Run your workflow

To run a workflow with the StreamFlow CLI, simply use the following command:

streamflow run /path/to/streamflow.yml

Note

For CWL workflows, StreamFlow also supports the cwl-runner interface (more details here).

The --outdir option specifies where StreamFlow must store the workflow results and the execution metadata. Metadata are collected and managed by the StreamFlow Database implementtion (see here). By default, StreamFlow uses the current directory as its output folder.

The --name option allows to specify a workflow name for the current execution. Note that multiple execution can have the same name, meaning that they are multiple instances of the same workflow. If a --name is not explicitly provided, StreamFlow will randomly generate a unique name for the current execution.

The --color option allows to print log preamble with colors related to the logging level, which can be useful for live demos and faster log inspections.

Run on Docker

The command below gives an example of how to execute a StreamFlow workflow in a Docker container:

docker run -d \
--mount type=bind,source="$(pwd)"/my-project,target=/streamflow/project \
--mount type=bind,source="$(pwd)"/results,target=/streamflow/results \
--mount type=bind,source="$(pwd)"/tmp,target=/tmp/streamflow \
alphaunito/streamflow \
streamflow run /streamflow/project/streamflow.yml

Note

A StreamFlow project, containing a streamflow.yml file and all the other relevant dependencies (e.g. a CWL description of the workflow steps and a Helm description of the execution environment) needs to be mounted as a volume inside the container, for example in the /streamflow/project folder.

By default, workflow outputs will be stored in the /streamflow/results folder. Therefore, it is necessary to mount such location as a volume in order to persist the results.

StreamFlow will save all its temporary files inside the /tmp/streamflow location. For debugging purposes, or in order to improve I/O performances in case of huge files, it could be useful to mount also such location as a volume.

By default, the StreamFlow Database stores workflow metadata in the ${HOME}/.streamflow folder. Mounting this floder as a volume preserve these metadata for further inspection (see here).

Warning

All the container-based connectors (i.e., DockerConnector, DockerComposeConnector and SingularityConnector) are not supported from inside a Docker container, as running nested containers is a non-trivial task.

Run on Kubernetes

It is also possible to execute the StreamFlow container as a Job in Kubernetes, with the same characteristics and restrictions discussed for the Docker case. A Helm template of a StreamFlow Job can be found here.

In this case, the StreamFlow HelmConnector is able to deploy Helm charts directly on the parent cluster, relying on ServiceAccount credentials. In order to do that, the inCluster option must be set to true for each involved module on the streamflow.yml file

deployments:
  helm-deployment:
    type: helm
    config:
      inCluster: true
      ...

A Helm template of a StreamFlow Job can be found here.

Warning

In case RBAC is active on the Kubernetes cluster, a proper RoleBinding must be attached to the ServiceAccount object, in order to give StreamFlow the permissions to manage deployments of pods and executions of tasks.