AWS:Set up account

Installation

Docker is required to run the aws-setup scripts. https://www.docker.com/get-started/

An Amazon AWS account is also required, with payment attached. https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/

On a linux/mac/windows computer with docker or docker desktop installed, run the following commands in a terminal:

docker pull btingle/aws-setup
docker run -v /var/run/docker.sock:/var/run/docker.sock -it btingle/aws-setup

It may be necessary to give the container additional privileges. When you enter the image, test this with the following command:

root@f54f423d64b1:/home/awsuser# docker ps

If you get a permission denied error, exit the container and run again with the --privileged option enabled:

docker run --privileged -v /var/run/docker.sock:/var/run/docker.sock -it btingle/aws-setup

If you're using a remote docker instance through the DOCKER_HOST environment variable, for example on windows WSL, you can use the following script in place of 'docker run':

host=$(basename $DOCKER_HOST | cut -d':' -f1)
port=$(basename $DOCKER_HOST | cut -d':' -f2)
prot=$(dirname $DOCKER_HOST)

if [ "$host" = "localhost" ] || [ "$host" == "127.0.0.1" ]; then
	host=host.docker.internal
fi

# essentially we are just forwarding the DOCKER_HOST information to the container (making sure to use host.docker.internal if DOCKER_HOST is localhost)
docker run --env DOCKER_HOST=$prot//$host:$port -it btingle/awsdock-setup

Container Environment

The container has a barebones software installation, with some additional utilities to help out. curl and vi are installed so you can download files and edit them, for example if you have a custom config file you want to download from pastebin. You can also install whatever software you like using "apt install", e.g "apt install git".

If you have files you'd like to access from the container, you can link them in using the docker "-v" option. By default we link the docker socket using this option ("-v /var/run/docker.sock:/var/run/docker.sock"), but you can link any number of directories or files in this manner. For example, if you would like the contents of the "/tmp" directory on your local machine to be available under "/temp" in the docker image, you would add the following option to your "docker run" command: "-v /tmp:/temp", for a final command of:

docker run -v /tmp:/temp -v /var/run/docker.sock:/var/run/docker.sock -it btingle/aws-setup:latest

If you're an advanced user and you'd like to create your own version of the aws-setup image with certain software preinstalled, you can request us for access to the aws-setup repository, which contains the scripts and Dockerfile we use to set up the docker image.

Running the Scripts

When you enter the docker image, you will be in /home/awsuser. There should be two directories in front of you, aws-setup and awsdock. We start off by going into the aws-setup directory and configuring our AWS credentials.

root@f54f423d64b1:/home/awsuser# cd aws-setup
root@f54f423d64b1:/home/awsuser# aws configure

You'll now be prompted to enter your AWS access key ID & AWS secret access key. If you already know what these are you can enter them and move on. Don't bother setting the output format, but do set your region.

If you don't know what your AWS secret key and access key are, follow this tutorial: https://aws.amazon.com/blogs/security/wheres-my-secret-access-key/.

Make sure to save your keys somewhere safe that you will remember!!

If it is your first time setting up an environment on your AWS account, you will need to run initialize-aws-batch.bash.

root@f54f423d64b1:/home/awsuser/aws-setup# bash initialize-aws-batch.bash

You should see this script spit out a bunch of JSON text. If you accidentally run this script when it has already been run before, you will see a bunch of errors along the lines of: "Service role name <blank> has been taken in this account". Don't worry about these, they don't mean anything. Any other errors you should report to me @ ben@tingle.org, I'll find you a solution.

Before setting up your environment, you will want to create an S3 bucket to store your data. You can accomplish this using the cli like so:

root@f54f423d64b1:/home/awsuser/aws-setup# aws s3api create-bucket --bucket <<name>>

Alternatively you can follow the amazon guide to creating an s3 container for more transparency on how s3 buckets work: https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-bucket.html

If you already have an s3 bucket you've created, you can use that instead of creating a new one. This bucket will be used in step 3 of create-aws-batch-env.bash, and should be configured for input,output.

Now we move on to creating the environment. If it is your first time creating an environment, you can use the included awsdock.config for an interactive environment creation experience.

root@f54f423d64b1:/home/awsuser/aws-setup# bash create-aws-batch-env.bash ~/awsdock/aws-setup-configs/awsdock.config

Enter the desired name for your environment.

What would you like this environment to be called? [default: "dockenv"]: <your env name here>

Enter the aws region for your environment

Which region is this environment based in? [default: us-west-1]: <your region here>

The name + region will serve as the unique identifier for this environment, e.g "mydockenv_us-west-1". You will refer to this identifier when submitting jobs through a script.

Attach the bucket you created to the environment

What bucket would you like to attach to this environment? <your bucket>

Set the I/O policy for your bucket to input,output.

Which action(s) would you like to perform on this bucket? Choose from input/output, if using multiple separate by commas. input,output

Confirm that you will use the btingle/dockaws image for this environment.

What docker image should this environment use? [default: btingle/dockaws:latest]: btingle/dockaws:latest

Set MAX_CPUS for your environment to desired value. If you will be running jobs at as large a scale as possible, give this a high value, e.g 9999.

How many CPUS would you like to allocate to this environment at maximum? [default: None]:

Set BID_PERCENTAGE for your environment to desired value. See "Advanced Usage" section for more explanation of this parameter.

What is your bid percentage threshold for spot instances? See the docs for more info on this parameter. [default: 100]: <your value>

All done!

Dealing With Errors

If you got an error while setting up your environment with create-aws-batch-env.bash, it may help to run the script again, making sure to provide the exact same parameters.

Advanced Usage/Configuration Usage

0. is for setting up the environment's NAME and REGION. These two properties in combination serve as the unique identifier for your batch environment.

• Your environment's NAME can be set by the ENV_NAME configuration variable, otherwise set interactively.

• Likewise, the REGION can be set by the ENV_AWS_REGION configuration variable, otherwise set interactively.

1. sets up the profile/role that will be used in this environment. This part will always be non-interactive, so this step is mostly here as a debug tool/progress marker.

2. sets up bucket policies for the environment. For example, if you would like to pull DB2 data from bkslab's zinc3d bucket, you would set that up here.

• You can set bucket policies using the ENV_BUCKET_CONFIGS configuration variable. Multiple buckets+policies can be included in this variable, for example:

• • ENV_BUCKET_POLICIES="zinc3d:input mybucket:output" will set up bkslab's zinc3d bucket as the input source for the environment and mybucket as the output destination.

• • ENV_BUCKET_POLICIES="zinc3d:input prompt" will set up zinc3d as the input source, and one other bucket policy through interactive prompt. This is the default setting of awsdock.config.

• • ENV_BUCKET_POLICIES="zinc3d:input mybucket1:input,output mybucket2:output" sets up 3 policies- indicating that zinc3d can be used for input, mybucket1 can be used for input/output, and mybucket2 can be used for just output.

• • ENV_BUCKET_POLICIES="prompt+" Will set up as many bucket policies as desired through interactive prompt.

3. sets up the docker image to be used in this environment. The name of the image is set by the JOB_IMAGE config variable.

• In awsdock.config, this is bkslab's btingle/awsdock image. This image is compatible with our script for submitting DOCK jobs to AWS.

• JOB_IMAGE can be set to any image you so desire, but keep in mind that our DOCK job submission/collection scripts are specialized to the input/output format our awsdock image uses.

4. creates all the AWS components needed to run jobs with AWS batch, including Job Queue, Compute Environment, and Job definition. The MAX_CPUS and BID_PERCENTAGE parameters are used/set during this step. The RETRY_STRATEGY and JOB_JSON_CONFIG parameters will also be used.

• MAX_CPUS is the maximum number of virtual cpus (vCPUs) allocated at any one time by AWS batch for this environment. The default JOB_JSON_CONFIG assigns one vcpu to one job, so a MAX_CPUS value of 100 means that a maximum of 100 jobs can run at any one given time.

• BID_PERCENTAGE is a value between (0, 100) and tells AWS what % of the on-demand price you are willing to pay for compute resources. A lower BID_PERCENTAGE value will result in better economy for your jobs, but also lower potential allocation.

• • A BID_PERCENTAGE value of 100 will still save money over the on-demand price in many cases, but if demand is high it will pay full price.

• • A BID_PERCENTAGE value of 50 will always save money, however during periods of high demand you may struggle to allocate machines for your jobs.

• RETRY_STRATEGY tells aws batch how many times to retry a failed job before giving up. By default awsdock.config tells the job to retry 5 times before giving up. Because we use the economical "spot fleet" strategy, jobs may fail because the machine they are on was allocated to a higher paying customer.

• • Our awsdock image will save progress when spot fleet re-allocates the machine it is running on. This way no compute time is wasted.

• JOB_JSON_CONFIG is a json structure specifying the resource parameters jobs will use. These values can be changed, for example if you would like jobs to have more memory you can change the "memory" attribute to a higher value.

• • If you are using our awsdock image, you should not need to change any of these parameters.

Bid Percentage

In order to use resources efficiently, our AWS environment uses AWS spot instances to buy compute resources. AWS spot instances basically allow us to purchase compute resources for a fraction of the price, with the caveat that service may be interrupted at any time. Our AWS docking image allows us to take advantage of this service by saving progress whenever the instance is about to be interrupted. The bid percentage parameter indicates what % of the on-demand price we are willing to pay for compute resources. If left at 100, the scheduler will pay the on-demand price for compute resources if no spot instances are available.