fix fairing install Incompatible dependencies

.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 +12,23 @@
 # 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__)
@@ -153,50 +149,41 @@ def generate_isvc(self):
             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)
-
+        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, container=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))
+            predictor = V1beta1PredictorSpec(
+                tensorflow=V1beta1TFServingSpec(storage_uri=storage_uri))
         elif self.framework == 'onnx':
-            predictor = V1alpha2PredictorSpec(
+            predictor = V1beta1PredictorSpec(
                 onnx=V1alpha2ONNXSpec(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))
+        # elif self.framework == 'custom':
+        #     predictor = V1beta1PredictorSpec(
+        #         custom=V1alpha2CustomSpec(container=container))
         else:
             raise RuntimeError("Unsupported framework {}".format(framework))
         return predictor

requirements.txt

@@ -1,28 +1,25 @@
-python-dateutil>=2.1,<=2.8.0
-numpy>=1.17.3
-kfserving>=0.3.0.2
+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