AI News, Setting up a Machine Learning Farm in the Cloud with Spot Instances + Auto Scaling
- On Monday, June 4, 2018
- By Read More
Setting up a Machine Learning Farm in the Cloud with Spot Instances + Auto Scaling
If you have a problem like this, you’re already familiar with some of the details, such as how much RAM is needed to compute a feature, whether the machine learning algorithm chosen can vectorize each instance independently, or whether it needs to process multiple instances.
So for now, let’s say we have a problem where each instance can be vectorized independently from all the other instances (and all of the features can be computed within the RAM limitations of a single machine). We’ll choose a typical master-worker coordination pattern where a master node queues instances and worker nodes that pull from that queue and vectorize instances.
Before I start setting up auto-scaling, I’ll create AWS images for the master and worker nodes. Then I’ll test out the application on the image and configure the worker node so that on boot it pulls a unit of work from the master on boot.
Creating an auto-scaled spot instance cluster Alright, we’ve figured out how we plan to distribute the computation of our problem over a cluster of machines. We’ve chosen an instance type that meets our price/performance requirements, tested out code on it, and created Amazon Machine Images for each of the roles. We’ll boot up our master server, and the idea is that the workers will be auto-scaled up.
Amazon lets you specify policies that scale in absolute number of machines or relative (percentage of current cluster size). How fast you add/subtract machines from the cluster will be determined by what you choose in the next step.
Alarms are events that trigger an action. There’s a single command to both define the alarm condition and associate it with the scaling policy we just created (using the super long policy ID from the previous step).
If you’re running an online learning system and want to reduce system latency, then you want to scale up quickly and scale down slowly (and pay the cost of over-provisioning) to reduce the queue size. The lower bound on responsiveness to load increases is going to be the boot time of the system —
(This happens rarely, but if you can’t be interrupted, it’s something you have to plan for.) A simple script can monitor the spot price and, if it’s above the on-demand price, replace your launch configuration with on-demand instances.
- On Thursday, February 20, 2020
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