Redun engine

The Redun adaptor submits tasks to a Redun scheduler. It bridges Redun’s asynchronous execution model with AiiDA provenance so you can analyse runs in both systems simultaneously.

Installation

1. Install the Redun extra:

   pip install node_graph_engine[redun]
   

2. Initialise a Redun project (for example inside your repository) if you do not have one already:

   redun init
   

   Adjust redun.ini to point to the storage backend you intend to use. The default in-memory database works for quick experiments.

3. Load an AiiDA profile in the environment that launches the Redun scheduler.

Example

from aiida import load_profile
from node_graph import task
from node_graph_engine.engines.redun import RedunEngine

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 = RedunEngine(name="redun-quick-start")
outputs = engine.run(graph)
print(outputs)

Use AiiDA commands to inspect the processes and their provenance:

verdi process list -a

Which will show something like:

2230  4s ago     NodeGraph<add_then_multiply>         ⏹ Finished [0]
2231  4s ago     add                                  ⏹ Finished [0]
2233  4s ago     multiply                             ⏹ Finished [0]

Then generate a provenance graph for a workflow:

verdi node graph generate 2230 -f png

Here is the resulting graph: