def test_pool_with_memmap_array_view(tmpdir):
"""Check that subprocess can access and update shared memory array"""
assert_array_equal = np.testing.assert_array_equal
# Fork the subprocess before allocating the objects to be passed
pool_temp_folder = tmpdir.mkdir('pool').strpath
p = MemmapingPool(10, max_nbytes=2, temp_folder=pool_temp_folder)
try:
filename = tmpdir.join('test.mmap').strpath
a = np.memmap(filename, dtype=np.float32, shape=(3, 5), mode='w+')
a.fill(1.0)
# Create an ndarray view on the memmap instance
a_view = np.asarray(a)
assert not isinstance(a_view, np.memmap)
assert has_shareable_memory(a_view)
p.map(inplace_double, [(a_view, (i, j), 1.0)
for i in range(a.shape[0])
for j in range(a.shape[1])])
# Both a and the a_view have been updated
assert_array_equal(a, 2 * np.ones(a.shape))
assert_array_equal(a_view, 2 * np.ones(a.shape))
# Passing memmap array view to the pool should not trigger the
# creation of new files on the FS
assert os.listdir(pool_temp_folder) == []
finally:
p.terminate()
del p
def test_memmaping_on_dev_shm():
"""Check that MemmapingPool uses /dev/shm when possible"""
p = MemmapingPool(3, max_nbytes=10)
try:
# Check that the pool has correctly detected the presence of the
# shared memory filesystem.
pool_temp_folder = p._temp_folder
folder_prefix = '/dev/shm/joblib_memmaping_pool_'
assert_true(pool_temp_folder.startswith(folder_prefix))
assert_true(os.path.exists(pool_temp_folder))
# Try with a file larger than the memmap threshold of 10 bytes
a = np.ones(100, dtype=np.float64)
assert_equal(a.nbytes, 800)
p.map(id, [a] * 10)
# a should have been memmaped to the pool temp folder: the joblib
# pickling procedure generate a .pkl and a .npy file:
assert_equal(len(os.listdir(pool_temp_folder)), 2)
b = np.ones(100, dtype=np.float64)
assert_equal(b.nbytes, 800)
p.map(id, [b] * 10)
# A copy of both a and b are not stored in the shared memory folder
assert_equal(len(os.listdir(pool_temp_folder)), 4)
finally:
# Cleanup open file descriptors
p.terminate()
del p
# The temp folder is cleaned up upon pool termination
assert_false(os.path.exists(pool_temp_folder))
def test_memmaping_on_dev_shm():
"""Check that MemmapingPool uses /dev/shm when possible"""
p = MemmapingPool(3, max_nbytes=10)
try:
# Check that the pool has correctly detected the presence of the
# shared memory filesystem.
pool_temp_folder = p._temp_folder
folder_prefix = '/dev/shm/joblib_memmaping_pool_'
assert pool_temp_folder.startswith(folder_prefix)
assert os.path.exists(pool_temp_folder)
# Try with a file larger than the memmap threshold of 10 bytes
a = np.ones(100, dtype=np.float64)
assert a.nbytes == 800
p.map(id, [a] * 10)
# a should have been memmaped to the pool temp folder: the joblib
# pickling procedure generate one .pkl file:
assert len(os.listdir(pool_temp_folder)) == 1
# create a new array with content that is different from 'a' so that
# it is mapped to a different file in the temporary folder of the
# pool.
b = np.ones(100, dtype=np.float64) * 2
assert b.nbytes == 800
p.map(id, [b] * 10)
# A copy of both a and b are now stored in the shared memory folder
assert len(os.listdir(pool_temp_folder)) == 2
finally:
# Cleanup open file descriptors
p.terminate()
del p
# The temp folder is cleaned up upon pool termination
assert not os.path.exists(pool_temp_folder)
def test_weak_array_key_map():
def assert_empty_after_gc_collect(container, retries=3):
for i in range(retries):
if len(container) == 0:
return
gc.collect()
assert len(container) == 0
a = np.ones(42)
m = _WeakArrayKeyMap()
m.set(a, 'a')
assert m.get(a) == 'a'
b = a
assert m.get(b) == 'a'
m.set(b, 'b')
assert m.get(a) == 'b'
del a
gc.collect()
assert len(m._data) == 1
assert m.get(b) == 'b'
del b
assert_empty_after_gc_collect(m._data)
c = np.ones(42)
m.set(c, 'c')
assert len(m._data) == 1
assert m.get(c) == 'c'
with raises(KeyError):
m.get(np.ones(42))
del c
assert_empty_after_gc_collect(m._data)
# Check that creating and dropping numpy arrays with potentially the same
# object id will not cause the map to get confused.
def get_set_get_collect(m, i):
a = np.ones(42)
with raises(KeyError):
m.get(a)
m.set(a, i)
assert m.get(a) == i
return id(a)
unique_ids = set([get_set_get_collect(m, i) for i in range(1000)])
if platform.python_implementation() == 'CPython':
# On CPython (at least) the same id is often reused many times for the
# temporary arrays created under the local scope of the
# get_set_get_collect function without causing any spurious lookups /
# insertions in the map.
assert len(unique_ids) < 100
def test_pool_with_memmap(tmpdir_path):
"""Check that subprocess can access and update shared memory memmap"""
assert_array_equal = np.testing.assert_array_equal
# Fork the subprocess before allocating the objects to be passed
pool_temp_folder = os.path.join(tmpdir_path, 'pool')
os.makedirs(pool_temp_folder)
p = MemmapingPool(10, max_nbytes=2, temp_folder=pool_temp_folder)
try:
filename = os.path.join(tmpdir_path, 'test.mmap')
a = np.memmap(filename, dtype=np.float32, shape=(3, 5), mode='w+')
a.fill(1.0)
p.map(inplace_double, [(a, (i, j), 1.0)
for i in range(a.shape[0])
for j in range(a.shape[1])])
assert_array_equal(a, 2 * np.ones(a.shape))
# Open a copy-on-write view on the previous data
b = np.memmap(filename, dtype=np.float32, shape=(5, 3), mode='c')
p.map(inplace_double, [(b, (i, j), 2.0)
for i in range(b.shape[0])
for j in range(b.shape[1])])
# Passing memmap instances to the pool should not trigger the creation
# of new files on the FS
assert os.listdir(pool_temp_folder) == []
# the original data is untouched
assert_array_equal(a, 2 * np.ones(a.shape))
assert_array_equal(b, 2 * np.ones(b.shape))
# readonly maps can be read but not updated
c = np.memmap(filename, dtype=np.float32, shape=(10,), mode='r',
offset=5 * 4)
assert_raises(AssertionError, p.map, check_array,
[(c, i, 3.0) for i in range(c.shape[0])])
# depending on the version of numpy one can either get a RuntimeError
# or a ValueError
assert_raises((RuntimeError, ValueError), p.map, inplace_double,
[(c, i, 2.0) for i in range(c.shape[0])])
finally:
# Clean all filehandlers held by the pool
p.terminate()
del p
def test_numpy_persistence_bufferred_array_compression():
big_array = np.ones((_IO_BUFFER_SIZE + 100), dtype=np.uint8)
filename = env['filename'] + str(random.randint(0, 1000))
numpy_pickle.dump(big_array, filename, compress=True)
arr_reloaded = numpy_pickle.load(filename)
np.testing.assert_array_equal(big_array, arr_reloaded)
def test_joblib_compression_formats():
compresslevels = (1, 3, 6)
filename = env['filename'] + str(random.randint(0, 1000))
objects = (np.ones(shape=(100, 100), dtype='f8'),
range(10),
{'a': 1, 2: 'b'}, [], (), {}, 0, 1.0)
for compress in compresslevels:
for cmethod in _COMPRESSORS:
dump_filename = filename + "." + cmethod
for obj in objects:
if not PY3_OR_LATER and cmethod in ('xz', 'lzma'):
# Lzma module only available for python >= 3.3
msg = "{} compression is only available".format(cmethod)
assert_raises_regex(NotImplementedError, msg,
numpy_pickle.dump, obj, dump_filename,
compress=(cmethod, compress))
else:
numpy_pickle.dump(obj, dump_filename,
compress=(cmethod, compress))
# Verify the file contains the right magic number
with open(dump_filename, 'rb') as f:
assert _detect_compressor(f) == cmethod
# Verify the reloaded object is correct
obj_reloaded = numpy_pickle.load(dump_filename)
assert isinstance(obj_reloaded, type(obj))
if isinstance(obj, np.ndarray):
np.testing.assert_array_equal(obj_reloaded, obj)
else:
assert obj_reloaded == obj
os.remove(dump_filename)
def get_set_get_collect(m, i):
a = np.ones(42)
with raises(KeyError):
m.get(a)
m.set(a, i)
assert m.get(a) == i
return id(a)
def test_memmaping_pool_for_large_arrays():
"""Check that large arrays are not copied in memory"""
assert_array_equal = np.testing.assert_array_equal
# Check that the tempfolder is empty
assert_equal(os.listdir(TEMP_FOLDER), [])
# Build an array reducers that automaticaly dump large array content
# to filesystem backed memmap instances to avoid memory explosion
p = MemmapingPool(3, max_nbytes=40, temp_folder=TEMP_FOLDER)
try:
# The tempory folder for the pool is not provisioned in advance
assert_equal(os.listdir(TEMP_FOLDER), [])
assert_false(os.path.exists(p._temp_folder))
small = np.ones(5, dtype=np.float32)
assert_equal(small.nbytes, 20)
p.map(check_array, [(small, i, 1.0) for i in range(small.shape[0])])
# Memory has been copied, the pool filesystem folder is unused
assert_equal(os.listdir(TEMP_FOLDER), [])
# Try with a file larger than the memmap threshold of 40 bytes
large = np.ones(100, dtype=np.float64)
assert_equal(large.nbytes, 800)
p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])
# The data has been dumped in a temp folder for subprocess to share it
# without per-child memory copies
assert_true(os.path.isdir(p._temp_folder))
dumped_filenames = os.listdir(p._temp_folder)
assert_equal(len(dumped_filenames), 2)
# Check that memmory mapping is not triggered for arrays with
# dtype='object'
objects = np.array(['abc'] * 100, dtype='object')
results = p.map(has_shareable_memory, [objects])
assert_false(results[0])
finally:
# check FS garbage upon pool termination
p.terminate()
assert_false(os.path.exists(p._temp_folder))
del p
def test_memmapping_pool_for_large_arrays(factory, tmpdir):
"""Check that large arrays are not copied in memory"""
# Check that the tempfolder is empty
assert os.listdir(tmpdir.strpath) == []
# Build an array reducers that automaticaly dump large array content
# to filesystem backed memmap instances to avoid memory explosion
p = factory(3, max_nbytes=40, temp_folder=tmpdir.strpath, verbose=2)
try:
# The temporary folder for the pool is not provisioned in advance
assert os.listdir(tmpdir.strpath) == []
assert not os.path.exists(p._temp_folder)
small = np.ones(5, dtype=np.float32)
assert small.nbytes == 20
p.map(check_array, [(small, i, 1.0) for i in range(small.shape[0])])
# Memory has been copied, the pool filesystem folder is unused
assert os.listdir(tmpdir.strpath) == []
# Try with a file larger than the memmap threshold of 40 bytes
large = np.ones(100, dtype=np.float64)
assert large.nbytes == 800
p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])
# The data has been dumped in a temp folder for subprocess to share it
# without per-child memory copies
assert os.path.isdir(p._temp_folder)
dumped_filenames = os.listdir(p._temp_folder)
assert len(dumped_filenames) == 1
# Check that memory mapping is not triggered for arrays with
# dtype='object'
objects = np.array(['abc'] * 100, dtype='object')
results = p.map(has_shareable_memory, [objects])
assert not results[0]
finally:
# check FS garbage upon pool termination
p.terminate()
assert not os.path.exists(p._temp_folder)
del p
def test_memmapping_on_large_enough_dev_shm(factory):
"""Check that memmapping uses /dev/shm when possible"""
orig_size = jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE
try:
# Make joblib believe that it can use /dev/shm even when running on a
# CI container where the size of the /dev/shm is not very large (that
# is at least 32 MB instead of 2 GB by default).
jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = int(32e6)
p = factory(3, max_nbytes=10)
try:
# Check that the pool has correctly detected the presence of the
# shared memory filesystem.
pool_temp_folder = p._temp_folder
folder_prefix = '/dev/shm/joblib_memmapping_folder_'
assert pool_temp_folder.startswith(folder_prefix)
assert os.path.exists(pool_temp_folder)
# Try with a file larger than the memmap threshold of 10 bytes
a = np.ones(100, dtype=np.float64)
assert a.nbytes == 800
p.map(id, [a] * 10)
# a should have been memmapped to the pool temp folder: the joblib
# pickling procedure generate one .pkl file:
assert len(os.listdir(pool_temp_folder)) == 1
# create a new array with content that is different from 'a' so
# that it is mapped to a different file in the temporary folder of
# the pool.
b = np.ones(100, dtype=np.float64) * 2
assert b.nbytes == 800
p.map(id, [b] * 10)
# A copy of both a and b are now stored in the shared memory folder
assert len(os.listdir(pool_temp_folder)) == 2
finally:
# Cleanup open file descriptors
p.terminate()
del p
# The temp folder is cleaned up upon pool termination
assert not os.path.exists(pool_temp_folder)
finally:
jmr.SYSTEM_SHARED_MEM_FS_MIN_SIZE = orig_size
def test_non_contiguous_array_pickling(tmpdir):
filename = tmpdir.join('test.pkl').strpath
for array in [ # Array that triggers a contiguousness issue with nditer,
# see https://github.com/joblib/joblib/pull/352 and see
# https://github.com/joblib/joblib/pull/353
np.asfortranarray([[1, 2], [3, 4]])[1:],
# Non contiguous array with works fine with nditer
np.ones((10, 50, 20), order='F')[:, :1, :]
]:
assert not array.flags.c_contiguous
assert not array.flags.f_contiguous
numpy_pickle.dump(array, filename)
array_reloaded = numpy_pickle.load(filename)
np.testing.assert_array_equal(array_reloaded, array)
def test_memory_usage():
# Verify memory stays within expected bounds.
filename = env['filename']
small_array = np.ones((10, 10))
big_array = np.ones(shape=100 * int(1e6), dtype=np.uint8)
small_matrix = np.matrix(small_array)
big_matrix = np.matrix(big_array)
for compress in (True, False):
for obj in (small_array, big_array, small_matrix, big_matrix):
size = obj.nbytes / 1e6
obj_filename = filename + str(np.random.randint(0, 1000))
mem_used = memory_used(numpy_pickle.dump,
obj, obj_filename, compress=compress)
# The memory used to dump the object shouldn't exceed the buffer
# size used to write array chunks (16MB).
write_buf_size = _IO_BUFFER_SIZE + 16 * 1024 ** 2 / 1e6
assert mem_used <= write_buf_size
mem_used = memory_used(numpy_pickle.load, obj_filename)
# memory used should be less than array size + buffer size used to
# read the array chunk by chunk.
read_buf_size = 32 + _IO_BUFFER_SIZE # MiB
assert mem_used < size + read_buf_size
def test_non_contiguous_array_pickling():
filename = env['filename'] + str(random.randint(0, 1000))
for array in [ # Array that triggers a contiguousness issue with nditer,
# see https://github.com/joblib/joblib/pull/352 and see
# https://github.com/joblib/joblib/pull/353
np.asfortranarray([[1, 2], [3, 4]])[1:],
# Non contiguous array with works fine with nditer
np.ones((10, 50, 20), order='F')[:, :1, :]]:
assert not array.flags.c_contiguous
assert not array.flags.f_contiguous
numpy_pickle.dump(array, filename)
array_reloaded = numpy_pickle.load(filename)
np.testing.assert_array_equal(array_reloaded, array)
os.remove(filename)
def test_memory_usage(tmpdir, compress):
# Verify memory stays within expected bounds.
filename = tmpdir.join('test.pkl').strpath
small_array = np.ones((10, 10))
big_array = np.ones(shape=100 * int(1e6), dtype=np.uint8)
small_matrix = np.matrix(small_array)
big_matrix = np.matrix(big_array)
for obj in (small_array, big_array, small_matrix, big_matrix):
size = obj.nbytes / 1e6
obj_filename = filename + str(np.random.randint(0, 1000))
mem_used = memory_used(numpy_pickle.dump,
obj, obj_filename, compress=compress)
# The memory used to dump the object shouldn't exceed the buffer
# size used to write array chunks (16MB).
write_buf_size = _IO_BUFFER_SIZE + 16 * 1024 ** 2 / 1e6
assert mem_used <= write_buf_size
mem_used = memory_used(numpy_pickle.load, obj_filename)
# memory used should be less than array size + buffer size used to
# read the array chunk by chunk.
read_buf_size = 32 + _IO_BUFFER_SIZE # MiB
assert mem_used < size + read_buf_size
def test_non_contiguous_array_pickling():
filename = env['filename'] + str(random.randint(0, 1000))
for array in [ # Array that triggers a contiguousness issue with nditer,
# see https://github.com/joblib/joblib/pull/352 and see
# https://github.com/joblib/joblib/pull/353
np.asfortranarray([[1, 2], [3, 4]])[1:],
# Non contiguous array with works fine with nditer
np.ones((10, 50, 20), order='F')[:, :1, :]
]:
nose.tools.assert_false(array.flags.c_contiguous)
nose.tools.assert_false(array.flags.f_contiguous)
numpy_pickle.dump(array, filename)
array_reloaded = numpy_pickle.load(filename)
np.testing.assert_array_equal(array_reloaded, array)
os.remove(filename)
def test_memory_numpy_check_mmap_mode(tmpdir):
"""Check that mmap_mode is respected even at the first call"""
memory = Memory(location=tmpdir.strpath, mmap_mode='r', verbose=0)
@memory.cache()
def twice(a):
return a * 2
a = np.ones(3)
b = twice(a)
c = twice(a)
assert isinstance(c, np.memmap)
assert c.mode == 'r'
assert isinstance(b, np.memmap)
assert b.mode == 'r'
def test_nested_parallelism_with_dask():
distributed = pytest.importorskip('distributed')
client = distributed.Client(n_workers=2, threads_per_worker=2) # noqa
# 10 MB of data as argument to trigger implicit scattering
data = np.ones(int(1e7), dtype=np.uint8)
for i in range(2):
with parallel_backend('dask'):
backend_types_and_levels = _recursive_backend_info(data=data)
assert len(backend_types_and_levels) == 4
assert all(name == 'DaskDistributedBackend'
for name, _ in backend_types_and_levels)
# No argument
with parallel_backend('dask'):
backend_types_and_levels = _recursive_backend_info()
assert len(backend_types_and_levels) == 4
assert all(name == 'DaskDistributedBackend'
for name, _ in backend_types_and_levels)
def test_memory_numpy_check_mmap_mode(tmpdir):
"""Check that mmap_mode is respected even at the first call"""
memory = Memory(cachedir=tmpdir.strpath, mmap_mode='r', verbose=0)
memory.clear(warn=False)
@memory.cache()
def twice(a):
return a * 2
a = np.ones(3)
b = twice(a)
c = twice(a)
assert isinstance(c, np.memmap)
assert c.mode == 'r'
assert isinstance(b, np.memmap)
assert b.mode == 'r'
def test_memory_numpy_check_mmap_mode():
"""Check that mmap_mode is respected even at the first call"""
memory = Memory(cachedir=env["dir"], mmap_mode="r", verbose=0)
memory.clear(warn=False)
@memory.cache()
def twice(a):
return a * 2
a = np.ones(3)
b = twice(a)
c = twice(a)
nose.tools.assert_true(isinstance(c, np.memmap))
nose.tools.assert_equal(c.mode, "r")
nose.tools.assert_true(isinstance(b, np.memmap))
nose.tools.assert_equal(b.mode, "r")
def test_memory_numpy_check_mmap_mode():
"""Check that mmap_mode is respected even at the first call"""
memory = Memory(cachedir=env['dir'], mmap_mode='r', verbose=0)
memory.clear(warn=False)
@memory.cache()
def twice(a):
return a * 2
a = np.ones(3)
b = twice(a)
c = twice(a)
nose.tools.assert_true(isinstance(c, np.memmap))
nose.tools.assert_equal(c.mode, 'r')
nose.tools.assert_true(isinstance(b, np.memmap))
nose.tools.assert_equal(b.mode, 'r')
def test_memmaping_pool_for_large_arrays_in_return(tmpdir):
"""Check that large arrays are not copied in memory in return"""
assert_array_equal = np.testing.assert_array_equal
# Build an array reducers that automaticaly dump large array content
# but check that the returned datastructure are regular arrays to avoid
# passing a memmap array pointing to a pool controlled temp folder that
# might be confusing to the user
# The MemmapingPool user can always return numpy.memmap object explicitly
# to avoid memory copy
p = MemmapingPool(3, max_nbytes=10, temp_folder=tmpdir.strpath)
try:
res = p.apply_async(np.ones, args=(1000,))
large = res.get()
assert not has_shareable_memory(large)
assert_array_equal(large, np.ones(1000))
finally:
p.terminate()
del p
def test_memmapping_pool_for_large_arrays_in_return(factory, tmpdir):
"""Check that large arrays are not copied in memory in return"""
assert_array_equal = np.testing.assert_array_equal
# Build an array reducers that automaticaly dump large array content
# but check that the returned datastructure are regular arrays to avoid
# passing a memmap array pointing to a pool controlled temp folder that
# might be confusing to the user
# The MemmappingPool user can always return numpy.memmap object explicitly
# to avoid memory copy
p = factory(3, max_nbytes=10, temp_folder=tmpdir.strpath)
try:
res = p.apply_async(np.ones, args=(1000, ))
large = res.get()
assert not has_shareable_memory(large)
assert_array_equal(large, np.ones(1000))
finally:
p.terminate()
del p
def test_memmaping_pool_for_large_arrays_disabled():
"""Check that large arrays memmaping can be disabled"""
# Set max_nbytes to None to disable the auto memmaping feature
p = MemmapingPool(3, max_nbytes=None, temp_folder=TEMP_FOLDER)
try:
# Check that the tempfolder is empty
assert_equal(os.listdir(TEMP_FOLDER), [])
# Try with a file largish than the memmap threshold of 40 bytes
large = np.ones(100, dtype=np.float64)
assert_equal(large.nbytes, 800)
p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])
# Check that the tempfolder is still empty
assert_equal(os.listdir(TEMP_FOLDER), [])
finally:
# Cleanup open file descriptors
p.terminate()
del p
def test_memmaping_pool_for_large_arrays_disabled(tmpdir):
"""Check that large arrays memmaping can be disabled"""
# Set max_nbytes to None to disable the auto memmaping feature
p = MemmapingPool(3, max_nbytes=None, temp_folder=tmpdir.strpath)
try:
# Check that the tempfolder is empty
assert os.listdir(tmpdir.strpath) == []
# Try with a file largish than the memmap threshold of 40 bytes
large = np.ones(100, dtype=np.float64)
assert large.nbytes == 800
p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])
# Check that the tempfolder is still empty
assert os.listdir(tmpdir.strpath) == []
finally:
# Cleanup open file descriptors
p.terminate()
del p
def test_memmapping_pool_for_large_arrays_disabled(factory, tmpdir):
"""Check that large arrays memmapping can be disabled"""
# Set max_nbytes to None to disable the auto memmapping feature
p = factory(3, max_nbytes=None, temp_folder=tmpdir.strpath)
try:
# Check that the tempfolder is empty
assert os.listdir(tmpdir.strpath) == []
# Try with a file largish than the memmap threshold of 40 bytes
large = np.ones(100, dtype=np.float64)
assert large.nbytes == 800
p.map(check_array, [(large, i, 1.0) for i in range(large.shape[0])])
# Check that the tempfolder is still empty
assert os.listdir(tmpdir.strpath) == []
finally:
# Cleanup open file descriptors
p.terminate()
del p
def test_memory_numpy_check_mmap_mode(tmpdir, monkeypatch):
"""Check that mmap_mode is respected even at the first call"""
memory = Memory(location=tmpdir.strpath, mmap_mode='r', verbose=0)
@memory.cache()
def twice(a):
return a * 2
a = np.ones(3)
b = twice(a)
c = twice(a)
assert isinstance(c, np.memmap)
assert c.mode == 'r'
assert isinstance(b, np.memmap)
assert b.mode == 'r'
# Corrupts the file, Deleting b and c mmaps
# is necessary to be able edit the file
del b
del c
gc.collect()
corrupt_single_cache_item(memory)
# Make sure that corrupting the file causes recomputation and that
# a warning is issued.
recorded_warnings = monkeypatch_cached_func_warn(twice, monkeypatch)
d = twice(a)
assert len(recorded_warnings) == 1
exception_msg = 'Exception while loading results'
assert exception_msg in recorded_warnings[0]
# Asserts that the recomputation returns a mmap
assert isinstance(d, np.memmap)
assert d.mode == 'r'
def test_joblib_compression_formats():
compresslevels = (1, 3, 6)
filename = env['filename'] + str(random.randint(0, 1000))
objects = (np.ones(shape=(100, 100), dtype='f8'), range(10), {
'a': 1,
2: 'b'
}, [], (), {}, 0, 1.0)
for compress in compresslevels:
for cmethod in _COMPRESSORS:
dump_filename = filename + "." + cmethod
for obj in objects:
if not PY3_OR_LATER and cmethod in ('xz', 'lzma'):
# Lzma module only available for python >= 3.3
msg = "{0} compression is only available".format(cmethod)
assert_raises_regex(NotImplementedError,
msg,
numpy_pickle.dump,
obj,
dump_filename,
compress=(cmethod, compress))
else:
numpy_pickle.dump(obj,
dump_filename,
compress=(cmethod, compress))
# Verify the file contains the right magic number
with open(dump_filename, 'rb') as f:
nose.tools.assert_equal(_detect_compressor(f), cmethod)
# Verify the reloaded object is correct
obj_reloaded = numpy_pickle.load(dump_filename)
nose.tools.assert_true(isinstance(obj_reloaded, type(obj)))
if isinstance(obj, np.ndarray):
np.testing.assert_array_equal(obj_reloaded, obj)
else:
nose.tools.assert_equal(obj_reloaded, obj)
os.remove(dump_filename)
def test_memory_numpy_check_mmap_mode(tmpdir, monkeypatch):
"""Check that mmap_mode is respected even at the first call"""
memory = Memory(location=tmpdir.strpath, mmap_mode='r', verbose=0)
@memory.cache()
def twice(a):
return a * 2
a = np.ones(3)
b = twice(a)
c = twice(a)
assert isinstance(c, np.memmap)
assert c.mode == 'r'
assert isinstance(b, np.memmap)
assert b.mode == 'r'
# Corrupts the file, Deleting b and c mmaps
# is necessary to be able edit the file
del b
del c
corrupt_single_cache_item(memory)
# Make sure that corrupting the file causes recomputation and that
# a warning is issued.
recorded_warnings = monkeypatch_cached_func_warn(twice, monkeypatch)
d = twice(a)
assert len(recorded_warnings) == 1
exception_msg = 'Exception while loading results'
assert exception_msg in recorded_warnings[0]
# Asserts that the recomputation returns a mmap
assert isinstance(d, np.memmap)
assert d.mode == 'r'
def test_joblib_compression_formats(tmpdir, compress, cmethod):
filename = tmpdir.join('test.pkl').strpath
objects = (np.ones(shape=(100, 100), dtype='f8'), range(10), {
'a': 1,
2: 'b'
}, [], (), {}, 0, 1.0)
dump_filename = filename + "." + cmethod
for obj in objects:
if not PY3_OR_LATER and cmethod in ('lzma', 'xz', 'lz4'):
# Lzma module only available for python >= 3.3
msg = "{} compression is only available".format(cmethod)
error = NotImplementedError
if cmethod == 'lz4':
error = ValueError
with raises(error) as excinfo:
numpy_pickle.dump(obj,
dump_filename,
compress=(cmethod, compress))
excinfo.match(msg)
elif cmethod == 'lz4' and with_lz4.args[0]:
# Skip the test if lz4 is not installed. We here use the with_lz4
# skipif fixture whose argument is True when lz4 is not installed
raise SkipTest("lz4 is not installed.")
else:
numpy_pickle.dump(obj, dump_filename, compress=(cmethod, compress))
# Verify the file contains the right magic number
with open(dump_filename, 'rb') as f:
assert _detect_compressor(f) == cmethod
# Verify the reloaded object is correct
obj_reloaded = numpy_pickle.load(dump_filename)
assert isinstance(obj_reloaded, type(obj))
if isinstance(obj, np.ndarray):
np.testing.assert_array_equal(obj_reloaded, obj)
else:
assert obj_reloaded == obj
def test_memmap_returned_as_regular_array(backend):
data = np.ones(int(1e3))
# Check that child processes send temporary memmaps back as numpy arrays.
[result] = Parallel(n_jobs=2, backend=backend, max_nbytes=100)(
delayed(check_memmap_and_send_back)(data) for _ in range(1))
assert _get_backing_memmap(result) is None
def generate_arrays(n):
for i in range(n):
yield np.ones(10, dtype=np.float32) * i
def __init__(self):
self.array_float = np.arange(100, dtype='float64')
self.array_int = np.ones(100, dtype='int32')
self.array_obj = np.array(['a', 10, 20.0], dtype='object')
def __init__(self):
self.array_float = np.arange(100, dtype='float64')
self.array_int = np.ones(100, dtype='int32')
self.array_obj = np.array(['a', 10, 20.0], dtype='object')
def generate_arrays(n):
for i in range(n):
yield np.ones(10, dtype=np.float32) * i
