README.md

# Python Performance Lab: Sharpening Your Instincts A PyCon US 2026 hands-on tutorial. You optimize intentionally slow Python code across three rounds plus a team challenge, measuring every change with [CodSpeed](https://codspeed.io). ## Rounds | Round | Topic | Skills | | -------------------------- | -------------------- | ------------------------------------- | | [1](rounds/1_histogram/) | Byte-pair histogram | Data representation, vectorization | | [2](rounds/2_corruption/) | Corruption scanner | Vectorization, parallelism | | [3](rounds/3_dna/) (final) | DNA sequence matcher | Everything above, as a team challenge | Each round ships an intentionally slow `baseline.py` (a read-only reference), a `solution.py` you edit, deterministic data generators, parametrized correctness tests, and benchmarks that run baseline and solution side-by-side. ## Setup You need [`uv`](https://docs.astral.sh/uv/). Python 3.15t will be downloaded directly. The order below matters: forking, logging in, and doing the first run on `main` register you on the live leaderboard, so every later push to your branch shows up as a side-by-side comparison against your own baseline. ```bash # 1. Fork github.com/CodSpeedHQ/pyconus-2026-tutorial, then clone your fork. git clone https://github.com//pyconus-2026-tutorial && cd pyconus-2026-tutorial # or better, use the GitHub CLI: gh repo fork CodSpeedHQ/pyconus-2026-tutorial --clone=true && cd pyconus-2026-tutorial # 2. Install deps + generate the datasets (~650 MB total). uv sync uv run scripts/setup.py # 3. Install the CodSpeed CLI and log in. curl -L https://codspeed.io/install.sh | sh codspeed auth login # 4. Make a first CodSpeed local run and access the performance report codspeed run --mode walltime -- uv run pytest --codspeed # 5. Branch off and create a pr on the main repo. Every push to this branch re-runs and re-ranks you. git checkout -b # Make a small change like adding your name in the README.md for example, and commit: echo "This is 's PR" >> README.md git add README.md git commit -m "Add to the README" # Push and open a PR against the main repo: gh pr create --title "[username] performance improvements" --base main --repo CodSpeedHQ/pyconus-2026-tutorial ``` Generate smaller datasets on lower-spec machines: ```bash uv run scripts/setup.py --round1-mb 10 --round2-mb 32 --round3-mb 100 ``` ## Working on a round Every round directory ships its own `README.md`. The commands are the same shape every time, illustrated here for Round 1: ```bash # Correctness tests against the small fixture. uv run pytest rounds/1_histogram/ # Walltime benchmark against the full dataset. uv run pytest --codspeed rounds/1_histogram/ # Same, run through the CodSpeed CLI with the walltime mode codspeed run --mode walltime -- uv run pytest --codspeed rounds/1_histogram/ ``` Edit `solution.py` to optimize. Leave `baseline.py` alone so the side-by-side comparison stays meaningful. Every test and benchmark is parametrized over both implementations, so the output always shows `[baseline]` versus `[solution]`. ## Layout ``` rounds/ 1_histogram/ # baseline.py, solution.py, gen_data.py, tests. 2_corruption/ 3_dna/ scripts/ setup.py # one-shot data generation across every round. ``` Each round's `data/` directory is generated locally and gitignored. This is Yuxiao's PR