Adapt latest kfserving and training oprator

.gitignore

@@ -1,7 +1,8 @@
 .metaparticle
 *.bak
 .vscode
-
+.idea
+.env
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

kubeflow/fairing/constants/constants.py

@@ -58,7 +58,7 @@
 KFSERVING_GROUP = "serving.kubeflow.org"
 KFSERVING_KIND = "InferenceService"
 KFSERVING_PLURAL = "inferenceservices"
-KFSERVING_VERSION = 'v1alpha2'
+KFSERVING_VERSION = 'v1beta1'
 KFSERVING_DEFAULT_NAME = 'fairing-kfserving-'
 KFSERVING_DEPLOYER_TYPE = 'kfservice'
 KFSERVING_CONTAINER_NAME = 'user-container'

kubeflow/fairing/deployers/kfserving/kfserving.py

@@ -12,27 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import uuid
 import logging
+import uuid
 
-from kubernetes import client as k8s_client
-
-from kfserving import V1alpha2EndpointSpec
-from kfserving import V1alpha2PredictorSpec
-from kfserving import V1alpha2TensorflowSpec
-from kfserving import V1alpha2ONNXSpec
-from kfserving import V1alpha2PyTorchSpec
-from kfserving import V1alpha2SKLearnSpec
-from kfserving import V1alpha2TritonSpec
-from kfserving import V1alpha2XGBoostSpec
-from kfserving import V1alpha2CustomSpec
-from kfserving import V1alpha2InferenceServiceSpec
-from kfserving import V1alpha2InferenceService
-
+from kfserving import V1beta1InferenceService
+from kfserving import V1beta1InferenceServiceSpec
+from kfserving import V1beta1PredictorSpec
+from kfserving import V1beta1SKLearnSpec
+from kfserving import V1beta1TFServingSpec
+from kfserving import V1beta1TorchServeSpec
+from kfserving import V1beta1TritonSpec
+from kfserving import V1beta1XGBoostSpec
+from kubeflow.fairing import utils
 from kubeflow.fairing.constants import constants
 from kubeflow.fairing.deployers.deployer import DeployerInterface
 from kubeflow.fairing.kubernetes.manager import KubeManager
-from kubeflow.fairing import utils
+from kubernetes import client as k8s_client
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -41,7 +36,7 @@
 class KFServing(DeployerInterface):
     """Serves a prediction endpoint using Kubeflow KFServing."""
 
-    def __init__(self, framework, default_storage_uri=None, canary_storage_uri=None,
+    def __init__(self, framework, default_storage_uri=None,
                  canary_traffic_percent=0, namespace=None, labels=None, annotations=None,
                  custom_default_container=None, custom_canary_container=None,
                  isvc_name=None, stream_log=False, cleanup=False, config_file=None,
@@ -50,7 +45,6 @@ def __init__(self, framework, default_storage_uri=None, canary_storage_uri=None,
         :param framework: The framework for the InferenceService, such as Tensorflow,
             XGBoost and ScikitLearn etc.
         :param default_storage_uri: URI pointing to Saved Model assets for default service.
-        :param canary_storage_uri: URI pointing to Saved Model assets for canary service.
         :param canary_traffic_percent: The amount of traffic to sent to the canary, defaults to 0.
         :param namespace: The k8s namespace where the InferenceService will be deployed.
         :param labels: Labels for the InferenceService, separate with commas if have more than one.
@@ -73,7 +67,6 @@ def __init__(self, framework, default_storage_uri=None, canary_storage_uri=None,
         self.framework = framework
         self.isvc_name = isvc_name
         self.default_storage_uri = default_storage_uri
-        self.canary_storage_uri = canary_storage_uri
         self.canary_traffic_percent = canary_traffic_percent
         self.annotations = annotations
         self.set_labels(labels)
@@ -135,68 +128,38 @@ def deploy(self, isvc):  # pylint:disable=arguments-differ,unused-argument
 
     def generate_isvc(self):
         """ generate InferenceService """
-
         api_version = constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION
-        default_predictor, canary_predictor = None, None
-
-        if self.framework == 'custom':
-            default_predictor = self.generate_predictor_spec(
-                self.framework, container=self.custom_default_container)
-        else:
-            default_predictor = self.generate_predictor_spec(
-                self.framework, storage_uri=self.default_storage_uri)
-
-        if self.framework != 'custom' and self.canary_storage_uri is not None:
-            canary_predictor = self.generate_predictor_spec(
-                self.framework, storage_uri=self.canary_storage_uri)
-        if self.framework == 'custom' and self.custom_canary_container is not None:
-            canary_predictor = self.generate_predictor_spec(
-                self.framework, container=self.custom_canary_container)
-
-        if canary_predictor:
-            isvc_spec = V1alpha2InferenceServiceSpec(
-                default=V1alpha2EndpointSpec(predictor=default_predictor),
-                canary=V1alpha2EndpointSpec(predictor=canary_predictor),
-                canary_traffic_percent=self.canary_traffic_percent)
-        else:
-            isvc_spec = V1alpha2InferenceServiceSpec(
-                default=V1alpha2EndpointSpec(predictor=default_predictor),
-                canary_traffic_percent=self.canary_traffic_percent)
-
-        return V1alpha2InferenceService(api_version=api_version,
-                                        kind=constants.KFSERVING_KIND,
-                                        metadata=k8s_client.V1ObjectMeta(
-                                            name=self.isvc_name,
-                                            generate_name=constants.KFSERVING_DEFAULT_NAME,
-                                            namespace=self.namespace),
-                                        spec=isvc_spec)
-
-
-    def generate_predictor_spec(self, framework, storage_uri=None, container=None):
+        default_predictor = self.generate_predictor_spec(
+            self.framework, storage_uri=self.default_storage_uri)
+        return V1beta1InferenceService(api_version=api_version,
+                                       kind=constants.KFSERVING_KIND,
+                                       metadata=k8s_client.V1ObjectMeta(
+                                           name=self.isvc_name,
+                                           generate_name=constants.KFSERVING_DEFAULT_NAME,
+                                           namespace=self.namespace),
+                                       spec=V1beta1InferenceServiceSpec(
+                                           predictor=default_predictor
+                                       ))
+
+    def generate_predictor_spec(self, framework, storage_uri=None):
         '''Generate predictor spec according to framework and
            default_storage_uri or custom container.
         '''
         if self.framework == 'tensorflow':
-            predictor = V1alpha2PredictorSpec(
-                tensorflow=V1alpha2TensorflowSpec(storage_uri=storage_uri))
-        elif self.framework == 'onnx':
-            predictor = V1alpha2PredictorSpec(
-                onnx=V1alpha2ONNXSpec(storage_uri=storage_uri))
+            predictor = V1beta1PredictorSpec(
+                tensorflow=V1beta1TFServingSpec(storage_uri=storage_uri))
         elif self.framework == 'pytorch':
-            predictor = V1alpha2PredictorSpec(
-                pytorch=V1alpha2PyTorchSpec(storage_uri=storage_uri))
+            predictor = V1beta1PredictorSpec(
+                pytorch=V1beta1TorchServeSpec(storage_uri=storage_uri))
         elif self.framework == 'sklearn':
-            predictor = V1alpha2PredictorSpec(
-                sklearn=V1alpha2SKLearnSpec(storage_uri=storage_uri))
+            predictor = V1beta1PredictorSpec(
+                sklearn=V1beta1SKLearnSpec(storage_uri=storage_uri))
         elif self.framework == 'triton':
-            predictor = V1alpha2PredictorSpec(
-                triton=V1alpha2TritonSpec(storage_uri=storage_uri))
+            predictor = V1beta1PredictorSpec(
+                triton=V1beta1TritonSpec(storage_uri=storage_uri))
         elif self.framework == 'xgboost':
-            predictor = V1alpha2PredictorSpec(
-                xgboost=V1alpha2XGBoostSpec(storage_uri=storage_uri))
-        elif self.framework == 'custom':
-            predictor = V1alpha2PredictorSpec(
-                custom=V1alpha2CustomSpec(container=container))
+            predictor = V1beta1PredictorSpec(
+                xgboost=V1beta1XGBoostSpec(storage_uri=storage_uri))
         else:
             raise RuntimeError("Unsupported framework {}".format(framework))
         return predictor

kubeflow/fairing/deployers/pytorchjob/pytorchjob.py

@@ -1,19 +1,19 @@
 import logging
-from kubernetes import client as k8s_client
-
-from kubeflow.pytorchjob import V1ReplicaSpec
-from kubeflow.pytorchjob import V1PyTorchJob
-from kubeflow.pytorchjob import V1PyTorchJobSpec
 
 from kubeflow.fairing.constants import constants
 from kubeflow.fairing.deployers.job.job import Job
+from kubeflow.training import V1PyTorchJob
+from kubeflow.training import V1PyTorchJobSpec
+from kubeflow.training import V1ReplicaSpec, V1RunPolicy
+from kubernetes import client as k8s_client
 
 logger = logging.getLogger(__name__)
 
 
 class PyTorchJob(Job):
     """ Handle all the k8s' template building to create pytorch
         training job using Kubeflow PyTorch Operator"""
+
     def __init__(self, namespace=None, master_count=1, worker_count=1,
                  runs=1, job_name=None, stream_log=True, labels=None,
                  pod_spec_mutators=None, cleanup=False, annotations=None,
@@ -81,12 +81,12 @@ def generate_deployment_spec(self, pod_template_spec):
 
         pytorchjob = V1PyTorchJob(
             api_version=constants.PYTORCH_JOB_GROUP + "/" + \
-                constants.PYTORCH_JOB_VERSION,
+                        constants.PYTORCH_JOB_VERSION,
             kind=constants.PYTORCH_JOB_KIND,
             metadata=k8s_client.V1ObjectMeta(name=self.job_name,
                                              generate_name=constants.PYTORCH_JOB_DEFAULT_NAME,
                                              labels=self.labels),
-            spec=V1PyTorchJobSpec(pytorch_replica_specs=pytorch_replica_specs)
+            spec=V1PyTorchJobSpec(pytorch_replica_specs=pytorch_replica_specs, run_policy=V1RunPolicy(clean_pod_policy="None"))
         )
 
         return pytorchjob

kubeflow/fairing/deployers/tfjob/tfjob.py

@@ -1,20 +1,21 @@
-import logging
 import copy
-from kubernetes import client as k8s_client
+import logging
 
-from kubeflow.tfjob import V1ReplicaSpec
-from kubeflow.tfjob import V1TFJob
-from kubeflow.tfjob import V1TFJobSpec
+from kubernetes import client as k8s_client
 
 from kubeflow.fairing.constants import constants
 from kubeflow.fairing.deployers.job.job import Job
+from kubeflow.training import V1ReplicaSpec, V1RunPolicy
+from kubeflow.training import V1TFJob
+from kubeflow.training import V1TFJobSpec
 
 logger = logging.getLogger(__name__)
 
 
 class TfJob(Job):
     """ Handle all the k8s' template building to create tensorflow
         training job using Kubeflow TFOperator"""
+
     def __init__(self, namespace=None, worker_count=1, ps_count=0,
                  chief_count=0, runs=1, job_name=None, stream_log=True,
                  labels=None, pod_spec_mutators=None, cleanup=False, annotations=None,
@@ -90,7 +91,8 @@ def generate_deployment_spec(self, pod_template_spec):
             metadata=k8s_client.V1ObjectMeta(name=self.job_name,
                                              generate_name=constants.TF_JOB_DEFAULT_NAME,
                                              labels=self.labels),
-            spec=V1TFJobSpec(tf_replica_specs=tf_replica_specs)
+            spec=V1TFJobSpec(tf_replica_specs=tf_replica_specs, run_policy=V1RunPolicy(clean_pod_policy="None"))
+
         )
 
         return tfjob

kubeflow/fairing/kubernetes/manager.py

@@ -5,8 +5,8 @@
 from kubernetes import client, config, watch
 from kfserving import KFServingClient
 
-from kubeflow.tfjob import TFJobClient
-from kubeflow.pytorchjob import PyTorchJobClient
+from kubeflow.training import TFJobClient
+from kubeflow.training import PyTorchJobClient
 
 from kubeflow.fairing.utils import is_running_in_k8s, camel_to_snake
 from kubeflow.fairing.constants import constants

requirements.txt

@@ -1,28 +1,26 @@
-python-dateutil>=2.1,<=2.8.0
-numpy>=1.17.3
-kfserving>=0.3.0.2
+ray[serve]==1.5.0
+kfserving>=0.6.1
 docker>=3.4.1
 notebook>=5.6.0
-kubernetes==10.0.1
+kubernetes>=12.0.0
 future>=0.17.1
 six>=1.11.0
 google-cloud-storage>=1.13.2
 google-cloud-logging>=1.13.0
-requests>=2.21.0,<2.23
+requests>=2.21.0
 setuptools>=34.0.0
 google-auth>=1.6.2
 httplib2>=0.12.0
 oauth2client>=4.0.0
 tornado>=6.0.1
 google-api-python-client>=1.7.8
 cloudpickle>=0.8,<=1.4.1
-urllib3==1.24.2
 boto3>=1.9.0
 azure-storage-file>=2.1.0
 azure-mgmt-storage>=9.0.0
 retrying>=1.3.3
-kubeflow-tfjob>=0.1.1
-kubeflow-pytorchjob>=0.1.1
+kubeflow-training>=1.3.0
 ibm-cos-sdk>=2.6.0
 grpcio>=1.27.2
 nbconvert>=5.6.1
+msrestazure>=0.6.4

setup.py

@@ -21,6 +21,7 @@
         'Programming Language :: Python :: 3.5',
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.9',
         "License :: OSI Approved :: Apache Software License",
         "Operating System :: OS Independent",
         'Topic :: Scientific/Engineering',