Getting Started

Getting Started

Install

git clone https://github.com/JonPark0/contilab && cd contilab
python3 -m venv .venv && .venv/bin/pip install -e '.[dev]'

Python ≥ 3.11 and PyTorch 2.x are required. A GPU is optional: the cpu profile is designed to finish on a 4-core machine, the gpu profile scales the same experiments up.

First run: every method on synthetic data

.venv/bin/contilab run configs/experiments/smoke_all.yaml
.venv/bin/contilab report runs/smoke_all

This runs 11 methods × 3 synthetic streams (class-IL, task-IL, text) × 2 seeds with the full metric tier and writes:

• runs/smoke_all/results.csv — one row per run, one column per scalar metric
• runs/smoke_all/report.md and report.html — tables, statistics and figures

A real benchmark

# Split MNIST, class-incremental, all methods, 3 seeds
.venv/bin/contilab run configs/experiments/cpu_main.yaml "matrix.preset=[split_mnist_class]"
.venv/bin/contilab report runs/cpu_main

Datasets download once into $CONTILAB_CACHE (default ~/.cache/contilab). Use contilab data prepare <benchmark> to fetch them ahead of time.

Reading the results

• ACC — average accuracy over all tasks after the last task.
• BWT / forgetting — how much earlier tasks lost (negative BWT = forgetting).
• min-ACC / stability gap — the worst dip of an old task during later training (continual evaluation).
• recency bias — in class-IL, how often old-task samples are predicted as the newest task's classes.

Every metric is defined in Metrics. Runs marked diverged (loss became NaN or exploded) are kept and flagged in the tables as (diverged k/n).

Next steps

• Change scale, model or hyperparameters: Configuration
• Compare merging and distillation: Consolidation
• Add your own method or metric: Extending