Welcome to Pydra: A simple dataflow engine with scalable semantics’s documentation!
Pydra is a new lightweight dataflow engine written in Python. Pydra is developed as an open-source project in the neuroimaging community, but it is designed as a general-purpose dataflow engine to support any scientific domain.
Scientific workflows often require sophisticated analyses that encompass a large collection of algorithms. The algorithms, that were originally not necessarily designed to work together, and were written by different authors. Some may be written in Python, while others might require calling external programs. It is a common practice to create semi-manual workflows that require the scientists to handle the files and interact with partial results from algorithms and external tools. This approach is conceptually simple and easy to implement, but the resulting workflow is often time consuming, error-prone and difficult to share with others. Consistency, reproducibility and scalability demand scientific workflows to be organized into fully automated pipelines. This was the motivation behind Pydra - a new dataflow engine written in Python.
The Pydra package is a part of the second generation of the Nipype ecosystem — an open-source framework that provides a uniform interface to existing neuroimaging software and facilitates interaction between different software components. The Nipype project was born in the neuroimaging community, and has been helping scientists build workflows for a decade, providing a uniform interface to such neuroimaging packages as FSL, ANTs, AFNI, FreeSurfer and SPM. This flexibility has made it an ideal basis for popular preprocessing tools, such as fMRIPrep and C-PAC. The second generation of Nipype ecosystem is meant to provide additional flexibility and is being developed with reproducibility, ease of use, and scalability in mind. Pydra itself is a standalone project and is designed as a general-purpose dataflow engine to support any scientific domain.
The goal of Pydra is to provide a lightweight dataflow engine for computational graph construction, manipulation, and distributed execution, as well as ensuring reproducibility of scientific pipelines. In Pydra, a dataflow is represented as a directed acyclic graph, where each node represents a Python function, execution of an external tool, or another reusable dataflow. The combination of several key features makes Pydra a customizable and powerful dataflow engine:
Composable dataflows: Any node of a dataflow graph can be another dataflow, allowing for nested dataflows of arbitrary depths and encouraging creating reusable dataflows.
Flexible semantics for creating nested loops over input sets: Any Task or dataflow can be run over input parameter sets and the outputs can be recombined (similar concept to Map-Reduce model, but Pydra extends this to graphs with nested dataflows).
A content-addressable global cache: Hash values are computed for each graph and each Task. This supports reusing of previously computed and stored dataflows and Tasks.
Support for Python functions and external (shell) commands: Pydra can decorate and use existing functions in Python libraries alongside external command line tools, allowing easy integration of existing code and software.
Native container execution support: Any dataflow or Task can be executed in an associated container (via Docker or Singularity) enabling greater consistency for reproducibility.
Auditing and provenance tracking: Pydra provides a simple JSON-LD-based message passing mechanism to capture the dataflow execution activities as a provenance graph. These messages track inputs and outputs of each task in a dataflow, and the resources consumed by the task.
- User Guide
- Release Notes
- Library API (application programmer interface)