Merge pull request #2574 from cliveseldon/2378_http_grpc_executor

ci_build_and_push_images.sh

@@ -68,10 +68,8 @@ function build_push_engine {
 function build_push_mock {
     make \
 	-C examples/models/mean_classifier \
-	build_rest \
-	build_grpc \
-	push_rest \
-	push_grpc
+	build \
+	push
     MOCK_MODEL_EXIT_VALUE=$?
 }
 
@@ -94,32 +92,32 @@ function build_push_request_logger {
 function build_push_sklearnserver {
     make \
 	-C servers/sklearnserver \
-        build_all \
-	push_all 
+        build \
+	push 
     SKLEARN_EXIT_VALUE=$?
 }
 
 function build_push_mlflowserver {
     make \
 	-C servers/mlflowserver \
-        build_all \
-	push_all 
+        build \
+	push 
     MLFLOW_EXIT_VALUE=$?
 }
 
 function build_push_xgboostserver {
     make \
 	-C servers/xgboostserver \
-        build_all \
-	push_all 
+        build \
+	push 
     XGBOOST_EXIT_VALUE=$?
 }
 
 function build_push_tfproxy {
     make \
-	-C integrations/tfserving \
-        build_all \
-	push_all 
+	-C integrations/tfserving_proxy \
+        build \
+	push 
     TFPROXY_EXIT_VALUE=$?
 }
 
@@ -139,6 +137,14 @@ function build_push_storage_initializer {
     STORAGE_INITIALIZER_EXIT_VALUE=$?
 }
 
+function build_push_mab {
+    make \
+	-C components/routers/epsilon-greedy \
+        build \
+	push 
+    MAB_EXIT_VALUE=$?
+}
+
 
 build_push_python
 build_push_operator
@@ -153,6 +159,7 @@ build_push_xgboostserver
 build_push_tfproxy
 build_push_alibi_explainer
 build_push_storage_initializer
+build_push_mab
 
 #######################################
 # EXIT STOPS COMMANDS FROM HERE ONWARDS
@@ -171,6 +178,7 @@ echo "Mock model exit value: $MOCK_MODEL_EXIT_VALUE"
 echo "Alibi Detect exit value: $ALIBI_DETECT_EXIT_VALUE"
 echo "Request Logger exit value: $LOGGER_EXIT_VALUE"
 echo "Tensorflow Proxy exit value: $TFPROXY_EXIT_VALUE"
+echo "MAB exit value: $MAB_EXIT_VALUE"
 
 exit $((${PYTHON_EXIT_VALUE} \
     + ${OPERATOR_EXIT_VALUE} \
@@ -184,6 +192,7 @@ exit $((${PYTHON_EXIT_VALUE} \
     + ${XGBOOST_EXIT_VALUE} \
     + ${TFPROXY_EXIT_VALUE} \
     + ${STORAGE_INITIALIZER_EXIT_VALUE} \
+    + ${MAB_EXIT_VALUE} \    
     + ${EXPLAIN_EXIT_VALUE}))
 
 

components/alibi-explain-server/tests/test_anchor_tabular.py

@@ -26,7 +26,7 @@
 import json
 from .utils import SKLearnServer
 ADULT_EXPLAINER_URI = "gs://seldon-models/sklearn/income/alibi/0.4.0"
-ADULT_MODEL_URI = "gs://seldon-models/sklearn/income/model-0.23.2"
+ADULT_MODEL_URI = "gs://seldon-models/sklearn/income/model"
 EXPLAINER_FILENAME = "explainer.dill"
 
 

components/routers/epsilon-greedy/Makefile

@@ -1,14 +1,12 @@
-IMAGE_VERSION=1.3
+VERSION := $(shell cat ../../../version.txt)
 IMAGE_NAME=seldonio/mab_epsilon_greedy
+KIND_NAME ?= kind
 
-build_rest:
-	s2i build . -E environment_rest seldonio/seldon-core-s2i-python3:0.15 $(IMAGE_NAME)_rest:$(IMAGE_VERSION)
+build:
+	s2i build . -E environment seldonio/seldon-core-s2i-python3:${VERSION} $(IMAGE_NAME):$(VERSION)
 
-push_to_dockerhub_rest:
-	docker push $(IMAGE_NAME)_rest:$(IMAGE_VERSION)
+push:
+	docker push $(IMAGE_NAME):$(VERSION)
 
-build_grpc:
-	s2i build . -E environment_grpc seldonio/seldon-core-s2i-python3:0.15 $(IMAGE_NAME)_grpc:$(IMAGE_VERSION)
-
-push_to_dockerhub_grpc:
-	docker push $(IMAGE_NAME)_grpc:$(IMAGE_VERSION)
+kind_load: build
+	kind load -v 3 docker-image ${IMAGE_NAME}:${VERSION} --name ${KIND_NAME}

components/routers/epsilon-greedy/environment_grpc → components/routers/epsilon-greedy/environment

@@ -1,4 +1,3 @@
 MODEL_NAME=EpsilonGreedy
-API_TYPE=GRPC
 SERVICE_TYPE=ROUTER
 PERSISTENCE=0

doc/source/examples/backwards_compatibility.nblink

@@ -0,0 +1,3 @@
+{
+  "path": "../../../notebooks/backwards_compatability.ipynb"
+}

doc/source/examples/istio.md

@@ -12,6 +12,3 @@ The addition of Istio is complementary to Seldon and is illustrated below where
 
 ![svc-graph-istio](./svc-graph-istio.png)
 
-## Worked Examples
-
-[An example step-by-step guide to canary deployments using Istio and Seldon is provided](./istio_canary.html)

doc/source/examples/notebooks.rst

@@ -140,7 +140,6 @@ Ingress
    Ambassador Shadow <ambassador_shadow>
    Ambassador Headers <ambassador_headers>
    Ambassador Custom Config <ambassador_custom>
-   Istio Canary <istio_canary>
    Istio Examples <istio_examples>   
 
 Infrastructure
@@ -161,4 +160,4 @@ Benchmarking and Load Tests
    Service Orchestrator <bench_svcOrch>
    Tensorflow <bench_tensorflow>   
    Argo Workflows Benchmarking <vegeta_bench_argo_workflows>   
-
+   Backwards Compatability Tests <backwards_compatibility>

doc/source/java-jni/README.md

@@ -132,7 +132,6 @@ Define the core parameters needed by our Java S2I images to wrap your model.
 An example is:
 
 ```bash
-API_TYPE=REST
 SERVICE_TYPE=MODEL
 JAVA_IMPORT_PATH=io.seldon.example.model.ExampleModelHandler
 ```
@@ -213,10 +212,6 @@ Import path for your Java model implementation.
 For instance, in the example above, this would be
 `io.seldon.example.model.ExampleModelHandler`.
 
-### API_TYPE
-
-API type to create.
-Can be REST or GRPC.
 
 ### SERVICE_TYPE
 

doc/source/python/index.rst

@@ -12,12 +12,11 @@ You can use the following links to navigate the Python seldon-core module:
 .. toctree::
    :maxdepth: 1
 
-   Seldon Core Python Module <python_module.md>
-   Your python class <python_component.md>
-   Wrap using S2I <python_wrapping_s2i.md>
-   Wrap using Docker <python_wrapping_docker.md>
-   Seldon Python Client <seldon_client.md>
-   Seldon Python Server <python_server.md>
-   Python API reference <api/modules>
-
+   Install the Seldon Core Python module <python_module.md>
+   Creating your Python inference class <python_component.md>
+   Create image with S2I <python_wrapping_s2i.md>
+   Create image with a Dockerfile <python_wrapping_docker.md>
+   Seldon Python server configuration <python_server.md>
+   Calling the Seldon API with the Seldon Python client <seldon_client.md>   
+   Python API reference <api/modules>   	      
 

doc/source/python/python_component.md

@@ -264,7 +264,7 @@ spec:
 
 ## Low level Methods
 
-If you want more control you can provide a low-level methods that will provide as input the raw proto buffer payloads. The signatures for these are shown below for release `sedon_core>=0.2.6.1`:
+If you want more control you can provide a low-level methods that will provide as input the raw proto buffer payloads. The signatures for these are shown below for release `seldon_core>=0.2.6.1`:
 
 ```python
     def predict_raw(self, msg: prediction_pb2.SeldonMessage) -> prediction_pb2.SeldonMessage:

doc/source/python/python_module.md

@@ -71,46 +71,6 @@ Keep in mind that this will include some dependencies which may not be used.
 Therefore, unless necessary, we recommend most users to install the default
 distribution of `seldon-core` as [documented above](#install).
 
-## Seldon Core Microservices
-
-Seldon allows you to easily take your runtime inference code and create a Docker container that can be managed by Seldon Core. Follow the [S2I instructions](../wrappers/python.md) to wrap your code.
-
-You can also create your own image and utilise the `seldon-core-microservice` executable to run your model code.
-
-
-## Seldon Core Python API Client
-
-The python package contains a module that provides a reference python client for the internal Seldon Core microservice API and the external APIs. More specifically it provides:
-
- * Internal microservice API
-    * Make REST or gRPC calls
-    * Test all methods: `predict`, `transform-input`, `transform-output`, `route`, `aggregate`
-    * Provide a numpy array, binary data or string data as payload or get random data generated as payload for given shape
-    * Send data as tensor, TFTensor or ndarray
- * External API
-    * Make REST or gRPC calls
-    * Call the API via Ambassador, Istio or Seldon's OAUTH API gateway.
-    * Test `predict` or `feedback` endpoints
-    * Provide a numpy array, binary data or string data as payload or get random data generated as payload for given shape
-    * Send data as tensor, TFTensor or ndarray
-
-Basic usage of the client is to create a `SeldonClient` object first. For example for a Seldon Deployment called "mymodel" running in the namespace `seldon` with Ambassador endpoint at "localhost:8003" (i.e., via port-forwarding):
-
-```python
-from seldon_core.seldon_client import SeldonClient
-sc = SeldonClient(deployment_name="mymodel",namespace="seldon", gateway_endpoint="localhost:8003")
-```
-
-Then make calls of various types. For example, to make a random prediction  via the Ambassador gateway using REST:
-
-```python
-r = sc.predict(gateway="ambassador",transport="rest")
-print(r)
-```
-
-Examples of using the `seldon_client` module can be found in the [example notebook](../examples/helm_examples.html).
-
-The API docs can be found [here](./api/seldon_core.html#module-seldon_core.seldon_client).
 
 ## Troubleshooting
 
@@ -146,3 +106,9 @@ also works:
 ```bash
 $ pip install azure-storage-blob==2.1.0 seldon-core
 ```
+
+## Next Steps
+
+[Create your python inference class](python_component.md)
+
+

doc/source/python/python_server.md

@@ -1,4 +1,10 @@
-# Seldon Python Server
+# Seldon Python Server Configuration
+
+ * [Workers](#workers)
+ * [Threads](#threads)
+ * [Flask Development Server](#development-server)
+ * [Server Configuration](#configuration)
+
 
 To serve your component, Seldon's Python wrapper will use
 [Gunicorn](https://gunicorn.org/) under the hood by default.

doc/source/python/python_wrapping_docker.md

@@ -65,11 +65,10 @@ EXPOSE 5000
 
 # Define environment variable
 ENV MODEL_NAME MyModel
-ENV API_TYPE REST
 ENV SERVICE_TYPE MODEL
 ENV PERSISTENCE 0
 
-CMD exec seldon-core-microservice $MODEL_NAME $API_TYPE --service-type $SERVICE_TYPE --persistence $PERSISTENCE
+CMD exec seldon-core-microservice $MODEL_NAME --service-type $SERVICE_TYPE --persistence $PERSISTENCE
 ```
 
 
@@ -87,10 +86,6 @@ The required environment variables understood by the builder image are explained
 ### MODEL_NAME
 The name of the class containing the model. Also the name of the python file which will be imported.
 
-### API_TYPE
-
-API type to create. Can be REST or GRPC
-
 ### SERVICE_TYPE
 
 The service type being created. Available options are:
@@ -157,7 +152,6 @@ These arguments can be set when deploying in a Seldon Deployment. An example can
 {
   "graph": {
     "name": "tfserving-proxy",
-    "endpoint": { "type": "REST" },
     "type": "MODEL",
     "children": [],
     "parameters": [

doc/source/python/python_wrapping_s2i.md

@@ -35,39 +35,8 @@ To use our s2i builder image to package your python model you will need:
 We will go into detail for each of these steps:
 
 ### Python file
-Your source code should contain a python file which defines a class of the same name as the file. For example, looking at our skeleton python model file at `wrappers/s2i/python/test/model-template-app/MyModel.py`:
 
-```python
-class MyModel(object):
-    """
-    Model template.
-    You can load your model parameters in __init__ from a location accessible at runtime.
-    """
-
-    def __init__(self):
-        """
-        Add any initialization parameters.
-        These will be passed at runtime from the graph definition parameters defined in your seldondeployment kubernetes resource manifest.
-        """
-        print("Initializing")
-
-    def predict(self,X,features_names):
-        """
-        Return a prediction.
-
-        Parameters
-        ----------
-        X : array-like
-        feature_names : array of feature names (optional)
-        """
-        print("Predict called - will run identity function")
-        return X
-```
-
- * The file is called MyModel.py and it defines a class MyModel
- * The class contains a predict method that takes an array (numpy) X and feature_names and returns an array of predictions.
- * You can add any required initialization inside the class init method.
- * Your return array should be at least 2-dimensional.
+Your source code should contain a python file which defines a class of the same name as the file. For further details see [details on creating your python class](python_component.md)
 
 ### Dependencies
 
@@ -119,13 +88,14 @@ Define the core parameters needed by our python builder image to wrap your model
 
 ```bash
 MODEL_NAME=MyModel
-API_TYPE=REST
 SERVICE_TYPE=MODEL
 PERSISTENCE=0
 ```
 
 These values can also be provided or overridden on the command line when building the image.
 
+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
 
@@ -181,10 +151,6 @@ The required environment variables understood by the builder image are explained
 
 The name of the class containing the model. Also the name of the python file which will be imported.
 
-### API_TYPE
-
-API type to create. Can be REST or GRPC
-
 ### SERVICE_TYPE
 
 The service type being created. Available options are:
@@ -304,7 +270,7 @@ The allowable `type` values for the parameters are defined in the [proto buffer
 
 
 ### Local Python Dependencies
-`from version 0.5-SNAPSHOT`
+`from version 0.5`
 
 To use a private repository for installing Python dependencies use the following build command: