88 python 3 compatibility

docs/getting_started/minikube.md

@@ -72,7 +72,7 @@ In this session, we show how to wrap the sklearn iris classifier in the [seldon-
 
 3. Wrap your saved model using the core-python-wrapper docker image:
     ```bash
-    docker run -v $(pwd):/model seldonio/core-python-wrapper:0.6 /model IrisClassifier 0.1 seldonio --force
+    docker run -v $(pwd):/model seldonio/core-python-wrapper:0.7 /model IrisClassifier 0.1 seldonio --force
     ```
 
 4. Build the docker image locally

docs/wrappers/h2o.md

@@ -44,7 +44,7 @@ Detailed steps:
 3. Run the python wrapping scripts, with the additional ````--base-image``` argument:
 
 	```bash
-	docker run -v /path/to/your/model/folder:/model seldonio/core-python-wrapper:0.6 /model H2OModel 0.1 myrepo --base-image=H2OBase:1.0
+	docker run -v /path/to/your/model/folder:/model seldonio/core-python-wrapper:0.7 /model H2OModel 0.1 myrepo --base-image=H2OBase:1.0
 	```
 
 	"0.1" is the version of the docker image that will be created. "myrepo" is the name of your dockerhub repository.
@@ -88,7 +88,7 @@ Here we give a step by step example in which we will train and save a [H2O model
 
     ```bash
     cd ../../
-	docker run -v models/h2o_example:my_model seldonio/core-python-wrapper:0.6 my_model H2OModel 0.1 myrepo --base-image=H2OBase:1.0
+	docker run -v models/h2o_example:my_model seldonio/core-python-wrapper:0.7 my_model H2OModel 0.1 myrepo --base-image=H2OBase:1.0
     ``` 
 
     This will create a docker image "seldonio/h2omodel:0.1", which is ready to be deployed in seldon-core.

docs/wrappers/python.md

@@ -61,13 +61,13 @@ After you have copied the required files in your model folder, you run the Seldo
 In order to make things even simpler (and because we love Docker!) we have dockerised the wrapper script so that you don't need to install anything on your machine to run it - except Docker.
 
 ```
-docker run -v /path/to/model/dir:/my_model seldonio/core-python-wrapper:0.6 /my_model MnistClassifier 0.1 seldonio
+docker run -v /path/to/model/dir:/my_model seldonio/core-python-wrapper:0.7 /my_model MnistClassifier 0.1 seldonio
 ```
 
 Let's explain each piece of this command in more details.
 
 
-``` docker run seldonio/core-python-wrapper:0.6 ``` : run the core-python-wrapper container (version 0.6)
+``` docker run seldonio/core-python-wrapper:0.7 ``` : run the core-python-wrapper container.
 
 ``` -v /path/to/model/dir:/my_model ``` : Tells docker to mount your local folder to /my_model in the container. This is used to access your files and generate the wrapped model files. 
 
@@ -76,7 +76,7 @@ Let's explain each piece of this command in more details.
 For reference, here is the complete list of arguments that can be passed to the script.
 
 ```
-docker run -v /path:<model_path> seldonio/core-python-wrapper:0.6 
+docker run -v /path:<model_path> seldonio/core-python-wrapper:0.7 
 	<model_path>
 	<model_name>
 	<image_version>
@@ -97,18 +97,18 @@ Required:
 * docker_repo: The name of your dockerhub repository. In our example seldonio.
 
 Optional:
-* out_folder: The folder that will be created to contain the output files. Defaults to ./build
-* service_type: The type of Seldon Service API the model will use. Defaults to MODEL. Other options are ROUTER, COMBINER, TRANSFORMER, OUTPUT_TRANSFORMER
-* base_image: The docker image your docker container will inherit from. Defaults to python:2.
-* image_name: The name of your docker image. Defaults to model_name in lowercase
+* out-folder: The folder that will be created to contain the output files. Defaults to ./build
+* service-type: The type of Seldon Service API the model will use. Defaults to MODEL. Other options are ROUTER, COMBINER, TRANSFORMER, OUTPUT_TRANSFORMER
+* base-image: The docker image your docker container will inherit from. Defaults to python:2.
+* image-name: The name of your docker image. Defaults to model_name in lowercase
 * force: When this flag is present, the build folder will be overwritten if it already exists. The wrapping is aborted by default.
 * persistence: When this flag is present, the model will be made persistent, its state being saved at a regular interval on redis.
 * grpc: When this flag is present, the model will expose a GRPC API rather than the default REST API
 
 Note that you can access the command line help of the script by using the -h or --help argument as follows:
 
 ```
-docker run seldonio/core-python-wrapper:0.6 -h
+docker run seldonio/core-python-wrapper:0.7 -h
 ```
 
 Note also that you could use the python script directly if you feel so enclined, but you would have to check out seldon-core and install some python libraries on your local machine - by using the docker image you don't have to care about these dependencies.

examples/models/mean_classifier/MeanClassifier.py

@@ -11,15 +11,16 @@ def __init__(self, intValue=0):
         assert type(intValue) == int, "intValue parameters must be an integer"
         self.int_value = intValue
 
-        print "Loading model here"
-        X = np.load(open("model.npy",'r'))
+        print("Loading model here")
+
+        X = np.load(open("model.npy",'rb'), encoding='latin1') 
         self.threshold_ = X.mean() + self.int_value
 
     def _meaning(self, x):
         return f(x.mean()-self.threshold_)
 
     def predict(self, X, feature_names):
-        print X
+        print(X)
         X = np.array(X)
         assert len(X.shape) == 2, "Incorrect shape"
 

examples/models/mean_classifier/requirements.txt

@@ -1,4 +1,2 @@
 numpy==1.11.2
-scikit-learn==0.17.1
 pandas==0.18.1
-scipy==0.18.1

wrappers-docker/Makefile

@@ -1,5 +1,5 @@
 IMAGE_NAME=docker.io/seldonio/core-python-wrapper
-IMAGE_VERSION=0.6
+IMAGE_VERSION=0.7
 
 SELDON_CORE_DIR=..
 

wrappers/Makefile

@@ -2,5 +2,5 @@ update_protos:
 	cp ../proto/prediction.proto ./python/proto
 
 build_protos:
-	python -m grpc.tools.protoc -I./python/proto --python_out=./python/proto --grpc_python_out=./python/proto ./python/proto/prediction.proto
+	python -m grpc.tools.protoc -I./python --python_out=./python --grpc_python_out=./python ./python/proto/prediction.proto
 

wrappers/python/microservice.py

@@ -180,6 +180,6 @@ def parse_parameters(parameters):
         server.add_insecure_port("0.0.0.0:{}".format(port))
         server.start()
 
-        print "GRPC Microservice Running on port {}".format(port)
+        print("GRPC Microservice Running on port {}".format(port))
         while True:
             time.sleep(1000)

wrappers/python/model_microservice.py

@@ -36,8 +36,8 @@ def get_rest_microservice(user_model,debug=False):
     @app.errorhandler(SeldonMicroserviceException)
     def handle_invalid_usage(error):
         response = jsonify(error.to_dict())
-        print "ERROR:"
-        print error.to_dict()
+        print("ERROR:")
+        print(error.to_dict())
         response.status_code = 400
         return response
 

wrappers/python/outlier_detector_microservice.py

@@ -27,8 +27,8 @@ def get_rest_microservice(user_model,debug=False):
     @app.errorhandler(SeldonMicroserviceException)
     def handle_invalid_usage(error):
         response = jsonify(error.to_dict())
-        print "ERROR:"
-        print error.to_dict()
+        print("ERROR:")
+        print(error.to_dict())
         response.status_code = 400
         return response
 
@@ -67,7 +67,7 @@ def TransformInput(self,request,context):
         outlier_score = score(self.user_model,features,datadef.names)
 
         request.meta.tags["outlierScore"] = outlier_score
-
+        
         return request
 
 def get_grpc_server(user_model,debug=False):

wrappers/python/persistence.py

@@ -1,7 +1,12 @@
 import threading
 import os
 import time
-import cPickle as pickle
+try:
+    # python 2
+    import cPickle as pickle
+except ImportError:
+    # python 3
+    import pickle
 import redis
 
 

wrappers/python/seldon_requirements.txt

@@ -1,6 +1,5 @@
 numpy==1.11.2
 pandas==0.18.1
-grpc==0.3.post19
 grpcio==1.1.3
 Flask==0.11.1
 futures

wrappers/python/transformer_microservice.py

@@ -48,8 +48,8 @@ def get_rest_microservice(user_model,debug=False):
     @app.errorhandler(SeldonMicroserviceException)
     def handle_invalid_usage(error):
         response = jsonify(error.to_dict())
-        print "ERROR:"
-        print error.to_dict()
+        print("ERROR:")
+        print(error.to_dict())
         response.status_code = 400
         return response
 

wrappers/python/wrap_model.py

@@ -15,7 +15,7 @@ def wrap_model(
         **wrapping_arguments):
     if os.path.isdir(build_folder):
         if not force_erase:
-            print "Build folder already exists. To force erase, use --force argument"
+            print("Build folder already exists. To force erase, use --force argument")
             exit(0)
         else:
             shutil.rmtree(build_folder)

wrappers/tester.py

@@ -123,36 +123,36 @@ def run(args):
     for i in range(args.n_requests):
         batch = generate_batch(contract,args.batch_size)
         if args.prnt:
-            print '-'*40
-            print "SENDING NEW REQUEST:"
+            print('-'*40)
+            print("SENDING NEW REQUEST:")
 
         if not args.grpc:
             REST_request = gen_REST_request(batch,features=feature_names,tensor=args.tensor)
             if args.prnt:
-                print REST_request
+                print(REST_request)
 
             response = requests.post(
                 REST_url,
                 data={"json":json.dumps(REST_request),"isDefault":True})
             jresp = response.json()
 
             if args.prnt:
-                print "RECEIVED RESPONSE:"
-                print jresp
-                print
+                print("RECEIVED RESPONSE:")
+                print(jresp)
+                print()
         else:
             GRPC_request = gen_GRPC_request(batch,features=feature_names,tensor=args.tensor)
             if args.prnt:
-                print GRPC_request
+                print(GRPC_request)
 
             channel = grpc.insecure_channel('{}:{}'.format(args.host,args.port))
             stub = prediction_pb2_grpc.ModelStub(channel)
             response = stub.Predict(GRPC_request)
 
             if args.prnt:
-                print "RECEIVED RESPONSE:"
-                print response
-                print
+                print("RECEIVED RESPONSE:")
+                print(response)
+                print()
 
 
 if __name__ == "__main__":