def update_deployment(self,
name,
model_uri=None,
flavor=None,
config=None):
"""Updates an existing model deployment in openshift. It can either update
the `model_uri` and/or the mandatory config items describing the container image,
i.e `image`, `docker_registry`, `tag`.
Notes:
In case more configurations need to be changed, consider deleting and creating
the deployment from scratch.
Special treatment for different model flavors are not implemented.
Args:
name (str): name of the deployment
model_uri (str): path where to find the mlflow packed model
flavor (str, optional): mlflow deployment flavor. Defaults to None
config (dict, optional): config items for the deployment. Defaults to {}
Raises:
MlflowException: if the updated deployment lead to an error in openshift
Returns:
dict: {'name': <name>, 'flavor': <flavor>}
"""
if not model_uri and not config:
raise MlflowException(
"Provide at least a new *model_uri* or *config*")
dc_obj = oc.selector("dc", labels={"app": name}).object()
if config:
if all(key in config
for key in ("image", "docker_registry", "tag")):
dc_obj = oc_helper.update_container_image(dc_obj, config)
else:
raise MlflowException(
"Not all of the necessary *config* items for updating are provided. "
"You need to provide: image, docker_registry, tag, auth_user and auth_password"
)
if model_uri:
dc_obj = oc_helper.update_model_uri(dc_obj, model_uri)
# hotfix for bug in openshift-client library -> normal apply()
dc_obj.modify_and_apply(lambda x: True, retries=0)
route_host = oc_helper.get_route_name(name)
auth_user, auth_password = oc_helper.get_authentication_info(name)
try:
oc_helper.check_succesful_deployment(name, route_host, auth_user,
auth_password)
except MlflowException as mlflow_exception:
self.delete_deployment(name)
raise mlflow_exception
return {'name': name, 'flavor': flavor}
def create_deployment(self, name, model_uri, flavor=None, config={}):
"""Creates all necessary artifacts for a model deployment in openshift.
Notes:
special treatment for different model flavors are not implemented.
Args:
name (str): name of the deployment
model_uri (str): path where to find the mlflow packed model
flavor (str, optional): mlflow deployment flavor. Defaults to None
config (dict, optional): config items for the deployment. Defaults to {}
Necessary config items: image, docker_registry, tag
Raises:
mlflow_exception: if the deployment failed in openshift or not all
mandatory config items are provided
Returns:
dict: {'name': <name>, 'flavor': <flavor>}
"""
if not all(key in config
for key in ("image", "docker_registry", "tag")):
raise MlflowException(
"not all mandatory config items (image, docker_registry, tag) "
"are provided.")
config = set_config_defaults(config)
template_path = os.path.join(os.path.dirname(__file__),
'templates/deploy_with_auth.yml')
config["NAME"] = name
config["MODEL_URI"] = model_uri
oc_helper.apply_deployment_config(config, template_path)
try:
route_host = oc_helper.get_route_name(name)
oc_helper.check_succesful_deployment(name, route_host,
config["BASIC_AUTH_USERNAME"],
config["BASIC_AUTH_PASSWORD"])
except MlflowException as mlflow_exception:
self.delete_deployment(name)
raise mlflow_exception
logger.info("\n" + "Endpoint available under: " + route_host)
return {'name': name, 'flavor': flavor}
def predict(self, deployment_name, df):
"""Makes predictions using the specified deployment name. This can be used for
making batch predictions using the openshift infrastrucutre, e.g. in automated
daily/weekly pipelines.
Args:
deployment_name (str): name of the deployment
df (pd.DataFrame): dataframe with the correct format the model expects
Returns:
np.ndarray: array containing the predictions
"""
auth_user, auth_password = oc_helper.get_authentication_info(
deployment_name)
route_host = oc_helper.get_route_name(deployment_name)
# send to https model deployment
payload = df.to_dict(orient='split')
response = requests.post("https://{0}/invocations".format(route_host),
headers={'Content-Type': 'application/json'},
auth=(auth_user, auth_password),
data=json.dumps(payload))
list_response = ast.literal_eval(response.content.decode("utf-8"))
return np.array(list_response)