Unlike the execution environments described in the previous section on Cloud where the user has to start condor workers on the cloud nodes, Amazon provides a managed service called AWS Batch. It automates the notion of provisioning nodes in the cloud, and setting up of a compute environment and a job queue that can submit jobs to those nodes.
Starting 4.9 release, Pegasus has support for executing horizontally clustered jobs on Amazon AWS Batch Service using the command line tool pegasus-aws-batch. In other words, you can get Pegasus to cluster each level of your workflow into a bag of tasks and run those clustered jobs on Amazon Cloud using AWS Batch Service. In upcoming releases, we plan to add support to pegasus-aws-batch to do dependency management that will allow us to execute the whole workflow in a single AWS Batch job.
To use AWS Batch as user you need to do some one time setup to get started at running. Please follow the instructions carefully in this section.
To use AWS Batch for your workflows, we need two credential files
AWS Credentials File: This is the file that you create and use whenever accessing Amazon EC2 and is located at ~/.aws/credentials. For our purposes we need the following information in that file.
$ cat ~/.aws/credentials [default] aws_access_key_id = XXXXXXXXXXXX aws_secret_access_key = XXXXXXXXXXX
S3 Config File: Pegasus workflows use pegasus-s3 command line tool to stage-in input data required by the tasks to S3 and push data output data generated to S3 when user application code runs. These credentials are specified in .s3cfg file usually put in the user home directory. This format of the file is described in the pegaus-s3 command line client's man page. A minimalistic file is illustrated below
$ cat ~/.s3cfg [amazon] # end point has to be consistent with the EC2 region you are using. Here we are referring to us-west-2 region. endpoint = http://s3-us-west-2.amazonaws.com # Amazon now allows 5TB uploads max_object_size = 5120 multipart_uploads = True ranged_downloads = True [user@amazon] access_key = XXXXXXXXXXXX secret_key = XXXXXXXXXXXX
All jobs in AWS Batch are run in a container via the Amazon EC2 container service. The Amazon EC2 container service does not give control over the docker run command for a container. Hence, Pegasus runs jobs on container that is based on the Amazon Fetch and Run Example . This container image allows us to fetch user executables automatically from S3. All container images referred used for Pegasus workflows must be based on the above example.
Additionally, the Docker file for your container image should include these additional Docker run commands to install the yum packages that Pegasus requires.
RUN yum -y install perl findutils
After you have pushed the Docker image to the Amazon ECR Repository, the image URL for that image you will use later to refer in the job definition to use for your jobs.
If you are using AWS Batch for the very first time, then you need to use the Amazon Web console to create a role with your user that will give the AWS Batch services privileges to execute to access other AWS services such as EC2 Container Service , CloudWatchLogs etc. The following roles need to be created
AWS Batch Service IAM Role: For convenience and ease of use make sure you name the role AWSBatchServiceRole , so that you don't have to make other changes. Complete the procedures listed at AWS Batch Service IAM Role.
Amazon ECS Instance Role: AWS Batch compute environments are populated with Amazon ECS container instances, and they run the Amazon ECS container agent locally. The Amazon ECS container agent makes calls to various AWS APIs on your behalf, so container instances that run the agent require an IAM policy and role for these services to know that the agent belongs to you. Complete the procedures listed at Amazon ECS Instance Role.
IAM Role: Whenever a Pegasus job runs via AWS Batch it needs to fetch data from S3 and push data back to S3. To create this job role follow the instructions at section Create an IAM role in Amazon Fetch and Run Example to create a IAM role named batchJobRole.
batchJobRole should have full write access to S3 i.e have the policy AmazonS3FullAccess attached to it.
It is important that you name the roles as listed above. Else, you will need to update the same job definition, compute environment, and job queue json files that you use to create the various Batch entities.
AWS Batch has a notion of
Job Definition - job definition is something that allows you to use your container image in Amazon EC2 Repository to run one or many AWS Batch jobs.
Compute Environment - what sort of compute nodes you want your jobs to run on.
Job Queue - the queue that feeds the jobs to a compute environment.
Currently, with Pegasus you can only use one of each for a workflow i.e the same job definition, compute environment and job queue need to be used for all jobs in the workflow.
To create the above entities we recommend you to use pegasus-aws-batch client . You can start with the sample json files present in share/pegasus/examples/awsbatch-black-nonsharedfs directory.
sample-job-definition.json : Edit the attribute named image and replace it with the ARN of the container image you built for your account
sample-compute-env.json : Edit the attributes subnets and securityGroupIds
Before running the pegasus-aws-batch client make sure your properties file has the following properties
pegasus.aws.region= [amazon ec2 region] pegasus.aws.account=[your aws account id - digits]
You can then use pegasus-aws-batch client to generate the job definition, the compute environment and job queue to use.
$ pegasus-aws-batch --conf ./conf/pegasusrc --prefix pegasus-awsbatch-example --create --compute-environment ./conf/sample-compute-env.json --job-definition ./conf/sample-job-definition.json --job-queue ./conf/sample-job-queue.json .. 2018-01-18 15:16:00.771 INFO [Synch] Created Job Definition arn:aws:batch:us-west-2:405596411149:job-definition/pegasus-awsbatch-example-job-definition:1 2018-01-18 15:16:07.034 INFO [Synch] Created Compute Environment arn:aws:batch:us-west-2:XXXXXXXXXX:compute-environment/pegasus-awsbatch-example-compute-env 2018-01-18 15:16:11.291 INFO [Synch] Created Job Queue arn:aws:batch:us-west-2:XXXXXXXXXX:job-queue/pegasus-awsbatch-example-job-queue 2018-01-18 15:16:11.292 INFO [PegasusAWSBatch] Time taken to execute is 12.194 seconds
You need to add the ARN's of created job definition, compute environment and job queue listed in pegasus-aws-batch output to your pegasusrc file
# Properties required to run on AWS Batch # the amazon region in which you are running workflows pegasus.aws.region=us-west-2 # your AWS account id ( in digits) # pegasus.aws.account=XXXXXXXXXX # ARN of the job definition that you create using pegasus-aws-batch # pegasus.aws.batch.job_definition=arn:aws:batch:us-west-2:XXXXXXXXXX:job-definition/fetch_and_run # ARN of the job definition that you create using pegasus-aws-batch # pegasus.aws.batch.compute_environment=arn:aws:batch:us-west-2:XXXXXXXXXX:compute-environment/pegasus-awsbatch-example-compute-env # ARN of the job queue that you create using pegasus-aws-batch # pegasus.aws.batch.job_queue=arn:aws:batch:us-west-2:XXXXXXXXXX:job-queue/pegasus-awsbatch-example-job-queue
To run jobs on AWS Batch, you need to have an execution site in your site catalog. Here is a sample site catalog to use for running workflows on AWS Batch
<?xml version="1.0" encoding="UTF-8"?> <sitecatalog xmlns="http://pegasus.isi.edu/schema/sitecatalog" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://pegasus.isi.edu/schema/sitecatalog http://pegasus.isi.edu/schema/sc-4.0.xsd" version="4.0"> <site handle="local" arch="x86_64" os="LINUX" osrelease="" osversion="" glibc=""> <directory path="/LOCAL/shared-scratch" type="shared-scratch" free-size="" total-size=""> <file-server operation="all" url="file:///LOCAL/shared-scratch"> </file-server> </directory> <directory path="/LOCAL/shared-storage" type="shared-storage" free-size="" total-size=""> <file-server operation="all" url="/LOCAL/shared-storage"> </file-server> </directory> <profile namespace="env" key="PEGASUS_HOME">/usr/bin/..</profile> </site> <site handle="aws-batch" arch="x86_64" os="LINUX"> <directory path="pegasus-batch-bamboo" type="shared-scratch" free-size="" total-size=""> <file-server operation="all" url="s3://user@amazon/pegasus-batch-bamboo"> </file-server> </directory> <profile namespace="pegasus" key="clusters.num">1</profile> <profile namespace="pegasus" key="style">condor</profile> </site> </sitecatalog>
Once the whole setup is complete, before running a workflow make sure you have the following properties in your configuration file
# get clustered jobs running using AWSBatch pegasus.clusterer.job.aggregator AWSBatch #cluster even single jobs on a level pegasus.clusterer.allow.single True # Properties required to run on AWS Batch # the amazon region in which you are running workflows pegasus.aws.region=us-west-2 # your AWS account id ( in digits) # pegasus.aws.account=XXXXXXXXXX # ARN of the job definition that you create using pegasus-aws-batch pegasus.aws.batch.job_definition=pegasus-awsbatch-example-job-definition # ARN of the job definition that you create using pegasus-aws-batch pegasus.aws.batch.compute_environment=pegasus-awsbatch-example-compute-env # ARN of the job queue that you create using pegasus-aws-batch pegasus.aws.batch.job_queue=pegasus-awsbatch-example-job-queue