FastStatisticalModels4Python

Fast Statistical Models for Python

This repository contains the PyCon US 2026 deck and reproducibility artifacts for Breaking the Speed Limit: Python 3.14, Numba, and JAX in Statistical Computing.

Public pages

The GitHub Pages version is the easiest way to view the talk materials:

Material	Open online	Source
Slides	View slides	slides/index.html
Poster	View poster	poster/index.html
Slides print mode	Open print/PDF view	slides/README.md

Core thesis:

• simulation is how statisticians write tests;
• speed only counts after the statistic is preserved;
• Python remains the scientific interface;
• Numba, JAX, Python 3.14, threads, processes, and A100 solve different bottlenecks.

Local preview

Use the project conda environment by default:

conda activate py312
python -m http.server 8000

Open the local pages:

• Slides: http://localhost:8000/slides/
• Poster: http://localhost:8000/poster/
• Slides print/PDF mode: http://localhost:8000/slides/index.html?print-pdf

The canonical deck is slides/index.html. Current structure is 36 slides total: 29 main-path slides and 7 backup slides. Slides 9 and 17 are video method-transition slides.

Results layout

• MacBook/local trust tier: experiments/results/macbook_air_long/latest/
• Linux server CPU scale tier: experiments/results/linux_server_cpu/long_safe_20260503_190133/
• Historical A100 long-safe tier: experiments/results/linux_server_a100/long_safe_20260503_190133/
• A100 permutation break-even notes: experiments/results/linux_server_a100/permutation_break_even/
• Slide-ready figures: experiments/results/presentation_figures/

Current result status

• MacBook k-means and permutation correctness/calibration results are the trust tier used in the main talk.
• Linux server CPU and k-means A100 results are scale evidence used in the main talk.
• The old A100 permutation matched slice is historical negative evidence: n=5,000, p=50,000, batch_R=512, before streamed reduction and the broader shape sweep.
• The current A100 permutation break-even narrative uses a100_streamed_reduction, larger batch_R, and a measured shape sweep. Speedup is scoped to the matched CPU matrix baseline divided by A100 streamed full end-to-end. Compile is excluded, transfer is included, and kernel-only rows are not used for CPU/A100 decisions.
• A100 permutation correctness wording is intentionally scoped: new accepted rows use pass_exact or pass_gpu_tolerance, while ambiguous rows use manual_check; historical rows marked check appear only in older follow-up notes.

Regenerate figures

MacBook figures:

python -m experiments.visualization.plot_macbook_air_evidence \
  --results-dir experiments/results/macbook_air_long/latest

Server CPU and k-means/A100 presentation figures:

python -m experiments.visualization.plot_server_talk_evidence

A100 permutation break-even figures, when the experiment CSVs are available:

python -m experiments.server.a100_permutation_break_even plot \
  --out-dir experiments/results/linux_server_a100/permutation_break_even \
  --presentation-dir experiments/results/presentation_figures

Deck QA

Review screenshots are local generated artifacts under slides/review/screenshots/. The QA pass checks browser mode, video poster fallbacks, broken media URLs, clipping, overlap, console errors, and A100 pending/experimental wording.

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