The StreamFlow framework is a container-native Workflow Management System (WMS) written in Python 3. It has been designed around two main principles:
- Allow the execution of tasks in multi-container environments, in order to support concurrent execution of multiple communicating tasks in a multi-agent ecosystem.
- Relax the requirement of a single shared data space, in order to allow for hybrid workflow executions on top of multi-cloud or hybrid cloud/HPC infrastructures.
The StreamFlow module is available on PyPI, so you can install it using pip.
pip install streamflow
Please note that StreamFlow requires
python >= 3.8. Then you can execute it directly from the CLI
streamflow run /path/to/streamflow.yml
StreamFlow Docker images are available on Docker Hub. In order to run a workflow inside the StreaFlow image
- A StreamFlow project, containing a
streamflow.ymlfile and all the other relevant dependencies (e.g. a CWL description of the workflow steps and a Helm description of the execution environment) need to be mounted as a volume inside the container, for example in the
- Workflow outputs, if any, will be stored in the
/streamflow/resultsfolder. 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/streamflowlocation. 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
- The path of the
streamflow.ymlfile inside the container (e.g.
/streamflow/project/streamflow.yml) must be passed as an argument to the Docker container
The script below gives an example of StreamFlow execution 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 run /streamflow/project/streamflow.yml
It is also possible to execute the StreamFlow container as a
Job in Kubernetes.
In this case, StreamFlow is able to deploy
Helm models directly on the parent cluster through the
ServiceAccount credentials. In order to do that, the
inCluster option must be set to
true for each
involved module on the
models: helm-model: type: helm config: inCluster: true ...
Helm template of a StreamFlow
Job can be found in the
Please note that, 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.
StreamFlow relies on the Common Workflow Language (CWL) standard to design workflow models. CWL conformance badges for StreamFlow are reported below.
Contribute to StreamFlow
As a first step, get StreamFlow from GitHub
git clone email@example.com:alpha-unito/streamflow.git
Then you can install all the requred packages using the
pip install command
cd streamflow pip install .
StreamFlow relies on GitHub Actions for PyPI and Docker Hub distributions. Therefore, in order to publish a
new version of the software, you only have to augment the version number in
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