AWS Parallel Cluster version 3

Setting up a multi-instance cluster

You can now configure pcluster to have multiple queues and compute instances in single cluster. This is a nice feature because it allows you to choose from different types of instances based in your job (e.g., memory vs compute optimized) or to choose instances based on spot availability.
To decide which instances to put into your cluster use the Amazon Spot Instance Advisor to find instances that have a low frequency of interruption (< 5%). We found that instances that have >5% frequency of interruption don't perform well as spot instances. They either take too long to get into the queue, or they get kicked out of the queue too often. Use the Spot Instance Advisor to find several available instances that meet your needs within a region of interest. You should be pairing your instance search with the EC2 spot pricing table to find less expensive options.
After finding the region and instances that will meet your needs build your pcluster config file. See the LADCO modeling instance example below.
Pcluster 3 YAML configuration file documentation

Modeling spot cluster config

A few key features of the this configuration

It's configured to run in the us-east-2 region with the Amazon Linux 2 OS
Head node is a c4.large instance with 40Gb of disk space
The Slurm scheduler will control the MPI jobs on the cluster
Computing configuration (Scheduling -> Compute Resources)
- There is 1 queue (slurmq1) with 3 compute instance options:
- r4.16xlarge - Memory optimized instance with 64 vCPUs, 488 Gb RAM, Intel Xeon E5-2686 v4 (Broadwell) processors
- m6g.16xlarge - General purpose instance with 64 vCPUs, 256 Gb RAM, AWS Graviton 2 processors
- c5.24xlarge - Compute optimized instance with 96 vCPUs, and 192 Gb RAM, Intel Xeon Scalable Processors (Cascade Lake)
- To access these different instances for a simulation, use a Slurm directive in your job script (note the connection of the --constraint directive to the ComputeResources -> Name in the config file):

#SBATCH --partition=slurmq1
#SBATCH --constraint="r416x"

Data storage configuration
- A preconfigured "apps" disk is mounted to the cluster as the "/ladco" directory. This disk is based on a snapshot of an EBS volume that has scripts and software (all built on ALinux2) pre-loaded. We frequently save snapshots of our applications disk to save specific configurations. New clusters build off recent or newest apps snapshots
- Three types of "data" disks are mounted to the cluster under the "/data" directory:
- /data/ebs - Elastic block store (EBS) provisioned volume with 1 Tb of space. As this is a provisioned disk you pay for the allocated storage regardless of how much is being used
- /data/efs - Elastic file system (EFS) flexible volume that gets provisioned dynamically. You pay for what you use
- /data/lustre - Flexible Lustre volume with a mount point directly to an S3 bucket

Region: us-east-2
 Image:
  Os: alinux2  # alinux2 | centos7 | ubuntu1804 | ubuntu2004
 HeadNode:
  InstanceType: c4.large
  Networking:
   SubnetId: subnet-????????
   ElasticIp: false  # true|false|EIP-id
  DisableSimultaneousMultithreading: false
  Ssh:
   KeyName: ????????
  LocalStorage:
   RootVolume:
     Size: 40
     Encrypted: false
     DeleteOnTermination: true
  Iam:
   S3Access:
   - BucketName: ????????
     EnableWriteAccess: true
Scheduling:
  Scheduler: slurm
  SlurmSettings:
   ScaledownIdletime: 15
  SlurmQueues:
   - Name: slurmq1 # slurm directive: -p slurmq1
     CapacityType: SPOT
     Networking:
       SubnetIds:
         - subnet-????????
       PlacementGroup:
         Enabled: True
     ComputeResources:
       - Name: r416x # slurm directive: -C "[r416x]"
         DisableSimultaneousMultithreading: false
         InstanceType: r4.16xlarge
         MaxCount: 50
       - Name: m6g16x # slurm directive: -C "[m6g16x]"
         DisableSimultaneousMultithreading: false
         InstanceType: m6g.16xlarge
         MaxCount: 50
       - Name: c524x # slurm directive: -C "[c524x]"
         DisableSimultaneousMultithreading: false
         InstanceType: c5.24xlarge
         MaxCount: 50
SharedStorage:
 - MountDir: /ladco
   StorageType: Ebs
   Name: appsvolume
   EbsSettings:
     Size: 120
     SnapshotId: snap-????????
     DeletionPolicy: Snapshot # Delete | Retain | Snapshot
 - MountDir: /data/efs
   StorageType: Efs
   Name: dataefs
   EfsSettings:
     Encrypted: false  # true
     PerformanceMode: generalPurpose  # generalPurpose | maxIO
     ThroughputMode: bursting  # bursting | provisioned
 - MountDir: /data/ebs
   StorageType: Ebs
   Name: dataebs
   EbsSettings:
     VolumeType: sc1 # gp2 | gp3 | io1 | io2 | sc1 | st1 | standard
     Size: 1000
     Encrypted: false  # true
     DeletionPolicy: Delete
 - MountDir: /data/lustre
   StorageType: FsxLustre
   Name: datalustre
   FsxLustreSettings:
     StorageCapacity: 2400  # 1200 | 2400| 3600
     DeploymentType: SCRATCH_1  # PERSISTENT_1 | SCRATCH_1 | SCRATCH_2
     ExportPath: s3://????????/lustre
     ImportPath: s3://????????
     AutoImportPolicy: NEW_CHANGED  # NEW | NEW_CHANGED
     StorageType: SSD  # HDD | SSD
Monitoring:
 DetailedMonitoring: false
 Logs:
   CloudWatch:
     Enabled: true
     RetentionInDays: 14
     DeletionPolicy: Delete
 Dashboards:
   CloudWatch:
     Enabled: true
CustomS3Bucket: ????????
 Tags:
 - Key: Name
   Value: Feb22Pcluster

Starting the cluster

Get familiar with the pcluster commands. Once your config file is set up, use this command to start a cluster called "my-cluster-name" in the us-west-1 region

pcluster create-cluster -c config.spot_us-west-1.pcluster3.22Feb2022.yaml -r us-west-1 -n my-cluster-name

Check the status of the build using:

pcluster list-clusters -r us-west-1

Connecting to the cluster

Once you get a successful build, connect to the cluster with the pcluster ssh command:

pcluster ssh -n my-cluster-name -i ~/.awskeys/mykey.pem

You can also log into the AWS Console, go to the service EC2 and look up your instance. There is a connection button to find and ssh command if you prefer to use an SSH client like putty or a tradition ssh command from a Linux command line.

Running jobs

AWS Parallel Cluster version 2

Install AWS-CLI
Install Pcluster
Don't set the spot price. When you do not set a spot price, AWS will give you the spot market price capped at the on-demand price. Setting the spot price makes the instance more prone to being reclaimed and having your job terminated. As there is currently no functionality automatically enabled on EC2 instances for checkpoint/restart, losing an instance is a show-stopper for WRF production runs. For WRF MPI applications it's not worth playing the spot market if the tradeoff is instance reliability.
Use the base alinux AMI for your version of pcluster; e.g., for v2.4.0 : https://github.com/aws/aws-parallelcluster/blob/v2.4.0/amis.txt

Configure the cluster with a config file:

spot pcluster config

[aws]
aws_region_name = us-east-2
 
[cluster ladcospot]
vpc_settings = ladco
ebs_settings = data,input,apps
scheduler = slurm
master_instance_type = c4.large
compute_instance_type = r4.16xlarge
master_root_volume_size = 40
cluster_type = spot
base_os = alinux2
key_name = ******
s3_read_resource = arn:aws:s3:::ladco-backup/*
s3_read_write_resource = arn:aws:s3:::ladco-wrf/*
post_install = s3://ladco-backup/post_install_users.sh
disable_hyperthreading = true
custom_ami = ami-0c283443a1ebb5c17
max_queue_size = 100
 
# Create a 10 Tb cold storage I/O directory 
[ebs data]
shared_dir = data
volume_type = sc1
volume_size = 10000
volume_iops = 1500
encrypted = false

# Attach an "apps" volume with pre-loaded software
[ebs apps]
shared_dir = ladco
ebs_volume_id = vol-*****

# Attach an "input" data volume with pre-loaded input data
[ebs input]
shared_dir = input
ebs_volume_id = vol-*****

[vpc public]
master_subnet_id = subnet-******
vpc_id = vpc-******

[global]
update_check = true
sanity_check = true
cluster_template = ladcospot

[aliases]
ssh = ssh -Y {CFN_USER}@{MASTER_IP} {ARGS}

on demand pcluster config

[aws]
aws_region_name = us-east-2
 
[cluster ladcowrf]
vpc_settings = public
ebs_settings = ladcowrf
scheduler = sge
master_instance_type = m4.large
compute_instance_type = m5a.4xlarge
placement = cluster
placement_group = DYNAMIC
master_root_volume_size = 40
cluster_type = ondemand
base_os = alinux
key_name = *****
min_vcpus = 0
max_vcpus = 64
desired_vcpus = 0
# Base AMI for pcluster v2.1.0
custom_ami = ami-0381cb7486cdc973f
 
[ebs ladcowrf]
shared_dir = data
volume_type = gp2
volume_size = 10000
volume_iops = 1500
encrypted = false

[vpc public]
master_subnet_id = subnet-******
vpc_id = vpc-******

[global]
update_check = true
sanity_check = true
cluster_template = ladcowrf

[aliases]
ssh = ssh -Y {CFN_USER}@{MASTER_IP} {ARGS}

Cluster Access

Start the cluster

 pcluster create -c config.spot ladcospot

Log in to the cluster

pcluster ssh ladcospot -i {name of your AWS Key}

Fault Tolerance

Script to monitor pcluster system logs for termination notice, and restart WRF.

#!/bin/bash
CASE=LADCO_2016_WRFv39_YNT_NAM
JSTART=2016095
wrk_dir=/data/apps/WRFV3.9.1/sims/LADCO_2016_WRFv39_YNT_NAM
while true
do
  if [ -z $(curl -s http://169.254.169.254/latest/meta-data/spot/termination-time | grep -q .*T.*Z ) ]; then
     echo "terminated"
     break
  else
     echo "Still running fine"
     sleep 3
  fi
done
echo "Restarting WRF job"
#qsub -N WRF_rest $wrk_dir/wrapper_restart_wrf.csh $JSTART 6

~

Working with AWS Parallel Cluster

Contents

AWS Parallel Cluster version 3

Setting up a multi-instance cluster

Modeling spot cluster config

Starting the cluster

Connecting to the cluster

Running jobs

AWS Parallel Cluster version 2

spot pcluster config

on demand pcluster config

Cluster Access

Fault Tolerance

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