stepit 0.5.0

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stepit

Yet another python decorator for persistent caching. stepit caches function results based on their source code and arguments, automatically invalidating the cache when either changes.

Key Features

• Smart Cache Invalidation: Cache is updated when function source code changes
• Recursive Awareness: Tracks changes in nested function calls
• Informative Logging: Color-coded progress and status messages
• Zero Configuration: Works out of the box with sensible defaults
• Customizable: Supports custom cache keys, directories, and serialization

Installation

pip install stepit

Quick Start

from stepit import stepit

@stepit
def expensive_calculation(x):
    # ... complex computation ...
    return result

# First call: computes and caches
result = expensive_calculation(42)
# Second call: uses cache
result = expensive_calculation(42)

Advanced Usage

Recursive Functions with Automatic Cache Invalidation

@stepit
def fibonacci(n):
    if n <= 1: return n
    return fibonacci(n-1) + fibonacci(n-2)

# First run: calculates and caches all intermediate results
fibonacci(100)

# Change the function
@stepit
def fibonacci(n):  # Different implementation
    if n <= 0: return 0
    if n == 1: return 1
    return fibonacci(n-1) + fibonacci(n-2)

# Cache is automatically invalidated due to source code change
fibonacci(100)

Custom Cache Configuration

@stepit(
    key="my_special_function",
    cache_dir="custom_cache"
)
def process_data(x):
    return x * 2

Informative Logging

import logging
logging.getLogger("stepit").setLevel(logging.DEBUG)

@stepit
def add(a, b):
    return a + b

add(1, 2)  # Outputs:
# ⏩ stepit 'add': Starting execution of `__main__.add()`
# ✅ stepit 'add': Successfully completed and cached [exec time 0 seconds, size 37 bytes]
# ♻️  stepit 'add': is up-to-date. Using cached result

Use Cases

• Data processing pipelines
• Scientific computations
• Machine learning model training
• Any expensive computations that may be repeated

How It Works

1. Creates a unique key based on:
   • Function's source code
   • Arguments
   • Any nested @stepit-decorated function calls
2. Checks if result exists in cache
3. Executes function if:
   • No cached result exists
   • Source code has changed
   • Arguments are different
4. Stores result in cache for future use

License

MIT License

Contributing

Contributions welcome! Please check our GitHub repository for guidelines.