pycaddy 0.2.0

A Python toolbox caddy for experiment tracking, parameter sweeping, and automation tasks

pycaddy

A Python toolbox caddy for experiment tracking, parameter sweeping, and automation tasks.

pycaddy helps you organize machine learning experiments, track runs with unique IDs, sweep hyperparameter grids, and manage experiment artifacts — all with minimal overhead and zero bloat.

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Table of Contents

• Installation
• Quick Start
• Key Features
• Core Components
• Documentation
• License

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Installation

pip install pycaddy

Requirements: Python ≥ 3.11

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Quick Start

Experiment Tracking

from pycaddy.project import Project

# Create a project for organizing experiments
project = Project(root="experiments").ensure_folder()

# Start a new experiment run
session = project.session("train", params={"lr": 0.001, "batch_size": 32})
session.start()

# Your experiment code here
model_path = session.path("model.pt")
# save_model(model_path)

# Mark as completed
session.done()

Parameter Sweeping

from pycaddy.sweeper import DictSweep, StrategyName

# Define parameter space
params = {
    'learning_rate': [0.01, 0.001],
    'batch_size': [16, 32, 64]
}

# Generate all combinations
sweep = DictSweep(parameters=params, strategy=StrategyName.PRODUCT)
for config in sweep.generate():
    print(config)
    # {'learning_rate': 0.01, 'batch_size': 16}
    # {'learning_rate': 0.01, 'batch_size': 32}
    # ... etc

Grid Search with Auto-Resume

from pycaddy.project import Project, ExistingRun

project = Project(root="experiments").ensure_folder()

for config in sweep.generate():
    session = project.session(
        "train",
        params=config,
        existing_run_strategy=ExistingRun.RESUME
    )

    if session.is_done():
        print(f"Skipping completed run: {session.uid}")
        continue

    session.start()
    # train_model(config, checkpoint_path=session.path("checkpoint.pt"))
    session.done()

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Key Features

• Project Management: Structured folder organization with automatic metadata tracking
• Session Tracking: Track experiment runs with unique IDs, status monitoring, and file attachments
• Parameter Sweeping: Generate parameter combinations with multiple strategies (product, zip, grid)
• Auto-Resume: Automatically detect and skip completed runs based on parameter hashing
• Concurrent Safe: File-based locking for multi-process experiment tracking
• Lightweight: Minimal dependencies, designed as a focused toolbox
• Flexible Storage: Choose between subfolder or prefix-based file organization

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Core Components

Project & Session

High-level API for organizing experiments into projects and tracking individual runs as sessions.

• Project: Manages a root folder and creates sessions
• Session: Represents one experiment run with status tracking and file management

→ Full Project & Session Guide

Ledger

Low-level JSON-backed registry that tracks all experiment runs in a single metadata.json file.

• Thread/process-safe with file locking
• Deterministic parameter hashing for run deduplication
• Fine-grained control for advanced use cases

→ Full Ledger Guide

Parameter Sweeping

Generate parameter configurations with various strategies:

• PRODUCT: Cartesian product of all parameter values
• ZIP: Zip parameters together element-wise
• Custom strategies for advanced sampling

→ Full Parameter Sweeping Guide

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Documentation

• Project & Session API — High-level experiment management
• Ledger API — Low-level run registry
• Parameter Sweeping — Grid search and parameter generation

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License

MIT License — see LICENSE file for details.

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Links

• Repository: https://github.com/HutoriHunzu/pycaddy
• PyPI: https://pypi.org/project/pycaddy/
• Author: Uri Goldblatt ([e-mail])