ai-content-maker/.venv/Lib/site-packages/joblib/memory.py

1161 lines
45 KiB
Python

"""
A context object for caching a function's return value each time it
is called with the same input arguments.
"""
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# Copyright (c) 2009 Gael Varoquaux
# License: BSD Style, 3 clauses.
import asyncio
import datetime
import functools
import inspect
import logging
import os
import pathlib
import pydoc
import re
import textwrap
import time
import tokenize
import traceback
import warnings
import weakref
from . import hashing
from ._store_backends import CacheWarning # noqa
from ._store_backends import FileSystemStoreBackend, StoreBackendBase
from .func_inspect import (filter_args, format_call, format_signature,
get_func_code, get_func_name)
from .logger import Logger, format_time, pformat
FIRST_LINE_TEXT = "# first line:"
# TODO: The following object should have a data store object as a sub
# object, and the interface to persist and query should be separated in
# the data store.
#
# This would enable creating 'Memory' objects with a different logic for
# pickling that would simply span a MemorizedFunc with the same
# store (or do we want to copy it to avoid cross-talks?), for instance to
# implement HDF5 pickling.
# TODO: Same remark for the logger, and probably use the Python logging
# mechanism.
def extract_first_line(func_code):
""" Extract the first line information from the function code
text if available.
"""
if func_code.startswith(FIRST_LINE_TEXT):
func_code = func_code.split('\n')
first_line = int(func_code[0][len(FIRST_LINE_TEXT):])
func_code = '\n'.join(func_code[1:])
else:
first_line = -1
return func_code, first_line
class JobLibCollisionWarning(UserWarning):
""" Warn that there might be a collision between names of functions.
"""
_STORE_BACKENDS = {'local': FileSystemStoreBackend}
def register_store_backend(backend_name, backend):
"""Extend available store backends.
The Memory, MemorizeResult and MemorizeFunc objects are designed to be
agnostic to the type of store used behind. By default, the local file
system is used but this function gives the possibility to extend joblib's
memory pattern with other types of storage such as cloud storage (S3, GCS,
OpenStack, HadoopFS, etc) or blob DBs.
Parameters
----------
backend_name: str
The name identifying the store backend being registered. For example,
'local' is used with FileSystemStoreBackend.
backend: StoreBackendBase subclass
The name of a class that implements the StoreBackendBase interface.
"""
if not isinstance(backend_name, str):
raise ValueError("Store backend name should be a string, "
"'{0}' given.".format(backend_name))
if backend is None or not issubclass(backend, StoreBackendBase):
raise ValueError("Store backend should inherit "
"StoreBackendBase, "
"'{0}' given.".format(backend))
_STORE_BACKENDS[backend_name] = backend
def _store_backend_factory(backend, location, verbose=0, backend_options=None):
"""Return the correct store object for the given location."""
if backend_options is None:
backend_options = {}
if isinstance(location, pathlib.Path):
location = str(location)
if isinstance(location, StoreBackendBase):
return location
elif isinstance(location, str):
obj = None
location = os.path.expanduser(location)
# The location is not a local file system, we look in the
# registered backends if there's one matching the given backend
# name.
for backend_key, backend_obj in _STORE_BACKENDS.items():
if backend == backend_key:
obj = backend_obj()
# By default, we assume the FileSystemStoreBackend can be used if no
# matching backend could be found.
if obj is None:
raise TypeError('Unknown location {0} or backend {1}'.format(
location, backend))
# The store backend is configured with the extra named parameters,
# some of them are specific to the underlying store backend.
obj.configure(location, verbose=verbose,
backend_options=backend_options)
return obj
elif location is not None:
warnings.warn(
"Instantiating a backend using a {} as a location is not "
"supported by joblib. Returning None instead.".format(
location.__class__.__name__), UserWarning)
return None
def _build_func_identifier(func):
"""Build a roughly unique identifier for the cached function."""
modules, funcname = get_func_name(func)
# We reuse historical fs-like way of building a function identifier
return os.path.join(*modules, funcname)
# An in-memory store to avoid looking at the disk-based function
# source code to check if a function definition has changed
_FUNCTION_HASHES = weakref.WeakKeyDictionary()
###############################################################################
# class `MemorizedResult`
###############################################################################
class MemorizedResult(Logger):
"""Object representing a cached value.
Attributes
----------
location: str
The location of joblib cache. Depends on the store backend used.
func: function or str
function whose output is cached. The string case is intended only for
instantiation based on the output of repr() on another instance.
(namely eval(repr(memorized_instance)) works).
argument_hash: str
hash of the function arguments.
backend: str
Type of store backend for reading/writing cache files.
Default is 'local'.
mmap_mode: {None, 'r+', 'r', 'w+', 'c'}
The memmapping mode used when loading from cache numpy arrays. See
numpy.load for the meaning of the different values.
verbose: int
verbosity level (0 means no message).
timestamp, metadata: string
for internal use only.
"""
def __init__(self, location, call_id, backend='local', mmap_mode=None,
verbose=0, timestamp=None, metadata=None):
Logger.__init__(self)
self._call_id = call_id
self.store_backend = _store_backend_factory(backend, location,
verbose=verbose)
self.mmap_mode = mmap_mode
if metadata is not None:
self.metadata = metadata
else:
self.metadata = self.store_backend.get_metadata(self._call_id)
self.duration = self.metadata.get('duration', None)
self.verbose = verbose
self.timestamp = timestamp
@property
def func(self):
return self.func_id
@property
def func_id(self):
return self._call_id[0]
@property
def args_id(self):
return self._call_id[1]
@property
def argument_hash(self):
warnings.warn(
"The 'argument_hash' attribute has been deprecated in version "
"0.12 and will be removed in version 0.14.\n"
"Use `args_id` attribute instead.",
DeprecationWarning, stacklevel=2)
return self.args_id
def get(self):
"""Read value from cache and return it."""
try:
return self.store_backend.load_item(
self._call_id,
timestamp=self.timestamp,
metadata=self.metadata,
verbose=self.verbose
)
except ValueError as exc:
new_exc = KeyError(
"Error while trying to load a MemorizedResult's value. "
"It seems that this folder is corrupted : {}".format(
os.path.join(self.store_backend.location, *self._call_id)))
raise new_exc from exc
def clear(self):
"""Clear value from cache"""
self.store_backend.clear_item(self._call_id)
def __repr__(self):
return '{}(location="{}", func="{}", args_id="{}")'.format(
self.__class__.__name__, self.store_backend.location,
*self._call_id
)
def __getstate__(self):
state = self.__dict__.copy()
state['timestamp'] = None
return state
class NotMemorizedResult(object):
"""Class representing an arbitrary value.
This class is a replacement for MemorizedResult when there is no cache.
"""
__slots__ = ('value', 'valid')
def __init__(self, value):
self.value = value
self.valid = True
def get(self):
if self.valid:
return self.value
else:
raise KeyError("No value stored.")
def clear(self):
self.valid = False
self.value = None
def __repr__(self):
if self.valid:
return ('{class_name}({value})'
.format(class_name=self.__class__.__name__,
value=pformat(self.value)))
else:
return self.__class__.__name__ + ' with no value'
# __getstate__ and __setstate__ are required because of __slots__
def __getstate__(self):
return {"valid": self.valid, "value": self.value}
def __setstate__(self, state):
self.valid = state["valid"]
self.value = state["value"]
###############################################################################
# class `NotMemorizedFunc`
###############################################################################
class NotMemorizedFunc(object):
"""No-op object decorating a function.
This class replaces MemorizedFunc when there is no cache. It provides an
identical API but does not write anything on disk.
Attributes
----------
func: callable
Original undecorated function.
"""
# Should be a light as possible (for speed)
def __init__(self, func):
self.func = func
def __call__(self, *args, **kwargs):
return self.func(*args, **kwargs)
def call_and_shelve(self, *args, **kwargs):
return NotMemorizedResult(self.func(*args, **kwargs))
def __repr__(self):
return '{0}(func={1})'.format(self.__class__.__name__, self.func)
def clear(self, warn=True):
# Argument "warn" is for compatibility with MemorizedFunc.clear
pass
def call(self, *args, **kwargs):
return self.func(*args, **kwargs)
def check_call_in_cache(self, *args, **kwargs):
return False
###############################################################################
# class `AsyncNotMemorizedFunc`
###############################################################################
class AsyncNotMemorizedFunc(NotMemorizedFunc):
async def call_and_shelve(self, *args, **kwargs):
return NotMemorizedResult(await self.func(*args, **kwargs))
###############################################################################
# class `MemorizedFunc`
###############################################################################
class MemorizedFunc(Logger):
"""Callable object decorating a function for caching its return value
each time it is called.
Methods are provided to inspect the cache or clean it.
Attributes
----------
func: callable
The original, undecorated, function.
location: string
The location of joblib cache. Depends on the store backend used.
backend: str
Type of store backend for reading/writing cache files.
Default is 'local', in which case the location is the path to a
disk storage.
ignore: list or None
List of variable names to ignore when choosing whether to
recompute.
mmap_mode: {None, 'r+', 'r', 'w+', 'c'}
The memmapping mode used when loading from cache
numpy arrays. See numpy.load for the meaning of the different
values.
compress: boolean, or integer
Whether to zip the stored data on disk. If an integer is
given, it should be between 1 and 9, and sets the amount
of compression. Note that compressed arrays cannot be
read by memmapping.
verbose: int, optional
The verbosity flag, controls messages that are issued as
the function is evaluated.
cache_validation_callback: callable, optional
Callable to check if a result in cache is valid or is to be recomputed.
When the function is called with arguments for which a cache exists,
the callback is called with the cache entry's metadata as its sole
argument. If it returns True, the cached result is returned, else the
cache for these arguments is cleared and the result is recomputed.
"""
# ------------------------------------------------------------------------
# Public interface
# ------------------------------------------------------------------------
def __init__(self, func, location, backend='local', ignore=None,
mmap_mode=None, compress=False, verbose=1, timestamp=None,
cache_validation_callback=None):
Logger.__init__(self)
self.mmap_mode = mmap_mode
self.compress = compress
self.func = func
self.cache_validation_callback = cache_validation_callback
self.func_id = _build_func_identifier(func)
self.ignore = ignore if ignore is not None else []
self._verbose = verbose
# retrieve store object from backend type and location.
self.store_backend = _store_backend_factory(backend, location,
verbose=verbose,
backend_options=dict(
compress=compress,
mmap_mode=mmap_mode),
)
if self.store_backend is not None:
# Create func directory on demand.
self.store_backend.store_cached_func_code([self.func_id])
self.timestamp = timestamp if timestamp is not None else time.time()
try:
functools.update_wrapper(self, func)
except Exception:
pass # Objects like ufunc don't like that
if inspect.isfunction(func):
doc = pydoc.TextDoc().document(func)
# Remove blank line
doc = doc.replace('\n', '\n\n', 1)
# Strip backspace-overprints for compatibility with autodoc
doc = re.sub('\x08.', '', doc)
else:
# Pydoc does a poor job on other objects
doc = func.__doc__
self.__doc__ = 'Memoized version of %s' % doc
self._func_code_info = None
self._func_code_id = None
def _is_in_cache_and_valid(self, call_id):
"""Check if the function call is cached and valid for given arguments.
- Compare the function code with the one from the cached function,
asserting if it has changed.
- Check if the function call is present in the cache.
- Call `cache_validation_callback` for user define cache validation.
Returns True if the function call is in cache and can be used, and
returns False otherwise.
"""
# Check if the code of the function has changed
if not self._check_previous_func_code(stacklevel=4):
return False
# Check if this specific call is in the cache
if not self.store_backend.contains_item(call_id):
return False
# Call the user defined cache validation callback
metadata = self.store_backend.get_metadata(call_id)
if (self.cache_validation_callback is not None and
not self.cache_validation_callback(metadata)):
self.store_backend.clear_item(call_id)
return False
return True
def _cached_call(self, args, kwargs, shelving):
"""Call wrapped function and cache result, or read cache if available.
This function returns the wrapped function output or a reference to
the cached result.
Arguments:
----------
args, kwargs: list and dict
input arguments for wrapped function
shelving: bool
True when called via the call_and_shelve function.
Returns
-------
Output of the wrapped function if shelving is false, or a
MemorizedResult reference to the value if shelving is true.
"""
args_id = self._get_args_id(*args, **kwargs)
call_id = (self.func_id, args_id)
_, func_name = get_func_name(self.func)
func_info = self.store_backend.get_cached_func_info([self.func_id])
location = func_info['location']
if self._verbose >= 20:
logging.basicConfig(level=logging.INFO)
_, signature = format_signature(self.func, *args, **kwargs)
self.info(
textwrap.dedent(
f"""
Querying {func_name} with signature
{signature}.
(argument hash {args_id})
The store location is {location}.
"""
)
)
# Compare the function code with the previous to see if the
# function code has changed and check if the results are present in
# the cache.
if self._is_in_cache_and_valid(call_id):
if shelving:
return self._get_memorized_result(call_id)
try:
start_time = time.time()
output = self._load_item(call_id)
if self._verbose > 4:
self._print_duration(time.time() - start_time,
context='cache loaded ')
return output
except Exception:
# XXX: Should use an exception logger
_, signature = format_signature(self.func, *args, **kwargs)
self.warn('Exception while loading results for '
'{}\n {}'.format(signature, traceback.format_exc()))
if self._verbose > 10:
self.warn(
f"Computing func {func_name}, argument hash {args_id} "
f"in location {location}"
)
return self._call(call_id, args, kwargs, shelving)
@property
def func_code_info(self):
# 3-tuple property containing: the function source code, source file,
# and first line of the code inside the source file
if hasattr(self.func, '__code__'):
if self._func_code_id is None:
self._func_code_id = id(self.func.__code__)
elif id(self.func.__code__) != self._func_code_id:
# Be robust to dynamic reassignments of self.func.__code__
self._func_code_info = None
if self._func_code_info is None:
# Cache the source code of self.func . Provided that get_func_code
# (which should be called once on self) gets called in the process
# in which self.func was defined, this caching mechanism prevents
# undesired cache clearing when the cached function is called in
# an environment where the introspection utilities get_func_code
# relies on do not work (typically, in joblib child processes).
# See #1035 for more info
# TODO (pierreglaser): do the same with get_func_name?
self._func_code_info = get_func_code(self.func)
return self._func_code_info
def call_and_shelve(self, *args, **kwargs):
"""Call wrapped function, cache result and return a reference.
This method returns a reference to the cached result instead of the
result itself. The reference object is small and pickeable, allowing
to send or store it easily. Call .get() on reference object to get
result.
Returns
-------
cached_result: MemorizedResult or NotMemorizedResult
reference to the value returned by the wrapped function. The
class "NotMemorizedResult" is used when there is no cache
activated (e.g. location=None in Memory).
"""
return self._cached_call(args, kwargs, shelving=True)
def __call__(self, *args, **kwargs):
return self._cached_call(args, kwargs, shelving=False)
def __getstate__(self):
# Make sure self.func's source is introspected prior to being pickled -
# code introspection utilities typically do not work inside child
# processes
_ = self.func_code_info
# We don't store the timestamp when pickling, to avoid the hash
# depending from it.
state = self.__dict__.copy()
state['timestamp'] = None
# Invalidate the code id as id(obj) will be different in the child
state['_func_code_id'] = None
return state
def check_call_in_cache(self, *args, **kwargs):
"""Check if function call is in the memory cache.
Does not call the function or do any work besides func inspection
and arg hashing.
Returns
-------
is_call_in_cache: bool
Whether or not the result of the function has been cached
for the input arguments that have been passed.
"""
call_id = (self.func_id, self._get_args_id(*args, **kwargs))
return self.store_backend.contains_item(call_id)
# ------------------------------------------------------------------------
# Private interface
# ------------------------------------------------------------------------
def _get_args_id(self, *args, **kwargs):
"""Return the input parameter hash of a result."""
return hashing.hash(filter_args(self.func, self.ignore, args, kwargs),
coerce_mmap=self.mmap_mode is not None)
def _hash_func(self):
"""Hash a function to key the online cache"""
func_code_h = hash(getattr(self.func, '__code__', None))
return id(self.func), hash(self.func), func_code_h
def _write_func_code(self, func_code, first_line):
""" Write the function code and the filename to a file.
"""
# We store the first line because the filename and the function
# name is not always enough to identify a function: people
# sometimes have several functions named the same way in a
# file. This is bad practice, but joblib should be robust to bad
# practice.
func_code = u'%s %i\n%s' % (FIRST_LINE_TEXT, first_line, func_code)
self.store_backend.store_cached_func_code([self.func_id], func_code)
# Also store in the in-memory store of function hashes
is_named_callable = (hasattr(self.func, '__name__') and
self.func.__name__ != '<lambda>')
if is_named_callable:
# Don't do this for lambda functions or strange callable
# objects, as it ends up being too fragile
func_hash = self._hash_func()
try:
_FUNCTION_HASHES[self.func] = func_hash
except TypeError:
# Some callable are not hashable
pass
def _check_previous_func_code(self, stacklevel=2):
"""
stacklevel is the depth a which this function is called, to
issue useful warnings to the user.
"""
# First check if our function is in the in-memory store.
# Using the in-memory store not only makes things faster, but it
# also renders us robust to variations of the files when the
# in-memory version of the code does not vary
try:
if self.func in _FUNCTION_HASHES:
# We use as an identifier the id of the function and its
# hash. This is more likely to falsely change than have hash
# collisions, thus we are on the safe side.
func_hash = self._hash_func()
if func_hash == _FUNCTION_HASHES[self.func]:
return True
except TypeError:
# Some callables are not hashable
pass
# Here, we go through some effort to be robust to dynamically
# changing code and collision. We cannot inspect.getsource
# because it is not reliable when using IPython's magic "%run".
func_code, source_file, first_line = self.func_code_info
try:
old_func_code, old_first_line = extract_first_line(
self.store_backend.get_cached_func_code([self.func_id]))
except (IOError, OSError): # some backend can also raise OSError
self._write_func_code(func_code, first_line)
return False
if old_func_code == func_code:
return True
# We have differing code, is this because we are referring to
# different functions, or because the function we are referring to has
# changed?
_, func_name = get_func_name(self.func, resolv_alias=False,
win_characters=False)
if old_first_line == first_line == -1 or func_name == '<lambda>':
if not first_line == -1:
func_description = ("{0} ({1}:{2})"
.format(func_name, source_file,
first_line))
else:
func_description = func_name
warnings.warn(JobLibCollisionWarning(
"Cannot detect name collisions for function '{0}'"
.format(func_description)), stacklevel=stacklevel)
# Fetch the code at the old location and compare it. If it is the
# same than the code store, we have a collision: the code in the
# file has not changed, but the name we have is pointing to a new
# code block.
if not old_first_line == first_line and source_file is not None:
if os.path.exists(source_file):
_, func_name = get_func_name(self.func, resolv_alias=False)
num_lines = len(func_code.split('\n'))
with tokenize.open(source_file) as f:
on_disk_func_code = f.readlines()[
old_first_line - 1:old_first_line - 1 + num_lines - 1]
on_disk_func_code = ''.join(on_disk_func_code)
possible_collision = (on_disk_func_code.rstrip() ==
old_func_code.rstrip())
else:
possible_collision = source_file.startswith('<doctest ')
if possible_collision:
warnings.warn(JobLibCollisionWarning(
'Possible name collisions between functions '
"'%s' (%s:%i) and '%s' (%s:%i)" %
(func_name, source_file, old_first_line,
func_name, source_file, first_line)),
stacklevel=stacklevel)
# The function has changed, wipe the cache directory.
# XXX: Should be using warnings, and giving stacklevel
if self._verbose > 10:
_, func_name = get_func_name(self.func, resolv_alias=False)
self.warn("Function {0} (identified by {1}) has changed"
".".format(func_name, self.func_id))
self.clear(warn=True)
return False
def clear(self, warn=True):
"""Empty the function's cache."""
func_id = self.func_id
if self._verbose > 0 and warn:
self.warn("Clearing function cache identified by %s" % func_id)
self.store_backend.clear_path([func_id, ])
func_code, _, first_line = self.func_code_info
self._write_func_code(func_code, first_line)
def call(self, *args, **kwargs):
"""Force the execution of the function with the given arguments.
The output values will be persisted, i.e., the cache will be updated
with any new values.
Parameters
----------
*args: arguments
The arguments.
**kwargs: keyword arguments
Keyword arguments.
Returns
-------
output : object
The output of the function call.
"""
call_id = (self.func_id, self._get_args_id(*args, **kwargs))
return self._call(call_id, args, kwargs)
def _call(self, call_id, args, kwargs, shelving=False):
self._before_call(args, kwargs)
start_time = time.time()
output = self.func(*args, **kwargs)
return self._after_call(call_id, args, kwargs, shelving,
output, start_time)
def _before_call(self, args, kwargs):
if self._verbose > 0:
print(format_call(self.func, args, kwargs))
def _after_call(self, call_id, args, kwargs, shelving, output, start_time):
self.store_backend.dump_item(call_id, output, verbose=self._verbose)
duration = time.time() - start_time
if self._verbose > 0:
self._print_duration(duration)
metadata = self._persist_input(duration, call_id, args, kwargs)
if shelving:
return self._get_memorized_result(call_id, metadata)
if self.mmap_mode is not None:
# Memmap the output at the first call to be consistent with
# later calls
output = self._load_item(call_id, metadata)
return output
def _persist_input(self, duration, call_id, args, kwargs,
this_duration_limit=0.5):
""" Save a small summary of the call using json format in the
output directory.
output_dir: string
directory where to write metadata.
duration: float
time taken by hashing input arguments, calling the wrapped
function and persisting its output.
args, kwargs: list and dict
input arguments for wrapped function
this_duration_limit: float
Max execution time for this function before issuing a warning.
"""
start_time = time.time()
argument_dict = filter_args(self.func, self.ignore,
args, kwargs)
input_repr = dict((k, repr(v)) for k, v in argument_dict.items())
# This can fail due to race-conditions with multiple
# concurrent joblibs removing the file or the directory
metadata = {
"duration": duration, "input_args": input_repr, "time": start_time,
}
self.store_backend.store_metadata(call_id, metadata)
this_duration = time.time() - start_time
if this_duration > this_duration_limit:
# This persistence should be fast. It will not be if repr() takes
# time and its output is large, because json.dump will have to
# write a large file. This should not be an issue with numpy arrays
# for which repr() always output a short representation, but can
# be with complex dictionaries. Fixing the problem should be a
# matter of replacing repr() above by something smarter.
warnings.warn("Persisting input arguments took %.2fs to run."
"If this happens often in your code, it can cause "
"performance problems "
"(results will be correct in all cases). "
"The reason for this is probably some large input "
"arguments for a wrapped function."
% this_duration, stacklevel=5)
return metadata
def _get_memorized_result(self, call_id, metadata=None):
return MemorizedResult(self.store_backend, call_id,
metadata=metadata, timestamp=self.timestamp,
verbose=self._verbose - 1)
def _load_item(self, call_id, metadata=None):
return self.store_backend.load_item(call_id, metadata=metadata,
timestamp=self.timestamp,
verbose=self._verbose)
def _print_duration(self, duration, context=''):
_, name = get_func_name(self.func)
msg = f"{name} {context}- {format_time(duration)}"
print(max(0, (80 - len(msg))) * '_' + msg)
# ------------------------------------------------------------------------
# Private `object` interface
# ------------------------------------------------------------------------
def __repr__(self):
return '{class_name}(func={func}, location={location})'.format(
class_name=self.__class__.__name__,
func=self.func,
location=self.store_backend.location,)
###############################################################################
# class `AsyncMemorizedFunc`
###############################################################################
class AsyncMemorizedFunc(MemorizedFunc):
async def __call__(self, *args, **kwargs):
out = super().__call__(*args, **kwargs)
return await out if asyncio.iscoroutine(out) else out
async def call_and_shelve(self, *args, **kwargs):
out = super().call_and_shelve(*args, **kwargs)
return await out if asyncio.iscoroutine(out) else out
async def call(self, *args, **kwargs):
out = super().call(*args, **kwargs)
return await out if asyncio.iscoroutine(out) else out
async def _call(self, call_id, args, kwargs, shelving=False):
self._before_call(args, kwargs)
start_time = time.time()
output = await self.func(*args, **kwargs)
return self._after_call(call_id, args, kwargs, shelving,
output, start_time)
###############################################################################
# class `Memory`
###############################################################################
class Memory(Logger):
""" A context object for caching a function's return value each time it
is called with the same input arguments.
All values are cached on the filesystem, in a deep directory
structure.
Read more in the :ref:`User Guide <memory>`.
Parameters
----------
location: str, pathlib.Path or None
The path of the base directory to use as a data store
or None. If None is given, no caching is done and
the Memory object is completely transparent. This option
replaces cachedir since version 0.12.
backend: str, optional
Type of store backend for reading/writing cache files.
Default: 'local'.
The 'local' backend is using regular filesystem operations to
manipulate data (open, mv, etc) in the backend.
mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional
The memmapping mode used when loading from cache
numpy arrays. See numpy.load for the meaning of the
arguments.
compress: boolean, or integer, optional
Whether to zip the stored data on disk. If an integer is
given, it should be between 1 and 9, and sets the amount
of compression. Note that compressed arrays cannot be
read by memmapping.
verbose: int, optional
Verbosity flag, controls the debug messages that are issued
as functions are evaluated.
bytes_limit: int | str, optional
Limit in bytes of the size of the cache. By default, the size of
the cache is unlimited. When reducing the size of the cache,
``joblib`` keeps the most recently accessed items first. If a
str is passed, it is converted to a number of bytes using units
{ K | M | G} for kilo, mega, giga.
**Note:** You need to call :meth:`joblib.Memory.reduce_size` to
actually reduce the cache size to be less than ``bytes_limit``.
**Note:** This argument has been deprecated. One should give the
value of ``bytes_limit`` directly in
:meth:`joblib.Memory.reduce_size`.
backend_options: dict, optional
Contains a dictionary of named parameters used to configure
the store backend.
"""
# ------------------------------------------------------------------------
# Public interface
# ------------------------------------------------------------------------
def __init__(self, location=None, backend='local',
mmap_mode=None, compress=False, verbose=1, bytes_limit=None,
backend_options=None):
Logger.__init__(self)
self._verbose = verbose
self.mmap_mode = mmap_mode
self.timestamp = time.time()
if bytes_limit is not None:
warnings.warn(
"bytes_limit argument has been deprecated. It will be removed "
"in version 1.5. Please pass its value directly to "
"Memory.reduce_size.",
category=DeprecationWarning
)
self.bytes_limit = bytes_limit
self.backend = backend
self.compress = compress
if backend_options is None:
backend_options = {}
self.backend_options = backend_options
if compress and mmap_mode is not None:
warnings.warn('Compressed results cannot be memmapped',
stacklevel=2)
self.location = location
if isinstance(location, str):
location = os.path.join(location, 'joblib')
self.store_backend = _store_backend_factory(
backend, location, verbose=self._verbose,
backend_options=dict(compress=compress, mmap_mode=mmap_mode,
**backend_options))
def cache(self, func=None, ignore=None, verbose=None, mmap_mode=False,
cache_validation_callback=None):
""" Decorates the given function func to only compute its return
value for input arguments not cached on disk.
Parameters
----------
func: callable, optional
The function to be decorated
ignore: list of strings
A list of arguments name to ignore in the hashing
verbose: integer, optional
The verbosity mode of the function. By default that
of the memory object is used.
mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional
The memmapping mode used when loading from cache
numpy arrays. See numpy.load for the meaning of the
arguments. By default that of the memory object is used.
cache_validation_callback: callable, optional
Callable to validate whether or not the cache is valid. When
the cached function is called with arguments for which a cache
exists, this callable is called with the metadata of the cached
result as its sole argument. If it returns True, then the
cached result is returned, else the cache for these arguments
is cleared and recomputed.
Returns
-------
decorated_func: MemorizedFunc object
The returned object is a MemorizedFunc object, that is
callable (behaves like a function), but offers extra
methods for cache lookup and management. See the
documentation for :class:`joblib.memory.MemorizedFunc`.
"""
if (cache_validation_callback is not None and
not callable(cache_validation_callback)):
raise ValueError(
"cache_validation_callback needs to be callable. "
f"Got {cache_validation_callback}."
)
if func is None:
# Partial application, to be able to specify extra keyword
# arguments in decorators
return functools.partial(
self.cache, ignore=ignore,
mmap_mode=mmap_mode,
verbose=verbose,
cache_validation_callback=cache_validation_callback
)
if self.store_backend is None:
cls = (AsyncNotMemorizedFunc
if asyncio.iscoroutinefunction(func)
else NotMemorizedFunc)
return cls(func)
if verbose is None:
verbose = self._verbose
if mmap_mode is False:
mmap_mode = self.mmap_mode
if isinstance(func, MemorizedFunc):
func = func.func
cls = (AsyncMemorizedFunc
if asyncio.iscoroutinefunction(func)
else MemorizedFunc)
return cls(
func, location=self.store_backend, backend=self.backend,
ignore=ignore, mmap_mode=mmap_mode, compress=self.compress,
verbose=verbose, timestamp=self.timestamp,
cache_validation_callback=cache_validation_callback
)
def clear(self, warn=True):
""" Erase the complete cache directory.
"""
if warn:
self.warn('Flushing completely the cache')
if self.store_backend is not None:
self.store_backend.clear()
# As the cache is completely clear, make sure the _FUNCTION_HASHES
# cache is also reset. Else, for a function that is present in this
# table, results cached after this clear will be have cache miss
# as the function code is not re-written.
_FUNCTION_HASHES.clear()
def reduce_size(self, bytes_limit=None, items_limit=None, age_limit=None):
"""Remove cache elements to make the cache fit its limits.
The limitation can impose that the cache size fits in ``bytes_limit``,
that the number of cache items is no more than ``items_limit``, and
that all files in cache are not older than ``age_limit``.
Parameters
----------
bytes_limit: int | str, optional
Limit in bytes of the size of the cache. By default, the size of
the cache is unlimited. When reducing the size of the cache,
``joblib`` keeps the most recently accessed items first. If a
str is passed, it is converted to a number of bytes using units
{ K | M | G} for kilo, mega, giga.
items_limit: int, optional
Number of items to limit the cache to. By default, the number of
items in the cache is unlimited. When reducing the size of the
cache, ``joblib`` keeps the most recently accessed items first.
age_limit: datetime.timedelta, optional
Maximum age of items to limit the cache to. When reducing the size
of the cache, any items last accessed more than the given length of
time ago are deleted.
"""
if bytes_limit is None:
bytes_limit = self.bytes_limit
if self.store_backend is None:
# No cached results, this function does nothing.
return
if bytes_limit is None and items_limit is None and age_limit is None:
# No limitation to impose, returning
return
# Defers the actual limits enforcing to the store backend.
self.store_backend.enforce_store_limits(
bytes_limit, items_limit, age_limit
)
def eval(self, func, *args, **kwargs):
""" Eval function func with arguments `*args` and `**kwargs`,
in the context of the memory.
This method works similarly to the builtin `apply`, except
that the function is called only if the cache is not
up to date.
"""
if self.store_backend is None:
return func(*args, **kwargs)
return self.cache(func)(*args, **kwargs)
# ------------------------------------------------------------------------
# Private `object` interface
# ------------------------------------------------------------------------
def __repr__(self):
return '{class_name}(location={location})'.format(
class_name=self.__class__.__name__,
location=(None if self.store_backend is None
else self.store_backend.location))
def __getstate__(self):
""" We don't store the timestamp when pickling, to avoid the hash
depending from it.
"""
state = self.__dict__.copy()
state['timestamp'] = None
return state
###############################################################################
# cache_validation_callback helpers
###############################################################################
def expires_after(days=0, seconds=0, microseconds=0, milliseconds=0, minutes=0,
hours=0, weeks=0):
"""Helper cache_validation_callback to force recompute after a duration.
Parameters
----------
days, seconds, microseconds, milliseconds, minutes, hours, weeks: numbers
argument passed to a timedelta.
"""
delta = datetime.timedelta(
days=days, seconds=seconds, microseconds=microseconds,
milliseconds=milliseconds, minutes=minutes, hours=hours, weeks=weeks
)
def cache_validation_callback(metadata):
computation_age = time.time() - metadata['time']
return computation_age < delta.total_seconds()
return cache_validation_callback