Introduction

Pacha is a unified registry for machine learning artifacts—models, datasets, and training recipes—with full lineage tracking, semantic versioning, and cryptographic integrity.

The Problem

Machine learning projects face critical challenges in artifact management:

  1. Model Versioning - How do you track which version of a model is in production?
  2. Dataset Provenance - Where did your training data come from? How was it processed?
  3. Reproducibility - Can you recreate a model from 6 months ago exactly?
  4. Lineage Tracking - Was this model fine-tuned from another? Quantized? Pruned?

The Solution

Pacha provides a content-addressed registry that:

  • Deduplicates artifacts using BLAKE3 hashing
  • Versions models, datasets, and recipes with semantic versioning
  • Documents artifacts with Model Cards and Datasheets
  • Tracks lineage through fine-tuning, distillation, quantization, and merging
  • Verifies integrity with cryptographic hashes

Core Principles

Toyota Way Methodology

Pacha follows Toyota Way principles:

  • Muda (Waste Elimination) - Content-addressed deduplication eliminates duplicate storage
  • Jidoka (Built-in Quality) - Cryptographic integrity verification catches corruption
  • Kaizen (Continuous Improvement) - Incremental lineage tracking enables iterative improvement

Sovereign AI Stack

Pacha integrates with the Pragmatic AI Labs Sovereign AI Stack:

ComponentPurposeFormat
alimentarData loading.ald encrypted
aprenderModel training.apr encrypted
entrenarTraining pipelinesTOML configs
realizarModel servinggRPC/REST
pachaArtifact registrySQLite + CAS

Quick Start

# Initialize registry
pacha init

# Register a model
pacha model register fraud-detector model.apr -v 1.0.0 -d "Fraud detection model"

# Register a dataset
pacha data register transactions data.ald -v 1.0.0 -p "Transaction data for fraud detection"

# Check model stage
pacha model get fraud-detector -v 1.0.0

# Promote to production
pacha model stage fraud-detector -v 1.0.0 -t production

Architecture Overview

~/.pacha/
├── registry.db      # SQLite metadata
└── objects/         # Content-addressed storage
    ├── ab/
    │   └── cdef1234...
    ├── cd/
    │   └── ef5678...
    └── ...

Key Features

Model Registry

  • Semantic versioning (MAJOR.MINOR.PATCH)
  • Model Cards (Mitchell et al., 2019)
  • Lifecycle stages (development → staging → production → archived)
  • Lineage tracking (fine-tuning, distillation, quantization, merging)

Data Registry

  • Dataset versioning
  • Datasheets (Gebru et al., 2021)
  • Provenance tracking (W3C PROV-DM)

Recipe Registry

  • Training recipes in TOML
  • Hyperparameter specifications
  • Environment dependencies
  • Hardware requirements

Experiment Tracking

  • Run tracking with metrics
  • Artifact association
  • Git commit tracking
  • Duration and status logging

Next Steps