def deploy_spec(self, model_spec: ModelSpec):
self._create_api_client()
env_api = EnvironmentApi(self.api_client)
user = env_api.read_user()
print(user)
# TODO: Use KubernetesSpec().spec
sd = SeldonDeployment(
kind="SeldonDeployment",
api_version="machinelearning.seldon.io/v1",
metadata=ObjectMeta(
name=model_spec.model_details.name,
namespace=model_spec.runtime_options.namespace, # type: ignore
),
spec=SeldonDeploymentSpec(predictors=[
PredictorSpec(
graph=PredictiveUnit(
implementation=KubernetesSpec.Implementations[
model_spec.model_details.platform],
model_uri=model_spec.model_details.uri,
name="model",
),
name="default",
replicas=model_spec.runtime_options.
replicas, # type: ignore
)
]),
)
dep_instance = SeldonDeploymentsApi(self.api_client)
dep_instance.create_seldon_deployment(
model_spec.runtime_options.namespace, sd) # type: ignore
def undeploy_spec(self, model_spec: ModelSpec):
self._create_api_client()
dep_instance = SeldonDeploymentsApi(self.api_client)
dep_instance.delete_seldon_deployment(
model_spec.model_details.name,
model_spec.runtime_options.k8s_options.namespace,
_preload_content=False)
def deploy(self, model_details: ModelDetails):
# TODO: Use KubernetesSpec().spec
sd = SeldonDeployment(
kind="SeldonDeployment",
api_version="machinelearning.seldon.io/v1",
metadata=ObjectMeta(
name=model_details.name, namespace=self._k8s_options.namespace
),
spec=SeldonDeploymentSpec(
predictors=[
PredictorSpec(
graph=PredictiveUnit(
implementation=KubernetesSpec.Implementations[
model_details.platform
],
model_uri=model_details.uri,
name="model",
),
name="default",
replicas=self._k8s_options.replicas,
)
]
),
)
api_client = self._get_api_client()
dep_instance = SeldonDeploymentsApi(api_client)
created = dep_instance.create_seldon_deployment(self._k8s_options.namespace, sd)
def wait_ready(self, model_details: ModelDetails, timeout_secs=None) -> bool:
ready = False
t0 = time.time()
api_client = self._get_api_client()
dep_instance = SeldonDeploymentsApi(api_client)
while not ready:
sdep: SeldonDeployment = dep_instance.read_seldon_deployment(
model_details.name, self._k8s_options.namespace
)
sdep_dict = sdep.to_dict()
ready = sdep_dict["status"]["state"] == "Available"
if timeout_secs is not None:
t1 = time.time()
if t1 - t0 > timeout_secs:
return ready
return ready
def wait_ready_spec(self,
model_spec: ModelSpec,
timeout_secs=None) -> bool:
self._create_api_client()
ready = False
t0 = time.time()
dep_instance = SeldonDeploymentsApi(self.api_client)
while not ready:
sdep: SeldonDeployment = dep_instance.read_seldon_deployment(
model_spec.model_details.name,
model_spec.runtime_options.k8s_options.namespace)
sdep_dict = sdep.to_dict()
ready = sdep_dict["status"]["state"] == "Available"
if timeout_secs is not None:
t1 = time.time()
if t1 - t0 > timeout_secs:
return ready
return ready
def undeploy(self, model_details: ModelDetails):
api_client = self._get_api_client()
dep_instance = SeldonDeploymentsApi(api_client)
dep_instance.delete_seldon_deployment(model_details.name,
self._k8s_options.namespace,
_preload_content=False)