def update_model(model_id,
model_file=None,
name=None,
new_tags=None,
remove_tags=None):
"""Update one of the project's models."""
model = ml.get_model(model_id)
if model_file is not None:
# Load a tflite file and upload it to Cloud Storage
print('Uploading to Cloud Storage...')
model_source = ml.TFLiteGCSModelSource.from_tflite_model_file(
model_file)
tflite_format = ml.TFLiteFormat(model_source=model_source)
model.model_format = tflite_format
if name is not None:
model.display_name = name
if new_tags is not None:
model.tags = new_tags if model.tags is None else model.tags + new_tags
if remove_tags is not None and model.tags is not None:
model.tags = list(set(model.tags).difference(set(remove_tags)))
updated_model = ml.update_model(model)
ml.publish_model(updated_model.model_id)
def test_publish_invalid_fails(firebase_model):
assert firebase_model.validation_error is not None
with pytest.raises(exceptions.FailedPreconditionError) as excinfo:
ml.publish_model(firebase_model.model_id)
check_operation_error(excinfo,
'Cannot publish a model that is not verified.')
def publish_model_to_firebase(tflite_model_name, model_name):
source = ml.TFLiteGCSModelSource.from_tflite_model_file(tflite_model_name)
model_format = ml.TFLiteFormat(model_source=source)
firebase_models = ml.list_models(
list_filter="display_name = {0}".format(model_name)).iterate_all()
for model in firebase_models:
custom_model = model
custom_model.model_format = model_format
model_to_publish = ml.update_model(custom_model)
ml.publish_model(model_to_publish.model_id)
def test_publish_unpublish_non_existing_model(firebase_model):
ml.delete_model(firebase_model.model_id)
with pytest.raises(exceptions.NotFoundError) as excinfo:
ml.publish_model(firebase_model.model_id)
check_operation_error(
excinfo,
'Model \'{0}\' was not found'.format(firebase_model.as_dict().get('name')))
with pytest.raises(exceptions.NotFoundError) as excinfo:
ml.unpublish_model(firebase_model.model_id)
check_operation_error(
excinfo,
'Model \'{0}\' was not found'.format(firebase_model.as_dict().get('name')))
def add_automl_model(model_ref, name, tags=None):
"""Add an AutoML tflite model file to the project and publish it."""
# Create the model object
model_source = ml.TFLiteAutoMlSource(model_ref)
model = ml.Model(display_name=name,
model_format=ml.TFLiteFormat(model_source=model_source))
if tags is not None:
model.tags = tags
# Add the model to your Firebase project and publish it
new_model = ml.create_model(model)
new_model.wait_for_unlocked()
ml.publish_model(new_model.model_id)
print('Model uploaded and published:')
print_models([new_model], headers=False)
def test_publish_unpublish_model(firebase_model):
assert firebase_model.published is False
published_model = ml.publish_model(firebase_model.model_id)
assert published_model.published is True
unpublished_model = ml.unpublish_model(published_model.model_id)
assert unpublished_model.published is False
def upload_model(model_file, name, tags=None):
"""Upload a tflite model file to the project and publish it."""
# Load a tflite file and upload it to Cloud Storage
print('Uploading to Cloud Storage...')
model_source = ml.TFLiteGCSModelSource.from_tflite_model_file(model_file)
# Create the model object
tflite_format = ml.TFLiteFormat(model_source=model_source)
model = ml.Model(display_name=name, model_format=tflite_format)
if tags is not None:
model.tags = tags
# Add the model to your Firebase project and publish it
new_model = ml.create_model(model)
ml.publish_model(new_model.model_id)
print('Model uploaded and published:')
print_models([new_model], headers=False)
