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| Screenshot of a Dockerflow workflow for DNA sequence analysis. |
In scientific computing, it’s really common to submit jobs to a local high-performance computing (HPC) cluster. There are tools to do that in the cloud, like Elasticluster and Starcluster. They replicate the local way of doing things, which means they require a bunch of infrastructure setup and management that the university IT department would otherwise do. Even after you’re set up, you still have to ssh into the cluster to do anything. And then there are a million different choices for workflow managers, each unsatisfactory in its own special way.
By day, I’m a product manager. I hadn’t done any serious coding in a few years. But I figured it shouldn’t be that hard to create a proof-of-concept, just to show that the Apache Beam API that Dataflow implements can be used for running scientific workflows. Now, Dataflow was created for a different purpose, namely, to support scalable data-parallel processing, like transforming giant data sets, or computing summary statistics, or indexing web pages. To use Dataflow for scientific workflows would require wrapping up shell steps that launch VMs, run some code, and shuttle data back and forth from an object store. It should be easy, right?
It wasn’t so bad. Over the weekend, I downloaded the Dataflow SDK, ran the wordcount examples, and started modifying. I had a “Hello, world” proof-of-concept in a day.
To really run scientific workflows would require more, of course. Varying VM shapes, a way to pass parameters from one step to the next, graph definition, scattering and gathering, retries. So I shifted into prototyping mode.
I created a new GitHub project called Dockerflow. With Dockerflow, workflows can be defined in YAML files. They can also be written in pretty compact Java code. You can run a batch of workflows at once by providing a CSV file with one row per workflow to define the parameters.
Dataflow and Docker complement each other nicely:
- Dataflow provides a fully managed service with a nice monitoring interface, retries, graph optimization and other niceties.
- Docker provides portability of the tools themselves, and there's a large library of packaged tools already available as Docker images.
While Dockerflow supports a simple YAML workflow definition, a similar approach could be taken to implement a runner for one of the open standards like CWL or WDL.
defn: name: HelloWorkflow steps: - defn: name: Hello inputParameters: name: message defaultValue: Hello, World! docker: imageName: ubuntu cmd: echo $message
And here’s the same example written in Java:
public class HelloWorkflow implements WorkflowDefn {
@Override
public Workflow createWorkflow(String[] args) throws IOException {
Task hello =
TaskBuilder.named("Hello").input("message", “Hello, World!”).docker(“ubuntu”).script("echo $message").build();
return TaskBuilder.named("HelloWorkflow").steps(hello).args(args).build();
}
}
Dockerflow is just a prototype at this stage, though it can run real workflows and includes many nice features, like dry runs, resuming failed runs from mid-workflow, and, of course, the nice UI. It uses Cloud Dataflow in a way that was never intended -- to run scientific batch workflows rather than large-scale data-parallel workloads. I wish I’d written it in Python rather than Java. The Dataflow Python SDK wasn’t quite as mature when I started.
Which is all to say, it’s been a great 20% project, and the future really depends on whether it solves a problem people have, and if others are interested in improving on it. We welcome your contributions and comments! How do you run and monitor scientific workflows today?
By Jonathan Bingham, Google Genomics and Verily Life Sciences
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