Add Tempo Prepackaged Server

doc/source/index.rst

@@ -94,6 +94,7 @@ Documentation Index
    MLflow Server <servers/mlflow.md>
    SKLearn Server <servers/sklearn.md>
    Triton Inference Server <servers/triton.md>
+   Tempo Server <servers/tempo.md>   
    Tensorflow Serving <servers/tensorflow.md>
    XGBoost Server <servers/xgboost.md>
 

doc/source/python/api/seldon_core.rst

@@ -97,14 +97,6 @@ seldon\_core.mlmd\_utils module
    :undoc-members:
    :show-inheritance:
 
-seldon\_core.persistence module
--------------------------------
-
-.. automodule:: seldon_core.persistence
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 seldon\_core.seldon\_client module
 ----------------------------------
 

doc/source/servers/tempo.md

@@ -0,0 +1,42 @@
+# Tempo Server
+
+[Tempo](https://github.com/SeldonIO/tempo) is a MLOps python SDK that allows packaging custom python servers and orchestration of multiple models from python. The Tempo python SDK allows packaging of the custom code as a conda-pack environment tar ball and Cloudpickle artifacts. It has a Seldon Core runtime which allows Tempo artifacts to be run under Seldon Core.
+
+For more details see the [Tempo documentation](https://tempo.readthedocs.io/en/latest/).
+
+An example Tempo model yaml for Seldon Core is shown below:
+
+```python
+apiVersion: machinelearning.seldon.io/v1
+kind: SeldonDeployment
+metadata:
+  annotations:
+    seldon.io/tempo-description: ''
+    seldon.io/tempo-model: '{"model_details": {"name": "numpyro-divorce", "local_folder":
+      "/home/clive/work/mlops/fork-tempo/docs/examples/custom-model/artifacts", "uri":
+      "s3://tempo/divorce", "platform": "custom", "inputs": {"args": [{"ty": "numpy.ndarray",
+      "name": "marriage"}, {"ty": "numpy.ndarray", "name": "age"}]}, "outputs": {"args":
+      [{"ty": "numpy.ndarray", "name": null}]}, "description": ""}, "protocol": "tempo.kfserving.protocol.KFServingV2Protocol",
+      "runtime_options": {"runtime": "tempo.seldon.SeldonKubernetesRuntime", "docker_options":
+      {"defaultRuntime": "tempo.seldon.SeldonDockerRuntime"}, "k8s_options": {"replicas":
+      1, "minReplicas": null, "maxReplicas": null, "authSecretName": "minio-secret",
+      "serviceAccountName": null, "defaultRuntime": "tempo.seldon.SeldonKubernetesRuntime",
+      "namespace": "production"}, "ingress_options": {"ingress": "tempo.ingress.istio.IstioIngress",
+      "ssl": false, "verify_ssl": true}}}'
+  labels:
+    seldon.io/tempo: 'true'
+  name: numpyro-divorce
+  namespace: production
+spec:
+  predictors:
+  - graph:
+      envSecretRefName: minio-secret
+      implementation: TEMPO_SERVER
+      modelUri: s3://tempo/divorce
+      name: numpyro-divorce
+      serviceAccountName: tempo-pipeline
+      type: MODEL
+    name: default
+    replicas: 1
+  protocol: kfserving
+```

helm-charts/seldon-core-operator/templates/customresourcedefinition_seldondeployments.machinelearning.seldon.io.yaml

@@ -6,7 +6,6 @@ metadata:
   annotations:
     cert-manager.io/inject-ca-from: '{{ include "seldon.namespace" . }}/seldon-serving-cert'
     controller-gen.kubebuilder.io/version: v0.4.1
-  creationTimestamp: null
   labels:
     app: seldon
     app.kubernetes.io/instance: '{{ .Release.Name }}'

helm-charts/seldon-core-operator/templates/customresourcedefinition_v1_seldondeployments.machinelearning.seldon.io.yaml

@@ -6,7 +6,6 @@ metadata:
   annotations:
     cert-manager.io/inject-ca-from: '{{ include "seldon.namespace" . }}/seldon-serving-cert'
     controller-gen.kubebuilder.io/version: v0.4.1
-  creationTimestamp: null
   name: seldondeployments.machinelearning.seldon.io
 spec:
   group: machinelearning.seldon.io

helm-charts/seldon-core-operator/templates/webhook.yaml

@@ -9,7 +9,6 @@ kind: ValidatingWebhookConfiguration
 metadata:
   annotations:
     cert-manager.io/inject-ca-from: '{{ include "seldon.namespace" . }}/seldon-serving-cert'
-  creationTimestamp: null
   labels:
     app: seldon
     app.kubernetes.io/instance: '{{ .Release.Name }}'

helm-charts/seldon-core-operator/values.yaml

@@ -118,7 +118,7 @@ predictor_servers:
         defaultImageVersion: "1.8.0-dev"
         image: seldonio/sklearnserver
       kfserving:
-        defaultImageVersion: "0.1.1"
+        defaultImageVersion: "0.3.2"
         image: seldonio/mlserver
   TENSORFLOW_SERVER:
     protocols:
@@ -134,13 +134,18 @@ predictor_servers:
         defaultImageVersion: "1.8.0-dev"
         image: seldonio/xgboostserver
       kfserving:
-        defaultImageVersion: "0.1.1"
+        defaultImageVersion: "0.3.2"
         image: seldonio/mlserver
   TRITON_SERVER:
     protocols:
       kfserving:
         defaultImageVersion: "20.08-py3"
         image: nvcr.io/nvidia/tritonserver
+  TEMPO_SERVER:
+    protocols:
+      kfserving:
+        defaultImageVersion: "0.3.2"
+        image: seldonio/mlserver
 
 # ## Other
 # You can choose the crds to not be installed if you already installed them

notebooks/resources/iris-sklearn-v2.yaml

@@ -9,7 +9,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/sklearn/iris
+      modelUri: gs://seldon-models/sklearn/iris-0.23.2/lr_model
       name: classifier
     name: default
     replicas: 1