Playbooks distributed with ElastiCluster

ElastiCluster uses Ansible to configure the VM cluster based on the options read from the configuration file. This chapter describes the Ansible playbooks bundled [1] with ElastiCluster and how to use them.

[1]ElastiCluster playbooks can be found in the elasticluster/share/playbooks directory of the source code. You are free to copy, customize and redistribute them under the terms of the GNU General Public License version 3 or (at your option) any later version.

Setup variables

In some cases, extra variables can be set to playbooks to modify its default behavior. In these cases, you can either define a variable global to the cluster using:

global_var_<varname>=<value>

or, if the variable must be defined only for a specific group of hosts:

<groupname>_var_<varname>=<value>

For example, to allow reboot of “worker” nodes only:

worker_var_allow_reboot=yes

General setup variables

The following customization variables apply to all ElastiCluster playbooks.

Variable name Default Description
allow_reboot no Allow rebooting nodes if needed. Be careful if this is set when resizing clusters, as you may lose running jobs.
disable_selinux no If yes, then SELinux is disabled at the beginning of the “setup” phase on all nodes of the cluster. Note that disabling SELinux may require a reboot; so, if allow_reboot (see above) is yes, ElastiCluster will reboot cluster nodes if needed while setting the cluster up, if allow_reboot is no (default) then the node will only be configured to disable SELinux after the next reboot but the “setup” phase will run with SELinux enabled. (Applies to RHEL/CentOS hosts only)
insecure_https_downloads no

If no (default), require that web sites, from where software is downloaded, present a valid SSL/TLS certificate. However, it may happen that the base OS trusted certificates repository is not fully up-to-date and this verification fails. (See, for instance, issue #539). In these cases, setting this option to yes allows the playbooks to continue (at the expense of some security).

Warning

Setting this option to yes also allows installing packages from unauthenticated sources on Debian/Ubuntu.
multiuser_cluster no Install NIS/YP. The first node in the master class will be assigned the role of the YP master server; additional nodes in the “master” class (if any) will be YP slave servers.
proxy_url none If this is set to a non-empty value (e.g., http://proxy.example.com:3128/), ElastiCluster configures all VMs to route HTTP(S) and FTP requests through this proxy host. This can be useful when running behind a firewall, or when the VMs have a private IPv4 address, or simply when you have access to a caching proxy (which can speed up downloading the same file from many hosts). By default, no proxy is used and direct HTTP/FTP connections are attempted.
ssh_password_auth yes Allow users to log in via SSH by providing a password. Note: the default in ElastiCluster is the opposite of what all major GNU/Linux distributions do.
upgrade_packages yes Upgrade all installed to the latest available version. Setting this to no allows speedier setup when starting from cluster snapshots. Note: even when set to no, some packages may still be upgraded to satisfy dependencies of other packages that need to be installed.

Feature variables

The following customization variables activate special features, which may depend on the cloud environment or cluster setup.

Azure files

ElastiCluster nodes can automatically mount an Azure files share; you need to set the following setup variables on all nodes where an Azure files share is to be mounted (made visibile):

Setup variables for mounting Azure files
Variable Default value Description
af_storage_account_name None, a value is mandatory Storage account name, as gotten from the the Storage account access keys view on the Azure portal.
af_storage_account_key None, a value is mandatory Storage account key, as gotten from the the Storage account access keys view on the Azure portal.
af_share_name None, a value is mandatory Name given to the Azure files share (see: Create a file share through the Azure portal on the Azure website)
af_mount_point /net/azfiles Local directory where the Azure files will be made visible.

See Section Setup variables above for the relevant configuration file syntax.

Compute clusters

The following playbooks are available for installing compute clusters (batch-queuing or otherwise) with ElastiCluster.

SLURM

Supported on:

  • Ubuntu 14.04 (“trusty”) and later
  • Debian 8 (“jessie”) and later
  • RHEL/CentOS 6.x and 7.x

This playbook installs the SLURM batch-queueing system.

You are supposed to only define one slurm_master and multiple slurm_worker. The first will act as login node, NFS server for the /home filesystem, and runs the SLURM scheduler and accounting database; the workers will only execute the jobs. A slurm_submit role allows you to optionally install “SLURM client” nodes, i.e., hosts whose only role in the cluster is to submit jobs and query the queue status.

Ansible group Action
slurm_master SLURM controller/scheduler node; also runs the accounting storage daemon slurmdbd and its MySQL/MariaDB backend.
slurm_worker SLURM execution node: runs the slurmd daemon.
slurm_submit SLURM client: has all the submission and query commands installed, but runs no daemon.

The following example configuration sets up a SLURM batch-queuing cluster using 1 front-end and 4 execution nodes:

[cluster/slurm]
master_nodes=1
worker_nodes=4
ssh_to=master
setup_provider=slurm
# ...

[setup/slurm]
master_groups=slurm_master
worker_groups=slurm_worker
# ...

You can combine the SLURM playbook with other add-on software. For instance, you can install the Ganglia monitoring system alongside with SLURM; in this case the setup stanza will look like:

[setup/slurm+ganglia]
frontend_groups=slurm_master,ganglia_master
compute_groups=slurm_worker,ganglia_monitor
...

When combined with the CUDA add-on, and if any actual NVIDIA GPU devices are found on the compute nodes, SLURM will be configured with the GPU devices are GRES resources so that you can request the use of GPUs in your jobs by passing the --gres=gpu:... option to sbatch:

# request 2 GPU devices
sbatch --gres=gpu:2 my_gpgpu_job.sh

Note that compute nodes need to be given the cuda add-on playbook in order for CUDA GPU detection and configuration to work. For eaxmple, the following configuration will detect and configure NVIDIA GPUs on all compute nodes (but not on the front-end):

[setup/slurm+gpu]
frontend_groups=slurm_master
compute_groups=slurm_worker,cuda
...

Extra variables can be set by editing the setup/ section:

SLURM tunables
Variable Default value Description
slurm_allowedramspace 1 100 Max percentage of RAM that can be allocated to a job. If slurm_constrainramspace (see below) is yes, then this limit is applied to a job’s real memory usage; otherwise, this limit is summed with slurm_allowedswapspace to cap the virtual memory usage (see SLURM’s VSizeFactor configuration parameter).
slurm_allowedswapspace 1 Max percentage of virtual memory (in addition to the real memory) that can be allocated to a job. This value is summed with slurm_allowedramspace to cap a job’s total virtual memory usage. You might want to set this limit to a much higher value when using GPUs, as GPU memory might be accounted in the job’s virtual memory.
slurm_conf_file use ElastiCluster’s built-in template

Filesystem path to a file that is deployed as slurm.conf on every node in the cluster. The file can use Jinja2 template syntax. By default, ElastiCluster’s built-in slurm.conf template is used; it is probably a good idea to use it as a starting point.

Note

The value of this variable should be an absolute filesystem path. In other words, you should use a path starting with / like /home/me/conf/slurm.conf.j2 instead of a relative path like my-slurm-config.j2.

Warning

SLURM will log a warning if configuration files on worker nodes do not have the exact same content (byte by byte) as the configuration file on the master node. It is therefore recommended to set slurm_conf_file as a cluster-global variable.
slurm_constrainramspace yes Only used if ``slurm_taskplugin`` is set to ``task/cgroup``. If set to yes then SLURM constrains the job’s RAM usage by setting the memory soft limit to the allocated memory and the hard limit to the allocated memory * slurm_allowedramspace (see below). This can add stability to a system when there are multiple misbehaving jobs that allocate large amounts of memory, but can be problematic with jobs using GPUs (since the memory used on the GPU seems to be accounted against the job’s own CPU RAM consumption).
slurm_constrainswapspace yes Only used if ``slurm_taskplugin`` is set to ``task/cgroup``. If set to yes then SLURM kills jobs whose virtual memory usage exceeds allocated memory * slurm_allowedswapspace (see below).
slurm_jobacctgatherfrequency 60 Value of JobAcctGatherFrequency in slurm.conf
slurm_jobacctgathertype jobacct_gather/linux Value of JobAcctGratherType in slurm.conf
slurm_maxarraysize 1000 Maximum size of an array job
slurm_maxjobcount 10000 Maximum nr. of jobs actively managed by the SLURM controller (i.e., pending and running)
slurm_proctracktype proctrack/linuxproc Value of ProcTrackType in slurm.conf
slurm_returntoservice 2 Value of ReturnToService in slurm.conf
slurm_selecttype select/cons_res Value of SelectType in slurm.conf
slurm_selecttypeparameters CR_Core_Memory Value of SelectTypeParameters in slurm.conf
slurm_taskplugin task/none Value of TaskPlugin in slurm.conf
slurm_version 18.08 Version of SLURM to install; valid values are 17.02, 17.11, and 18.08. Note: only applicable to RHEL/CentOS 7 clusters, in all other cases the version of SLURM provided by the Linux distribution is used.

Note that the slurm_* extra variables need to be set globally (e.g., global_var_slurm_selectype) because the SLURM configuration file must be identical across the whole cluster.

Global variable multiuser_cluster controls whether the NIS/YP software is installed on the cluster (NIS master on the cluster master node, compute nodes are NIS slaves) to make it easier to add users to the cluster (just run the adduser command on the master).

The “SLURM” playbook depends on the following Ansible roles being available:

In order for the NFS exported home directory to be mountable from the cluster’s compute nodes, security groups on OpenStack need to permit all UDP traffic between all cluster nodes.

GridEngine

Tested on:

  • CentOS 6.x and 7.x
  • Ubuntu 14.04 (“trusty”) and 16.04 (“xenial”)
  • Debian 8 (“jessie”) and later
ansible groups role
gridengine_master Scheduler, admin, and submission host
gridengine_worker Compute (exec) node and submission host

This playbook installs GridEngine using the packages distributed with Ubuntu, Debian, or CentOS, and creates a basic working configuration.

You are supposed to only define one gridengine_master and multiple gridengine_worker. The first acts as login node, fileserver, and runs the master scheduler (SGE qmaster), whereas the others will only execute jobs (SGE execd).

The /home filesystem is exported from the gridengine “master” to the worker nodes. The cell directory $SGE_ROOT/$SGE_CELL/common directory is shared from the gridengine server to the compute nodes (via NFS).

A snippet of a typical configuration for a gridengine cluster is:

[cluster/gridengine]
frontend_nodes=1
compute_nodes=5
ssh_to=frontend
setup_provider=ansible_gridengine
...

[setup/ansible_gridengine]
frontend_groups=gridengine_master
compute_groups=gridengine_worker
...

You can combine the gridengine playbooks with ganglia. In this case the setup configuration stanza looks like:

[setup/ansible_gridengine]
frontend_groups=gridengine_master,ganglia_master
compute_groups=gridengine_worker,ganglia_monitor
...

Global variable multiuser_cluster controls whether the NIS/YP software is installed on the cluster (NIS master on the cluster master node, compute nodes are NIS slaves) to make it easier to add users to the cluster (just run the adduser command on the master).

Hadoop + Spark

Supported on:

  • CentOS 7
  • Debian 9 (“stretch”)
  • Ubuntu 16.04

This playbook installs a Hadoop 2.x cluster with Spark and Hive, using the packages provided by the Apache Bigtop project. The cluster comprises the HDFS and YARN services: each worker node acts both as a HDFS “DataNode” and as a YARN execution node; there is a single master node, running YARN’s “ResourceManager” and “JobHistory”, and Hive’s “MetaStore” services.

Ansible group Action
hadoop_master Install the Hadoop cluster master node: run YARN “ResourceManager” and Hive “MetaStore” server. In addition, install a PostgreSQL server to host Hive metastore tables.
hadoop_worker Install a YARN+HDFS node: run YARN’s “NodeManager” and HDFS’ “DataNode” services. This is the group of nodes that actually provide the storage and execution capacity for the Hadoop cluster.

HDFS is only formatted upon creation; if you want to reformat/zero out the HDFS filesystem you need to run the hdfs namenode -format command yourself. No rebalancing is done when adding or removing data nodes from the cluster.

Nota bene:

  1. Currently ElastiCluster turns off HDFS permission checking: therefore Hadoop/HDFS clusters installed with ElastiCluster are only suitable for shared usage by mutually trusting users.
  2. Currently ElastiCluster has no provision to vacate an HDFS data node before removing it. Be careful when shrinking a cluster, as this may lead to data loss!

The following example configuration sets up a Hadoop cluster using 4 storage+execution nodes:

[cluster/hadoop+spark]
master_nodes=1
worker_nodes=4
ssh_to=master

setup_provider=hadoop+spark
# ...

[setup/hadoop+spark]
provider=ansible
master_groups=hadoop_master
worker_groups=hadoop_worker

Global variable multiuser_cluster controls whether the NIS/YP software is installed on the cluster (NIS master on the cluster master node, compute nodes are NIS slaves) to make it easier to add users to the cluster (just run the adduser command on the master).

The following variables can be used to control the defaults for running Spark applications. (Note that they set a default, hence can be overridden by applications when creating a Spark context; on the other hand, these defaults are exactly what is used when running pyspark or a Jupyter notebook with Spark support.)

Spark settings
Variable Default value Description
spark_driver_memory_mb 1 (Free memory on master node / nr. of CPUs of master node) Used to set spark.driver.memory: maximum amount of memory (counted in MBs) that a Spark “driver” process is allowed to use.
spark_driver_maxresultsize_mb 1 (80% of ``spark_driver_memory_mb``) Used to set spark.driver.maxResultSize: Limit of total size (amount in MBs) of serialized results of all partitions for each Spark action (e.g. collect)
spark_executor_memory_mb 1 (Max free memory on worker node / max nr. of CPUs on a node) Used to set spark.executor.memory: Maximum amount of memory (counted in MBs) that a Spark “executor” process is allowed to use.
spark_python_worker_memory_mb 1 (50% of ``spark_executor_memory_mb``) Used to set spark.python.worker.memory: Maximum amount of memory (counted in MBs) to use per Python worker process during aggregation. If the memory used during aggregation goes above this amount, Spark starts spilling the data into disks.

The following variables can be used to change the version of the installed software:

BigTop distribution settings
Variable Default value Description
bigtop_release 1 1.4.0

Release of Apache BigTop to use for installing Spark/Hadoop packages. As of June 2019, ElastiCluster supports BigTop 1.4.0 (default), 1.3.0, 1.2.1, and 1.2.0.

BigTop release 1.4.0 installs Hadoop 2.8.5, Hive 2.3.3, and Spark 2.2.3; it supports CentOS 7, Debian 9 (“stretch”) and Ubuntu 16.04 (“xenial”) as base operating systems.

BigTop release 1.3.0 installs Hadoop 2.8.4, Hive 2.3.3, and Spark 2.2.1; it supports CentOS 7, Debian 9 (“stretch”) and Ubuntu 16.04 (“xenial”) as base operating systems.

BigTop release 1.2.x installs Hadoop 2.7.3, Hive 1.2.1, and Spark 2.1.1; it supports CentOS 7, Debian 8 (“jessie”) and Ubuntu 16.04 (“xenial”) as base operating systems.
bigtop_mirror http://mirrors.ibiblio.org/apache URL of the mirror to use; should point at the root of the Apache mirror (i.e., there should be a bigtop/ directory immediately under it). The complete list of Apache mirrors can be found at: http://www.apache.org/mirrors/. There is little reason to ever change this value from the default: the mirror server is only used to download the GPG key and the repository definition file; it is not used for actual download of the Apache BigTop software.

HTCondor

Tested on:

  • Debian 8.x
  • Ubuntu 14.04
ansible groups role
condor_master Act as scheduler, submission and execution host.
condor_worker Act as execution host only.

This playbook will install the HTCondor workload management system using the packages provided by the Center for High Throughput Computing at UW-Madison.

The /home filesystem is exported from the condor master to the compute nodes.

A snippet of a typical configuration for a slurm cluster is:

[cluster/condor]
setup_provider=htcondor
frontend_nodes=1
compute_nodes=2
ssh_to=frontend
# ...

[setup/htcondor]
frontend_groups=condor_master
compute_groups=condor_worker
# ...

Kubernetes

Supported on:

  • Ubuntu 16.04, 18.04
  • RHEL/CentOS 7.x

This playbook installs the Kubernetes container management system on each host. It is configured using kubeadm; flannel is used for networking. Currently only 1 node is supported in the control plane.

ansible groups role
kubernetes_master Host in control plane. (Only 1 allowed.)
kubernetes_worker Act as execution host only.

To force the playbook to run, add the Ansible group kubernetes. The following example configuration example sets up a kubernetes cluster using 1 master and 2 worker nodes:

[cluster/kubernetes]
master_nodes=1
worker_nodes=4
setup_provider=kubernetes
# ...

[setup/kubernetes]
master_groups=kubernetes_master
worker_groups=kubernetes_worker
# ...

SSH into the cluster and execute sudo kubectl --kubeconfig /etc/kubernetes/admin.conf get nodes to view the cluster.

The following variables can be used to change the version of the installed software:

Kubernetes optional settings
Variable Default value Description
kubernetes_version latest Version of the Kubernetes packages to install; packages are always installed from apt.kubernetes.io. The version string should either match the version of a set of packages in the repository, or be the special string latest (default), which installs the latest version available on the repository.

PBSPro

Supported on:

  • CentOS 7.x
ansible groups role
pbspro_master Act as scheduler, submission and communication host.
pbspro_worker Act as execution host only.

This playbook will install the PBSPro workload manager and job scheduler using the packages released on GitHub.

The PBSPro server will be configured with a single queue, named workq; all worker nodes will belong to this queue. The only deviation from the stock configuration of the PBSPro server is that job history is enabled.

The /home filesystem is exported from the pbspro_master node to the pbspro_worker nodes.

A snippet of a typical configuration for a PBSPro cluster is:

[cluster/pbspro]
setup=pbspro
frontend_nodes=1
compute_nodes=4
# ...

[setup/pbspro]
frontend_groups=pbspro_master
compute_groups=pbspro_worker
# ...

The installed version can be controlled by editing the setup/ section:

PBSPro settings
Variable Default value Description
pbspro_version 18.1.3 What release of PBSPro to install. Must match the numeric part of a release tag on GitHub.

TORQUE

Supported on:

  • CentOS 7.x
  • CentOS 6.x
ansible groups role
torque_master Act as scheduler, submission and execution host.
torque_worker Act as execution host only.

This playbook will install the TORQUE workload management system using the packages provided in the EPEL repository.

Note

Due to a change in the licensing policy, the last available version in EPEL is TORQUE 4.2.10 (released in March 2015) while the latest version (as of this writing, April 2018) is 6.1.2; the cluster installed by ElastiCluster will thus lack recent features.

For the same licensing reasons, TORQUE is no longer available in recent Debian and Ubuntu distributions and hence cannot currently be installed by ElastiCluster.

The TORQUE server will be configured with a single queue, named default; all worker nodes will belong to this queue.

Note

The MAUI scheduler, which has been the only open-source companion scheduler for TORQUE, is no longer supported nor packaged for any modern distribution. Therefore, TORQUE is installed and configured with the default FIFO scheduler. This is probably sub-optimal for almost any workload; consider using PBSPro instead, which has a compatible interface and a flexible scheduler.

The /home filesystem is exported from the torque_master node to the torque_worker nodes.

A snippet of a typical configuration for a TORQUE cluster is:

[cluster/torque]
setup=torque
frontend_nodes=1
compute_nodes=4
# ...

[setup/torque]
frontend_groups=torque_master
compute_groups=torque_worker
# ...

Filesystems and storage

The following playbooks are available for installing storage clusters and distributed filesystems with ElastiCluster. These can be used standalone, or mixed with compute clusters to provide additional storage space or more performant cluster filesystems.

CephFS

Supported on:

  • Ubuntu 16.04, 14.04
  • Debian 8 (“jessie”), 9 (“stretch”)
  • CentOS 6.x and 7.x
Ansible group Action
ceph_mon Install Ceph server software and configure this host to run the MON (monitor) service. There must be at least one MON node in any Ceph cluster.
ceph_osd Install Ceph server software and configure this host to run the OSD (object storage device) service. There must be at least three OSD nodes in a Ceph cluster.
ceph_mds Install Ceph server software and configure this host to run the MDS (meta-data server) service. This node is optional but CephFS is only available if at least one MDS is available in the cluster.
ceph_client Install required packages to make usage of Ceph Storage Cluster and CephFS possible. Any mount point listed in CEPH_MOUNTS will be created and the corresponding filesystem mounted.

This will install a Ceph Storage Cluster with CephFS. Actual data storage is done in the OSD nodes, on each node under directory /var/lib/ceph/osd/ceph-NNN. All hosts in group ceph_client can then mount this filesystem, using either the CephFS kernel driver or the CephFS FUSE driver.

Management of the cluster is possible from any node (incl. ceph_client nodes) using the client.admin key (deployed in file /etc/ceph/ceph.client.admin.keyring, by default only readable to the root user).

The Ceph and CephFS behavior can be changed by defining the following variables in the setup/ section:

Ceph/CephFS variables in ElastiCluster
Variable Default value Description
ceph_release luminous Name of Ceph release to install, e.g. “luminous” or “jewel”. Note that not all releases are available on all platforms; for instance, selecting the “hammer” release on Ubuntu 16.04 will install “jewel” instead.
ceph_osd_pool_size 2 Default number of object replicas in a pool.
ceph_osd_pg_num computed according to: http://docs.ceph.com/docs/master/rados/operations/placement-groups/#a-preselection-of-pg-num Default number of PGs in a pool.
ceph_metadata_pg_num 1/8 of ceph_osd_pool_size Number of PGs for the CephFS metadata pool.
ceph_data_pg_num 7/8 of ceph_osd_pool_size Number of PGs for the CephFS data pool.

More detailed information can be found in the ceph role README.

Note

  • In contrast with similar ElastiCluster playbooks, the CephFS playbook does not automatically mount CephFS on client nodes.

  • This playbook’s defaults differ from Ceph’s upstream defaults in the following ways:

    • default replication factor for objects in a pool is 2 (Ceph’s upstream is 3)
    • the minimum number of copies of an object that a pool should have to continue being operational is 1 (Ceph’s upstream is 2).

    In both cases, the different default is motivated by the assumption that cloud-based storage is “safer” than normal disk-based storage due to redundancy and fault-tolerance mechanisms at the cloud IaaS level.

  • The CephFS kernel driver for the “Luminous” release requires features that are only present in the Linux kernel from version 4.5 on. At the time of this writing, a >4.5 kernel is only installed by default on Debian 9 “stretch”. To mount a “Luminous” CephFS on any other Linux distribution, you will have to either use the CephFS FUSE driver or tell Ceph not tu use tunables v5:

    sudo ceph osd crush tunables hammer

The following example configuration sets up a CephFS cluster using 1 MON+MDS node, 5 OSD nodes and providing 3 replicas for each object:

[setup/ceph1]
mon_groups=ceph_mon,ceph_mds
osd_groups=ceph_osd
client_groups=ceph_client

global_var_ceph_release=luminous
global_var_ceph_osd_pool_size=3

[cluster/ceph1]
setup=ceph1

mon_nodes=1
osd_nodes=5
client_nodes=1
ssh_to=client

# .. cloud-specific params ...

This example configuration sets up a CephFS cluster using 3 MON+OSD nodes, 1 MDS nodes and sets explicitly the number of PGs to use for CephFS metadata and data:

[setup/ceph2]
mon_groups=ceph_mon,ceph_osd
mds_groups=ceph_mds
client_groups=ceph_client

global_var_ceph_release=luminous
global_var_ceph_metadata_pg_num=1024
global_var_ceph_data_pg_num=8192

[cluster/ceph2]
setup=ceph2

mon_nodes=3
mds_nodes=1
client_nodes=1
ssh_to=client

# .. cloud-specific params ...

GlusterFS

Supported on:

  • Ubuntu 16.04 and later
  • Debian 9 and later
  • RHEL/CentOS 6.x, 7.x
ansible groups action
glusterfs_server Run a GlusterFS server with a single brick
glusterfs_client Install gluster client and (optionally) mount a GlusterFS filesystem.

This will install a GlusterFS using all the glusterfs_server nodes as servers with a single brick located in directory /srv/glusterfs, and any glusterfs_client to mount this filesystem over directory /glusterfs.

To manage the GlusterFS filesystem you need to connect to a glusterfs_server node.

Several versions of GlusterFS are concurrently maintained and packaged upstream; the actual version of GlusterFS installed by ElastiCluster can be set using global variable glusterfs_version in the [setup/...] section (see below).

Note

Not all versions of GlusterFS are available on all the operating systems listed above; check out the availability matrix at https://gluster.readthedocs.io/en/latest/Install-Guide/Community_Packages/

Note

If GlusterFS is already installed on a cluster, changing the value of glusterfs_version and running elasticluster setup may not correctly upgrade the software. To upgrade GlusterFS to a more recent version, you need to follow the procedure detailed at: https://docs.gluster.org/en/latest/Upgrade-Guide/

By default the GlusterFS volume is “pure distributed”: i.e., there is no redundancy in the server setup (if a server goes offline, so does the data that resides there), and neither is the data replicated nor striped, i.e., replica and stripe number is set to 1. This can be changed by defining the following variables in the setup/ section:

Note that setting glusterfs_redundancy to a non-zero value will force the volume to be “dispersed”, which is incompatible with striping and replication. In other words, the glusterfs_redundancy option is incompatible with glusterfs_stripes and/or glusterfs_replicas. You can read more about the GlusterFS volume types and permitted combinations at http://docs.gluster.org/en/latest/Administrator%20Guide/Setting%20Up%20Volumes/.

variable name default description
glusterfs_version 6 version of GlusterFS to be installed
glusterfs_stripes no stripe set the stripe value for default volume
glusterfs_replicas no replica set replica value for default volume
glusterfs_redundancy 0 nr. of servers that can fail or be offline without affecting data availability

The following example configuration sets up a GlusterFS cluster using 8 data nodes and providing 2 replicas for each file:

[cluster/gluster]
  client_nodes=1
  server_nodes=8
  ssh_to=client

  setup_provider=gluster
  # ... rest of cluster params as usual ...

[setup/gluster]
  provider=ansible

  client_groups=glusterfs_client
  server_groups=glusterfs_server,glusterfs_client

  # set replica and stripe parameters
  server_var_glusterfs_replicas=2
  server_var_glusterfs_stripes=1

The following example configuration sets up a dispersed GlusterFS volume using 6 data nodes with redundancy 2, i.e., two servers can be offlined without impacting data availability:

[cluster/gluster]
  client_nodes=1
  server_nodes=6
  ssh_to=client

  setup_provider=gluster
  # ... rest of cluster params as usual ...

[setup/gluster]
  provider=ansible

  client_groups=glusterfs_client
  server_groups=glusterfs_server,glusterfs_client

  # set redundancy and force "dispersed" volume
  server_var_glusterfs_redundancy=2

The following example configuration sets up a pure distributed GlusterFS volume over 3 server nodes, installing GlusterFS version 4.1:

[cluster/gluster]

client_nodes=1 server_nodes=3 ssh_to=client

setup_provider=gluster # … rest of cluster params as usual …

[setup/gluster]

provider=ansible

client_groups=glusterfs_client server_groups=glusterfs_server,glusterfs_client

# set redundancy and force “dispersed” volume global_var_glusterfs_version=4.1

The “GlusterFS” playbook depends on the following Ansible roles being available:

OrangeFS/PVFS2

Tested on:

  • Ubuntu 14.04
ansible groups role
pvfs2_meta Run the pvfs2 metadata service
pvfs2_data Run the pvfs2 data service
pvfs2_client configure as pvfs2 client and mount the filesystem

The OrangeFS/PVFS2 playbook will configure a pvfs2 cluster. It downloads the software from the OrangeFS website, compile and install it on all the machine, and run the various server and client daemons.

In addiction, it will mount the filesystem in /pvfs2 on all the clients.

You can combine, for instance, a SLURM cluster with a PVFS2 cluster:

[cluster/slurm+orangefs]
frontend_nodes=1
compute_nodes=10
orangefs_nodes=10
ssh_to=frontend
setup_provider=ansible_slurm+orangefs
...

[setup/ansible_slurm+orangefs]
frontend_groups=slurm_master,pvfs2_client
compute_groups=slurm_worker,pvfs2_client
orangefs_groups=pvfs2_meta,pvfs2_data
...

This configuration will create a SLURM cluster with 10 compute nodes, 10 data nodes and a frontend, and will mount the /pvfs2 directory from the data nodes to both the compute nodes and the frontend.

Add-on software

The following playbooks add functionality or software on top of a cluster (e.g., monitoring with web-based dashboard). They can also be used stand-alone (e.g., JupyterHub).

Anaconda

Supported on:

  • Ubuntu 14.04 and later
  • RHEL/CentOS 6.x and 7.x
Ansible group Action
anaconda Install the Anaconda Python distribution

This playbook installs the Anaconda Python distribution. Using customization variables you can choose whether to install the Python 2.7 or Python 3 version, and whether to make the Anaconda Python interpreter the default Python interpreter for logged-in users.

Note

Starting release 2020.02, Anaconda only supports Python 3. If you want to use Python 2 from the Anaconda distribution, set anaconda_version (see below) to 2019.10 or an earlier version.

The following variables may be set to alter the role behavior:

Anaconda role variables in ElastiCluster
Variable Default value Description
anaconda_version 2020.07 Version of the Anaconda Python distribution to install
anaconda_python_version 3 Anaconda comes with either a Python2 or a Python3 interpreter – choose which one you want here. (Note: Py2 is only available in Anaconda 2019.10 or earlier.)
anaconda_in_path yes whether the Python interpreter from Anaconda should be made the first match in users’ shell $PATH

For instance, the following configuration snippet requests that ElastiCluster and Ansible install Anaconda Python3 on a SLURM cluster, and make it the default interpreter on the frontend node only:

[setup/slurm+anaconda]
# ... same as usual SLURM setup, but:
master_groups=slurm_master,anaconda
worker_groups=slurm_worker,anaconda

# use Anaconda Python3 flavor
global_var_anaconda_python_version=3

# make it default for logged-in users on master node only
master_var_anaconda_in_path=yes
worker_var_anaconda_in_path=no

The code from this role is a minor modification of the ansible-anaconda playbook written by Andrew Rothstein, and as such maintains the original distribution license. See the accompanying LICENSE file for details.

Ansible

Supported on:

  • Ubuntu 12.04 and later
  • RHEL/CentOS 6.x and 7.x

This playbook installs the Ansible orchestration and configuration management system on each host. There is not much clustering happening here; this playbook is provided in case you want to be able to run Ansible playbooks from inside the cluster (as opposed to always running them from the ElastiCluster host).

To force the playbook to run, add the Ansible group ansible to any node. The following example configuration sets up a SLURM batch-queuing cluster using 1 front-end and 4 execution nodes, and additionally installs Ansible on the front-end:

[cluster/slurm]
master_nodes=1
worker_nodes=4
ssh_to=master
setup_provider=slurm+ansible
# ...

[setup/slurm+ansible]
master_groups=slurm_master,ansible
worker_groups=slurm_worker
# ...

CUDA

Tested on:

  • Ubuntu 14.04, 16.04
  • CentOS 6.x, 7.x
ansible groups role
cuda Install the NVIDIA device drivers and the CUDA toolkit and runtime.

This playbook will detect NVIDIA GPU devices on every host where it is run, and install the CUDA toolkit and runtime software, thus enabling the use of GPU accelerators.

Note that in some cases a reboot is necessary in order to correctly install the NVIDIA GPU device drivers; this is only allowed if the global variable allow_reboot is true – by default, reboots are not allowed so the playbook will fail if a reboot is needed.

A variable can be used to control the version of the CUDA toolkit that will be installed on the nodes:

Variable name Default value Description
cuda_version 8.0 What version of the CUDA toolkit to install

The default is to install CUDA tooklit and runtime version 8.0

Docker CE

Supported on:

  • Debian 9 (“stretch”) and later
  • Ubuntu 16.04 and later
  • RHEL/CentOS 7.x
Ansible group Action
docker Install Docker CE

This playbook installs the Docker CE engine and client. Using customization variables you can choose what update channel to download the software from (“stable”, “test”, or “nightly”), and what users should be able to use Docker.

The following variables may be set to alter the role behavior:

Docker CE role variables in ElastiCluster
Variable Default value Description
docker_release_channel stable What release/update channel to download the Docker software from; choose among stable, test, or nightly. (See Docker CE about page for an explanation of the different policies in each channel.)
docker_compose_release 1.25.5 What release of *docker-compose* to install; set it to 0 or the empty string to skip installing docker-compose.
docker_credential_helpers_release v0.6.3 What release of *Docker credential helpers* to install; set it to 0 or the empty string to skip installing them. Note that all release tags start with the letter v (as of April 2020).
docker_group_members [] List of users that are authorized to call the docker client. Note: the distribution’s default user (e.g., user ubuntu on Ubuntu VMs) is always authorized to run docker by ElastiCluster.
docker_prune_frequency daily How often should a cron job run, to delete old unused Docker images. Must be a valid string for Ansible’s cron module *special_time*; in practice, hourly, daily, weekly, or monthly are likely the only useful settings.

For instance, the following configuration snippet requests that ElastiCluster and Ansible install Docker CE on the worker nodes of a SLURM cluster:

[setup/slurm+docker]
# ... same as usual SLURM setup, but:
master_groups=slurm_master
worker_groups=slurm_worker,docker

EasyBuild

Supported on:

  • Ubuntu 16.04, 14.04
  • Debian 8 (“jessie”), 9 (“stretch”)
  • CentOS 6.x and 7.x
Ansible group Action
easybuild Install the EasyBuild HPC package manager.

This playbook installs EasyBuild and its dependencies to provide a working build environment for HPC clusters.

EasyBuild is configured with the following options:

The following variables may be set to alter the role behavior:

EasyBuild role variables in ElastiCluster
Variable Default value Description
EASYBUILD_VERSION 2.8.2 The version of EasyBuild to install. Interpolated into the (default) source archive name.
EASYBUILD_PREFIX /opt/easybuild Root directory of all the EasyBuild-related paths: source archive, .eb` files repository, installed software, etc.
EASYBUILD_INSTALL Build no software during cluster setup.

List of .eb recipes to build. This is a YAML list, i.e., a comma-separated list of recipe names enclosed in square brackets; for example:

global_var_EASYBUILD_INSTALL=[RELION,OpenMPI]

Beware: the initial EasyBuild invocation will have to build the entire toolchain, so it can take a couple of hours even to install a small and relatively simple package. For this reason, the default value of this variable is the empty list (i.e., do not install any software through EasyBuild).
EASYBUILD_OPTARCH GENERIC Optimization flags for building software, see: http://easybuild.readthedocs.io/en/latest/Controlling_compiler_optimization_flags.html#controlling-target-architecture-specific-optimizations-via-optarch By default the “GENERIC” value is used which should produce code compatible with any x86-64 processor.

It is advised to install EasyBuild on the master/frontend node only, and export the software directories from there to compute nodes via NFS, to cut down on build times and to avoid coherency issues.

Ganglia

Tested on:

  • Ubuntu 12.04, 14.04, 16.04
  • CentOS 6.x, 7.x
ansible groups role
ganglia_master Run gmetad and web interface. It also run the monitor daemon.
ganglia_monitor Run ganglia monitor daemon.

This playbook will install Ganglia monitoring tool using the packages distributed with Ubuntu or CentOS and will configure frontend and monitors.

You should run only one ganglia_master. This will install the gmetad daemon to collect all the metrics from the monitored nodes and will also run apache.

If the machine in which you installed ganglia_master has IP 10.2.3.4, the ganglia web interface will be available at the address http://10.2.3.4/ganglia/

This playbook is supposed to be compatible with all the other available playbooks.

IPython cluster

Tested on:

  • Ubuntu 12.04
  • CentOS 6.3
  • Debian 7.1 (GCE)
  • CentOS 6.2 (GCE)
ansible groups role
ipython_controller Run an IPython cluster controller
ipython_engine Run a number of ipython engine for each core

This playbook will install an IPython cluster to run python code in parallel on multiple machines.

One of the nodes should act as controller of the cluster (ipython_controller), running the both the hub and the scheduler. Other nodes will act as engine, and will run one “ipython engine” per core. You can use the controller node for computation too by assigning the ipython_engine class to it as well.

A snippet of typical configuration for an Hadoop cluster is:

[cluster/ipython]
setup_provider=ansible_ipython
controller_nodes=1
worker_nodes=4
ssh_to=controller
...

[setup/ansible_ipython]
controller_groups=ipython_controller,ipython_engine
worker_groups=ipython_engine
...

In order to use the IPython cluster, using the default configuration, you are supposed to connect to the controller node via ssh and run your code from there.

HPC common

Supported on:

  • Ubuntu 14.04 (“trusty”) and later
  • Debian 9 (“stretch”)
  • CentOS 6.x and 7.x
Ansible group Action
hpc Install commonly-used HPC libraries and tools

This playbook installs some libraries and tools commonly found on HPC clusters, including the GNU FORTRAN compiler, ATLAS, HDF5, LAPACK, NetCDF, OpenBLAS, and the OpenMPI development environment and compiler wrappers.

You probably want to install this add-on if you want to be able to install scientific software on the cluster by compiling it from sources.

For instance, the following configuration snippet requests that ElastiCluster and Ansible install the “HPC common” packages on the worker nodes of a SLURM cluster:

[setup/slurm+hpc]
# ... same as usual SLURM setup, but:
master_groups=slurm_master
worker_groups=slurm_worker,hpc

Julia language

Supported on:

  • Ubuntu 16.04 (“xenial”) and later
  • Debian 9 (“stretch”) and later
  • CentOS 7.x and 8.x
Ansible group Action
julia Install the Julia language environment.

This playbook installs the Julia language system. If the VM is also part of the`jupyterhub` group (see JupyterHub below), then support for running Julia-language notebooks in Jupyter is also installed.

The following extra variables can be set to control installation of the Julia language system:

Variable name Default Description
julia_version 1.6.0 Version of Julia to install
julia_in_path yes Should the julia command be added to all users’ default program search path? If not, you will have to invoke Julia by full path name (e.g., /opt/julia/bin/julia)
julia_home /opt/julia Where should the Julia software archive be unpacked?

The following example (incomplete) shows how to install JupyterHub with support for interactive Julia notebooks:

# JupyterHub software to be configured by Ansible # # (There is nothing Google-specific in the “setup” section; in fact, it can be # re-used verbatim with any cloud provider or base image) # [setup/jupyterhub] provider=ansible server_groups=jupyterhub,julia

# Bring all of the elements together to define a cluster called “jupyterhub” [cluster/jupyterhub+julia] security_group=tcp_port_443

setup=jupyterhub server_nodes=1 ssh_to=server

# …fill in details about cloud, login, and image_id/flavor

Please read the notes in the JupyterHub section about how to create users to log in to JupyterHub via web.

A full example of how to install a JupyterHub stand-alone server with R and Julia language support can be found at: https://github.com/elasticluster/elasticluster/blob/master/examples/jupyterhub-on-google.conf

JupyterHub

Supported on:

  • Ubuntu 14.04 (“trusty”) and later
  • Debian 8 (“jessie”), 9 (“stretch”)
  • CentOS 6.x and 7.x
Ansible group Action
jupyterhub Install Jupyter and JupyterHub to work with interactive computational notebooks.

Install JupyterHub to grant multiple users access to Jupyter notebooks thorugh a web interface. Kernels are installed to run code written in Python 2.7 and 3.x (with Anaconda Python), BASH (using the OS-provided shell), PySpark (in conjunction with the Hadoop+Spark playbook), R language (if the R add-on is installed, see below), and MATLAB (if installed).

Note

JupyterHub is configured to authenticate users with the GNU/Linux /etc/passwd database. So, in order to log in you need to create users first (or set passwords to existing users).

In order to create a new user, run the following commands at the node’s shell prompt:

# replace `user_name` with an actual name (e.g. `jsmith`)
sudo adduser user_name

In order to set the password for an existing user, run the following commands at the node’s shell prompt:

# replace `user_name` with an actual name (e.g. `jsmith`)
sudo passwd user_name

To use the JupyterHub server:

  1. Note down the IP address of the server VM created by ElastiCluster
  2. In your browser, open https://server.ip/
  3. Accept the self-signed SSL certificate in the browser
  4. Log in using username and password

Note

You must edit the VM security group to allow connections to port 443! (ElastiCluster will not do this automatically.)

The JupyterHub role can be combined with other playbooks (it is advised to add it to the frontend/master node), or can be used to install a stand-alone server. A full example of how to install a JupyterHub stand-alone server can be found at: https://github.com/elasticluster/elasticluster/blob/master/examples/jupyterhub-on-google.conf

OpenCPU

Supported on:

  • Ubuntu 16.04, 18.04
Ansible group Action
opencpu Install OpenCPU server.

This playbook installs the OpenCPU server; OpenCPU “provides a reliable and interoperable HTTP API for data analysis based on R”.

The R language (see below) is installed as a dependency of OpenCPU; you can use any of the node variables described below to control and customize the installation of R.

R language

Supported on:

  • Ubuntu 18.04, 16.04, 14.04
  • Debian 10 (“buster”), 9 (“stretch”), 8 (“jessie”)
  • CentOS 6.x and 7.x
Ansible group Action
r Install the interpreter and a basic libraries for the GNU R language and statistical system.

This playbook installs the R language interpreter (version 3.5) and a few additional libraries. R binaries installed by ElastiCluster come from 3rd-party repositories which (normally) provide more up-to-date releases compared to the OS packages.

The following extra variables can be set to control installation of additional R libraries:

Variable name Default Description
r_libraries [devtools] List of R packages to install
r_cluster_support “yes” if installing R on more then 1 node, “no” otherwise Whether to install Rmpi and other packages for distributing work across a computing cluster

By default, the devtools library is installed so that R packages can be installed directly from their GitHub location. Additionally, R support for MPI and distribution of work across a cluster is available if R support is being deployed to more than 1 host.

Note

Note that the r_libraries variable is a YAML list. In order to customize its value, you must provide a comma-separated list of library names, enclosed in square brackets.

For instance, the following configuration snippet requests that ElastiCluster and Ansible install R libraries devtools and doSnow on the compute nodes of a SLURM cluster:

[setup/slurm+r]
# ...
compute_groups=slurm_worker,r
compute_r_libraries=[devtools,doSnow]

R Studio Server

Supported on:

  • Ubuntu 16.04, 14.04
  • Debian 8 (“jessie”), 9 (“stretch”)
Ansible group Action
rstudio Install R Studio Server (Open-Source Edition)

This playbook installs the R Studio Server web-based IDE for the R language. Any valid user of the system can log in to the R Studio Server web interface and use the IDE features.

To use the R Studio Server server:

  1. Note down the IP address of the server VM created by ElastiCluster

    Note

    R Studio Server is configured to authenticate users with the GNU/Linux /etc/passwd database. So, in order to log in you need to create users first (or set passwords to existing users).

    In order to create a new user, run the following commands at the node’s shell prompt:

    # replace `user_name` with an actual name (e.g. `jsmith`)
sudo adduser user_name

    In order to set the password for an existing user, run the following commands at the node’s shell prompt:

    # replace `user_name` with an actual name (e.g. `jsmith`)
sudo passwd user_name

  2. In your browser, open https://server.ip:8787/

    Note

    You must edit the VM security group to allow connections to port 8787! (ElastiCluster will not do this automatically.)

    Warning

    Connections to port 8787 will not be encrypted by default: any username/password combination will be sent insecurely over the network! Do not use passwords for R Studio that you have used or would use elsewhere!

  3. Log in using username and password

The R Studio Server playbooks automatically installs the R language (see above), so variables that control installation of R apply to R Studio as well.

The R Studio Server role can be combined with other playbooks, or can be used to install a stand-alone server. A full example of how to install a R Studio stand-alone server can be found at: https://github.com/elasticluster/elasticluster/blob/master/examples/rstudio-on-google.conf

SAMBA

Supported on:

  • Ubuntu 14.04 and later
  • Debian 8 (“jessie”), 9 (“stretch”)
  • CentOS 6.x and 7.x
Ansible group Action
samba Install and configure the SAMBA suite for serving local files over the network with the SMB/CIFS protocol

This playbook installs the SAMBA server suite, which implements a server for the SMB/CIFS network filesystem, plus other utilities for integrating Linux/UNIX systems in a Windows environment. Note that ElastiCluster only configures the smbd daemon for serving files as a “standalone SMB server” – no other integration with Windows services is included here.

The following extra variables can be set to control the way the SMB server is set up:

Variable name Default Description
smb_workgroup elasticluster NetBIOS workgroup name
smb_shares (no additional shares) Define additional SMB shares (see example below)

By default, ElastiCluster only configures sharing users’ home directories over SMB/CIFS. Additional shares can be defined by adding a smb_shares variable in the setup/ section. The value of this variable should be a list (comma-separated, enclosed in [ and ]) of share definitions; a share definition is enclosed in { and } and is comprised of comma-separated key:value pair; see example below. The following key:value pair will be acted upon:

name
The SMB share name; the string that clients must use to connect to this share
path
Local path to the root directory being served
readonly
Whether writes are allowed to the share. If no (default), then no writes are allowed by any client.
public
If yes, any user that can connect to the server can read (and also write, depending on the readonly setting above) files in the share. If no (default), only authenticated users can access the share.

For instance, the following ElastiCluster configuration snippet configures two additional shares, one named public which serves files off local path /data/public to any user who can connect, and one named secret which serves files off local path /data/secret to authenticated users:

[setup/samba]
server_groups=samba
server_var_smb_shares=[
  { name: 'public', path: '/data/public',  readonly: yes, public: yes },
  { name: 'secret', path: '/data/secret',  readonly: yes, public: no },
  ]