Merge commit 'e51c556ec37945a5dedc88dcf2fbc939ad5c881a' into v1.5.1-r…

…elease

doc/source/examples/notebooks.rst

@@ -119,7 +119,7 @@ Production Configurations and Integrations
 
    Example Helm Deployments <helm_examples>
    Max gRPC Message Size <max_grpc_msg_size>
-   REST timeouts <rest_timeouts>
+   Configurable timeouts <timeouts>
    Deploy Multiple Seldon Core Operators <multiple_operators>
    Protocol Examples <protocol_examples>
    Custom Protobuf Data Example <customdata_example>

doc/source/python/developer_notes.md

@@ -0,0 +1,49 @@
+# Development Tips
+
+
+## Running locally for testing
+
+Sometimes it is useful to be able to test your model locally without the need to build image with s2i or docker.
+
+This can be easily done with `seldon-core` as its installed the CLI command that starts the microservice.
+
+Assuming we have a simple model saved in `MyModel.py` file:
+```python
+class MyModel:
+
+    def predict(self, X, features_names=None):
+        """
+        Return a prediction.
+
+        Parameters
+        ----------
+        X : array-like
+        feature_names : array of feature names (optional)
+        """
+        print("Predict called - will run identity function")
+        return X
+```
+
+We can start Seldon Core microservice with
+```bash
+seldon-core-microservice MyModel --service-type MODEL
+```
+
+Then in other terminal we can send `curl` requests to test REST endpoint:
+```bash
+curl http://localhost:9000/api/v1.0/predictions \
+    -H 'Content-Type: application/json' \
+    -d '{"data": {"names": ["input"], "ndarray": ["data"]}}'
+```
+
+
+And assuming that `seldon-core` code is accessible at `${SELDON_CORE_DIR}` we can use `grpcurl` to send gRPC request:
+```bash
+cd ${SELDON_CORE_DIR}/executor/proto && grpcurl \
+    -d '{"data": {"names": ["input"], "ndarray": ["data"]}}' \
+    -plaintext -proto ./prediction.proto  0.0.0.0:5000 seldon.protos.Seldon/Predict
+```
+
+The `grpcurl` tool can be obtained using binaries released on [GitHub](https://github.com/fullstorydev/grpcurl) or using [asdf-vm](https://github.com/asdf-vm/asdf-plugins).
+
+See [Python Server](./python_server.html#configuration) documentation for config options.

doc/source/python/index.rst

@@ -17,6 +17,6 @@ You can use the following links to navigate the Python seldon-core module:
    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>   	      
-
+   Calling the Seldon API with the Seldon Python client <seldon_client.md>
+   Python API reference <api/modules>
+   Development Tips <developer_notes>

examples/ambassador/canary/ambassador_canary.ipynb

@@ -25,7 +25,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Error from server (AlreadyExists): namespaces \"seldon\" already exists\r\n"
+      "Error from server (AlreadyExists): namespaces \"seldon\" already exists\n"
      ]
     }
    ],
@@ -42,7 +42,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Context \"kind-kind\" modified.\r\n"
+      "Context \"kind-seldon\" modified.\n"
      ]
     }
    ],
@@ -72,7 +72,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.5.0-dev'"
+       "'1.6.0-dev'"
       ]
      },
      "execution_count": 4,
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,14 +130,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io/example created\r\n"
+      "seldondeployment.machinelearning.seldon.io/example created\n"
      ]
     }
    ],
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -204,12 +204,12 @@
       "  tensor {\n",
       "    shape: 1\n",
       "    shape: 1\n",
-      "    values: 0.5531305252881753\n",
+      "    values: 0.71309135369705\n",
       "  }\n",
       "}\n",
       "\n",
       "Response:\n",
-      "{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.08599584459790496]}}, 'meta': {}}\n"
+      "{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.09942988397161616]}}, 'meta': {'requestPath': {'classifier': 'seldonio/mock_classifier:1.6.0-dev'}}}\n"
      ]
     }
    ],
@@ -230,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,14 +280,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io/example created\r\n"
+      "Warning: kubectl apply should be used on resource created by either kubectl create --save-config or kubectl apply\n",
+      "seldondeployment.machinelearning.seldon.io/example configured\n"
      ]
     }
    ],
@@ -297,13 +298,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Waiting for deployment \"example-canary-0-classifier\" rollout to finish: 0 of 1 updated replicas are available...\n",
       "deployment \"example-canary-0-classifier\" successfully rolled out\n",
       "deployment \"example-main-0-classifier\" successfully rolled out\n"
      ]
@@ -323,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -337,15 +339,15 @@
        "  tensor {\n",
        "    shape: 1\n",
        "    shape: 1\n",
-       "    values: 0.08077854180978328\n",
+       "    values: 0.8694648161558428\n",
        "  }\n",
        "}\n",
        "\n",
        "Response:\n",
-       "{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.0554153788429373]}}, 'meta': {}}"
+       "{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.11433542420010957]}}, 'meta': {'requestPath': {'classifier': 'seldonio/mock_classifier:1.6.0-dev'}}}"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -356,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -367,34 +369,41 @@
     "    r = sc.predict(gateway=\"ambassador\",transport=\"rest\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Following checks number of prediction requests processed by default/canary predictors respectively."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "default_count=!kubectl logs $(kubectl get pod -lseldon-app=example-main -o jsonpath='{.items[0].metadata.name}') classifier | grep \"/predict\" | wc -l "
+    "default_count=!kubectl logs $(kubectl get pod -lseldon-app=example-main -o jsonpath='{.items[0].metadata.name}') classifier | grep \"root:predict\" | wc -l "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
-    "canary_count=!kubectl logs $(kubectl get pod -lseldon-app=example-canary -o jsonpath='{.items[0].metadata.name}') classifier | grep \"/predict\" | wc -l"
+    "canary_count=!kubectl logs $(kubectl get pod -lseldon-app=example-canary -o jsonpath='{.items[0].metadata.name}') classifier | grep \"root:predict\" | wc -l"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.2948717948717949\n"
+      "0.32894736842105265\n"
      ]
     }
    ],
@@ -406,27 +415,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io \"example\" deleted\r\n"
+      "seldondeployment.machinelearning.seldon.io \"example\" deleted\n"
      ]
     }
    ],
    "source": [
     "!kubectl delete -f canary.yaml"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -446,7 +448,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.8.6"
   },
   "varInspector": {
    "cols": {
@@ -479,5 +481,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }