Up and running on AWS in less that 60 seconds

Inspired by @radek’s blog post here and his two forum posts (here, and here), I present a generic way to get up and running on AWS in less than 60 seconds (well, 60 seconds after you go through it the first time :slight_smile: ).


  1. You must have created a persistent p2.xlarge instance in EC2 and give it a name. I simply followed the instructions from the introductory workshop material here and named my instance “fastai-part1v2”. For storage, I went with 50GB.

  2. You must have the AWC CLI installed and configured with your AWS Access Key, Secret Access Key, and default region.


  1. Clone my repo here: https://github.com/ohmeow/aws_setup

  2. cd into the aws_setup directory

  3. In your terminal run
    $ bash start-aws-instance.sh {name-of-instance | defaults to fastai-part1v2}
    This will spin up your instance and ssh you into it.

  4. Once your ssh’d into your AWS instance, get and run my version of the install script (this step only needs to be ran once) :
    $ wget https://raw.githubusercontent.com/ohmeow/aws_setup/master/fastai-install-gpu-part1-v2.sh
    $ bash fastai-install-gpu-part1-v2.sh

  5. After that is done, start your jupyter notebook
    $ ./start-jupyter-notebook
    This will activate the fastai anaconda environment that is created for you in the fastai-install-gpu-part1-v2.sh script and launch jupyter notebook.

  6. In your browser, go to https://localhost:8888 and get to work

  7. When you are done, open up another terminal on your machine and run:
    $ bash stop-aws-instance.sh {name-of-instance | defaults to fastai-part1v2}
    This will shut down your EC2 instance

Once you’ve gone through the steps above, you just have to …

  • $ bash start-aws-instance.sh {name-of-instance | defaults to fastai-part1v2}
  • $ ./start-jupyter-notebook
  • Go to https://localhost:8888 and do your work
  • $ bash stop-aws-instance.sh {name-of-instance | defaults to fastai-part1v2} when done.


  1. You’ll notice that while relying heavily on @radek’s version of the install script in step 4, I did make a few changes. The biggest one is simply using the environment.yml file in the fastai repo to create a “fastai” environment and install all the required packages for the course from it.

  2. This is for setting up a persistent EC2 instance. If you are into saving $$$ and want to see how to get things working with spot instances, see @radek’s posts mentioned above. I purposely decided to go with using persistent images (for the time-being at least) because it was much more straight-forward to get operational quickly. The scripts are more generic and will allow you to completely avoid having to log into AWS to be operational.

  3. This is a work in progress so please feel free to post recommendations and submit pull-requests if you got ideas on how to improve.

  4. I haven’t tested on anything outside of a p2.xlarge instance so if you are using other GPU friendly builds, let me know if the above still works or not.


Heh - sorry just posted an AMI that makes this somewhat obsolete… :wink:



I mean, that’s awesome.

At least it was a useful learning experience :slight_smile: Perhaps at least the scripts will be helpful for folks.


thanks @wgpubs for you time

I’m still using this setup because I know it works and I don’t have to mess with AWS console to work (I think it might even work for folks that aren’t using the us-west-2 region).