Create setup function, trainer-script updates, lambda-function skeleton.

docs/CONFIGURATION.md

@@ -28,3 +28,8 @@ Do note that the `cost-limit` parameter is an estimation, and **is not a guarant
 }
 ``` 
 
+## `training-script-filename` ##
+
+This package comes pre-loaded with a `trainer-script.sh` script to get you started. The Lambda function will find this file in the package and feed it into the CloudFormation template for you when invoked. 
+
+* Example: `trainer-script.sh`

requirements.txt

@@ -0,0 +1,7 @@
+boto3==1.5.2
+botocore==1.8.16
+docutils==0.14
+jmespath==0.9.3
+python-dateutil==2.6.1
+s3transfer==0.1.12
+six==1.11.0

setup.py

@@ -0,0 +1,74 @@
+# After setting up the config.json, lambda-function.py, and trainer-script.sh files,
+# run this script to create the necessary Lambda function. Execution of the Lambda
+# function will be triggered manually by you from the AWS CLI or web console, a separate
+# program of yours via the AWS SDK, an IoT button, or whatever other trigger you make use of
+# for firing off your algorithm's training session.
+
+import boto3
+import json
+import logging
+
+
+def lambda_creation(config={}):
+    """
+    Create the lambda function, return the response, give the AWS CLI command to execute it.
+    :return:
+    """
+    try:
+        return True
+
+    except Exception as e:
+        return False
+
+
+def parse_config():
+    """
+    Parse and return the config. This'll be a lot shorter than the _test function.
+    :return:
+    """
+    try:
+        config = {}
+        return config
+
+    except Exception as e:
+        return False
+
+
+def _test_lambda_creation():
+    """
+    Parse and return the config. This'll be a lot shorter than the _test function.
+    :return:
+    """
+    try:
+        return True
+
+    except Exception as e:
+        return False
+
+
+def _test_parse_config():
+    """
+    Try parsing the JSON config. Check for file existence, valid parsing, and of course
+    that valid inputs are configured. (Numerical inputs for time and cost, for example.)
+    :return:
+    """
+    try:
+        return True
+
+    except Exception as e:
+        return False
+
+
+if __name__ == '__main__':
+    """
+    Parse the config, craft a CloudFormation template, create the Lambda function.
+    """
+
+    parse_config_test_result = _test_parse_config()
+    lambda_creation_test_result = _test_lambda_creation()
+
+    # If the test have all passed, go ahead with function creation.
+
+    lambda_creation(config=parse_config())
+
+    logging.info('setup.py finished.')

src/trainer-script.sh

@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+# This script is what will run on the EC2 instance once it launches.
+# You'll want to verify that this functions as expected before launching
+# an expensive instance type with it, as simple typos can become costly
+# if you're launching and terminating instances every time you have a
+# bug.
+
+# You'll want to include some kind of logging facility with this script
+# in case things go wrong.
+
+# TODO Does sudo need to be used to run any of the install/poweroff commands?
+
+# Run setup of your training session. Your commands will invariably look different.
+cd /tmp
+yum install -y git
+git clone https://github.com/jgreenemi/MXNet-Familiarity-Project.git
+cd MXNet-Familiarity-Project
+pip install -r requirements.txt
+
+# Setup done - now run the training job.
+python classifier\trainer.py
+
+# If the script has completed, go ahead and turn off the server to eliminate any
+# additional costs.
+poweroff now