Updated documentation

doc/source/servers/overview.md

@@ -6,6 +6,7 @@ Seldon provides several prepacked servers you can use to deploy trained models:
 - [XGBoost Server](xgboost.html)
 - [Tensorflow Serving](tensorflow.html)
 - [MLflow Server](mlflow.html)
+- [Custom Servers](custom.md)
 
 For these servers you only need the location of the saved model in a local filestore, Google bucket, S3 bucket, azure or minio. An example manifest with an sklearn server is shown below:
 
@@ -158,18 +159,18 @@ and you can [read more](https://kubernetes.io/docs/concepts/configuration/secret
 
 In order for your SeldonDeployment to know what is the name of the secret, we have to specify the name of the secret we created - in the example above we named the secret `seldon-init-container-secret`.
 
-#### 3.1 Default Seldon Core Manager Controller value
+#### Option 1: Default Seldon Core Manager Controller value
 
 You can set a global default when you install Seldon Core through the Helm chart through the `values.yaml` variable `executor.defaultEnvSecretRefName`. You can see all the variables available in the [Advanced Helm Installation Page](../reference/helm.rst).
 
 ```yaml
 # ... other variables
-executor:
-  defaultEnvSecretRefName: seldon-core-init-container-secret
+predictiveUnit:
+  defaultEnvSecretRefName: seldon-init-container-secret
 # ... other variables
 ```
 
-#### 3.2 Override through SeldonDeployment config
+#### Option 2: Override through SeldonDeployment config
 
 It is also possible to provide an override value when you deploy your model using the SeldonDeploymen YAML. You can do this through the `envSecretRefName` value:
 

doc/source/servers/tensorflow.md

@@ -1,6 +1,6 @@
 # Tensorflow Serving
 
-If you have a trained Tensorflow model you can deploy this directly via REST or gRPC servers.
+If you have a trained Tensorflow model you can deploy this directly via REST or gRPC servers. 
 
 ## MNIST Example
 
@@ -79,4 +79,4 @@ spec:
 ```
 
 
-Try out a [worked notebook](../examples/server_examples.html)
+Try out a [worked notebook](../examples/server_examples.html)

doc/source/servers/xgboost.md

@@ -5,7 +5,9 @@ If you have a trained XGBoost model saved you can deploy it simply using Seldon'
 Prequisites:
 
   * Use xgboost v0.82
-  * The model pickle must be named `model.bst`
+  * The model must be named `model.bst`
+  * You must save your model using `bst.save_model(file_path)`
+  * The model is loaded with `xgb.Booster(model_file=model_file)`
 
 An example for a saved Iris prediction model:
 

examples/models/sklearn_iris/sklearn_iris.ipynb

@@ -56,6 +56,39 @@
     "    main()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wrap model with Python Wrapper Class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting IrisClassifier.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile IrisClassifier.py\n",
+    "from sklearn.externals import joblib\n",
+    "\n",
+    "class IrisClassifier(object):\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        self.model = joblib.load('IrisClassifier.sav')\n",
+    "\n",
+    "    def predict(self,X,features_names):\n",
+    "        return self.model.predict_proba(X)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -145,6 +178,51 @@
     "!kubectl config set-context $(kubectl config current-context) --namespace=seldon"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create Seldon Core config file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting sklearn_iris_deployment.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile sklearn_iris_deployment.yaml\n",
+    "apiVersion: machinelearning.seldon.io/v1alpha2\n",
+    "kind: SeldonDeployment\n",
+    "metadata:\n",
+    "  name: seldon-deployment-example\n",
+    "spec:\n",
+    "  name: sklearn-iris-deployment\n",
+    "  predictors:\n",
+    "  - componentSpecs:\n",
+    "    - spec:\n",
+    "        containers:\n",
+    "        - image: seldonio/sklearn-iris:0.1\n",
+    "          imagePullPolicy: IfNotPresent\n",
+    "          name: sklearn-iris-classifier\n",
+    "    graph:\n",
+    "      children: []\n",
+    "      endpoint:\n",
+    "        type: REST\n",
+    "      name: sklearn-iris-classifier\n",
+    "      type: MODEL\n",
+    "    name: sklearn-iris-predictor\n",
+    "    replicas: 1"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -243,7 +321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.4"
   },
   "varInspector": {
    "cols": {

examples/models/sklearn_iris_jsondata/IrisClassifier.py

@@ -1,19 +1,18 @@
 from sklearn.externals import joblib
 import sys
 
+
 def eprint(*args, **kwargs):
     print(*args, file=sys.stderr, **kwargs)
 
-class IrisClassifier(object):
 
+class IrisClassifier(object):
     def __init__(self):
-        self.model = joblib.load('IrisClassifier.sav')
+        self.model = joblib.load("IrisClassifier.sav")
 
-    def predict(self,X,features_names):
+    def predict(self, X, features_names):
         eprint("--------------------")
         eprint("Input dict")
         eprint(X)
         eprint("--------------------")
         ndarray = X["some_data"]["some_ndarray"]
-        return self.model.predict_proba(ndarray)
-

examples/models/sklearn_iris_jsondata/sklearn_iris_jsondata.ipynb

@@ -56,6 +56,48 @@
     "    main()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wrap your model with a Python wrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting IrisClassifier.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile IrisClassifier.py\n",
+    "from sklearn.externals import joblib\n",
+    "import sys\n",
+    "\n",
+    "\n",
+    "def eprint(*args, **kwargs):\n",
+    "    print(*args, file=sys.stderr, **kwargs)\n",
+    "\n",
+    "\n",
+    "class IrisClassifier(object):\n",
+    "    def __init__(self):\n",
+    "        self.model = joblib.load(\"IrisClassifier.sav\")\n",
+    "\n",
+    "    def predict(self, X, features_names):\n",
+    "        eprint(\"--------------------\")\n",
+    "        eprint(\"Input dict\")\n",
+    "        eprint(X)\n",
+    "        eprint(\"--------------------\")\n",
+    "        ndarray = X[\"some_data\"][\"some_ndarray\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -67,7 +109,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Wrap model using s2i"
+    "Wrap your Python model using s2i"
    ]
   },
   {
@@ -152,6 +194,51 @@
     "!kubectl config set-context $(kubectl config current-context) --namespace=seldon"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a seldon config file to deploy the containerized image you just created"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting sklearn_iris_jsondata_deployment.yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile sklearn_iris_jsondata_deployment.yaml\n",
+    "apiVersion: machinelearning.seldon.io/v1alpha2\n",
+    "kind: SeldonDeployment\n",
+    "metadata:\n",
+    "  name: seldon-deployment-example\n",
+    "spec:\n",
+    "  name: sklearn-iris-deployment\n",
+    "  predictors:\n",
+    "  - componentSpecs:\n",
+    "    - spec:\n",
+    "        containers:\n",
+    "        - image: seldonio/sklearn-iris-jsondata:0.1\n",
+    "          imagePullPolicy: IfNotPresent\n",
+    "          name: sklearn-iris-classifier\n",
+    "    graph:\n",
+    "      children: []\n",
+    "      endpoint:\n",
+    "        type: REST\n",
+    "      name: sklearn-iris-classifier\n",
+    "      type: MODEL\n",
+    "    name: sklearn-iris-predictor\n",
+    "    replicas: 1"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -253,7 +340,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.4"
   },
   "varInspector": {
    "cols": {

examples/models/sklearn_spacy_text/RedditClassifier.py

@@ -2,16 +2,17 @@
 
 from ml_utils import CleanTextTransformer, SpacyTokenTransformer
 
+
 class RedditClassifier(object):
     def __init__(self):
-        
+
         self._clean_text_transformer = CleanTextTransformer()
         self._spacy_tokenizer = SpacyTokenTransformer()
-        
-        with open('tfidf_vectorizer.model', 'rb') as model_file:
+
+        with open("tfidf_vectorizer.model", "rb") as model_file:
             self._tfidf_vectorizer = dill.load(model_file)
-           
-        with open('lr.model', 'rb') as model_file:
+
+        with open("lr.model", "rb") as model_file:
             self._lr_model = dill.load(model_file)
 
     def predict(self, X, feature_names):
@@ -20,4 +21,3 @@ def predict(self, X, feature_names):
         tfidf_features = self._tfidf_vectorizer.transform(spacy_tokens)
         predictions = self._lr_model.predict_proba(tfidf_features)
         return predictions
-