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Caching API Reference

Cache expensive computations in memory or on disk for better performance.

Cache Decorators

mo.cache

In-memory cache based on function arguments.

import marimo as mo

@mo.cache
def expensive_computation(x, y):
    # Result cached based on (x, y)
    return complex_calculation(x, y)

# First call computes
result1 = expensive_computation(1, 2)

# Second call with same args returns cached result
result2 = expensive_computation(1, 2)  # Instant

# Different args compute fresh
result3 = expensive_computation(3, 4)

mo.persistent_cache

Disk-based cache that persists across sessions.

@mo.persistent_cache
def very_expensive(data_path):
    # Result saved to disk
    return load_and_process(data_path)

# First run: computes and saves to disk
result = very_expensive("data.csv")

# Subsequent runs (even after restart): loads from disk
result = very_expensive("data.csv")  # Instant

Options:

@mo.persistent_cache(
    save_path="./cache",        # Custom cache directory
    method="pickle"             # "pickle" (default) or "json"
)
def cached_function(x):
    return process(x)

mo.lru_cache

Bounded memory cache (LRU eviction).

@mo.lru_cache(maxsize=128)
def bounded_cache(x):
    return compute(x)

# Keeps most recent 128 unique argument combinations
# Evicts least recently used when full

Cache Key Construction

Cache keys are built from:

1. Function arguments: Primitives, UI elements, arrays, pickleable objects
2. Closed-over variables: Variables referenced from outer scope
3. Source code: Used when variables can't be hashed/pickled

threshold = 0.5  # Closed-over variable

@mo.cache
def filter_data(data):
    # Cache key includes: data + threshold + source code
    return data[data > threshold]

# Changing threshold invalidates cache
threshold = 0.7
filter_data(data)  # Recomputes

Parameters

Common Parameters

@mo.cache(
    pin_modules=False  # If True, invalidate when module versions change
)
def func(x):
    return process(x)

persistent_cache Parameters

@mo.persistent_cache(
    save_path="./my_cache",  # Default: __marimo__/cache/
    method="pickle",          # "pickle" or "json"
    pin_modules=False,
    name="my_cache"           # Identifier for context manager usage
)
def func(x):
    return process(x)

Context Manager Usage

Use caching as context manager for blocks of code.

with mo.persistent_cache(name="data_processing"):
    # Everything in this block is cached
    df = load_large_file("data.parquet")
    processed = transform(df)
    result = aggregate(processed)

# result is cached

Async Support

All cache decorators support async functions.

@mo.cache
async def fetch_data(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.json()

# Usage
data = await fetch_data("https://api.example.com/data")

Cache Behavior

What Gets Cached

• Return values only
• Side effects are NOT cached (skipped on cache hit)

@mo.cache
def with_side_effect(x):
    print("Computing...")  # Only prints on cache miss
    return x * 2

with_side_effect(5)  # Prints "Computing...", returns 10
with_side_effect(5)  # No print, returns 10 (cached)

Cache Persistence

• mo.cache: Cleared when notebook restarts
• mo.persistent_cache: Persists across sessions (saved to disk)
• mo.lru_cache: Cleared when notebook restarts, bounded size

Cache Invalidation

Caches invalidate when:

• Arguments change
• Closed-over variables change
• Source code changes (if variables can't be hashed)
• pin_modules=True and module versions change

Best Practices

Isolate Cached Functions

Put cached functions in their own cells to prevent invalidation from unrelated changes.

# Cell 1: Cached function (isolated)
@mo.cache
def process_data(df):
    return expensive_transform(df)

# Cell 2: Use the function
result = process_data(my_df)

Use Closures for Unhashable Objects

# Close over unhashable objects instead of passing as arguments
model = load_model()  # Unhashable

@mo.cache
def predict(input_data):
    # model is closed over, not an argument
    return model.predict(input_data)

Return Serializable Values

For mo.persistent_cache, return values must be pickleable:

@mo.persistent_cache
def good_cache(x):
    return {"result": compute(x)}  # Dict is pickleable

@mo.persistent_cache
def bad_cache(x):
    return lambda: x  # Lambda is NOT pickleable - will fail

Cache at the Right Granularity

# TOO GRANULAR - many small caches
@mo.cache
def step1(x): return x + 1

@mo.cache
def step2(x): return x * 2

# BETTER - cache the whole pipeline
@mo.cache
def pipeline(x):
    result = x + 1
    result = result * 2
    return result

Clearing Caches

# Clear specific function's cache
expensive_computation.cache_clear()

# For persistent cache, delete the cache directory
import shutil
shutil.rmtree("__marimo__/cache")

Debugging Cache

@mo.cache
def my_func(x):
    print(f"Cache miss for x={x}")
    return x * 2

# Monitor cache hits/misses via print output