Google Cloud



The support for Google Cloud requires Nextflow version 20.01.0. To install it define the following variables in your system environment:

export NXF_VER=20.01.0
export NXF_MODE=google


Credentials for submitting requests to the Google LifeSciences API are picked up from your environment using Application Default Credentials. Application Default Credentials are designed to use the credentials most natural to the environment in which a tool runs.

The most common case will be to pick up your end-user Google credentials from your workstation. You can create these by running the command:

gcloud auth application-default login

and running through the authentication flow. This will write a credential file to your gcloud configuration directory that will be used for any tool you run on your workstation that picks up default credentials.

The next most common case would be when running on a Compute Engine VM. In this case, Application Default Credentials will pick up the Compute Engine Service Account credentials for that VM.

See the Application Default Credentials documentation for how to enable other use cases.

Finally, the GOOGLE_APPLICATION_CREDENTIALS environment variable can be used to specify location of the Google credentials file.

If you don’t have it, the credentials file can be download from the Google Cloud Console following these steps:

  • Open the Google Cloud Console

  • Go to APIs & Services → Credentials

  • Click on the Create credentials (blue) drop-down and choose Service account key, in the following page

  • Select an existing Service account or create a new one if needed

  • Select JSON as Key type

  • Click the Create button and download the JSON file giving a name of your choice e.g. creds.json.

Then, define the following variable replacing the path in the example with the one of your credentials file just downloaded:

export GOOGLE_APPLICATION_CREDENTIALS=/path/your/file/creds.json

Cloud Life Sciences

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 Cloud Life Sciences API which allows the seamless deployment of a Nextflow pipeline in the cloud, offloading the process executions through the Google Cloud service.


This features requires Nextflow 20.01.0-edge or later.


This API works well for coarse-grained workloads i.e. long running jobs. It’s not suggested the use this feature for pipelines spawning many short lived tasks.


Make sure to have defined in your environment the GOOGLE_APPLICATION_CREDENTIALS variable. See the section Requirements for details.


Make sure to have enabled Cloud Life Sciences API to use this feature. To learn how to enable it follow this link.

Create a nextflow.config file in the project root directory. The config must specify the following parameters:

  • Google Life Sciences as Nextflow executor i.e. process.executor = 'google-lifesciences'.

  • The Docker container images to be used to run pipeline tasks e.g. process.container = 'biocontainers/salmon:0.8.2--1'.

  • The Google Cloud project ID to run in e.g. google.project = 'rare-lattice-222412'.

  • The Google Cloud region or zone. This is where the Compute Engine VMs will be started. You need to specify either one, not both. Multiple regions or zones can be specified by separating them with a comma e.g. = 'us-central1-f,us-central-1-b'.


process {
    executor = 'google-lifesciences'
    container = 'your/container:latest'

google {
    project = 'your-project-id'
    zone = 'europe-west1-b'


Make sure to specify in the above setting the project ID not the project name.


A container image must be specified to deploy the process execution. You can use a different Docker image for each process using one or more Process selectors.

The following configuration options are available:




The Google Project Id to use for the pipeline execution.


The Google region where the computation is executed in Compute Engine VMs. Multiple regions can be provided separating them by a comma. Do not specify if a zone is provided. See available Compute Engine regions and zones

The Google zone where the computation is executed in Compute Engine VMs. Multiple zones can be provided separating them by a comma. Do not specify if a region is provided. See available Compute Engine regions and zones


The Google location where the job executions are deployed to Cloud Life Sciences API. See available Cloud Life Sciences API locations (default: the same as the region or the zone specified).


When true uses the configured Google project id as the billing project for storage access. This is required when accessing data from requester pays enabled buckets. See Requester Pays on Google Cloud Storage documentation (default: false)


Set the minimum CPU Platform e.g. ‘Intel Skylake’. See Specifying a minimum CPU Platform for VM instances (default: none).


Set the size of the virtual machine boot disk e.g 50.GB (default: none).


The container image run to copy input and output files. It must include the gsutil tool (default: google/cloud-sdk:alpine).


When true copies the /google debug directory in that task bucket directory (default: false)


When true enables the usage of preemptible virtual machines or false otherwise (default: true)


When true the VM will NOT be provided with a public IP address, and only contain an internal IP. If this option is enabled, the associated job can only load docker images from Google Container Registry, and the job executable cannot use external services other than Google APIs (default: false). Requires version 20.03.0-edge or later.


When true runs SSH daemon in the VM carrying out the job to which it’s possible to connect for debugging purposes (default: false).


The container image used to run the SSH daemon (default:

Process definition

Processes can be defined as usual and by default the cpus and memory directives are used to instantiate a custom machine type with the specified compute resources. If memory is not specified, 1GB of memory is allocated per cpu. A persistent disk will be created with size corresponding to the disk directive. If disk is not specified, the instance default is chosen to ensure reasonable I/O performance.

The process machineType directive may optionally be used to specify a predefined Google Compute Platform machine type If specified, this value overrides the cpus and memory directives. If the cpus and memory directives are used, the values must comply with the allowed custom machine type specifications . Extended memory is not directly supported, however high memory or cpu predefined instances may be utilized using the machineType directive


process custom_resources_task {
    cpus 8
    memory '40 GB'
    disk '200 GB'

    <Your script here>

process predefined_resources_task {
    machineType 'n1-highmem-8'

    <Your script here>


This feature requires Nextflow 19.07.0 or later.

Pipeline execution

The pipeline can be launched either in a local computer or a cloud instance. Pipeline input data can be stored either locally or in a Google Storage bucket.

The pipeline execution must specify a Google Storage bucket where the workflow’s intermediate results are stored using the -work-dir command line options. For example:

nextflow run <script or project name> -work-dir gs://my-bucket/some/path


Any input data not stored in a Google Storage bucket will automatically be transferred to the pipeline work bucket. Use this feature with caution being careful to avoid unnecessary data transfers.

Preemptible instances

Preemptible instances are supported adding the following setting in the Nextflow config file:

google {
    lifeSciences.preemptible = true

Since this type of virtual machines can be retired by the provider before the job completion, it is advisable to add the following retry strategy to your config file to instruct Nextflow to automatically re-execute a job if the virtual machine was terminated preemptively:

process {
  errorStrategy = { task.exitStatus==14 ? 'retry' : 'terminate' }
  maxRetries = 5

Hybrid execution

Nextflow allows the use of multiple executors in the same workflow application. This feature enables the deployment of hybrid workloads in which some jobs are executed in the local computer or local computing cluster and some other jobs are offloaded to Google Pipelines service.

To enable this feature use one or more Process selectors in your Nextflow configuration file to apply the Google Pipelines executor only to a subset of processes in your workflow. For example:

process {
    withLabel: bigTask {
        executor = 'google-lifesciences'
        container = 'my/image:tag'

google {
    project = 'your-project-id'
    zone = 'europe-west1-b'

Then deploy the workflow execution using the -bucket-dir to specify a Google Storage path for the jobs computed by the Google Pipeline service and, optionally, the -work-dir to specify the local storage for the jobs computed locally:

nextflow run <script or project name> -bucket-dir gs://my-bucket/some/path


The Google Storage path needs to contain at least sub-directory. Don’t use only the bucket name e.g. gs://my-bucket.


  • Currently it’s not possible to specify a disk type different from the default one assigned by the service depending the chosen instance type.


  • Make sure to have enabled Compute Engine API, Life Sciences API and Cloud Storage Service in the APIs & Services Dashboard page.

  • Make sure to have enough compute resources to run your pipeline in your project Quotas (i.e. Compute Engine CPUs, Compute Engine Persistent Disk, Compute Engine In-use IP addresses, etc).

  • Make sure your security credentials allows you to access any Google Storage bucket where input data and temporary files are stored.

  • Check the directory google/ created in the task work directory (in the bucket storage) created when on job failure and containing useful information of the job execution. The creation can be enabled as default setting the option google.lifeSciences.debug = true in the Nextflow config file

  • Enable the optional SSH daemon in the job VM using the option google.lifeSciences.sshDaemon = true

  • Make sure you are choosing a location where Cloud Life Sciences API is available, and a region or zone where Compute Engine is available.