calibrax 0.1.0

Calibrax: Unified benchmarking framework for the JAX scientific ML ecosystem

Calibrax

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Early Development — API is unstable and subject to breaking changes. Pin to specific commits if stability is required.

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Calibrax (Calibrate + JAX) is a unified benchmarking and metrics framework for the JAX scientific ML ecosystem. It extracts and consolidates shared benchmarking, profiling, statistical analysis, and evaluation functionality from Datarax, Artifex, and Opifex.

Features

Metrics (110+ registered, 17 domains, 4 tiers)

Calibrax provides a 4-tier metric system covering the full spectrum of ML evaluation:

Tier	Name	Pattern	Examples
0	Pure Functions	fn(predictions, targets) -> scalar	MSE, cosine distance, BLEU
1	Frozen Backbone	update() -> compute() -> reset()	FID, BERTScore, Inception Score
2	Learned	nnx.Module with trainable weights	LPIPS
3	Metric Learning	Differentiable embedding loss	Contrastive, Triplet, ArcFace

Functional domains: regression, classification, calibration, segmentation, distance, divergence, information, ranking, statistical, clustering, fairness, image, text, audio, geometric, graph, manifold

Key capabilities:

• MetricRegistry with axiom-based discovery (list_true_metrics(), list_by_invariance("rotation"))
• Geometric distance hierarchy — Euclidean, Riemannian (SPD, Grassmann, Stiefel), pseudo-Riemannian (ultrahyperbolic), Finsler (Randers)
• Graph metrics — spectral distance, resistance distance, Floyd-Warshall shortest paths
• Composition — MetricCollection, WeightedMetric, MetricSuite, ThresholdMetric
• Wrappers — BootstrapMetric (confidence intervals), ClasswiseWrapper, MetricTracker, MinMaxTracker
• Metric learning losses — contrastive, triplet margin, NTXent, ArcFace, CosFace, ProxyNCA, ProxyAnchor, with hard/semi-hard negative mining

Benchmarking & Profiling

• Timing — Warm-up aware timing with JIT compilation separation
• Resource monitoring — CPU, memory, GPU memory/clock/power tracking
• Energy & carbon — Energy measurement with carbon footprint estimation
• FLOPS & roofline — XLA-level FLOP counting, roofline performance analysis
• Compilation — XLA compilation profiling and tracing
• Complexity — Algorithmic complexity analysis
• Hardware — Automatic hardware detection and capability reporting

Analysis & Infrastructure

• Statistical analysis — Bootstrap confidence intervals, hypothesis testing, effect sizes, outlier detection
• Regression detection — Direction-aware detection with configurable severity levels
• Comparison & ranking — Cross-configuration comparison, Pareto front analysis, aggregate scoring
• Validation — Convergence analysis and accuracy assessment
• Storage — JSON-per-run file backend with baseline management
• Exporters — W&B and MLflow integration, publication-ready LaTeX/HTML/CSV tables and matplotlib plots
• CI integration — Regression gate with git bisect automation
• Monitoring — Production alerting with configurable thresholds
• CLI — calibrax ingest|export|check|baseline|trend|summary|profile

Quick Start

import jax.numpy as jnp
from calibrax.metrics import MetricRegistry, calculate_all
from calibrax.metrics.functional.regression import mse, mae, r_squared

predictions = jnp.array([1.1, 2.3, 2.8, 4.2, 4.7])
targets = jnp.array([1.0, 2.0, 3.0, 4.0, 5.0])

# Individual metrics
print(f"MSE: {mse(predictions, targets):.4f}")
print(f"R²:  {r_squared(predictions, targets):.4f}")

# Batch computation of all registered metrics
results = calculate_all(predictions, targets, metrics=["mse", "mae", "rmse", "r_squared"])

# Registry discovery
registry = MetricRegistry()
true_metrics = registry.list_true_metrics()
rotation_inv = registry.list_by_invariance("rotation")

Installation

# Basic installation
uv pip install calibrax

# With statistical analysis (scipy)
uv pip install "calibrax[stats]"

# With GPU monitoring
uv pip install "calibrax[gpu]"

# With image quality plugins (FID, Inception Score)
uv pip install "calibrax[image]"

# With text quality plugins (BERTScore)
uv pip install "calibrax[text]"

# With publication export (matplotlib)
uv pip install "calibrax[publication]"

Development Setup

The recommended way to set up a development environment is with the included setup.sh script. It auto-detects your platform (Linux CUDA, macOS Intel, Apple Silicon), creates a virtual environment, installs all dependencies, and generates an activation script.

git clone https://github.com/avitai/calibrax.git
cd calibrax

# Standard setup with automatic GPU detection
./setup.sh

# Activate the environment
source ./activate.sh

setup.sh Options

Flag	Description
--cpu-only	Force CPU-only setup, skip GPU/Metal detection
--metal	Enable Metal acceleration on Apple Silicon Macs
--deep-clean	Clear JAX cache, pip cache, pytest cache, and other artifacts
--force	Force reinstallation even if environment exists
--verbose, -v	Show detailed output during setup

# Examples
./setup.sh --cpu-only         # CPU-only development
./setup.sh --metal            # Apple Silicon with Metal
./setup.sh --force --verbose  # Force reinstall with full output
./setup.sh --deep-clean       # Clean everything and start fresh

Manual Setup

If you prefer to set up manually:

git clone https://github.com/avitai/calibrax.git
cd calibrax
uv venv
uv pip install -e ".[dev,test,stats]"
uv run pre-commit install

Architecture

src/calibrax/
├── core/          Data models, protocols, adapters, result container, registry
├── profiling/     Timing, resources, GPU, energy, FLOPS, roofline, compilation,
│                  complexity, hardware, tracing, carbon
├── statistics/    Statistical analyzer, significance testing
├── analysis/      Regression, comparison, ranking, scaling, Pareto, changepoint
├── validation/    Convergence, accuracy, validation framework
├── monitoring/    Alerts, production monitoring
├── storage/       JSON store, baselines
├── exporters/     W&B, MLflow, publication-ready output
├── metrics/
│   ├── functional/   110+ Tier 0 pure functions across 17 domains
│   ├── stateful/     Tier 1–2 base classes (FrozenBackboneMetric, LearnedMetric)
│   ├── learning/     Tier 3 metric learning losses and miners
│   ├── plugins/      Optional-dependency metrics (FID, BERTScore, LPIPS)
│   ├── composition.py   MetricCollection, WeightedMetric, MetricSuite, ThresholdMetric
│   ├── wrappers.py      BootstrapMetric, ClasswiseWrapper, MetricTracker, MinMaxTracker
│   └── _registry.py     MetricRegistry singleton with axiom-based discovery
├── ci/            CI regression gate, bisection engine
└── cli/           Command-line interface

Examples

Runnable examples are in examples/metrics/, available as both Python scripts and Jupyter notebooks:

Example	Level	Topics
01_quickstart.py	Beginner	Individual metrics, calculate_all, registry queries
02_regression_deep_dive.py	Beginner	All 12 regression metrics, outlier sensitivity
03_classification.py	Intermediate	Classification, calibration, segmentation
04_distances.py	Intermediate	Euclidean, hyperbolic, divergences, information theory
05_composition.py	Intermediate	Collections, weighted metrics, quality gates, tracking
06_image_quality.py	Intermediate	PSNR, SSIM, MS-SSIM, BLEU, ROUGE
07_metric_learning.py	Advanced	Contrastive, triplet, NTXent, ArcFace, mining
08_manifold_graph.py	Advanced	SPD, Grassmann, spectral distance, Floyd-Warshall

Development

# Run tests
uv run pytest tests/ -v --cov=calibrax --cov-report=term-missing

# Lint & format
uv run ruff check src/ tests/ --fix
uv run ruff format src/ tests/

# Type check
uv run pyright src/

# All quality checks
uv run pre-commit run --all-files

# Build documentation
uv run mkdocs build

# Convert examples to Jupyter notebooks
uv run python scripts/jupytext_converter.py batch-py-to-nb examples/metrics/

License

MIT