tidyrun 0.0.1

TidyRun

TidyRun

A tool to orchestrate the compute and storage of Python DAGs

Features

Serialization Framework

TidyRun includes a comprehensive serialization system for storing and retrieving Python objects with smart format selection:

• Type-Aware Encoding: Automatically selects the best format (folder, Parquet, HDF5, JSON, or pickle) based on value type
• Lazy Evaluation: Directories deserialize into LazyDict objects that load values on-demand
• Recursive Concatenation: Aggregate DataFrames across nested structures with LazyDict.concat()
• Metadata Sidecars: Each output is tracked with .tidyrun metadata files for format versioning
• Extensible Pipeline: Customize encoders or add support for custom types
• Intelligent Fallback: Parquet → HDF5 → JSON → Pickle chain ensures robust serialization

Quick Example:

from tidyrun.serialize import serialize, deserialize

# Save nested data with smart format selection
serialize({
    "metrics": pd.DataFrame(...),
    "config": {"lr": 0.001},
}, "./results/exp_001")

# Load with lazy evaluation
results = deserialize("./results/exp_001")
df = results["metrics"]  # Loads on access, cached

# Aggregate across nested structures
combined = results.concat(names=["run_id"])

Learn More:

• Quick Start — Local docs workflow and publishing notes
• Serialization Guide — Complete API reference, quick reference, and examples
• Design Decisions — Architecture rationale and trade-offs