def save_model(model, filename):
"""
Save a model. Includes DELTA configuration.
"""
model.save(filename, save_format='h5')
with h5py.File(filename, 'r+') as f:
f.attrs['delta'] = config.export()
def test_save(tmp_path):
tmp_path = tmp_path / 'temp.h5'
inputs = keras.layers.Input((None, None, 1))
out = keras.layers.Conv2D(filters=9, kernel_size=2)(inputs)
m = keras.Model(inputs, out)
io.save_model(m, tmp_path)
with h5py.File(tmp_path, 'r') as f:
assert f.attrs['delta'] == config.export()
def _mlflow_train_setup(model, dataset, training_spec):
mlflow.set_tracking_uri(config.mlflow.uri())
mlflow.set_experiment(config.mlflow.experiment())
mlflow.start_run()
_log_mlflow_params(model, dataset, training_spec)
temp_dir = tempfile.mkdtemp()
fname = os.path.join(temp_dir, 'config.yaml')
with open(fname, 'w') as f:
f.write(config.export())
mlflow.log_artifact(fname)
os.remove(fname)
return _MLFlowCallback(temp_dir)
def save_model(model, filename):
"""
Save a model. Includes DELTA configuration.
Parameters
----------
model: tensorflow.keras.models.Model
The model to save.
filename: str
Output filename.
"""
model.save(filename, save_format='h5')
with h5py.File(filename, 'r+') as f:
f.attrs['delta'] = config.export()
def save_model(model, filename):
"""
Save a model. Includes DELTA configuration.
Parameters
----------
model: tensorflow.keras.models.Model
The model to save.
filename: str
Output filename.
"""
if str(filename).endswith('.h5'):
model.save(filename, save_format='h5')
with h5py.File(filename, 'r+') as f:
f.attrs['delta'] = config.export()
else: # SavedModel format
model.save(filename)
# Record the config values into a subfolder of the savedmodel output folder
config_copy_folder = os.path.join(filename, 'assets.extra')
config_copy_path = os.path.join(config_copy_folder, 'delta_config.yaml')
if not os.path.exists(config_copy_folder):
os.mkdir(config_copy_folder)
with open(config_copy_path, 'w') as f:
f.write(config.export())
def test_dump():
config_reset()
assert config.to_dict() == yaml.load(config.export())
