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Local installation

Contents:

Quickstart from source

Install Gen3Workflow

Install required software:

Then use poetry install to install the dependencies. Before that, a virtualenv is recommended. If you don't manage your own, Poetry will create one for you during poetry install, and you must activate it with poetry shell. You may also need to upgrade pip to the latest version.

Create configuration file

Gen3Workflow requires a configuration file to run. We have a command line utility to help you create one based on a default configuration.

The configuration file itself will live outside of this repo (to prevent accidentally checking in sensitive information like database passwords).

To create a new configuration file from the default configuration:

python cfg_help.py create

This file will be placed in one of the default search directories for Gen3Workflow.

To get the exact path where the new configuration file was created, use:

python cfg_help.py get

The file should have detailed information about each of the configuration variables. Remember to fill out the new configuration file!

To use a configuration file in a custom location, you can set the GEN3WORKFLOW_CONFIG_PATH environment variable.

Start the Gen3Workflow service

You will need to run a TES server for Gen3Workflow to talk to. For example, you can start a local Funnel server: https://ohsu-comp-bio.github.io/funnel/#intro.

Update your configuration file:

  • set TES_SERVER_URL to the TES server URL
  • set MOCK_AUTH to true, so that no attempts to interact with Arborist are made.

Start the Gen3Workflow app:

python run.py

Try out the API at http://localhost:8080/_status or http://localhost:8080/docs (you might have to set DOCS_URL_PREFIX to "" in your configuration file for the docs endpoint to work).

Note: Although the Gen3Workflow service can run as a standalone component, a complete end-to-end experience with Funnel and the Funnel plugin requires interaction with the Fence service. While support for this flow is planned for future releases, it is not currently supported out of the box.

Run Nextflow workflows with Gen3Workflow

  • Hit the /storage/setup endpoint to get your working directory
  • Configure Nextflow. Example Nextflow configuration:
plugins {
	id 'nf-ga4gh'
}
process {
	executor = 'tes'
	container = 'quay.io/nextflow/bash'
}
tes {
	endpoint = '<Gen3Workflow URL>/ga4gh/tes'
	oauthToken = "${GEN3_TOKEN}"
}
aws {
	accessKey = "${GEN3_TOKEN}"
	secretKey = 'N/A'
	region = 'us-east-1'
	client {
		s3PathStyleAccess = true
		endpoint = '<Gen3Workflow URL>/s3'
	}
}
workDir = '<your working directory>'

A Gen3 access token is expected by most endpoints to verify the user's access (see Authorization documentation).

The Gen3Workflow URL should be set to http://localhost:8080 in this case; this is where the service runs by default when started with python run.py.

  • Run a workflow:

When setting your token manually:

export GEN3_TOKEN=<your token>
nextflow run hello

Or, with the Gen3 Python SDK configured with an API key:

gen3 run nextflow run hello

AWS access

For full functionality, the Gen3Workflow service requires access to perform a number of operations in AWS. The full policy can be found here: gen3-workflow/templates/crossplane.yaml

Quickstart with Helm

You can now deploy individual services via Helm!

If you are looking to deploy all Gen3 services, that can be done via the Gen3 Helm chart. Instructions for deploying all Gen3 services with Helm can be found here.

To deploy the Gen3Workflow service:

helm repo add gen3 https://helm.gen3.org
helm repo update
helm upgrade --install gen3/gen3workflow

These commands will add the Gen3 helm chart repo and install the Gen3Workflow service to your Kubernetes cluster.

Deploying Gen3Workflow this way will use the defaults that are defined in this values.yaml file

You can learn more about these values by accessing the Gen3Workflow README.md

If you would like to override any of the default values, simply copy the above values.yaml file into a local file and make any changes needed.

To deploy the service independant of other services (for testing purposes), you can set the .postgres.separate value to "true". This will deploy the service with its own instance of Postgres:

  postgres:
    separate: true

You can then supply your new values file with the following command:

helm upgrade --install gen3/gen3workflow -f values.yaml

If you are using Docker Build to create new images for testing, you can deploy them via Helm by replacing the .image.repository value with the name of your local image. You will also want to set the .image.pullPolicy to "never" so kubernetes will look locally for your image. Here is an example:

image:
  repository: <image name from docker image ls>
  pullPolicy: Never
  # Overrides the image tag whose default is the chart appVersion.
  tag: ""

Re-run the following command to update your helm deployment to use the new image:

helm upgrade --install gen3/gen3workflow

You can also store your images in a local registry. Kind and Minikube are popular for their local registries:

Dependencies: