release v1.18.2-release

README.md

@@ -68,7 +68,7 @@ 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/v1.19.0-dev/sklearn/iris/model.joblib
+gs://seldon-models/v1.18.2/sklearn/iris/model.joblib
 ```
 
 Create a namespace to run your model in:
@@ -91,7 +91,7 @@ spec:
   predictors:
   - graph:
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/sklearn/iris
       name: classifier
     name: default
     replicas: 1

components/alibi-detect-server/Dockerfile

@@ -5,7 +5,7 @@ FROM ${BASE_IMAGE}:${VERSION} as base
 ARG VERSION
 LABEL name="Seldon Alibi Detect Server" \
       vendor="Seldon Technologies" \
-      version="1.19.0-dev" \
+      version="1.18.2" \
       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

@@ -5,7 +5,7 @@ FROM ${BASE_IMAGE}:${VERSION} as base
 ARG VERSION
 LABEL name="Seldon Alibi Wrapper" \
       vendor="Seldon Technologies" \
-      version="1.19.0-dev" \
+      version="1.18.2" \
       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"

doc/source/graph/protocols.md

@@ -66,7 +66,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/sklearn/iris
       name: classifier
       parameters:
         - name: method

doc/source/python/python_wrapping_s2i.md

@@ -16,7 +16,7 @@ In this guide, we illustrate the steps needed to wrap your own python model in a
 To check everything is working you can run
 
 ```bash
-s2i usage seldonio/seldon-core-s2i-python3:1.19.0-dev
+s2i usage seldonio/seldon-core-s2i-python3:1.18.2
 ```
 
 
@@ -100,13 +100,13 @@ Use `s2i build` to create your Docker image from source code. You will need Dock
 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-python3:1.19.0-dev <my-image-name>
+s2i build <src-folder> seldonio/seldon-core-s2i-python3:1.18.2 <my-image-name>
 ```
 
 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-python3:1.19.0-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-python3:1.18.2 seldon-core-template-model
 ```
 
 The above s2i build invocation:
@@ -121,13 +121,13 @@ 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-python3:1.19.0-dev seldon-core-template-model
+s2i build wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:1.18.2 seldon-core-template-model
 ```
 
 For more help see:
 
 ```bash
-s2i usage seldonio/seldon-core-s2i-python3:1.19.0-dev
+s2i usage seldonio/seldon-core-s2i-python3:1.18.2
 s2i build --help
 ```
 
@@ -172,7 +172,7 @@ 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-python3:1.19.0-dev \
+   seldonio/seldon-core-s2i-python3:1.18.2 \
    <my-image-name>
 ```
 
@@ -265,7 +265,7 @@ 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-python3:1.19.0-dev <my-image-name>
+s2i build -i <python-wheel-folder>:/whl <src-folder> seldonio/seldon-core-s2i-python3:1.18.2 <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/upgrading.md

@@ -56,7 +56,7 @@ The label on services pointing to containers for each node in your inference gra
 ### OpenShift default storage initializer
 
 In this patch release we fixed the default storage initializer used on OpenShift (both Community and Certified operators).
-The image used now is `seldonio/rclone-storage-initializer:1.19.0-dev` which is the same as one used for the non-OpenShift releases (*).
+The image used now is `seldonio/rclone-storage-initializer:1.18.2` which is the same as one used for the non-OpenShift releases (*).
 
 For Certified operator you will find this image defined as
 ```yaml
@@ -177,7 +177,7 @@ 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:1.19.0-dev
+  image: seldonio/rclone-storage-initializer:1.18.2
 ```
 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/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/sklearn/iris
       implementation: SKLEARN_SERVER
   - name: candidate
     traffic: 0
@@ -360,7 +360,7 @@ spec:
   - name: default
     graph:
       name: classifier
-      modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/sklearn/iris
       implementation: SKLEARN_SERVER
 ```
 

doc/source/servers/overview.md

@@ -23,7 +23,7 @@ spec:
       graph:
         name: classifier
         implementation: SKLEARN_SERVER
-        modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris
+        modelUri: gs://seldon-models/v1.18.2/sklearn/iris
 ```
 
 By default only public models published to Google Cloud Storage will be accessible.
@@ -37,7 +37,7 @@ Seldon Core uses [Init Containers](https://kubernetes.io/docs/concepts/workloads
 
 ```yaml
 storageInitializer:
-  image: seldonio/rclone-storage-initializer:1.19.0-dev
+  image: seldonio/rclone-storage-initializer:1.18.2
 ```
 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
@@ -79,7 +79,7 @@ spec:
 
         initContainers:
         - name: classifier-model-initializer
-          image: seldonio/rclone-storage-initializer:1.19.0-dev
+          image: seldonio/rclone-storage-initializer:1.18.2
           imagePullPolicy: IfNotPresent
           args:
             - "s3://sklearn/iris"
@@ -130,7 +130,7 @@ spec:
       name: classifier
       implementation: SKLEARN_SERVER
       modelUri: s3://sklearn/iris
-      storageInitializerImage: seldonio/rclone-storage-initializer:1.19.0-dev  # Specify custom image here
+      storageInitializerImage: seldonio/rclone-storage-initializer:1.18.2  # 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.
@@ -160,7 +160,7 @@ spec:
     graph:
       name: classifier
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/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/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/sklearn/iris
       name: classifier
     name: default
     replicas: 1
@@ -68,7 +68,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/sklearn/iris
       name: classifier
       parameters:
         - name: method
@@ -104,7 +104,7 @@ spec:
   - graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/sklearn/iris
       name: classifier
       parameters:
         - name: method
@@ -152,7 +152,7 @@ spec:
     graph:
       children: []
       implementation: SKLEARN_SERVER
-      modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/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-python3:1.19.0-dev model:0.1
+s2i build . seldonio/seldon-core-s2i-python3:1.18.2 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/v1.19.0-dev/sklearn/iris/model.joblib
+$ gsutil cp model.joblib gs://seldon-models/v1.18.2/sklearn/iris/model.joblib
 
-[ Saved into gs://seldon-models/v1.19.0-dev/sklearn/iris/model.joblib ]
+[ Saved into gs://seldon-models/v1.18.2/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/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/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-python3:1.19.0-dev sklearn_iris:0.1
+s2i build . seldonio/seldon-core-s2i-python3:1.18.2 sklearn_iris:0.1
 ```
 
 **5. Deploy to Kubernetes**

doc/source/wrappers/s2i.md

@@ -16,7 +16,7 @@ The general work flow is:
 
     ```bash
     s2i build https://github.com/seldonio/seldon-core.git \
-        --context-dir=wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:1.19.0-dev \
+        --context-dir=wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:1.18.2 \
         seldon-core-template-model
     ```
 

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/v1.19.0-dev/sklearn/iris
+  modelUri: gs://seldon-models/v1.18.2/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/v1.19.0-dev/sklearn/iris
+      modelUri: gs://seldon-models/v1.18.2/sklearn/iris
       name: classifier
       logger:
         mode: all

examples/batch/hdfs-argo-workflows/hdfs-batch.ipynb

@@ -237,7 +237,7 @@
     "  - graph:\n",
     "      children: []\n",
     "      implementation: SKLEARN_SERVER\n",
-    "      modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris\n",
+    "      modelUri: gs://seldon-models/v1.18.2/sklearn/iris\n",
     "      name: classifier\n",
     "      logger:\n",
     "        mode: all\n",
@@ -536,7 +536,7 @@
     "\n",
     "  - name: process-batch-inputs\n",
     "    container:\n",
-    "      image: seldonio/seldon-core-s2i-python37:1.19.0-dev\n",
+    "      image: seldonio/seldon-core-s2i-python37:1.18.2\n",
     "\n",
     "      volumeMounts:\n",
     "      - mountPath: /assets\n",

examples/batch/kubeflow-pipelines-batch/README.ipynb

@@ -222,7 +222,7 @@
     "def nlp_pipeline(\n",
     "        namespace=\"kubeflow\",\n",
     "        seldon_server=\"SKLEARN_SERVER\",\n",
-    "        model_path=\"gs://seldon-models/v1.19.0-dev/sklearn/iris\",\n",
+    "        model_path=\"gs://seldon-models/v1.18.2/sklearn/iris\",\n",
     "        gateway_endpoint=\"istio-ingressgateway.istio-system.svc.cluster.local\",\n",
     "        retries=3,\n",
     "        replicas=10,\n",
@@ -284,7 +284,7 @@
     "\n",
     "    batch_process_step = dsl.ContainerOp(\n",
     "        name='data_downloader',\n",
-    "        image='seldonio/seldon-core-s2i-python37:1.19.0-dev        command=\"seldon-batch-processor\",\n",
+    "        image='seldonio/seldon-core-s2i-python37:1.18.2        command=\"seldon-batch-processor\",\n",
     "        arguments=[\n",
     "            \"--deployment-name\", \"{{workflow.name}}\",\n",
     "            \"--namespace\", namespace,\n",

examples/batch/kubeflow-pipelines-batch/README.md

@@ -126,7 +126,7 @@ from kubernetes import client as k8s
 def nlp_pipeline(
         namespace="kubeflow",
         seldon_server="SKLEARN_SERVER",
-        model_path="gs://seldon-models/v1.19.0-dev/sklearn/iris",
+        model_path="gs://seldon-models/v1.18.2/sklearn/iris",
         gateway_endpoint="istio-ingressgateway.istio-system.svc.cluster.local",
         retries=3,
         replicas=10,

examples/batch/kubeflow-pipelines-batch/assets/seldon-batch-pipeline.py

@@ -9,7 +9,7 @@
 def nlp_pipeline(
     namespace="kubeflow",
     seldon_server="SKLEARN_SERVER",
-    model_path="gs://seldon-models/v1.19.0-dev/sklearn/iris",
+    model_path="gs://seldon-models/v1.18.2/sklearn/iris",
     gateway_endpoint="istio-ingressgateway.istio-system.svc.cluster.local",
     retries=3,
     replicas=10,

examples/cicd/sig-mlops-jenkins-classic/models/image_classifier/seldon_custom_server_README.ipynb

@@ -494,7 +494,7 @@
    ],
    "source": [
     "%%bash\n",
-    "SELDON_BASE_WRAPPER=\"seldonio/seldon-core-s2i-python37:1.19.0-dev
+    "SELDON_BASE_WRAPPER=\"seldonio/seldon-core-s2i-python37:1.18.2
     "s2i build src/. $SELDON_BASE_WRAPPER sklearn-server:0.1 \\\n",
     "    --environment-file src/seldon_model.conf"
    ]

examples/cicd/sig-mlops-jenkins-classic/models/news_classifier/seldon_custom_server_README.ipynb

@@ -494,7 +494,7 @@
    ],
    "source": [
     "%%bash\n",
-    "SELDON_BASE_WRAPPER=\"seldonio/seldon-core-s2i-python37:1.19.0-dev
+    "SELDON_BASE_WRAPPER=\"seldonio/seldon-core-s2i-python37:1.18.2
     "s2i build src/. $SELDON_BASE_WRAPPER sklearn-server:0.1 \\\n",
     "    --environment-file src/seldon_model.conf"
    ]

examples/cicd/sig-mlops-jenkins-classic/servers/torchserver/Makefile

@@ -3,13 +3,13 @@ FOLDER=torchserver
 IMAGE_BASE=seldonio/${FOLDER}
 
 build_rest:
-	s2i build -E environment_rest ./${FOLDER} seldonio/seldon-core-s2i-python37:1.19.0-dev ${IMAGE_BASE}_rest:${VERSION}
+	s2i build -E environment_rest ./${FOLDER} seldonio/seldon-core-s2i-python37:1.18.2 ${IMAGE_BASE}_rest:${VERSION}
 
 push_rest:
 	docker push ${IMAGE_BASE}_rest:${VERSION}
 
 build_grpc:
-	s2i build -E environment_grpc ./${FOLDER} seldonio/seldon-core-s2i-python37:1.19.0-dev ${IMAGE_BASE}_grpc:${VERSION}
+	s2i build -E environment_grpc ./${FOLDER} seldonio/seldon-core-s2i-python37:1.18.2 ${IMAGE_BASE}_grpc:${VERSION}
 
 push_grpc:
 	docker push ${IMAGE_BASE}_grpc:${VERSION}

examples/cicd/sig-mlops-jenkins-classic/servers/torchserver/test/sklearn_iris.ipynb

@@ -89,7 +89,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "s2i build -E environment_rest ./sklearnserver seldonio/seldon-core-s2i-python37:1.19.0-dev seldonio/sklearnserver_rest:0.1\n",
+      "s2i build -E environment_rest ./sklearnserver seldonio/seldon-core-s2i-python37:1.18.2 seldonio/sklearnserver_rest:0.1\n",
       "---> Installing application source...\n",
       "---> Installing dependencies ...\n",
       "Looking in links: /whl\n",
@@ -284,7 +284,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "s2i build -E environment_grpc ./sklearnserver seldonio/seldon-core-s2i-python37:1.19.0-dev seldonio/sklearnserver_grpc:0.1\n",
+      "s2i build -E environment_grpc ./sklearnserver seldonio/seldon-core-s2i-python37:1.18.2 seldonio/sklearnserver_grpc:0.1\n",
       "---> Installing application source...\n",
       "---> Installing dependencies ...\n",
       "Looking in links: /whl\n",