updated examples and readme files with new modelData system

Author: zeryx

README.md

@@ -93,22 +93,21 @@ from Algorithmia import ADK
 # API calls will begin at the apply() method, with the request body passed as 'input'
 # For more details, see algorithmia.com/developers/algorithm-development/languages
 
-def apply(input, globals):
+def apply(input, modelData):
     # If your apply function uses state that's loaded into memory via load, you can pass that loaded state to your apply
     # function by defining an additional "globals" parameter in your apply function.
-    return "hello {} {}".format(str(input), str(globals['payload']))
+    return "hello {} {}".format(str(input), str(modelData.user_data['payload']))
 
 
-def load():
+def load(modelData):
     # Here you can optionally define a function that will be called when the algorithm is loaded.
     # The return object from this function can be passed directly as input to your apply function.
     # A great example would be any model files that need to be available to this algorithm
     # during runtime.
 
     # Any variables returned here, will be passed as the secondary argument to your 'algorithm' function
-    globals = {}
-    globals['payload'] = "Loading has been completed."
-    return globals
+    modelData.user_data['payload'] = "Loading has been completed."
+    return modelData
 
 
 # This turns your library code into an algorithm that can run on the platform.
@@ -175,27 +174,26 @@ def infer_image(image_url, n, globals):
     return result
 
 
-def load(manifest):
+def load(modelData):
 
-    state = {}
-    state["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
-    state["model"] = load_model(manifest.get_model("squeezenet"))
-    state["labels"] = load_labels(manifest.get_model("labels"))
-    return state
+    modelData.user_data["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
+    modelData.user_data["model"] = load_model(modelData.get_model("squeezenet"))
+    modelData.user_data["labels"] = load_labels(modelData.get_model("labels"))
+    return modelData
 
 
-def apply(input, state):
+def apply(input, modelData):
     if isinstance(input, dict):
         if "n" in input:
             n = input["n"]
         else:
             n = 3
         if "data" in input:
             if isinstance(input["data"], str):
-                output = infer_image(input["data"], n, state)
+                output = infer_image(input["data"], n, modelData.user_data)
             elif isinstance(input["data"], list):
                 for row in input["data"]:
-                    row["predictions"] = infer_image(row["image_url"], n, state)
+                    row["predictions"] = infer_image(row["image_url"], n, modelData.user_data)
                 output = input["data"]
             else:
                 raise Exception("\"data\" must be a image url or a list of image urls (with labels)")

examples/loaded_state_hello_world/src/Algorithm.py

@@ -4,22 +4,21 @@
 # API calls will begin at the apply() method, with the request body passed as 'input'
 # For more details, see algorithmia.com/developers/algorithm-development/languages
 
-def apply(input, globals):
+def apply(input, modelData):
     # If your apply function uses state that's loaded into memory via load, you can pass that loaded state to your apply
     # function by defining an additional "globals" parameter in your apply function.
-    return "hello {} {}".format(str(input), str(globals['payload']))
+    return "hello {} {}".format(str(input), str(modelData.user_data['payload']))
 
 
-def load():
+def load(modelData):
     # Here you can optionally define a function that will be called when the algorithm is loaded.
     # The return object from this function can be passed directly as input to your apply function.
     # A great example would be any model files that need to be available to this algorithm
     # during runtime.
 
     # Any variables returned here, will be passed as the secondary argument to your 'algorithm' function
-    globals = {}
-    globals['payload'] = "Loading has been completed."
-    return globals
+    modelData.user_data['payload'] = "Loading has been completed."
+    return modelData
 
 
 # This turns your library code into an algorithm that can run on the platform.

examples/pytorch_image_classification/src/Algorithm.py

@@ -51,27 +51,26 @@ def infer_image(image_url, n, globals):
     return result
 
 
-def load(manifest):
+def load(modelData):
 
-    state = {}
-    state["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
-    state["model"] = load_model(manifest.get_model("squeezenet"))
-    state["labels"] = load_labels(manifest.get_model("labels"))
-    return state
+    modelData.user_data["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
+    modelData.user_data["model"] = load_model(modelData.get_model("squeezenet"))
+    modelData.user_data["labels"] = load_labels(modelData.get_model("labels"))
+    return modelData
 
 
-def apply(input, state):
+def apply(input, modelData):
     if isinstance(input, dict):
         if "n" in input:
             n = input["n"]
         else:
             n = 3
         if "data" in input:
             if isinstance(input["data"], str):
-                output = infer_image(input["data"], n, state)
+                output = infer_image(input["data"], n, modelData.user_data)
             elif isinstance(input["data"], list):
                 for row in input["data"]:
-                    row["predictions"] = infer_image(row["image_url"], n, state)
+                    row["predictions"] = infer_image(row["image_url"], n, modelData.user_data)
                 output = input["data"]
             else:
                 raise Exception("\"data\" must be a image url or a list of image urls (with labels)")