cache.rst

Caching

Basics

Use .Computer.cache to decorate another function, func, that will be added as the computation/callable in a task. Caching is useful when .get is called multiple times on the same Computer, or across processes, invoking a slow func each time.

# Configure the directory for cache file storage
c = Computer(cache_path=Path("/some/directory"))

@c.cache
def myfunction(*args, **kwargs):
    # Expensive operations, e.g. load large files;
    # invoke external programs
    return data

c.add("myvar", (myfunction,))

# Data is cached in /some/directory/myfunction-*.pkl
c.get("myvar")

# Cached value is loaded and returned
c.get("myvar")

A cache key is computed from:

1. the name of func.
2. the arguments to func, and
3. the compiled bytecode of func (see hash_code).

If a file exists in cache_path with a matching key, it is loaded and returned instead of calling func.

If no matching file exists (a “cache miss”) or the cache_skip configuration option is True, func is executed and its return value is cached in the cache directory, cache_path (see Configuration → Caching <config-cache>). A cache miss will occur if any part of the key changes; that is, if:

1. the function is renamed in the source code,
2. the function is called with different arguments, or
3. the function source code is modified.

Cache data loaded from files

Consider a function that loads a very large file, or performs some slow processing on its contents:

from pathlib import Path

import pandas as pd
from genno import Quantity

@c.cache
def slow_data_load(path, _extra_cache_key=None):
    # Load data in some way
    result = pd.read_xml(path, ...)
    # … further processing …
    return Quantity(result)

We want to cache the result of slow_data_load, but have the cache refreshed when the file contents change. We do this using the _extra_cache_key argument to the function. This argument is not used in the function, but does affect the value of the cache key.

When calling the function, pass some value that indicates whether the contents of path have changed. One possibility is the modification time, via .Path.stat:

def load_cached_1(path):
    return slow_data_load(path, path.stat().st_mtime)

Another possibility is to hash the entire file. hash_contents is provided for this purpose:

from genno.caching import hash_contents

def load_cached_2(path):
    return slow_data_load(path, hash_contents(path))

Warning

For very large files, even hashing the file in this way can be slow, and this check must always be performed in order to check for a matching cache key.

The decorated functions can be used as computations in the graph, or called directly:

c.add("A1", load_cached_1, "example-file-A.xml")
c.add("A2", load_cached_2, "example-file-A.xml")

# Load and process the contents of example-file-A.xml
c.get("A1")

# Load again; the value is retrieved from cache if the
# file has not been modified
c.get("A1")

# Same without using the Computer
load_cached1("example-file-A.xml")
load_cached1("example-file-A.xml")

Integrate and extend

• .Encoder may be configured to handle (or ignore) additional/custom types that may appear as arguments to functions decorated with .Computer.cache. See the examples for .Encoder.register and .Encoder.ignore.

• .decorate can be used entirely independently of any .Computer by passing the cache_path (and optional cache_skip) keyword arguments:

  from functools import partial
  
  from genno.caching import decorate
  
  # Create a decorator with a custom cache path
  mycache = partial(decorate, cache_path=Path("/path/to/cache"))
  
  @mycache
  def func(a, b=2):
      return a ** b

  In this usage, it offers a subset of the feature-set of joblib.Memory

Internals and utilities

genno.caching