def update(model_id,
name=None,
desc=None,
condition_refcode=None,
condition_name=None,
feature_set_id=None,
raise_abort=True):
m = get(model_id, raise_abort=raise_abort)
User.check_request_for_logged_in_user(m.creator_id)
if name:
m.name = name
if desc:
m.description = desc
if condition_refcode:
m.condition_refcode = condition_refcode
if condition_name:
m.condition_name = condition_name
if feature_set_id:
fs = FeatureSet.get(feature_set_id, raise_abort=raise_abort)
m.feature_set_id = fs.id
if feature_set_id == 0:
m.feature_set_id = None
db.session.commit()
return m, 200
def create(name,
desc,
env_id,
feature_set_id,
condition_refcode=None,
condition_name=None,
create_example_model=False,
raise_abort=True):
e = Environment.get(env_id, raise_abort=raise_abort)
m = MLModel()
if feature_set_id:
fs = FeatureSet.get(feature_set_id, raise_abort=raise_abort)
m.feature_set_id = fs.id
m.environment_id = e.id
m.name = name
m.description = desc
m.creator_id = g.user.id
if condition_refcode:
m.condition_refcode = condition_refcode
if condition_name:
m.condition_name = condition_name
params = None
if create_example_model:
params = {'createExampleModel': create_example_model}
resp = requests.post('http://' + e.container_name + ':5000/models',
params=params).json()
m.ml_model_name = str(resp['modelName'])
db.session.add(m)
db.session.commit()
return m
def load_model(file, environment_id=None, feature_set_id=None, raise_abort=True):
# generate temporary path to save file to
tmp_uuid = str(uuid.uuid4().hex)
tmp_path = '/tmp/' + tmp_uuid
# create temporary directory
os.makedirs(tmp_path, mode=0o777)
# save zip-file to temporary directory and unzip it
file.save(tmp_path + '/' + file.filename)
unpack_archive(tmp_path + '/' + file.filename, tmp_path, 'zip')
os.remove(tmp_path + '/' + file.filename)
# load metadata
metadata = None
with open(tmp_path + METADATA_DIR + '/metadata.json', 'r') as infile:
metadata = json.load(infile)
# first of all: get the image of the environment to create
i = metadata[0]
create_image = Image.get_by_name(image_name=i['name'])
# create and start the new environment
env_created = None
e = metadata[1]
if not environment_id or environment_id <= 0:
env_created = Environment.create(name=e['name'], desc=e['description'], image_id=create_image.id)
else:
env_created = Environment.get(environment_id, raise_abort=raise_abort)
# create the model which is to be loaded
m = metadata[2]
model_created = MLModel.create(name=m['name'], desc=m['description'], env_id=env_created.id, create_example_model=False, feature_set_id=None)
if len(metadata) > 3:
# create the features
features_created = list()
features = metadata[3]
for f in features:
# do not create duplicate features
feature = Feature.get_by_res_par_val(resource=f['resource'], parameter_name=f['parameter_name'], value=f['value'])
if not feature:
feature = Feature.create(resource=f['resource'], parameter_name=f['parameter_name'], value=f['value'], name=f['name'], desc=f['description'])
features_created.append(feature)
# create the feature set with the features and model assigned
feature_set_created = None
fs = metadata[4]
if not feature_set_id or feature_set_id <= 0:
feature_set_created = FeatureSet.create(name=fs['name'], desc=fs['description'])
else:
feature_set_created = FeatureSet.get(feature_set_id, raise_abort=raise_abort)
feature_set_created.features = features_created
feature_set_created.ml_models.append(model_created)
db.session.commit()
# remove temporarily created directory and files
if os.path.isdir(env_created.get_data_directory() + '/' + model_created.ml_model_name):
rmtree(env_created.get_data_directory() + '/' + model_created.ml_model_name)
os.makedirs(env_created.get_data_directory() + '/' + model_created.ml_model_name, mode=0o777)
for filename in os.listdir(tmp_path):
move(tmp_path + '/' + filename, env_created.get_data_directory() + '/' + model_created.ml_model_name)
rmtree(env_created.get_data_directory() + '/' + model_created.ml_model_name + METADATA_DIR)
def get(self, feature_set_id):
return FeatureSet.get(feature_set_id), 200