Jobflow engine

The Jobflow adaptor maps tasks to Jobflow jobs, letting you integrate with materials-science pipelines and store rich provenance alongside Jobflow execution metadata.

Installation

1. Install the Jobflow extra:

   pip install node_graph_engine[jobflow]
   

2. (Optional) Configure a Jobflow JobStore (for example a MongoStore) if you want persistent storage beyond the default in-memory execution used by jobflow.run_locally().

3. Load an AiiDA profile prior to launching the engine.

Example

from aiida import load_profile
from node_graph import task
from node_graph_engine.engines.jobflow import JobflowEngine

load_profile()

@task()
def add(x, y):
    return x + y

@task()
def multiply(x, y):
    return x * y

@task.graph()
def add_then_multiply(x, y, z):
    the_sum = add(x=x, y=y).result
    return multiply(x=the_sum, y=z).result

graph = add_then_multiply.build(x=1, y=2, z=3)

engine = JobflowEngine(name="jobflow-quick-start")
outputs = engine.run(graph)
print(outputs)

Jobflow executes the jobs locally by default.

Use AiiDA commands to inspect the processes and their provenance:

verdi process list -a

Which will show something like:

2222  4s ago     NodeGraph<add_then_multiply>         ⏹ Finished [0]
2223  4s ago     add                                  ⏹ Finished [0]
2225  4s ago     multiply                             ⏹ Finished [0]

Then generate a provenance graph for a workflow:

verdi node graph generate 2222 -f png

Here is the resulting graph: