def test_mulitple_serializations_third(keras_model):
skl3 = KerasModel(artifact=keras_model)
skl3.store(name='nn3')
assert len(os.listdir(SAVED_MODELS)) == 3
# cleanup
for root, dirs, files in os.walk(SAVED_MODELS):
for f in files:
os.unlink(os.path.join(root, f))
for d in dirs:
shutil.rmtree(os.path.join(root, d))
def test_loader(keras_model):
skl = KerasModel(artifact=keras_model)
skl.store(name='nn')
K.clear_session()
reloaded = skl.load(name='nn')
assert isinstance(reloaded, KerasBaseModel)
for root, dirs, files in os.walk(SAVED_MODELS):
for f in files:
os.unlink(os.path.join(root, f))
for d in dirs:
shutil.rmtree(os.path.join(root, d))
def test_mulitple_serializations_third(keras_model, project_manager):
skl3 = KerasModel(artifact=keras_model)
skl3.store(name='nn3')
assert len(os.listdir(project_manager.CONFIG['saved-models'])) == 3 + 1 # .gitkeep
# cleanup
for root, dirs, files in os.walk(project_manager.CONFIG['saved-models']):
for f in files:
os.unlink(os.path.join(root, f))
for d in dirs:
shutil.rmtree(os.path.join(root, d))
with open(os.path.join(project_manager.CONFIG['saved-models'], '.gitkeep'), 'w') as gitkeep:
gitkeep.write('empty')
def test_serialization(keras_model):
skl = KerasModel(artifact=keras_model)
skl.store(name='nn')
assert os.path.exists(os.path.join(skl.model_path, 'nn' + '.h5'))
assert os.path.exists(os.path.join(skl.model_path, 'nn' + '.json'))
assert os.path.exists(
os.path.join(skl.model_path, 'tf', 'saved_model' + '.pb'))
assert os.path.isdir(os.path.join(skl.model_path, 'tf', 'variables'))
for root, dirs, files in os.walk(SAVED_MODELS):
for f in files:
os.unlink(os.path.join(root, f))
for d in dirs:
shutil.rmtree(os.path.join(root, d))
def test_trainable_model_from_file(keras_model):
skl = KerasModel(artifact=keras_model)
skl.store(name='nn')
K.clear_session()
trainable = TrainableModel.from_file(run_number=1,
name='nn',
model_type='keras')
assert isinstance(trainable.model, KerasBaseModel)
for root, dirs, files in os.walk(SAVED_MODELS):
for f in files:
os.unlink(os.path.join(root, f))
for d in dirs:
shutil.rmtree(os.path.join(root, d))
def test_trainable_model_from_file(keras_model, project_manager):
skl = KerasModel(artifact=keras_model)
skl.store(name='nn')
K.clear_session()
trainable = TrainableModel.from_file(run_number=1, name='nn', model_type='keras')
assert isinstance(trainable.model, KerasBaseModel)
for root, dirs, files in os.walk(project_manager.CONFIG['saved-models']):
for f in files:
os.unlink(os.path.join(root, f))
for d in dirs:
shutil.rmtree(os.path.join(root, d))
with open(os.path.join(project_manager.CONFIG['saved-models'], '.gitkeep'), 'w') as gitkeep:
gitkeep.write('empty')
def test_loader(keras_model, project_manager):
skl = KerasModel(artifact=keras_model)
skl.store(name='nn')
K.clear_session()
reloaded = skl.load(name='nn')
assert isinstance(reloaded, KerasBaseModel)
for root, dirs, files in os.walk(project_manager.CONFIG['saved-models']):
for f in files:
os.unlink(os.path.join(root, f))
for d in dirs:
shutil.rmtree(os.path.join(root, d))
with open(os.path.join(project_manager.CONFIG['saved-models'], '.gitkeep'), 'w') as gitkeep:
gitkeep.write('empty')
def test_serialization(keras_model, project_manager):
skl = KerasModel(artifact=keras_model)
skl.store(name='nn')
assert os.path.exists(os.path.join(skl.model_path, 'nn' + '.h5'))
assert os.path.exists(os.path.join(skl.model_path, 'nn' + '.json'))
assert os.path.exists(os.path.join(skl.model_path, 'tf.txt', 'saved_model' + '.pb'))
assert os.path.isdir(os.path.join(skl.model_path, 'tf.txt', 'variables'))
for root, dirs, files in os.walk(project_manager.CONFIG['saved-models']):
for f in files:
os.unlink(os.path.join(root, f))
for d in dirs:
shutil.rmtree(os.path.join(root, d))
with open(os.path.join(project_manager.CONFIG['saved-models'], '.gitkeep'), 'w') as gitkeep:
gitkeep.write('empty')
def test_mulitple_serializations_second(keras_model):
skl2 = KerasModel(artifact=keras_model)
skl2.store(name='nn2')
assert os.path.exists(skl2.model_path)
def test_mulitple_serializations_first(keras_model):
skl1 = KerasModel(artifact=keras_model)
skl1.store(name='nn1')
assert os.path.exists(skl1.model_path)
def serializer():
skl = KerasModel(artifact=keras_model)
skl.store(name='nn')