pyiron_workflow

Overview

pyiron_workflow is a framework for constructing workflows as computational graphs from simple python functions. Its objective is to make it as easy as possible to create reliable, reusable, and sharable workflows, with a special focus on research workflows for HPC environments.

Nodes are formed from python functions with simple decorators, and the resulting nodes can have their data inputs and outputs connected.

By allowing (but not demanding, in the case of data DAGs) users to specify the execution flow, both cyclic and acyclic graphs are supported.

By scraping type hints from decorated functions, both new data values and new graph connections are (optionally) required to conform to hints, making workflows strongly typed.

Individual node computations can be shipped off to parallel processes for scalability. (This is a beta-feature at time of writing; standard python executors like concurrent.futures.ThreadPoolExecutor and ProcessPoolExecutor work, and the Executor executor from executorlib is supported and tested; executorlib's more powerful flux- and slurm- based executors have not been tested and may fail.)

Once you're happy with a workflow, it can be easily turned it into a macro for use in other workflows. This allows the clean construction of increasingly complex computation graphs by composing simpler graphs.

Nodes (including macros) can be stored in plain text as python code, and imported by future workflows for easy access. This encourages and supports an ecosystem of useful nodes, so you don't need to re-invent the wheel. When these python files are in a properly managed git repository and released in a stable channel (e.g. conda-forge), they fulfill most requirements of the FAIR principles.

Executed or partially-executed graphs can be stored to file, either by explicit call or automatically after running. These can be reloaded (automatically on instantiation, in the case of workflows) and examined/rerun, etc.

Installation

conda install -c conda-forge pyiron_workflow

User introduction

pyiron_workflow offers a single-point-of-entry in the form of the Workflow object, and uses decorators to make it easy to turn regular python functions into "nodes" that can be put in a computation graph.

Decorating your python function as a node means that it's actually now a class, so you'll need to instantiate it before you can call it -- but otherwise it's a lot like a regular python function. You can put regular python code inside it, and it that code will run whenever you run the node.

>>> from pyiron_workflow import Workflow
>>>
>>> @Workflow.wrap.as_function_node
... def HelloWorld(greeting="Hello", subject="World"):
...     hello = f"{greeting} {subject}"
...     return hello
>>>
>>> hello_node = HelloWorld()  # Instantiate a node instance
>>> hello_node(greeting="Salutations")  # Use it just like a function
'Salutations World'

The intent of this node form is to build up a collection of function calls into a directed graph that gives a formal definition of your workflow. Under the hood, the node above has labelled input and output data channels:

>>> print(hello_node.inputs.labels)
['greeting', 'subject']

>>> hello_node.outputs.hello.value
'Salutations World'

Each time it runs, the Function node is taking its input, passing it to the function we decorated, executing that, and then putting the result into the node's output channels. These inputs and outputs can be chained together to form a computational graph. Inputs and outputs aren't actually just the data they hold -- they are data channels -- but you can perform most python operations on them as though they were raw objects. If a node only has a single output, you can reference it directly in place of its single output channel. This dynamically creates a new node to delay the operation and handle it at runtime:

>>> first = HelloWorld("Welcome", "One")
>>> second = HelloWorld("Greetings", "All")
>>> combined = first + " and " + second
>>> print(type(combined))
<class 'pyiron_workflow.nodes.standard.Add'>
>>> combined()
'Welcome One and Greetings All'

Sets of nodes can be collected under the umbrella of a living Workflow object, that can have nodes add to and removed from it. Let's build the above graph as a Workflow, and leverage one of the built-in standard nodes to hold input and fork it to two different downstream nodes:

>>> wf = Workflow("readme")
>>> wf.greeting = Workflow.create.standard.UserInput("Hi")
>>> wf.first = HelloWorld(greeting=wf.greeting)
>>> wf.second = HelloWorld(greeting=wf.greeting)
>>> wf.combined = wf.first + " and " + wf.second
>>> wf()
{'combined__add': 'Hi World and Hi World'}

Here we see that the output comes as a dictionary, with keys according to the node lable ('combined' and the channel name ('add'). Workflows return all unconnected output, and take any unconnected input as input arguments with similar keyword rules. Let's exploit this to easily re-run our workflow with different values:

>>> wf(greeting__user_input="Hey", first__subject="you")
{'combined__add': 'Hey you and Hey World'}

Once we have a workflow we like and think is useful, we may wish to package it as a Macro node. These are a lot like workflow, but "crystallized". Like Function nodes, they have a fixed set of input and output. They also let you have a bit more control over what gets exposed as IO, unlike workflows which (by default) expose all the unconnected bits. Defining a Macro is also a lot like defining a Function -- it can be done by decorating a simple python function. However, where Function nodes execute their decorated function at each run and can hold arbitrary python code, Macro nodes decorate a function that defines the graph they hold, it is executed once at instantiation, the input values are themselves all data channels and not the raw data, and from then on running the node runs that entire graph:

>>> @Workflow.wrap.as_macro_node
... def Combined(wf, greeting="Hey", subject1="You", subject2="World"):
...     wf.first = HelloWorld(greeting=greeting, subject=subject1)
...     wf.second = HelloWorld(greeting=greeting, subject=subject2)
...     wf.combined = wf.first + " and " + wf.second
...     return wf.combined
>>> 
>>> hello_macro = Combined()
>>> hello_macro(subject2="everyone")
{'combined': 'Hey You and Hey everyone'}

Not only does this give us a bit more control with how people interface with the graph (i.e. what IO to expose, what defaults (if any) to use), but Macro nodes are composable -- we can stick them into other macros or workflows as nodes, i.e. we can nest a sub-graph inside our graph. Let's do that, and also give a first example of a node with multiple outputs:

>>> @Workflow.wrap.as_macro_node
... def Composition(self, greeting):
...     self.compose = Combined(greeting=greeting)
...     self.simple = greeting + " there"
...     return self.compose, self.simple
>>>
>>> composed = Composition()
>>> composed(greeting="Hi")
{'compose': 'Hi You and Hi World', 'simple': 'Hi there'}

(Note that we also renamed the first variable to python's canonical self. It doesn't matter what the first variable is called -- but it must be there and represents the macro instance! If it's easier to use python's self, go for it; if you're copying and pasting from a workflow you wrote, wf or whatever your workflow variable was will be easier.)

Although the macro exposes only particular data for IO, you can always dig into the object to see what's happening:

>>> composed.compose.second.outputs.hello.value
'Hi World'

This lets us build increasingly complex workflows by composing simpler blocks. These building blocks are shareable and reusable by storing your macro in a .py file, or even releasing them as a python package. These workflows are formally defined, so unlike a plain python script it's easy to give them non-code representations, e.g. we can .draw our workflows or nodes at a high level:

Or dive in and resolving macro nodes to a specified depth:

To explore other benefits of pyiron_workflow, look at the quickstart.ipynb in the demo notebooks. There we explore

• Making nodes (optionally) strongly-typed
• Saving and loading (perhaps partially) executed workflows
• Parallelizing workflow computation by assigning executors to specific nodes
• Iterating over data with for-loops

For more advanced topics, like cyclic graphs, check the deepdive.ipynb notebook, explore the docstrings, or look at the