Seldon Components Usage Examples

doc/source/examples/combiner_spam_model.nblink

@@ -0,0 +1,3 @@
+{
+  "path": "../../../examples/combiners/spam_clf_combiner/spam-classification.ipynb"
+}

doc/source/examples/notebooks.rst

@@ -47,4 +47,6 @@ Notebooks
    Sklearn, Xgboost, Tensorflow Server Examples <server_examples>
    Tensorflow MNIST <deep_mnist>
    TFserving MNIST <tfserving_mnist>
+   Spam Classification TRANSFORMER component <transformer_spam_model>
+   Spam Classification TRANSFORMER + COMBINER Component <combiner_spam_model>
 

doc/source/examples/transformer_spam_model.nblink

@@ -0,0 +1,3 @@
+{
+  "path": "../../../examples/input_tranformer/spam-classification.ipynb"
+}

examples/combiners/spam_clf_combiner/.gitignore

@@ -0,0 +1 @@
+**/.DS_Store

examples/combiners/spam_clf_combiner/Combiner/.s2i/environment

@@ -0,0 +1,4 @@
+MODEL_NAME=Combiner
+API_TYPE=REST
+SERVICE_TYPE=COMBINER
+PERSISTENCE=0

examples/combiners/spam_clf_combiner/Combiner/Combiner.py

@@ -0,0 +1,22 @@
+import numpy as np
+
+class Combiner(object):
+
+    def aggregate(self, Xs, features_names=None):
+    	"""average out the probabilities from multiple classifier and return that as a result"""
+    	return np.mean([float(x[0]) for x in Xs])
+
+
+
+# if __name__== "__main__":
+#     clf1_res = np.array(['0.80', 'Spam'])
+#     clf2_res = np.array(['0.9959868467126312e-04', 'Spam'])
+
+#     example = np.array([clf1_res, clf2_res])
+#     combine = Combiner()
+#     res = combine.aggregate(example, features_names=None)
+#     print(res)
+
+
+
+

examples/combiners/spam_clf_combiner/Combiner/README.md

@@ -0,0 +1,33 @@
+# **Combiner**
+
+
+
+#### Activate Virtual Env
+
+Create a Python virtual environment:
+
+```
+    $ python3 -m venv venv
+    $ source venv/bin/activate
+```
+
+#### Install s2i
+
+
+    linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/
+    mac:  brew install source-to-image
+
+
+#### Build
+
+```
+$ s2i build Combiner/ seldonio/seldon-core-s2i-python3:0.7 combiner:1.0.0.1
+$ docker push <yourdockerhubusername>/combiner:1.0.0.1
+```
+
+#### Test
+
+```
+$ docker run --name "Combiner" --rm <yourdockerhubusername>/combiner:1.0.0.1
+
+```

examples/combiners/spam_clf_combiner/README.md

@@ -0,0 +1,23 @@
+
+#### Model Deployment using Seldon Transformer + Combiner Component
+
+Consider, we want to predict whether a text message is spam or not. The text data may contain multiple languages as well as each individual text may also have words from different languages. As per your needs, you are only interested in building a model on English language. So, you want to first translate the text message in English and then pass it to model to classify whether it is a spam or not. Moreover, we want to train two models: one that uses Neural network architecture using keras library and other that uses traditional machine learning classifier from scikit-learn. At runtime, we want to run both mdoels on a given input and output the average of the both predictions:
+
+Example: <br/>
+If classifier 1 outputs the probability of 0.7 for the message to be spam <br/>
+Classifier 2 outputs the probability of 0.8 for the message to be spam <br/>
+The final model using seldon-core graph would output the average probability of both classifiers: 0.75 
+
+
+
+![Model Pipeline](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/combiners/spam_clf_combiner/seldon_inference_graph.png)
+
+
+Each component in Seldon Inference graph is a microservice – specifically a docker container. The Seldon graph links those docker containers (in deploy.yaml file) to result in an end output. 
+
+
+![Graph Explanation](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/combiners/spam_clf_combiner/graph_explained.png)
+
+The repository contains the folders for for each docker image needed for seldon graph to work. The images used for this example are already published in public dockerhub. So, deploy the model on k8s cluster using:
+
+    kubectl apply -f deploy.yaml

examples/combiners/spam_clf_combiner/Translator/.s2i/environment

@@ -0,0 +1,4 @@
+MODEL_NAME=Translator
+API_TYPE=REST
+SERVICE_TYPE=TRANSFORMER
+PERSISTENCE=0

examples/combiners/spam_clf_combiner/Translator/README.md

@@ -0,0 +1,36 @@
+# **Translator**
+
+
+
+#### Activate Virtual Env
+
+Create a Python virtual environment:
+
+```
+$ python3 -m venv venv
+$ source venv/bin/activate
+```
+
+#### Install s2i
+
+
+    linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/
+    mac:  brew install source-to-image
+
+
+#### Build
+
+```
+$ s2i build . seldonio/seldon-core-s2i-python3:0.7 translator:1.0.0.1
+$ docker push <yourdockerhubusername>/translator:1.0.0.1
+```
+
+#### Test
+
+```
+$ docker run --name "translator" --rm <yourdockerhubusername>/translator:1.0.0.1
+
+curl -g http://localhost:5000/transform_input --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["Wie läuft dein Tag"]}}'
+
+
+```

examples/combiners/spam_clf_combiner/Translator/Translator.py

@@ -0,0 +1,26 @@
+import goslate
+import numpy as np
+
+class Translator():
+    def __init__(self):
+    	self.gs = goslate.Goslate()
+
+
+
+    def transform_input(self, text_msg, feature_names=None):
+
+        """the translator logic will go here. This shows a use of simple library. But translator service can be a Machine Learning model itself"""
+        translated = self.gs.translate(text_msg[0], 'en')
+        return np.array([translated])
+
+
+
+# if __name__== "__main__":
+#     t = Translator()
+#     example = np.array(['Wie läuft dein Tag'])
+#     translated = t.transform_input(example)
+#     print(translated)
+
+
+
+

examples/combiners/spam_clf_combiner/Translator/requirements.txt

@@ -0,0 +1,2 @@
+goslate
+numpy

examples/combiners/spam_clf_combiner/deploy.yaml

@@ -0,0 +1,58 @@
+---
+apiVersion: machinelearning.seldon.io/v1alpha2
+kind: SeldonDeployment
+metadata:
+  labels:
+    app: seldon
+  name: seldon-spam-classifier
+  namespace: default
+spec:
+  annotations:
+    project_name: seldon-spam-classifier
+    deployment_version: v1
+    seldon.io/rest-read-timeout: '100000'
+    seldon.io/rest-connection-timeout: '100000'
+    seldon.io/grpc-read-timeout: '100000'
+  name: seldon-spam-classifier
+  predictors:
+  - componentSpecs:
+    - spec:
+        containers:
+        - name: combiner
+          image: sandhya1594/combiner:1.0.0.1
+          imagePullPolicy: IfNotPresent
+        - name: spam-classifier
+          image: sandhya1594/spam-classifier:1.0.0.1
+          imagePullPolicy: IfNotPresent
+        - name: keras-spam-classifier
+          image: sandhya1594/keras-spam-classifier:1.0.0.1
+          imagePullPolicy: IfNotPresent
+        - name: translator
+          image: sandhya1594/translator:1.0.0.1
+          imagePullPolicy: IfNotPresent
+        terminationGracePeriodSeconds: 20
+    graph:
+      children:
+      - name: combiner
+        endpoint:
+          type: REST
+        type: COMBINER
+        children:
+        - name: keras-spam-classifier
+          endpoint:
+            type: REST
+          type: MODEL
+          children: []
+        - name: spam-classifier
+          endpoint: 
+            type: REST
+          type: MODEL
+          children: []
+      name: translator
+      endpoint:
+        type: REST
+      type: TRANSFORMER
+    name: seldon-combiner
+    replicas: 1
+    annotations:
+      predictor_version: v1

examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/environment

@@ -0,0 +1,4 @@
+MODEL_NAME=KerasSpamClassifier
+API_TYPE=REST
+SERVICE_TYPE=MODEL
+PERSISTENCE=0

examples/combiners/spam_clf_combiner/keras-spam-classifier/KerasSpamClassifier.py

@@ -0,0 +1,44 @@
+from pathlib import Path
+import numpy as np
+import pickle
+from sklearn.externals import joblib 
+from keras.preprocessing import sequence
+from keras.engine.saving import model_from_json
+
+class KerasSpamClassifier():
+
+    def __init__(self, model_path = Path('./model')):
+        self._architecture_path = model_path.joinpath('architecture.json')
+        self._weights_path = model_path.joinpath('weights.h5')
+        tokenizer_path = model_path.joinpath('tokenizer.pkl')
+
+        self.max_len = 150
+        # loading of trained tokenizer
+        with tokenizer_path.open('rb') as handle:
+            self.tokenizer = joblib.load(handle)
+
+        with self._architecture_path.open() as f:
+            self.model = model_from_json(f.read())
+            self.model.load_weights(self._weights_path.as_posix())
+            self.model._make_predict_function()
+
+
+
+    def predict(self, text, feature_names=None): #List[Tuple[float, float]]:
+        """
+        Predict on a english text you got from translator service. The output returns the probability of text being spam
+        """
+        seq= self.tokenizer.texts_to_sequences([text])
+        padded = sequence.pad_sequences(seq, maxlen=self.max_len)
+        prob = self.model.predict(padded)
+        prob = prob[0][0]
+        return np.array([prob, "spam"])
+
+
+# if __name__== "__main__":
+
+#     text = 'please click this link to win the price'
+#     #vect_path=Path('/Users/sandhya.sandhya/Desktop/data/doc/data'),
+#     sc = KerasSpamClassifier(model_path=Path('./model'))
+#     res = sc.predict(text, feature_names=None)
+#     print(res)

examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md

@@ -0,0 +1,45 @@
+# **Spam Classifier**
+
+
+
+#### Activate Virtual Env
+
+Create a Python virtual environment:
+
+```
+    $ python3 -m venv venv
+    $ source venv/bin/activate
+```
+
+#### Install s2i
+
+
+    linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/
+    mac:  brew install source-to-image
+
+
+#### Build
+
+```
+$ s2i build . seldonio/seldon-core-s2i-python3:0.7 keras-spam-classifier:1.0.0.1
+$ docker push <yourdockerhubusername>/keras-spam-classifier:1.0.0.1
+```
+
+#### Test
+
+```
+$ docker run --name "spam-classifier" --rm -d -p 5000:5000  keras-spam-classifier:1.0.0.1
+
+curl -g http://localhost:5000/predict --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["please click this link to win the price"]}}'
+
+Result:
+
+{"data":{"ndarray":["0.91942614","spam"]},"meta":{}}
+
+
+```
+
+
+
+
+

examples/combiners/spam_clf_combiner/keras-spam-classifier/requirements.txt

@@ -0,0 +1,3 @@
+scikit-learn==0.21.2
+numpy>=1.9.2
+keras

examples/combiners/spam_clf_combiner/skl-spam-classifier/.s2i/environment

@@ -0,0 +1,4 @@
+MODEL_NAME=SpamClassifier
+API_TYPE=REST
+SERVICE_TYPE=MODEL
+PERSISTENCE=0

examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md

@@ -0,0 +1,43 @@
+# **Spam Classifier**
+
+
+
+#### Activate Virtual Env
+
+Create a Python virtual environment:
+
+```
+    $ python3 -m venv venv
+    $ source venv/bin/activate
+```
+
+#### Install s2i
+
+
+    linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/
+    mac:  brew install source-to-image
+
+
+#### Build
+
+```
+$ s2i build . seldonio/seldon-core-s2i-python3:0.7 spam-classifier:1.0.0.1
+$ docker push <yourdockerhubusername>/spam-classifier:1.0.0.1
+```
+
+#### Test
+
+```
+$ docker run --name "spam-classifier" --rm -d -p 5000:5000  spam-classifier:1.0.0.1
+
+curl -g http://localhost:5000/predict --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["click here to win the price"]}}'
+
+Result:
+
+{"data":{"ndarray":["0.9785519250237192","spam"]},"meta":{}}
+
+```
+
+
+
+