def save(filepath, obj, on_overwrite='ignore'):
"""
Serialize `object` to a file denoted by `filepath`.
Parameters
----------
filepath : str
A filename. If the suffix is `.joblib` and joblib can be
imported, `joblib.dump` is used in place of the regular
pickling mechanisms; this results in much faster saves by
saving arrays as separate .npy files on disk. If the file
suffix is `.npy` than `numpy.save` is attempted on `obj`.
Otherwise, (c)pickle is used.
obj : object
A Python object to be serialized.
on_overwrite : str, optional
A string specifying what to do if the file already exists.
Possible values include:
- "ignore" : Just overwrite the existing file.
- "backup" : Make a backup copy of the file (<filepath>.bak).
Save the new copy. Then delete the backup copy. This allows
recovery of the old version of the file if saving the new one
fails.
"""
filepath = preprocess(filepath)
if os.path.exists(filepath):
if on_overwrite == 'backup':
backup = filepath + '.bak'
shutil.move(filepath, backup)
save(filepath, obj)
try:
os.remove(backup)
except Exception as e:
warnings.warn("Got an error while trying to remove " + backup +
":" + str(e))
return
else:
assert on_overwrite == 'ignore'
try:
_save(filepath, obj)
except RuntimeError as e:
""" Sometimes for large theano graphs, pickle/cPickle exceed the
maximum recursion depth. This seems to me like a fundamental
design flaw in pickle/cPickle. The workaround I employ here
is the one recommended to someone who had a similar problem
on stackexchange:
http://stackoverflow.com/questions/2134706/hitting-maximum-recursion-depth-using-pythons-pickle-cpickle
Obviously this does not scale and could cause a crash
but I don't see another solution short of writing our
own implementation of pickle.
"""
if str(e).find('recursion') != -1:
logger.warning('pylearn2.utils.save encountered the following '
'error: ' + str(e) +
'\nAttempting to resolve this error by calling ' +
'sys.setrecusionlimit and retrying')
old_limit = sys.getrecursionlimit()
try:
sys.setrecursionlimit(50000)
_save(filepath, obj)
finally:
sys.setrecursionlimit(old_limit)
def save(filepath, obj, on_overwrite='ignore'):
"""
Serialize `object` to a file denoted by `filepath`.
Parameters
----------
filepath : str
A filename. If the suffix is `.joblib` and joblib can be
imported, `joblib.dump` is used in place of the regular
pickling mechanisms; this results in much faster saves by
saving arrays as separate .npy files on disk. If the file
suffix is `.npy` than `numpy.save` is attempted on `obj`.
Otherwise, (c)pickle is used.
obj : object
A Python object to be serialized.
on_overwrite : str, optional
A string specifying what to do if the file already exists.
Possible values include:
- "ignore" : Just overwrite the existing file.
- "backup" : Make a backup copy of the file (<filepath>.bak).
Save the new copy. Then delete the backup copy. This allows
recovery of the old version of the file if saving the new one
fails.
"""
filepath = preprocess(filepath)
if os.path.exists(filepath):
if on_overwrite == 'backup':
backup = filepath + '.bak'
shutil.move(filepath, backup)
save(filepath, obj)
try:
os.remove(backup)
except Exception as e:
warnings.warn("Got an error while trying to remove " + backup
+ ":" + str(e))
return
else:
assert on_overwrite == 'ignore'
try:
_save(filepath, obj)
except RuntimeError as e:
""" Sometimes for large theano graphs, pickle/cPickle exceed the
maximum recursion depth. This seems to me like a fundamental
design flaw in pickle/cPickle. The workaround I employ here
is the one recommended to someone who had a similar problem
on stackexchange:
http://stackoverflow.com/questions/2134706/hitting-maximum-recursion-depth-using-pythons-pickle-cpickle
Obviously this does not scale and could cause a crash
but I don't see another solution short of writing our
own implementation of pickle.
"""
if str(e).find('recursion') != -1:
logger.warning('pylearn2.utils.save encountered the following '
'error: ' + str(e) +
'\nAttempting to resolve this error by calling ' +
'sys.setrecusionlimit and retrying')
old_limit = sys.getrecursionlimit()
try:
sys.setrecursionlimit(50000)
_save(filepath, obj)
finally:
sys.setrecursionlimit(old_limit)
def _load(filepath, recurse_depth=0, retry=True):
"""
Recursively tries to load a file until success or maximum number of
attempts.
Parameters
----------
filepath : str
A path to a file to load. Should be a pickle, Matlab, or NumPy
file; or a .txt or .amat file that numpy.loadtxt can load.
recurse_depth : int, optional
End users should not use this argument. It is used by the function
itself to implement the `retry` option recursively.
retry : bool, optional
If True, will make a handful of attempts to load the file before
giving up. This can be useful if you are for example calling
show_weights.py on a file that is actively being written to by a
training script--sometimes the load attempt might fail if the
training script writes at the same time show_weights tries to
read, but if you try again after a few seconds you should be able
to open the file.
Returns
-------
loaded_object : object
The object that was stored in the file.
"""
try:
import joblib
joblib_available = True
except ImportError:
joblib_available = False
if recurse_depth == 0:
filepath = preprocess(filepath)
if filepath.endswith('.npy') or filepath.endswith('.npz'):
return np.load(filepath)
if filepath.endswith('.amat') or filepath.endswith('txt'):
try:
return np.loadtxt(filepath)
except Exception:
raise Exception("{0} cannot be loaded by serial.load (trying "
"to use np.loadtxt)".format(filepath))
if filepath.endswith('.mat'):
global io
if io is None:
import scipy.io
io = scipy.io
try:
return io.loadmat(filepath)
except NotImplementedError as nei:
if str(nei).find('HDF reader') != -1:
global hdf_reader
if hdf_reader is None:
import h5py
hdf_reader = h5py
return hdf_reader.File(filepath, 'r')
else:
raise
# this code should never be reached
assert False
# for loading PY2 pickle in PY3
encoding = {'encoding': 'latin-1'} if six.PY3 else {}
def exponential_backoff():
if recurse_depth > 9:
logger.info('Max number of tries exceeded while trying to open '
'{0}'.format(filepath))
logger.info('attempting to open via reading string')
with open(filepath, 'rb') as f:
content = f.read()
return cPickle.loads(content, **encoding)
else:
nsec = 0.5 * (2.0**float(recurse_depth))
logger.info("Waiting {0} seconds and trying again".format(nsec))
time.sleep(nsec)
return _load(filepath, recurse_depth + 1, retry)
try:
if not joblib_available:
with open(filepath, 'rb') as f:
obj = cPickle.load(f, **encoding)
else:
try:
obj = joblib.load(filepath)
except Exception as e:
if os.path.exists(filepath) and not os.path.isdir(filepath):
raise
raise_cannot_open(filepath)
except MemoryError as e:
# We want to explicitly catch this exception because for MemoryError
# __str__ returns the empty string, so some of our default printouts
# below don't make a lot of sense.
# Also, a lot of users assume any exception is a bug in the library,
# so we can cut down on mail to pylearn-users by adding a message
# that makes it clear this exception is caused by their machine not
# meeting requirements.
if os.path.splitext(filepath)[1] == ".pkl":
improve_memory_error_message(
e, ("You do not have enough memory to "
"open %s \n"
" + Try using numpy.{save,load} "
"(file with extension '.npy') "
"to save your file. It uses less "
"memory when reading and "
"writing files than pickled files.") % filepath)
else:
improve_memory_error_message(
e, "You do not have enough memory to "
"open %s" % filepath)
except (BadPickleGet, EOFError, KeyError) as e:
if not retry:
raise Exception(e.__class__('Failed to open {0}'.format(filepath)))
obj = exponential_backoff()
except ValueError:
logger.exception
if not retry:
raise Exception(ValueError('Failed to open {0}'.format(filepath)))
obj = exponential_backoff()
except Exception:
# assert False
raise Exception("Couldn't open {0}".format(filepath))
# if the object has no yaml_src, we give it one that just says it
# came from this file. could cause trouble if you save obj again
# to a different location
if not hasattr(obj, 'yaml_src'):
try:
obj.yaml_src = '!pkl: "' + os.path.abspath(filepath) + '"'
except Exception:
pass
return obj
def _load(filepath, recurse_depth=0, retry=True):
"""
Recursively tries to load a file until success or maximum number of
attempts.
Parameters
----------
filepath : str
A path to a file to load. Should be a pickle, Matlab, or NumPy
file; or a .txt or .amat file that numpy.loadtxt can load.
recurse_depth : int, optional
End users should not use this argument. It is used by the function
itself to implement the `retry` option recursively.
retry : bool, optional
If True, will make a handful of attempts to load the file before
giving up. This can be useful if you are for example calling
show_weights.py on a file that is actively being written to by a
training script--sometimes the load attempt might fail if the
training script writes at the same time show_weights tries to
read, but if you try again after a few seconds you should be able
to open the file.
Returns
-------
loaded_object : object
The object that was stored in the file.
"""
try:
import joblib
joblib_available = True
except ImportError:
joblib_available = False
if recurse_depth == 0:
filepath = preprocess(filepath)
if filepath.endswith('.npy') or filepath.endswith('.npz'):
return np.load(filepath)
if filepath.endswith('.amat') or filepath.endswith('txt'):
try:
return np.loadtxt(filepath)
except Exception:
raise Exception("{0} cannot be loaded by serial.load (trying "
"to use np.loadtxt)".format(filepath))
if filepath.endswith('.mat'):
global io
if io is None:
import scipy.io
io = scipy.io
try:
return io.loadmat(filepath)
except NotImplementedError as nei:
if str(nei).find('HDF reader') != -1:
global hdf_reader
if hdf_reader is None:
import h5py
hdf_reader = h5py
return hdf_reader.File(filepath, 'r')
else:
raise
# this code should never be reached
assert False
# for loading PY2 pickle in PY3
encoding = {'encoding': 'latin-1'} if six.PY3 else {}
def exponential_backoff():
if recurse_depth > 9:
logger.info('Max number of tries exceeded while trying to open '
'{0}'.format(filepath))
logger.info('attempting to open via reading string')
with open(filepath, 'rb') as f:
content = f.read()
return cPickle.loads(content, **encoding)
else:
nsec = 0.5 * (2.0 ** float(recurse_depth))
logger.info("Waiting {0} seconds and trying again".format(nsec))
time.sleep(nsec)
return _load(filepath, recurse_depth + 1, retry)
try:
if not joblib_available:
with open(filepath, 'rb') as f:
obj = cPickle.load(f, **encoding)
else:
try:
obj = joblib.load(filepath)
except Exception as e:
if os.path.exists(filepath) and not os.path.isdir(filepath):
raise
raise_cannot_open(filepath)
except MemoryError as e:
# We want to explicitly catch this exception because for MemoryError
# __str__ returns the empty string, so some of our default printouts
# below don't make a lot of sense.
# Also, a lot of users assume any exception is a bug in the library,
# so we can cut down on mail to pylearn-users by adding a message
# that makes it clear this exception is caused by their machine not
# meeting requirements.
if os.path.splitext(filepath)[1] == ".pkl":
improve_memory_error_message(e,
("You do not have enough memory to "
"open %s \n"
" + Try using numpy.{save,load} "
"(file with extension '.npy') "
"to save your file. It uses less "
"memory when reading and "
"writing files than pickled files.")
% filepath)
else:
improve_memory_error_message(e,
"You do not have enough memory to "
"open %s" % filepath)
except (BadPickleGet, EOFError, KeyError) as e:
if not retry:
raise Exception(e.__class__('Failed to open {0}'.format(filepath)))
obj = exponential_backoff()
except ValueError:
logger.exception
if not retry:
raise Exception(ValueError('Failed to open {0}'.format(filepath)))
obj = exponential_backoff()
except Exception:
# assert False
raise Exception("Couldn't open {0}".format(filepath))
# if the object has no yaml_src, we give it one that just says it
# came from this file. could cause trouble if you save obj again
# to a different location
if not hasattr(obj, 'yaml_src'):
try:
obj.yaml_src = '!pkl: "' + os.path.abspath(filepath) + '"'
except Exception:
pass
return obj