Putting aside the question of hardware+electricity vs. g2.2xlarge service charges, I think it's worth mentioning that there's a lot more to putting these models together than just getting the hardware and paying to operate it. I tend to spend quite a while mucking with configurations, writing data preprocessing/formatting code, and doing component-wise checking of each piece of the giant ball of software it inevitably becomes. For these tasks, it can be a LOT more convenient to be running locally.
As soon as you're dealing with EC2, you have to take on the mental overhead of making sure that all your configuration persists between restarts (especially if you're using spot instances!), running start up tasks, mounting EBS and paying for volumes, etc. and in my experience this all really adds up. That said, I still do use EC2 for some things. If I wave five similar models I want to run in parallel, it's as simple as spinning up five identical instances. Also, once I start training new models I can continue to use my workstation without any slowdowns.
As soon as you're dealing with EC2, you have to take on the mental overhead of making sure that all your configuration persists between restarts (especially if you're using spot instances!), running start up tasks, mounting EBS and paying for volumes, etc. and in my experience this all really adds up. That said, I still do use EC2 for some things. If I wave five similar models I want to run in parallel, it's as simple as spinning up five identical instances. Also, once I start training new models I can continue to use my workstation without any slowdowns.