task end to end. This wall-clock total is your headline number and the easiest thing to track over time.
import time
start = time.perf_counter()
run_app()
print(time.perf_counter() - start)Profile by Function
Python's cProfile breaks the run down per function, so you can see which calls eat the most time across the whole program.
python -m cProfile -s cumtime app.pyRead the Hot Functions
Sort the report by total time and look at the top rows. The hot path is usually one or two functions doing the real heavy lifting.
Spot the Tight Loop
Inside the hot function, hunt for a loop over many elements doing math. That inner loop is almost always the part worth porting to Mojo.
def compute(data):
total = 0.0
for x in data:
total += x * x
return totalCheck the Input Size
A loop is only worth porting if it runs a lot. Note the data size, since a million iterations matters far more than ten.
Save the Numbers
Write down the baseline time and the hot function's share. Later you will compare against these numbers to prove the port paid off.
Watch for I/O Time
If most time is spent reading files or the network, that is I/O, not compute. Mojo speeds up math, so be sure the bottleneck is CPU work.
Confirm It Is Compute-Bound
A good port target is compute-bound: lots of arithmetic over many elements with little waiting. That is exactly where Mojo shines.
Pick One Target
Choose the single slowest compute hot path to rewrite first. One clear target keeps your capstone focused and easy to measure.
Quick Check
Pick the best first move when accelerating a Python app.
Recap
Measure the Python baseline, profile to find the compute-bound hot loop, and pick one clear target. Now you know exactly what to port. 🎯
Frequently asked questions
Is the “Profiling the Python Baseline” lesson free?
Yes — the full text of “Profiling the Python Baseline” is free to read here on the web, and the Mojo Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Mojo Academy course, upgrade to CoddyKit PRO.
What will I learn in “Profiling the Python Baseline”?
Find the slow path worth porting. You practise Mojo Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Mojo Academy?
No prior experience is required. Mojo Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Profiling the Python Baseline” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Mojo Academy lesson?
Yes. Every Mojo Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Profiling the Python Baseline
- Rewriting the Hot Path in Mojo
- Parallelizing and Tuning the Core
- Shipping the Accelerated Project