Efficiently design and manage flexible workflows with AiiDA, featuring an interactive GUI, checkpoints, provenance tracking, and remote execution capabilities.
Here is a detailed comparison between the WorkGraph
with two AiiDA built-in workflow components.
Aspect | WorkFunction | WorkChain | WorkGraph |
---|---|---|---|
Use Case | Short-running jobs | Long-running jobs | Long-running jobs |
Checkpointing | No |
Yes | Yes |
Execution order | Sequential |
Hybrid Sequential-Parallel |
Directed Acyclic Graph |
Non-blocking | No |
Yes | Yes |
Implementation | Easy | Difficult |
Easy |
Dynamic | No |
No |
Yes |
Ready to Use | Yes | Need PYTHONPATH |
Yes |
Subprocesses Handling | No |
Launches & waits | Launches & waits |
Flow Control | All | if , while |
if , while , match |
Termination | Hard exit |
ExitCode | ExitCode |
Data Passing | Direct passing | Context | Link & Context |
Output Recording | Limited support | Out & validates | Out |
Port Exposing | Limited support | Manual & automatic | Manual |
pip install aiida-workgraph
To install the latest version from source, first clone the repository and then install using pip
:
git clone https://github.com/aiidateam/aiida-workgraph
cd aiida-workgraph
pip install -e .
To install the jupyter widget support you need to in addition build the JavaScript packages:
pip install .[widget]
# build widget
cd aiida_workgraph/widget/
npm install
npm run build
# build web frontend
cd ../../aiida_workgraph/web/frontend/
npm install
npm run build
Explore the comprehensive documentation to discover all the features and capabilities of AiiDA Workgraph.
Visit the Workgraph Collections repository to see demonstrations of how to utilize AiiDA Workgraph for different computational codes.
Suppose we want to calculate (x + y) * z
in two steps. First, add x
and y
, then multiply the result with z
.
from aiida.engine import calcfunction
from aiida_workgraph import WorkGraph
# define add calcfunction
@calcfunction
def add(x, y):
return x + y
# define multiply calcfunction
@calcfunction
def multiply(x, y):
return x*y
# Create a workgraph to link the tasks.
wg = WorkGraph("test_add_multiply")
wg.add_task(add, name="add1")
wg.add_task(multiply, name="multiply1")
wg.add_link(wg.tasks["add1"].outputs["result"], wg.tasks["multiply1"].inputs["x"])
Prepare inputs and submit the workflow:
from aiida import load_profile
load_profile()
wg.submit(inputs = {"add1": {"x": 2, "y": 3}, "multiply1": {"y": 4}}, wait=True)
print("Result of multiply1 is", wg.tasks["multiply1"].outputs[0].value)
Start the web app, open a terminal and run:
workgraph web start
Then visit the page http://127.0.0.1:8000/workgraph, you should find a first_workflow
WorkGraph, click the pk and view the WorkGraph.
One can also generate the node graph from the process:
verdi node generate pk
To contribute to this repository, please enable pre-commit so the code in commits are conform to the standards.
pip install -e .[tests,pre-commit]
pre-commit install
See the README.md
See the README.md