Saturday, July 9, 2016

AWS Auto Scaling Lifecycle Hook with Lambda and CloudFormation



There are a lot of advantages to place instances in AWS Auto Scaling Groups, scaling is the obvious one. Even for a single instance appliance, Auto Scaling provides resiliency, health monitoring and auto recovery. In many cases, ASG High Availability model is superior to running active/standby appliances in terms of seamless automation and cost effectiveness.

However, Auto Scaling has limitations, not all instance actions and properties can be defined with an ASG. For example, instance launched in an ASG can have only one interface. Auto Scaling currently does not support attaching multiple interfaces. AWS Lambda, on the other hand, is great for defining custom actions executed efficiently and on demand. Putting the two together, AWS Auto Scaling lifecycle hook allows Lambda defined custom actions to be inserted during ASG instance launch or termination, which is powerful and flexible.

Reference links below for more details about Auto Scaling lifecycle hooks, as well as an excellent example and implementation steps using AWS console written by Vyom Nagrani  

To automate ASG and lifecycle hook actions, Cloudformation is used to define ASG and lifecycle hook. In the following example, a lifecycle hook is defined to send notification via SNS when instance launches. A Lambda function will be triggered via subscription to the SNS topic.
"GatewayAutoscalingGroupHook" : {
                "Type" : "AWS::AutoScaling::LifecycleHook",
                "Properties" : {
                                "AutoScalingGroupName" : { "Ref": "GatewayAutoscalingGroup" },
                                "HeartbeatTimeout" : 300,
                                "LifecycleTransition" : "autoscaling:EC2_INSTANCE_LAUNCHING",
                                "NotificationMetadata" : { "Fn::Join" : ["", [
                                                "{",
                                                "\"ENI1\"",
                                                ":",
                                                "\"",
                                                { "Ref" : "GatewayInstanceENI1" },
                                                "\"",
                                                ",",
                                                "\"ENI2\"",
                                                ":",
                                                "\"",
                                                { "Ref" : "GatewayInstanceENI2" },
                                                "\"",
                                                "}"
                                ]]},
                                "NotificationTargetARN" : "arn:aws:sns:us-east-1:697686697680:gateway-asg-lifecycle-hook",
                                "RoleARN" : "arn:aws:iam::697686697680:role/gateway-sns-hook-role"
                }
},

There is an odd behavior with Cloudformation when it is used to define ASG lifecycle hook. According to AWS, Lifecycle hook is defined AFTER the first instance in ASG is created. As a result, the first instance launches without the expected lifecycle hook action. Only when the first instance is deleted, the next instance kicks off lifecycle action, and triggers Lambda function as expected. AWS suggests several workarounds, including launching ASG with 0 instance and increasing to 1 later, or use custom resources.

Use Lambda monitoring features to see if/when the function is triggered by Lifecycle hooks. It is helpful to log the receiving message. AWS sends out a TEST notification when lifecycle hook is initially created. The TEST notification won’t have the complete notification content but it still will trigger Lambda. Since it currently can’t be turned off, Lambda function need to have some error handling for it.

Saturday, June 18, 2016

AWS Kaggle Machine – turnkey data science “Lab in a Box”



The advancement in data science and machine learning has not only brought breakthroughs like AlphaGO, but also starting to have broad impact in our everyday lives (Airbnb uses data to predict traveler’s destinations). Gone are the days when data science is only accessible by those in the ivory tower with million dollar proprietary software, new trends have emerged:
  • open source software and tools
  • compute capacity at cloud scale, with dramatic cost reduction
  • public data set, community based problem solving (Kaggle)

AWS provides both cost efficiency and scalable capabilities. It makes sense for data scientist to tap into the power of public cloud. An AWS image is developed here which:
  • Automates the installation and configuration of a comprehensive set of open source data science tools
  • Allows instance sizing based on needs
  • Control cost (shut down or terminate when done, launch in a few minutes)

What it is
An AWS AMI which provides “data science server in a box” with current open source toolkit (RStudio, Jupyter Notebook, Anaconda, Xgboost…). Builds automatically, fully configured ready for use in less than five minutes.

How to build a Kaggle Machine
Using the community AMI named “kaggle machine”,  build your Kaggle Machine in AWS, with one of the following method. Note the AMIs are currently available in us-east-1 and us-west-2. For other regions, you can build your machine in the above two regions, and copy AMI across regions.
Build Kaggle Machine from AWS console
Launch EC2 instance, search for “Kaggle-machine” in Community AMIs, specify a key pair. After instance creation, add a Security Group which allows port 8787, 9999 and 22 for ssh.

Build Kaggle Machine using CloudFormation Stack

A CloudFormation template can be used to build Kaggle Machine and Security Group automatically. Download the template and use it to build your stack in us-east-1 or us-west-2. The template can be found at http://github.com/seanxwang/kagglemachine/.

How to use it
After instance creation, note public DNS name of the machine, from any client on the internet, access services by pointing your browser to:
Rstudio: http://:8787 (default ruser/ruser)
Jupyter: http://:9999 (default password jupyter)

Change the default password immediately. The EC2 instance runs on Ubuntu, you can ssh to it
Cost
The cost is based on AWS EC2 usage. You only pay hourly when instance running, shutdown the instance when done. When you are done with your project and no longer need data to be saved on server, terminate the instance.

The development of Kaggle Machine originates from the needs of data scientists participating in Kaggle challenges, hope it will be provide you a useful toolset as well.