Merge pull request #55 from Techainer/no_proto

Removee GRPC due to not using + Increase version of some requirements

Author: duonglong289

docs/3_Concepts/1_Server.md

@@ -72,7 +72,7 @@ name: mlchain-server # name of service
 entry_file: server.py # python file contains object ServeModel
 host: localhost # host service
 port: 5000 # port service
-server: flask # server option flask or grpc 
+server: flask # server option flask or starlette  
 wrapper: gunicorn # wrapper option None or gunicorn
 gunicorn: # config gunicorn wrapper
   timeout: 60 # max time limit for the server to process
@@ -107,7 +107,7 @@ Port to serve on.
 #### server:
 ```--server STRING```
 
-Type of server to run. Currently we support flask or grpc.
+Type of server to run. Currently we support flask or starlette.
 
 #### wrapper:
 ```--wrapper STRING```

docs/4_Tutorials/2_Keras.md

@@ -275,7 +275,7 @@ name: Fashion-MNIST classifier # name of service
 entry_file: main.py # python file contains object ServeModel
 host: localhost # host service
 port: 5000 # port service
-server: flask # option flask or starlette or grpc
+server: flask # option flask or starlette
 wrapper: None # option None or gunicorn
 cors: true
 dump_request: None # None or path folder log request

docs/Model Deployment/mlconfig.md

@@ -22,7 +22,7 @@ name: mlchain-server # name of service
 entry_file: server.py # python file contains object ServeModel
 host: localhost # host service
 port: 5000 # port service
-server: flask # option flask or grpc
+server: flask # option flask or starlette
 wrapper: gunicorn # option None or gunicorn
 gunicorn: # config apm-server if uses gunicorn wrapper
   timeout: 60

docs/Model Deployment/tutorial.md

@@ -168,7 +168,7 @@ name: Digit-Recognizer # name of service
 entry_file: app.py # python file contains object ServeModel
 host: localhost # host service
 port: 5000 # port
-server: flask # option flask or grpc
+server: flask # option flask or starlette
 wrapper: None # option None or gunicorn
 cors: true
 gunicorn: # config apm-server if uses gunicorn wrapper

docs/commands/init.md

@@ -28,7 +28,7 @@ name: mlchain-server # name of service
 entry_file: server.py # python file contains object ServeModel
 host: localhost # host service
 port: 2222 # port service
-server: flask # option flask or starlette or grpc
+server: flask # option flask or starlette
 trace: False # option True or False
 queue: None # option None or rabbit or redis
 wrapper: None # option None or gunicorn

docs/getstarted/core_concepts.md

@@ -15,7 +15,7 @@ real life example using our service.
 ## ML Deployment
 Simple Machine Learning model deployment is the central feature of ML Chain library.
 Our ServeModel function allows user to deploy their model without requiring software engineering knowledge.
-We support Flask and grpc for website hosting.
+We support Flask and starlette for website hosting.
 
 [Read More...](../Model Deployment/general.md)
 

mlchain/__init__.py

@@ -7,7 +7,7 @@
 )
 
 # Parameters of MLchain
-__version__ = "0.2.8"
+__version__ = "0.2.9"
 
 HOST = "https://www.api.mlchain.ml"
 WEB_HOST = HOST

mlchain/cli/main.py

@@ -5,7 +5,6 @@
 import click
 import flask
 import starlette
-import grpc
 from .init import init_command
 from .run import run_command
 from .artifact import artifact_command

mlchain/cli/mlconfig.yaml

@@ -8,7 +8,7 @@ host: 0.0.0.0            # Host of service
 port: 8001               # Port service
 
 # Server config 
-server: flask        # Option flask or starlette or grpc
+server: flask        # Option flask or starlette
 wrapper: gunicorn        # Option None or gunicorn
 cors: true               # Auto enable CORS
 cors_allow_origins:      # Allow origins for CORS

mlchain/cli/run.py

@@ -65,7 +65,6 @@ def get_env(_k):
 op_gunicorn = click.option("--gunicorn", "wrapper", flag_value="gunicorn", help="Run server with gunicorn or not")
 op_flask = click.option("--flask", "server", flag_value="flask", help="Run with Flask server")
 op_starlette = click.option("--starlette", "server", flag_value="starlette", help="Run with Starlette server")
-op_grpc = click.option("--grpc", "server", flag_value="grpc", help="Run with gRPC server")
 op_worker = click.option("--workers", "-w", "workers", default=None, type=int, help="Number of workers")
 op_thread = click.option("--threads", "-t", "threads", default=None, type=int, help="Number of threads")
 op_mode = click.option("--mode", "-m", "mode", default=None, type=str, help="The mode of mlconfig")
@@ -84,7 +83,6 @@ def get_env(_k):
 @op_gunicorn
 @op_flask
 @op_starlette
-@op_grpc
 @op_worker
 @op_thread
 @op_config
@@ -280,25 +278,7 @@ def run_command(
         logger.info("Ngrok url: {0}".format(endpoint))
         os.environ["NGROK_URL"] = endpoint
 
-
-    ############
-    # Run with grpc 
-    ############
-    if server == "grpc":
-        from mlchain.server.grpc_server import GrpcServer
-
-        app = get_model(entry_file, serve_model=True)
-
-        if app is None:
-            raise Exception(
-                "Can not init model class from {0}. Please check mlconfig.yaml or {0} or mlchain run -m {{mode}}!".format(
-                    entry_file
-                )
-            )
-
-        app = GrpcServer(app, name=name)
-        app.run(host, port)
-    elif wrapper == "gunicorn":
+    if wrapper == "gunicorn":
         ############
         # Run with gunicorn 
         ############
@@ -482,7 +462,7 @@ def load(self):
         elif app.__class__.__name__ == "GrpcServer":
             app.run(host, port, debug=debug)
     elif isinstance(app, ServeModel):
-        if server not in ["starlette", "grpc"]:
+        if server != "starlette":
             server = "flask"
         if server == "flask":
             from mlchain.server.flask_server import FlaskServer
@@ -532,13 +512,6 @@ def load(self):
                 debug=debug
             )
 
-        elif server == "grpc":
-            from mlchain.server.grpc_server import GrpcServer
-
-            app = GrpcServer(app, name=name)
-            app.run(host, port)
-
-
 def get_model(module, serve_model=False):
     """
     Get the serve_model from entry_file