Release 1.11.0

README.md

@@ -60,7 +60,7 @@ We provide optimized model servers for some of the most popular Deep Learning an
 You only have to upload your model binaries into your preferred object store, in this case we have a trained scikit-learn iris model in a Google bucket:
 
 ```console
-gs://seldon-models/v1.11.0-dev/sklearn/iris/model.joblib
+gs://seldon-models/v1.11.0/sklearn/iris/model.joblib
 ```
 
 Create a namespace to run your model in:
@@ -83,7 +83,7 @@ spec:
   predictors:
   - graph:
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       name: classifier
     name: default
     replicas: 1

components/alibi-detect-server/Dockerfile

@@ -1,8 +1,8 @@
 # TODO: Add to release script
-FROM docker.io/seldonio/seldon-core-s2i-python37-ubi8:1.11.0-dev
+FROM docker.io/seldonio/seldon-core-s2i-python37-ubi8:1.11.0
 LABEL name="Seldon Alibi Detect Server" \
       vendor="Seldon Technologies" \
-      version="1.11.0-dev" \
+      version="1.11.0" \
       release="1" \
       summary="Alibi Detect Server for Seldon Core" \
       description="The Alibi Detect Server provides outlier, drift and adversarial detection services for Seldon Core"

components/alibi-explain-server/Dockerfile

@@ -1,8 +1,8 @@
 ARG VERSION
-FROM seldonio/seldon-core-s2i-python37-ubi8:$VERSION
+FROM seldonio/seldon-core-s2i-python37-ubi8:1.11.0
 LABEL name="Seldon Alibi Wrapper" \
       vendor="Seldon Technologies" \
-      version="1.11.0-dev" \
+      version="1.11.0" \
       release="1" \
       summary="Alibi Explainer Wrapper for Seldon Core" \
       description="Allows Seldon Core inference models to run with a black box model explanation model from the Alibi:Explain project"

components/seldon-request-logger/Dockerfile

@@ -1,7 +1,7 @@
 FROM registry.access.redhat.com/ubi8/python-38
 LABEL name="Seldon Request Logger" \
       vendor="Seldon Technologies" \
-      version="1.11.0-dev" \
+      version="1.11.0" \
       release="1" \
       summary="The payload logger for Seldon Core" \
       description="The Seldon Payload Logger allows request and response payloads from a Seldon Core inference graph to be processed and sent to an ELK endpoint"

components/storage-initializer/Dockerfile

@@ -1,7 +1,7 @@
 FROM registry.access.redhat.com/ubi8/python-36
 LABEL name="Storage Initializer" \
       vendor="Seldon Technologies" \
-      version="1.11.0-dev" \
+      version="1.11.0" \
       release="1" \
       summary="Storage Initializer for Seldon Core" \
       description="Allows Seldon Core to download artifacts from cloud and local storage to a local volume"

doc/source/graph/protocols.md

@@ -66,7 +66,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       name: classifier
       parameters:
         - name: method

doc/source/python/python_wrapping_s2i.md

@@ -17,7 +17,7 @@ If you are not familiar with s2i you can read [general instructions on using s2i
 To check everything is working you can run
 
 ```bash
-s2i usage seldonio/seldon-core-s2i-python3:1.11.0-dev
+s2i usage seldonio/seldon-core-s2i-python3:1.11.0
 ```
 
 
@@ -98,22 +98,22 @@ See below for the possible keys and values for this file.
 ## Step 3 - Build your image
 Use `s2i build` to create your Docker image from source code. You will need Docker installed on the machine and optionally git if your source code is in a public git repo. You can choose from three python builder images
 
- * Python 3.6 : seldonio/seldon-core-s2i-python36:1.11.0-dev seldonio/seldon-core-s2i-python3:1.11.0-dev
+ * Python 3.6 : seldonio/seldon-core-s2i-python36:1.11.0 seldonio/seldon-core-s2i-python3:1.11.0
    * Note there are [issues running TensorFlow under Python 3.7](https://github.com/tensorflow/tensorflow/issues/20444) (Nov 2018) and Python 3.7 is not officially supported by TensorFlow (Dec 2018).
  * Python 3.6 plus ONNX support via [Intel nGraph](https://github.com/NervanaSystems/ngraph) : seldonio/seldon-core-s2i-python3-ngraph-onnx:0.1
 
 Using s2i you can build directly from a git repo or from a local source folder. See the [s2i docs](https://github.com/openshift/source-to-image/blob/master/docs/cli.md#s2i-build) for further details. The general format is:
 
 ```bash
-s2i build <src-folder> seldonio/seldon-core-s2i-python3:1.11.0-dev <my-image-name>
+s2i build <src-folder> seldonio/seldon-core-s2i-python3:1.11.0 <my-image-name>
 ```
 
 Change to seldonio/seldon-core-s2i-python3 if using python 3.
 
 An example invocation using the test template model inside seldon-core:
 
 ```bash
-s2i build https://github.com/seldonio/seldon-core.git --context-dir=wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:1.11.0-dev seldon-core-template-model
+s2i build https://github.com/seldonio/seldon-core.git --context-dir=wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:1.11.0 seldon-core-template-model
 ```
 
 The above s2i build invocation:
@@ -128,13 +128,13 @@ For building from a local source folder, an example where we clone the seldon-co
 ```bash
 git clone https://github.com/seldonio/seldon-core.git
 cd seldon-core
-s2i build wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:1.11.0-dev seldon-core-template-model
+s2i build wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:1.11.0 seldon-core-template-model
 ```
 
 For more help see:
 
 ```bash
-s2i usage seldonio/seldon-core-s2i-python3:1.11.0-dev
+s2i usage seldonio/seldon-core-s2i-python3:1.11.0
 s2i build --help
 ```
 
@@ -179,7 +179,7 @@ s2i build \
    -e EXTRA_INDEX_URL=https://<pypi-user>:<pypi-auth>@mypypi.example.com/simple \
    -e PIP_TRUSTED_HOST=mypypi.example.com \
    <src-folder> \
-   seldonio/seldon-core-s2i-python3:1.11.0-dev \
+   seldonio/seldon-core-s2i-python3:1.11.0 \
    <my-image-name>
 ```
 
@@ -271,7 +271,7 @@ The allowable `type` values for the parameters are defined in the [proto buffer
 To use a private repository for installing Python dependencies use the following build command:
 
 ```bash
-s2i build -i <python-wheel-folder>:/whl <src-folder> seldonio/seldon-core-s2i-python3:1.11.0-dev <my-image-name>
+s2i build -i <python-wheel-folder>:/whl <src-folder> seldonio/seldon-core-s2i-python3:1.11.0 <my-image-name>
 ```
 
 This command will look for local Python wheels in the `<python-wheel-folder>` and use these before searching PyPI.

doc/source/reference/images.md

@@ -5,44 +5,44 @@
 
 | Description | Image URL | Stable Version | Development |
 |-------------|-----------|----------------|-------------|
-| [Seldon Operator](../workflow/install.md) | [seldonio/seldon-core-operator](https://hub.docker.com/r/seldonio/seldon-core-operator/tags/) | 1.10.0 | 1.11.0-dev |
-| [Seldon Service Orchestrator (Go)](../graph/svcorch.md)| [seldonio/seldon-core-executor](https://hub.docker.com/r/seldonio/executor/tags/) | 1.10.0 | 1.11.0-dev |
+| [Seldon Operator](../workflow/install.md) | [seldonio/seldon-core-operator](https://hub.docker.com/r/seldonio/seldon-core-operator/tags/) | 1.11.0 | 1.12.0-dev |
+| [Seldon Service Orchestrator (Go)](../graph/svcorch.md)| [seldonio/seldon-core-executor](https://hub.docker.com/r/seldonio/executor/tags/) | 1.11.0 | 1.12.0-dev |
 
 ## Pre-packaged servers
 
 
 | Description | Image URL | Version |
 |-------------|-----------|---------|
-| [MLFlow Server](../servers/mlflow.md) | [seldonio/mlflowserver](https://hub.docker.com/r/seldonio/mlflowserver/tags/) | 1.10.0 |
-| [SKLearn Server](../servers/sklearn.md) | [seldonio/sklearnserver](https://hub.docker.com/r/seldonio/sklearnserver/tags/) | 1.10.0 |
-| [XGBoost Server](../servers/xgboost.md) | [seldonio/xgboostserver](https://hub.docker.com/r/seldonio/xgboostserver/tags/) | 1.10.0 |
+| [MLFlow Server](../servers/mlflow.md) | [seldonio/mlflowserver](https://hub.docker.com/r/seldonio/mlflowserver/tags/) | 1.11.0 |
+| [SKLearn Server](../servers/sklearn.md) | [seldonio/sklearnserver](https://hub.docker.com/r/seldonio/sklearnserver/tags/) | 1.11.0 |
+| [XGBoost Server](../servers/xgboost.md) | [seldonio/xgboostserver](https://hub.docker.com/r/seldonio/xgboostserver/tags/) | 1.11.0 |
 
 ## Language wrappers
 
 | Description | Image URL | Stable Version | Development |
 |-------------|-----------|----------------|-------------|
-| [Seldon Python 3 (3.8) Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python3](https://hub.docker.com/r/seldonio/seldon-core-s2i-python3/tags/) | 1.10.0 | 1.11.0-dev |
-| [Seldon Python 3.6 Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python36](https://hub.docker.com/r/seldonio/seldon-core-s2i-python36/tags/) | 1.10.0 | 1.11.0-dev |
-| [Seldon Python 3.7 Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python37](https://hub.docker.com/r/seldonio/seldon-core-s2i-python37/tags/) | 1.10.0 | 1.11.0-dev |
-| [Seldon Python 3.8 Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python38](https://hub.docker.com/r/seldonio/seldon-core-s2i-python38/tags/) |  1.10.0  | 1.11.0-dev |
-| [Seldon Python 3.6 GPU Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python36-gpu](https://hub.docker.com/r/seldonio/seldon-core-s2i-python36-gpu/tags/) | 1.10.0 | 1.11.0-dev |
-| [Seldon Python 3.7 GPU Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python37-gpu](https://hub.docker.com/r/seldonio/seldon-core-s2i-python37-gpu/tags/) | 1.10.0 | 1.11.0-dev |
-| [Seldon Python 3.8 GPU Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python38-gpu](https://hub.docker.com/r/seldonio/seldon-core-s2i-python38-gpu/tags/) | 1.10.0 | 1.11.0-dev |
+| [Seldon Python 3 (3.7) Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python3](https://hub.docker.com/r/seldonio/seldon-core-s2i-python3/tags/) | 1.11.0 | 1.12.0-dev |
+| [Seldon Python 3.6 Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python36](https://hub.docker.com/r/seldonio/seldon-core-s2i-python36/tags/) | 1.11.0 | 1.12.0-dev |
+| [Seldon Python 3.7 Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python37](https://hub.docker.com/r/seldonio/seldon-core-s2i-python37/tags/) | 1.11.0 | 1.12.0-dev |
+| [Seldon Python 3.7 Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python38](https://hub.docker.com/r/seldonio/seldon-core-s2i-python38/tags/) |       | 1.12.0-dev |
+| [Seldon Python 3.6 GPU Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python36-gpu](https://hub.docker.com/r/seldonio/seldon-core-s2i-python36-gpu/tags/) | 1.11.0 | 1.12.0-dev |
+| [Seldon Python 3.7 GPU Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python37-gpu](https://hub.docker.com/r/seldonio/seldon-core-s2i-python37-gpu/tags/) | 1.11.0 | 1.12.0-dev |
+| [Seldon Python 3.8 GPU Wrapper for S2I](../python/python_wrapping_s2i.md) | [seldonio/seldon-core-s2i-python38-gpu](https://hub.docker.com/r/seldonio/seldon-core-s2i-python38-gpu/tags/) |       | 1.12.0-dev |
 
 ## Server proxies
 
 | Description | Image URL | Stable Version |
 |-------------|-----------|----------------|
 | [NVIDIA inference server proxy](integration_nvidia_link.rst) | [seldonio/nvidia-inference-server-proxy](https://hub.docker.com/r/seldonio/nvidia-inference-server-proxy/tags/) | 0.1 |
 | [SageMaker proxy](https://github.com/SeldonIO/seldon-core/tree/master/integrations/sagemaker) | [seldonio/sagemaker-proxy](https://hub.docker.com/r/seldonio/sagemaker-proxy/tags/) | 0.1 |
-| [Tensorflow Serving proxy](../servers/tensorflow.md) | [seldonio/tfserving-proxy](https://hub.docker.com/r/seldonio/tfserving-proxy/tags/) | 1.10.0 |
+| [Tensorflow Serving proxy](../servers/tensorflow.md) | [seldonio/tfserving-proxy](https://hub.docker.com/r/seldonio/tfserving-proxy/tags/) | 1.11.0 |
 
 
 ## Python modules
 
 | Description | Python Version | Version |
 |-------------|----------------|---------|
-| [seldon-core](https://pypi.org/project/seldon-core/) | >3.4,<3.9 | 1.10.0 |
+| [seldon-core](https://pypi.org/project/seldon-core/) | >3.4,<3.9 | 1.11.0 |
 | [seldon-core](https://pypi.org/project/seldon-core/) | 2,>=3,<3.7 | 0.2.6 (deprecated) |
 
 

doc/source/reference/upgrading.md

@@ -38,7 +38,7 @@ In Seldon Core 1.8 the rclone-based [storage initializer](https://github.com/Sel
 The storage initailizer image that is being used is controlled by the helm value:
 ```yaml
 storageInitializer:
-  image: seldonio/rclone-storage-initializer:1.11.0-dev
+  image: seldonio/rclone-storage-initializer:1.11.0
 ```
 and can be customised on per-deployment basis as described in [Prepackaged Model Servers](../servers/overview.md) documentation by setting value of `storageInitializerImage` variable in the graph definition.
 

doc/source/rollouts/abtests.md

@@ -46,7 +46,7 @@ spec:
     traffic: 100    
     graph:
       name: classifier
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       implementation: SKLEARN_SERVER
   - name: candidate
     traffic: 0
@@ -360,7 +360,7 @@ spec:
   - name: default
     graph:
       name: classifier
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       implementation: SKLEARN_SERVER
 ```
 

doc/source/servers/kfserving-storage-initializer.md

@@ -8,7 +8,7 @@ storageInitializer:
   image: gcr.io/kfserving/storage-initializer:v0.4.0
 ```
 
-> :warning: **NOTE:** Current default storage initializer is `seldonio/rclone-storage-initializer:1.11.0-dev` is described [here](./overview.md).
+> :warning: **NOTE:** Current default storage initializer is `seldonio/rclone-storage-initializer:1.11.0` is described [here](./overview.md).
 
 
 When `kfserving/storage-initializer` is used `modeluri` supports the following four object storage providers:
@@ -182,7 +182,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       serviceAccountName: user-gcp-sa
       name: classifier
     name: default

doc/source/servers/overview.md

@@ -22,7 +22,7 @@ spec:
       graph:
         name: classifier
         implementation: SKLEARN_SERVER
-        modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+        modelUri: gs://seldon-models/v1.11.0/sklearn/iris
 ```
 
 By default only public models published to Google Cloud Storage will be accessible.
@@ -36,7 +36,7 @@ Seldon Core uses [Init Containers](https://kubernetes.io/docs/concepts/workloads
 
 ```yaml
 storageInitializer:
-  image: seldonio/rclone-storage-initializer:1.11.0-dev
+  image: seldonio/rclone-storage-initializer:1.11.0
 ```
 in our default [helm values](../charts/seldon-core-operator.html#values).
 See the [Dockerfile](https://github.com/SeldonIO/seldon-core/blob/master/components/rclone-storage-initializer/Dockerfile
@@ -81,7 +81,7 @@ spec:
 
         initContainers:
         - name: classifier-model-initializer
-          image: seldonio/rclone-storage-initializer:1.11.0-dev
+          image: seldonio/rclone-storage-initializer:1.11.0
           imagePullPolicy: IfNotPresent
           args:
             - "s3://sklearn/iris"
@@ -132,7 +132,7 @@ spec:
       name: classifier
       implementation: SKLEARN_SERVER
       modelUri: s3://sklearn/iris
-      storageInitializerImage: seldonio/rclone-storage-initializer:1.11.0-dev  # Specify custom image here
+      storageInitializerImage: seldonio/rclone-storage-initializer:1.11.0  # Specify custom image here
       envSecretRefName: seldon-init-container-secret                          # Specify custom secret here
 ```
 Note that image and secret used by Storage Initializer can be customised per-deployment.
@@ -162,7 +162,7 @@ spec:
     graph:
       name: classifier
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
 ```
 
 The image name and other details will be added when this is deployed automatically.

doc/source/servers/sklearn.md

@@ -40,7 +40,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       name: classifier
     name: default
     replicas: 1
@@ -68,7 +68,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       name: classifier
       parameters:
         - name: method
@@ -111,7 +111,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       name: classifier
       parameters:
         - name: method
@@ -159,7 +159,7 @@ spec:
     graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       name: classifier
     name: default
     replicas: 1

doc/source/workflow/overview.md

@@ -111,7 +111,7 @@ To complete containerisation process you need two more components:
 
 Once these are in place you can use a simple s2i command
 ```bash
-s2i build . seldonio/seldon-core-s2i-python3:1.11.0-dev model:0.1
+s2i build . seldonio/seldon-core-s2i-python3:1.11.0 model:0.1
 ```
 to create ready to use Docker image.
 

doc/source/workflow/quickstart.md

@@ -82,9 +82,9 @@ You can upload your models into any of the object stores supported by our pre-pa
 For simplicity we have already uploaded it to the bucket so you can just proceed to the next step and run your model on Seldon Core.
 
 ```console
-$ gsutil cp model.joblib gs://seldon-models/v1.11.0-dev/sklearn/iris/model.joblib
+$ gsutil cp model.joblib gs://seldon-models/v1.11.0/sklearn/iris/model.joblib
 
-[ Saved into gs://seldon-models/v1.11.0-dev/sklearn/iris/model.joblib ]
+[ Saved into gs://seldon-models/v1.11.0/sklearn/iris/model.joblib ]
 ```
 
 **3. Deploy to Seldon Core in Kubernetes**
@@ -103,7 +103,7 @@ spec:
   predictors:
   - graph:
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       name: classifier
     name: default
     replicas: 1
@@ -217,7 +217,7 @@ Now we can use the Seldon Core utilities to convert our python class into a full
 The result below is a container with the name `sklearn_iris` and the tag `0.1` which we will be able to deploy using Seldon Core.
 
 ```console
-s2i build . seldonio/seldon-core-s2i-python3:1.11.0-dev sklearn_iris:0.1
+s2i build . seldonio/seldon-core-s2i-python3:1.11.0 sklearn_iris:0.1
 ```
 
 **5. Deploy to Kubernetes**

engine/Dockerfile.redhat

@@ -14,7 +14,7 @@ RUN yum install -y java-11-openjdk.x86_64
 RUN yum -y update-minimal --security --sec-severity=Important --sec-severity=Critical
 LABEL name="Seldon Engine" \
       vendor="Seldon Technologies" \
-      version="1.11.0-dev" \
+      version="1.11.0" \
       release="1" \
       summary="The Seldon Engine that is deployed for each model to handle the data flow of each ML computational graph." \
       description="The Seldon Engine that is deployed for each model to handle the data flow of each ML computational graph."

engine/pom.xml

@@ -10,7 +10,7 @@ xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xs
   </parent>
   <groupId>io.seldon.engine</groupId>
   <artifactId>seldon-engine</artifactId>
-  <version>1.11.0-dev</version>
+  <version>1.11.0</version>
   <packaging>jar</packaging>
   <name>engine</name>
   <url>http://maven.apache.org</url>

examples/batch/argo-workflows-batch/helm-charts/seldon-batch-workflow/values.yaml

@@ -19,7 +19,7 @@ seldonDeployment:
   # Prepackaged model server to use [see https://docs.seldon.io/projects/seldon-core/en/latest/servers/overview.html]
   server: SKLEARN_SERVER
   # The URL for the model that is to be used
-  modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+  modelUri: gs://seldon-models/v1.11.0/sklearn/iris
   # The number of seldon deployment replicas to launch
   replicas: 2
   # Waiting time before checks for deployment to ensure kubernetes cluster registers create

examples/batch/hdfs-argo-workflows/deployment.yaml

@@ -9,7 +9,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.11.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.11.0/sklearn/iris
       name: classifier
       logger:
         mode: all