Leveraging Elastic Fabric Adapter to run HPC and ML Workloads on AWS Batch

 This post is contributed by  Sean Smith, Software Development Engineer II, AWS ParallelCluster & Arya Hezarkhani, Software Development Engineer II, AWS Batch and HPC

 

On August 2, 2019, AWS Batch announced support for Elastic Fabric Adapter (EFA). This enables you to run highly performant, distributed high performance computing (HPC) and machine learning (ML) workloads by using AWS Batch’s managed resource provisioning and job scheduling.

EFA is a network interface for Amazon EC2 instances that enables you to run applications requiring high levels of inter-node communications at scale on AWS. Its custom-built operating system bypasses the hardware interface and enhances the performance of inter-instance communications, which is critical to scaling these applications. With EFA, HPC applications using the Message Passing Interface (MPI) and ML applications using NVIDIA Collective Communications Library (NCCL) can scale to thousands of cores or GPUs. As a result, you get the application performance of on-premises HPC clusters with the on-demand elasticity and flexibility of the AWS Cloud.

AWS Batch is a cloud-native batch scheduler that manages instance provisioning and job scheduling. AWS Batch automatically provisions instances according to job specifications, with the appropriate placement group, networking configurations, and any user-specified file system. It also automatically sets up the EFA interconnect to the instances it launches, which you specify through a single launch template parameter.

In this post, we walk through the setup of EFA on AWS Batch and run the NAS Parallel Benchmark (NPB), a benchmark suite that evaluates the performance of parallel supercomputers, using the open source implementation of MPI, OpenMPI.

 

Prerequisites

This walk-through assumes:

 

Configuring your compute environment

First, configure your compute environment to launch instances with the EFA device.

Creating an EC2 placement group

The first step is to create a cluster placement group. This is a logical grouping of instances within a single Availability Zone. The chief benefit of a cluster placement group is non-blocking, non-oversubscribed, fully bi-sectional network connectivity. Use a Region that supports EFA—currently, that is us-east-1, us-east-2, us-west-2, and eu-west-1. Run the following command:

$ aws ec2 create-placement-group –group-name “efa” –strategy “cluster” –region [your-region]

Creating an EC2 launch template

Next, create a launch template that contains a user-data script to install EFA libraries onto the instance. Launch templates enable you to store launch parameters so that you do not have to specify them every time you launch an instance. This will be the launch template used by AWS Batch to scale the necessary compute resources in your AWS Batch Compute Environment.

First, encode the user data into base64-encoding. This example uses the CLI utility base64 to do so.

 

$ echo “MIME-Version: 1.0

Content-Type: multipart/mixed; boundary=”==MYBOUNDARY==”

 

–==MYBOUNDARY==

Content-Type: text/cloud-boothook; charset=”us-ascii” cloud-init-per once yum_wget yum install -y wget

cloud-init-per once wget_efa wget -q –timeout=20 https://s3-us-west-2.amazonaws.com/

cloud-init-per once tar_efa tar -xf /tmp/aws-efa-installer-latest.tar.gz -C /tmp pushd /tmp/aws-efa-installer

cloud-init-per once install_efa ./efa_installer.sh -y pop /tmp/aws-efa-installer

cloud-init-per once efa_info /opt/amazon/efa/bin/fi_info -p efa

–==MYBOUNDARY==–” | base64

 

Save the base64-encoded output, because you need it to create the launch template.

 

Next, make sure that your default security group is configured correctly. On the EC2 console, select the default security group associated with your default VPC and edit the inbound rules to allow SSH and All traffic to itself. This must be set explicitly to the security group ID for EFA to work, as seen in the following screenshot.

SecurityGroupInboundRules

SecurityGroupInboundRules

 

Then edit the outbound rules and add a rule that allows all inbound traffic from the security group itself, as seen in the following screenshot. This is a requirement for EFA to work.

SecurityGroupOutboundRules

SecurityGroupOutboundRules

 

Now, create an ecsInstanceRole, the Amazon ECS instance profile that will be applied to Amazon EC2 instances in a Compute Environment. To create a role, follow these steps.

  1. Choose Roles, then Create Role.
  2. Select AWS Service, then EC2.
  3. Choose Permissions.
  4. Attach the managed policy AmazonEC2ContainerServiceforEC2Role.
  5. Name the role ecsInstanceRole.

 

You will create the launch template using the ID of the security group, the ID of a subnet in your default VPC, and the ecsInstanceRole that you created.

Next, choose an instance type that supports EFA, that’s denoted by the n in the instance name. This example uses c5n.18xlarge instances.

You also need an Amazon Machine Image (AMI) ID. This example uses the latest ECS-optimized AMI based on Amazon Linux 2. Grab the AMI ID that corresponds to the Region that you are using.

This example uses UserData to install EFA. This adds 1.5 minutes of bootstrap time to the instance launch. In production workloads, bake the EFA installation into the AMI to avoid this additional bootstrap delay.

Now create a file called launch_template.json with the following content, making sure to substitute the account ID, security group, subnet ID, AMI ID, and key name.

{

“LaunchTemplateName”: “EFA-Batch-LaunchTemplate”, “LaunchTemplateData”: {

“InstanceType”: “c5n.18xlarge”,

“IamInstanceProfile”: {

“Arn”: “arn:aws:iam::<Account Id>:instance-profile/ecsInstanceRole”

},

“NetworkInterfaces”: [

{

"DeviceIndex”: 0,

"Groups”: [

"<Security Group>”

],

“SubnetId”: “<Subnet Id>”,

“InterfaceType”: “efa”,

“Description”: “NetworkInterfaces Configuration For EFA and Batch”

}

],

“Placement”: {

“GroupName”: “efa”

},

“TagSpecifications”: [

[

{

"ResourceType”: "instance”,

"Tags”: [

{

"Key”: "from-lt”,

"Value”: "networkInterfacesConfig-EFA-Batch”

}

]p>}

],

“UserData”: “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”

}

}

Create a launch template from that file:

 

$ aws ec2 create-launch-template –cli-input-json file://launch_template.json

{

“LaunchTemplate”: {

“LatestVersionNumber”: 1,

“LaunchTemplateId”: “lt-*****************”, “LaunchTemplateName”: “EFA-Batch-LaunchTemplate”, “DefaultVersionNumber”: 1,

“CreatedBy”: “arn:aws:iam::************:user/desktop-user”, “CreateTime”: “2019-09-23T13:00:21.000Z”

}

}

 

Creating a compute environment

Next, create an AWS Batch Compute Environment. This uses the information from the launch template

EFA-Batch-Launch-Template created earlier.

 

{

“computeEnvironmentName”: “EFA-Batch-ComputeEnvironment”,

“type”: “MANAGED”,

“state”: “ENABLED”,

“computeResources”: {

“type”: “EC2”,

“minvCpus”: 0,

“maxvCpus”: 2088,

“desiredvCpus”: 0,

“instanceTypes”: [

"c5n.18xl[

"c5n.18xlarge”

]: [<[

"<same-s[

"<same-subnet-as-in-LaunchTemplate>”

]”: “arn:aws:iam::<account-id>:instance-profile/ecsInstanceRole”,

“launchTemplate”: {

“launchTemplateName”: “EFA-Batch-LaunchTemplate”,

“version”: “$Latest”

}

},

“serviceRole”: “arn:aws:iam::<account-id>:role/service-role/AWSBatchServiceRole”

}

 

Now, create the compute environment:

$ aws batch create-compute-environment –cli-input-json file://compute_environment.json

{

“computeEnvironmentName”: “EFA-Batch-ComputeEnvironment”, “computeEnvironmentArn”: “arn:aws:batch:us-east-1:<Account Id>:compute-environment”

}

 

Building the container image

To build the container, clone the repository that contains the Dockerfile used in this example.

First, install git:

$ git clone https://github.com/aws-samples/aws-batch-efa.git

 

In that repository, there are several files, one of which is the following Dockerfile.

 

FROM amazonlinux:1 ENV USER efauser

 

RUN yum update -y

RUN yum install -y which util-linux make tar.x86_64 iproute2 gcc-gfortran openssh-serv RUN pip-2.7 install supervisor

 

RUN useradd -ms /bin/bash $USER ENV HOME /home/$USER

 

##################################################### ## SSH SETUP

ENV SSHDIR $HOME/.ssh

RUN mkdir -p ${SSHDIR} \

&& touch ${SSHDIR}/sshd_config \

&& ssh-keygen -t rsa -f ${SSHDIR}/ssh_host_rsa_key -N ” \

&& cp ${SSHDIR}/ssh_host_rsa_key.pub ${SSHDIR}/authorized_keys \ && cp ${SSHDIR}/ssh_host_rsa_key ${SSHDIR}/id_rsa \

&& echo ”  IdentityFile ${SSHDIR}/id_rsa” >> ${SSHDIR}/config \ && echo ”  StrictHostKeyChecking no” >> ${SSHDIR}/config \

&& echo ”  UserKnownHostsFile /dev/null” >> ${SSHDIR}/config \ && echo ”  Port 2022″ >> ${SSHDIR}/config \

&& echo ‘Port 2022’ >> ${SSHDIR}/sshd_config \

&& echo ‘UsePrivilegeSeparation no’ >> ${SSHDIR}/sshd_config \

&& echo “HostKey ${SSHDIR}/ssh_host_rsa_key” >> ${SSHDIR}/sshd_config \ && echo “PidFile ${SSHDIR}/sshd.pid” >> ${SSHDIR}/sshd_config \

&& chmod -R 600 ${SSHDIR}/* \

&& chown -R ${USER}:${USER} ${SSHDIR}/

 

# check if ssh agent is running or not, if not, run RUN eval `ssh-agent -s` && ssh-add ${SSHDIR}/id_rsa

 

################################################# ## EFA and MPI SETUP

RUN curl -O https://s3-us-west-2.amazonaws.com/aws-efa-installer/aws-efa-installer-1. && tar -xf aws-efa-installer-1.5.0.tar.gz \

&& cd aws-efa-installer \

&& ./efa_installer.sh -y –skip-kmod –skip-limit-conf –no-verify

 

RUN wget https://www.nas.nasa.gov/assets/npb/NPB3.3.1.tar.gz \ && tar xzf NPB3.3.1.tar.gz

COPY make.def_efa /NPB3.3.1/NPB3.3-MPI/config/make.def COPY suite.def      /NPB3.3.1/NPB3.3-MPI/config/suite.def

 

RUN cd /NPB3.3.1/NPB3.3-MPI \

&& make suite \

&& chmod -R 755 /NPB3.3.1/NPB3.3-MPI/

 

###################################################

## supervisor container startup

 

ADD conf/supervisord/supervisord.conf /etc/supervisor/supervisord.conf ADD supervised-scripts/mpi-run.sh supervised-scripts/mpi-run.sh

RUN chmod 755 supervised-scripts/mpi-run.sh

 

EXPOSE 2022

ADD batch-runtime-scripts/entry-point.sh batch-runtime-scripts/entry-point.sh RUN chmod 755 batch-runtime-scripts/entry-point.sh

 

CMD /batch-runtime-scripts/entry-point.sh

 

To build this Dockerfile, run the included Makerfile with:

make

Now, push the created container image to Amazon Elastic Container Registry (ECR), so you can use it in your AWS Batch JobDefinition:

From the AWS CLI, create an ECR repository, we’ll call it aws-batch-efa:

$ aws ecr create-repository –repository-name aws-batch-efa

{

“repository”: {

“registryId”: “<Account-Id>”,
“repositoryName”: “aws-batch-efa”,
“repositoryArn”: “arn:aws:ecr:us-east-2:<Account-Id>:repository/aws-batch-efa”,
“createdAt”: 1568154893.0,
“repositoryUri”: “<Account-Id>.dkr.ecr.us-east-2.amazonaws.com/aws-batch-efa”

}
}

Edit the top of the makefile and add your AWS account number and AWS Region.

AWS_REGION=<REGION>
ACCOUNT_ID=<ACCOUNT-ID>

To push the image to the ECR repository, run:

make tag
make push

 

Run the application

To run the application using AWS Batch multi-node parallel jobs, follow these steps.

 

Setting up the AWS Batch multi-node job definition

Set up the AWS Batch multi-node job definition and expose the EFA device to the container by following these steps.

 

First, create a file called job_definition.json with the following contents. This file holds the configurations for the AWS Batch JobDefinition. Specifically, this JobDefinition uses the newly supported field LinuxParameters.Devices to expose a particular device—in this case, the EFA device path /dev/infiniband/uverbs0—to the container. Be sure to substitute the image URI with the one you pushed to ECR in the previous step. This is used to start the container.

 

{

“jobDefinitionName”: “EFA-MPI-JobDefinition”,

“type”: “multinode”,

“nodeProperties”: {

“numNodes”: 8,

“mainNode”: 0,

“nodeRangeProperties”: [

{

"tar[

{

"targetNodes”: "0:”,

"container”: {

"user”: "efauser”,

"image”: "<Docker Image From Previous Section>”,

"vcpus”: 72,

"memory”: 184320,

"linuxParameters”: {

"devices”: [

{

"hostPath”: "/dev/infiniband/uverbs0”

}

]”: [

[

{

"hardLimit”: -1,

"n[

{

"hardLimit”: -1,

"name”: "memlock”,

"softLimit”: -1

}

]> 

$ aws batch register-job-definition –cli-input-json file://job_definition.json

{

“jobDefinitionArn”: “arn:aws:batch:us-east-1:<account-id>:job-definition/EFA-MPI-JobDefinition”,

“jobDefinitionName”: “EFA-MPI-JobDefinition”,

“revision”: 1

}

 

Run the job

Next, create a job queue. This job queue points at the compute environment created before. When jobs are submitted to it, they queue until instances are available to run them.

 

{

“jobQueueName”: “EFA-Batch-JobQueue”,

“state”: “ENABLED”,

“priority”: 10,

“computeEnvironmentOrder”: [

{[

{

"order”: 1,

"computeEnviron[

{

"order”: 1,

"computeEnvironment”: "EFA-Batch-ComputeEnvironment”

}

]ut-json  file://job_queue.json

Now that you’ve created all the resources, submit the job. The numNodes=8 parameter tells the job definition to use eight nodes.

aws   batch submit-job –job-name      example-mpi-job –job-queue EFA-Batch-JobQueue –job-definition EFA-MPI-JobDefinition –node-overrides numNodes=8

 

NPB overview

NPB is a small set of benchmarks derived from computational fluid dynamics (CFD) applications. They consist of five kernels and three pseudo-applications. This example runs the 3D Fast Fourier Transform (FFT) benchmark, as it tests all-to-all communication. For this run, use c5n.18xlarge, as configured in the AWS compute environment earlier. This is an excellent choice for this workload as it has an Intel Skylake processor (72 hyperthreaded cores) and 100 GB Enhanced Networking (ENA), which you can take advantage of with EFA.

 

This test runs the FT “C” Benchmark with eight nodes * 72 vcpus = 576 vcpus.

 

NAS Parallel Benchmarks 3.3 — FT Benchmark

No input file inputft.data. Using compiled defaults Size : 512x 512x 512

Iterations : 20

Number of processes : 512 Processor array : 1x 512 Layout type : 1D

Initialization time = 1.3533580760000063

T = 1 Checksum = 5.195078707457D+02 5.149019699238D+02

T = 2 Checksum = 5.155422171134D+02 5.127578201997D+02
T = 3 Checksum = 5.144678022222D+02 5.122251847514D+02
T = 4 Checksum = 5.140150594328D+02 5.121090289018D+02
T = 5 Checksum = 5.137550426810D+02 5.121143685824D+02
T = 6 Checksum = 5.135811056728D+02 5.121496764568D+02
T = 7 Checksum = 5.134569343165D+02 5.121870921893D+02
T = 8 Checksum = 5.133651975661D+02 5.122193250322D+02
T = 9 Checksum = 5.132955192805D+02 5.122454735794D+02
T = 10 Checksum = 5.132410471738D+02 5.122663649603D+02
T = 11 Checksum = 5.131971141679D+02 5.122830879827D+02
T = 12 Checksum = 5.131605205716D+02 5.122965869718D+02
T = 13 Checksum = 5.131290734194D+02 5.123075927445D+02
T = 14 Checksum = 5.131012720314D+02 5.123166486553D+02
T = 15 Checksum = 5.130760908195D+02 5.123241541685D+02
T = 16 Checksum = 5.130528295923D+02 5.123304037599D+02
T = 17 Checksum = 5.130310107773D+02 5.123356167976D+02
T = 18 Checksum = 5.130103090133D+02 5.123399592211D+02
T = 19 Checksum = 5.129905029333D+02 5.123435588985D+02
T = 20 Checksum = 5.129714421109D+02 5.123465164008D+02

 

Result verification successful class = C

FT Benchmark Completed. Class = C

Size = 512x 512x 512 Iterations = 20

Time in seconds = 1.92 Total processes = 512 Compiled procs = 512 Mop/s total = 206949.17 Mop/s/process = 404.20

Operation type = floating point Verification = SUCCESSFUL

 

Summary

In this post, we covered how to run MPI Batch jobs with an EFA-enabled elastic network interface using AWS Batch multi-node parallel jobs and an EC2 launch template. We used a launch template to configure the AWS Batch compute environment to launch an instance with the EFA device installed. We showed you how to expose the EFA device to the container. You also learned how to package an MPI benchmarking application, the NPB, as a Docker container, and how to run the application as an AWS Batch multi-node parallel job.

We hope you found the information in this post helpful and encouraging as to all the possibilities for HPC on AWS.

from AWS Compute Blog