Executors

In the Nextflow framework architecture, the executor is the component that determines the system where a pipeline process is run and supervises its execution.

The executor provides an abstraction between the pipeline processes and the underlying execution system. This allows you to write the pipeline functional logic independently from the actual processing platform.

In other words you can write your pipeline script once and have it running on your computer, a cluster resource manager or the cloud by simply changing the executor definition in the Nextflow configuration file.

AWS Batch

Nextflow supports AWS Batch service which allows submitting jobs in the cloud without having to spin out and manage a cluster of virtual machines. AWS Batch uses Docker containers to run tasks, which makes deploying pipelines much simpler.

The pipeline processes must specify the Docker image to use by defining the container directive, either in the pipeline script or the nextflow.config file.

To enable this executor set the property process.executor = 'awsbatch' in the nextflow.config file.

The pipeline can be launched either in a local computer or a EC2 instance. The latter is suggested for heavy or long running workloads. Moreover a S3 bucket must be used as pipeline work directory.

Resource requests and other job characteristics can be controlled via the following process directives:

• accelerator

• cpus

• memory

• queue

See the AWS Batch page for further configuration details.

Azure Batch

Nextflow supports Azure Batch service which allows submitting jobs in the cloud without having to spin out and manage a cluster of virtual machines. Azure Batch uses Docker containers to run tasks, which makes deploying pipelines much simpler.

The pipeline processes must specify the Docker image to use by defining the container directive, either in the pipeline script or the nextflow.config file.

To enable this executor set the property process.executor = 'azurebatch' in the nextflow.config file.

The pipeline can be launched either in a local computer or a cloud virtual machine. The latter is suggested for heavy or long running workloads. Moreover a Azure Blob storage container must be used as pipeline work directory.

See the Azure Batch page for further configuration details.

Bridge

Bridge is an abstraction layer to ease batch system and resource manager usage in heterogeneous HPC environments.

It is open source software and can be installed on top of existing classical job schedulers such as Slurm or LSF, or other schedulers. Bridge allows to submit jobs, get information on running jobs, stop jobs, get information on the cluster system, etc.

For more details on how to install the Bridge system, see the documentation.

To enable the Bridge executor simply set process.executor = 'bridge' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• memory

• queue

• time

GA4GH TES

Warning

This is an experimental feature and it may change in a future release. It requires Nextflow version 0.31.0 or later.

The Task Execution Schema (TES) project by the GA4GH standardisation initiative is an effort to define a standardized schema and API for describing batch execution tasks in portable manner.

Nextflow includes an experimental support for the TES API providing a tes executor which allows the submission of workflow tasks to a remote execution back-end exposing a TES API endpoint.

To use this feature define the following variables in the workflow launching environment:

export NXF_MODE=ga4gh
export NXF_EXECUTOR=tes
export NXF_EXECUTOR_TES_ENDPOINT='http://back.end.com'

It is important that the endpoint is specified without the trailing slash; otherwise, the resulting URLs will be not normalized and the requests to TES will fail.

Then you will be able to run your workflow over TES using the usual Nextflow command line. Be sure to specify the Docker image to use, i.e.:

nextflow run rnaseq-nf -with-docker alpine

Note

If the variable NXF_EXECUTOR_TES_ENDPOINT is omitted the default endpoint is http://localhost:8000.

Tip

You can use a local Funnel server using the following launch command line:

./funnel server --Server.HTTPPort 8000 --LocalStorage.AllowedDirs $HOME run

(tested with version 0.8.0 on macOS)

Warning

Make sure the TES back-end can access the workflow work directory when data is exchanged using a local or shared file system.

Known Limitations

• Automatic deployment of workflow scripts in the bin folder is not supported.

• Process output directories are not supported. For details see #76.

• Glob patterns in process output declarations are not supported. For details see #77.

Google Cloud Batch

Google Cloud Batch is a managed computing service that allows the execution of containerized workloads in the Google Cloud Platform infrastructure.

Nextflow provides built-in support for the Batch API which allows the seamless deployment of a Nextflow pipeline in the cloud, offloading the process executions as pipelines (it requires Nextflow 22.07.1-edge or later).

The pipeline processes must specify the Docker image to use by defining the container directive, either in the pipeline script or the nextflow.config file. Moreover the pipeline work directory must be located in a Google Storage bucket.

To enable this executor set the property process.executor = 'google-batch' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• accelerator

• container

• containerOptions

• cpus

• disk

• machineType

• memory

• time

• resourceLabels

See the Google Cloud Batch page for further configuration details.

Google Life Sciences

Google Cloud Life Sciences is a managed computing service that allows the execution of containerized workloads in the Google Cloud Platform infrastructure.

Nextflow provides built-in support for the Life Sciences API which allows the seamless deployment of a Nextflow pipeline in the cloud, offloading the process executions as pipelines (it requires Nextflow 20.01.0 or later).

The pipeline processes must specify the Docker image to use by defining the container directive, either in the pipeline script or the nextflow.config file. Moreover the pipeline work directory must be located in a Google Storage bucket.

To enable this executor set the property process.executor = 'google-lifesciences' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• accelerator

• cpus

• disk

• machineType

• memory

• time

See the Google Life Sciences page for further configuration details.

HyperQueue

Warning

This is an incubating feature. It may change in future Nextflow releases.

The hyperqueue executor allows you to run your pipeline script by using the HyperQueue job scheduler.

Nextflow manages each process as a separate job that is submitted to the cluster by using the hq command line tool.

Being so, the pipeline must be launched from a node where the hq command is available, that is, in a common usage scenario, the cluster head node.

To enable the HTCondor executor simply set process.executor = 'hyperqueue' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• accelerator

• clusterOptions

• cpus

• memory

• time

HTCondor

Warning

This is an incubating feature. It may change in future Nextflow releases.

The condor executor allows you to run your pipeline script by using the HTCondor resource manager.

Nextflow manages each process as a separate job that is submitted to the cluster by using the condor_submit command.

Being so, the pipeline must be launched from a node where the condor_submit command is available, that is, in a common usage scenario, the cluster head node.

Note

The HTCondor executor for Nextflow does not support at this time the HTCondor ability to transfer input/output data to the corresponding job computing node. Therefore the data needs to be made accessible to the computing nodes using a shared file system directory from where the Nextflow workflow has to be executed (or specified via the -w option).

To enable the HTCondor executor simply set process.executor = 'condor' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• disk

• memory

• time

Ignite

Danger

This feature has been phased out and is no longer supported as of version 22.01.x.

The ignite executor allows you to run a pipeline on an Apache Ignite cluster.

To enable this executor set process.executor = 'ignite' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• cpus

• disk

• memory

See the Apache Ignite page to learn how to configure Nextflow to deploy and run an Ignite cluster in your infrastructure.

Kubernetes

The k8s executor allows you to run a pipeline on a Kubernetes cluster.

Resource requests and other job characteristics can be controlled via the following process directives:

• accelerator

• cpus

• memory

• pod

• time

See the Kubernetes page to learn how to set up a Kubernetes cluster for running Nextflow pipelines.

Local

The local executor is used by default. It runs the pipeline processes in the computer where Nextflow is launched. The processes are parallelised by spawning multiple threads and by taking advantage of multi-cores architecture provided by the CPU.

In a common usage scenario, the local executor can be useful to develop and test your pipeline script in your computer, switching to a cluster facility when you need to run it on production data.

LSF

The lsf executor allows you to run your pipeline script by using a Platform LSF cluster.

Nextflow manages each process as a separate job that is submitted to the cluster by using the bsub command.

Being so, the pipeline must be launched from a node where the bsub command is available, that is, in a common usage scenario, the cluster head node.

To enable the LSF executor simply set process.executor = 'lsf' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• memory

• queue

• time

Note

LSF supports both per-core and per-job memory limit. Nextflow assumes that LSF works in the per-core memory limits mode, thus it divides the requested memory by the number of requested cpus.

This is not required when LSF is configured to work in per-job memory limit mode. You will need to specified that adding the option perJobMemLimit in Scope executor in the Nextflow configuration file.

See also the Platform LSF documentation.

Moab

The moab executor allows you to run your pipeline script by using the Moab resource manager by Adaptive Computing.

Nextflow manages each process as a separate job that is submitted to the cluster by using the msub command provided by the resource manager.

Being so, the pipeline must be launched from a node where the msub command is available, that is, in a common usage scenario, the compute cluster login node.

To enable the Moab executor simply set process.executor = 'moab' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• memory

• queue

• time

NQSII

The nsqii executor allows you to run your pipeline script by using the NQSII resource manager.

Nextflow manages each process as a separate job that is submitted to the cluster by using the qsub command provided by the scheduler.

Being so, the pipeline must be launched from a node where the qsub command is available, that is, in a common usage scenario, the cluster login node.

To enable the NQSII executor simply set process.executor = 'nqsii' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• memory

• queue

• time

OAR

The oar executor allows you to run your pipeline script by using the OAR resource manager.

Nextflow manages each process as a separate job that is submitted to the cluster by using the oarsub command.

Being so, the pipeline must be launched from a node where the oarsub command is available, that is, in a common usage scenario, the cluster head node.

To enable the OAR executor simply set process.executor = 'oar' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• memory

• queue

• time

Known Limitations

• clusterOptions should be given, if more than one, semicolon separated. It ensures the OAR batch script to be accurately formatted:

  clusterOptions = '-t besteffort;--project myproject'
  

PBS/Torque

The pbs executor allows you to run your pipeline script by using a resource manager belonging to the PBS/Torque family of batch schedulers.

Nextflow manages each process as a separate job that is submitted to the cluster by using the qsub command provided by the scheduler.

Being so, the pipeline must be launched from a node where the qsub command is available, that is, in a common usage scenario, the cluster login node.

To enable the PBS executor simply set process.executor = 'pbs' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• memory

• queue

• time

PBS Pro

The pbspro executor allows you to run your pipeline script by using the PBS Pro resource manager.

Nextflow manages each process as a separate job that is submitted to the cluster by using the qsub command provided by the scheduler.

Being so, the pipeline must be launched from a node where the qsub command is available, that is, in a common usage scenario, the cluster login node.

To enable the PBS Pro executor simply set process.executor = 'pbspro' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• memory

• queue

• time

SGE

The sge executor allows you to run your pipeline script by using a Sun Grid Engine cluster or a compatible platform (Open Grid Engine, Univa Grid Engine, etc).

Nextflow manages each process as a separate grid job that is submitted to the cluster by using the qsub command.

Being so, the pipeline must be launched from a node where the qsub command is available, that is, in a common usage scenario, the cluster head node.

To enable the SGE executor simply set process.executor = 'sge' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• memory

• penv

• queue

• time

SLURM

The slurm executor allows you to run your pipeline script by using the SLURM resource manager.

Nextflow manages each process as a separate job that is submitted to the cluster by using the sbatch command.

Being so, the pipeline must be launched from a node where the sbatch command is available, that is, in a common usage scenario, the cluster head node.

To enable the SLURM executor simply set process.executor = 'slurm' in the nextflow.config file.

Resource requests and other job characteristics can be controlled via the following process directives:

• clusterOptions

• cpus

• memory

• queue

• time

Note

SLURM partitions can be considered jobs queues. Nextflow allows you to set partitions by using the above queue directive.

Tip

Nextflow does not provide a direct support for SLURM multi-clusters feature. If you need to submit workflow executions to a cluster that is not the current one, specify it setting the SLURM_CLUSTERS variable in the launching environment.