Add Task connect_configuration, connect_label_enumeration, and support for nested dictionary with connect

Add Task.get_tasks

examples/manual_model_config.py

@@ -12,22 +12,32 @@ task = Task.init(project_name='examples', task_name='Manual model configuration'
 # create a model
 model = torch.nn.Module
 
-# store dictionary of definition for a specific network design
+# Connect a local configuration file
+config_file = 'samples/sample.json'
+config_file = task.connect_configuration(config_file)
+# then read configuration as usual, the backend will contain a copy of it.
+# later when executing remotely, the returned `config_file` will be a temporary file
+# containing a new copy of the configuration retrieved form the backend
+# # model_config_dict = json.load(open(config_file, 'rt'))
+
+# Or Store dictionary of definition for a specific network design
 model_config_dict = {
     'value': 13.37,
-    'dict': {'sub_value': 'string'},
+    'dict': {'sub_value': 'string', 'sub_integer': 11},
     'list_of_ints': [1, 2, 3, 4],
 }
-task.set_model_config(config_dict=model_config_dict)
+model_config_dict = task.connect_configuration(model_config_dict)
 
-# or read form a config file (this will override the previous configuration dictionary)
-# task.set_model_config(config_text='this is just a blob\nof text from a configuration file')
+# We now update the dictionary after connecting it, and the changes will be tracked as well.
+model_config_dict['new value'] = 10
+model_config_dict['value'] *= model_config_dict['new value']
 
-# store the label enumeration the model is training for
-task.set_model_label_enumeration({'background': 0, 'cat': 1, 'dog': 2})
-print('Any model stored from this point onwards, will contain both model_config and label_enumeration')
+# store the label enumeration of the training model
+labels = {'background': 0, 'cat': 1, 'dog': 2}
+task.connect_label_enumeration(labels)
 
 # storing the model, it will have the task network configuration and label enumeration
+print('Any model stored from this point onwards, will contain both model_config and label_enumeration')
 
 torch.save(model, os.path.join(gettempdir(), "model"))
 print('Model saved')

examples/samples/sample.json

@@ -0,0 +1,8 @@
+{
+    "list_of_ints": [1,2,3,4],
+    "dict": {
+        "sub_value": "string",
+        "sub_integer": 11
+    },
+    "value": 13.37
+}