Combining Recipes

Here, we will show how to use combine quacc jobs and workflows into your own custom workflows that can be run with your chosen workflow engine.

Running a User-Constructed Serial Workflow

We will now try running a simple workflow where we relax a bulk Cu structure using EMT and take the output of that calculation as the input to a follow-up static calculation with EMT.

graph LR
  A[Input] --> B(Relax) --> C(Static) --> D[Output];

CovalentDaskParslPrefectRedunJobflow

Important

If you haven't done so yet, make sure you update the quacc WORKFLOW_ENGINE configuration variable and start the Covalent server:

quacc set WORKFLOW_ENGINE covalent
covalent start

import covalent as ct
from ase.build import bulk
from quacc import flow
from quacc.recipes.emt.core import relax_job, static_job


# Define the workflow
@flow  # (1)!
def workflow(atoms):
    # Define Job 1
    result1 = relax_job(atoms)

    # Define Job 2, which takes the output of Job 1 as input
    result2 = static_job(result1["atoms"])

    return result2


# Make an Atoms object of a bulk Cu structure
atoms = bulk("Cu")

# Dispatch the workflow to the Covalent server
# with the bulk Cu Atoms object as the input
dispatch_id = ct.dispatch(workflow)(atoms)

# Fetch the result from the server
result = ct.get_result(dispatch_id, wait=True)
print(result)

1. Because the workflow was defined with a @flow decorator, it will be sent to the Covalent server and a dispatch ID will be returned.

Modifying the Decorator of a Pre-Made Job

If you want to modify the decorator of a pre-made job, such as to modify the executor, you can use the quacc.wflow_tools.customizers.redecorate function:

from quacc import redecorate

relax_job_ = redecorate(relax_job, job(executor="local"))  # (1)!

1. This is the same as simply re-decorating it directly and using this new function instead:

   from quacc import job
   
   
   @job(executor="local")
   def relax_job_(*args, **kwargs):
       return relax_job(*args, **kwargs)
   

Important

If you haven't done so yet, make sure you update the quacc WORKFLOW_ENGINE configuration variable and load the default Dask cluster:

terminal

quacc set WORKFLOW_ENGINE dask

python

from dask.distributed import Client

client = Client()

from ase.build import bulk
from quacc.recipes.emt.core import relax_job, static_job


# Define the workflow
def workflow(atoms):
    # Define Job 1
    delayed1 = relax_job(atoms)

    # Define Job 2, which takes the output of Job 1 as input
    delayed2 = static_job(delayed1["atoms"])

    return delayed2


# Make an Atoms object of a bulk Cu structure
atoms = bulk("Cu")

# Dispatch the workflow
delayed = workflow(atoms)

# Fetch the result
result = client.compute(delayed).result()
print(result)

Modifying the Decorator of a Pre-Made Job

If you want to modify the decorator of a pre-made job for any reason, you can use the quacc.wflow_tools.customizers.redecorate function:

from quacc import redecorate

relax_job_ = redecorate(relax_job, job(name="my_custom_name"))  # (1)!

1. This is the same as simply re-decorating it directly and using this new function instead:

   from quacc import job
   
   
   @job(name="my_custom_name")
   def relax_job_(*args, **kwargs):
       return relax_job(*args, **kwargs)
   

Important

If you haven't done so yet, make sure you update the quacc WORKFLOW_ENGINE configuration variable and load the default Parsl configuration:

terminal

quacc set WORKFLOW_ENGINE parsl

python

import parsl

parsl.load()

from ase.build import bulk
from quacc.recipes.emt.core import relax_job, static_job


# Define the workflow
def workflow(atoms):
    # Define Job 1
    future1 = relax_job(atoms)

    # Define Job 2, which takes the output of Job 1 as input
    future2 = static_job(future1["atoms"])  # (1)!

    return future2


# Make an Atoms object of a bulk Cu structure
atoms = bulk("Cu")

# Dispatch the workflow
future = workflow(atoms)

# Fetch the result
result = future.result()
print(result)

1. Parsl PythonApp objects will implicitly know to call .result() on any AppFuture it receives, and it is good to rely on this fact to avoid unnecessary blocking.

Modifying the Decorator of a Pre-Made Job

If you want to modify the decorator of a pre-made job, such as to modify the allowed executors of a given function, you can use the quacc.wflow_tools.customizers.redecorate function:

from quacc import redecorate

relax_job_ = redecorate(relax_job, job(executors=["all"]))  # (1)!

1. This is the same as simply re-decorating it directly and using this new function instead:

   from quacc import job
   
   
   @job(executors=["all"])
   def relax_job_(*args, **kwargs):
       return relax_job(*args, **kwargs)
   

Important

If you haven't done so yet, make sure you update the quacc WORKFLOW_ENGINE configuration variable:

terminal

quacc set WORKFLOW_ENGINE prefect

from ase.build import bulk
from quacc import flow
from quacc.recipes.emt.core import relax_job, static_job


# Define the workflow
@flow
def workflow(atoms):
    # Call Task 1
    future1 = relax_job(atoms)

    # Call Task 2, which takes the output of Task 1 as input
    return static_job(future1["atoms"])  # (1)!


# Make an Atoms object of a bulk Cu structure
atoms = bulk("Cu")

# Run the workflow with Prefect tracking
result = workflow(atoms)
print(result)

1. Prefect Task objects will implicitly know to call .result() on any PrefectFuture it receives, and it is good to rely on this fact to avoid unnecessary blocking.

Modifying the Decorator of a Pre-Made Job

If you want to modify the decorator of a pre-made job, such as to modify the number of retries, you can use the quacc.wflow_tools.customizers.redecorate function:

from quacc import redecorate

relax_job_ = redecorate(relax_job, job(retries=2))  # (1)!

1. This is the same as simply re-decorating it directly and using this new function instead:

   from quacc import job
   
   
   @job(retries=2)
   def relax_job_(*args, **kwargs):
       return relax_job(*args, **kwargs)
   

Important

If you haven't done so yet, make sure you update the quacc WORKFLOW_ENGINE configuration variable:

quacc set WORKFLOW_ENGINE redun

from ase.build import bulk
from redun import Scheduler
from quacc import flow
from quacc.recipes.emt.core import relax_job, static_job

# Instantiate the scheduler
scheduler = Scheduler()


# Define the workflow
@flow  # (1)!
def workflow(atoms):
    # Define Job 1
    result1 = relax_job(atoms)

    # Define Job 2, which takes the output of Job 1 as input
    result2 = static_job(result1["atoms"])

    return result2


# Make an Atoms object of a bulk Cu structure
atoms = bulk("Cu")

# Dispatch the workflow
result = scheduler.run(workflow(atoms))
print(result)

1. The @flow decorator defines the workflow that will be executed. It is the same as the @task decorator in Redun.

Modifying the Decorator of a Pre-Made Job

If you want to modify the decorator of a pre-made job for any reason, you can use the quacc.wflow_tools.customizers.redecorate function:

from quacc import redecorate

relax_job_ = redecorate(relax_job, job(name="my_custom_name"))  # (1)!

1. This is the same as simply re-decorating it directly and using this new function instead:

   from quacc import job
   
   
   @job(name="my_custom_name")
   def relax_job_(*args, **kwargs):
       return relax_job(*args, **kwargs)
   

Important

If you haven't done so yet, make sure you update the quacc WORKFLOW_ENGINE configuration variable:

quacc set WORKFLOW_ENGINE jobflow

import jobflow as jf
from ase.build import bulk
from quacc.recipes.emt.core import relax_job, static_job

# Make an Atoms object of a bulk Cu structure
atoms = bulk("Cu")

# Define Job 1
job1 = relax_job(atoms)

# Define Job 2, which takes the output of Job 1 as input
job2 = static_job(job1.output["atoms"])  # (1)!

# Define the workflow
workflow = jf.Flow([job1, job2])  # (2)!

# Run the workflow locally
responses = jf.run_locally(workflow)

# Get the result
result = responses[job2.uuid][1].output
print(result)

1. In Jobflow, each Job is only a reference and so the .output must be explicitly passed between jobs.

2. We must stitch the individual Job objects together into a jf.Flow, which can be easily achieved by passing them as a list to the jf.Flow() constructor.

Running a User-Constructed Parallel Workflow

Now we will define a workflow where we will carry out two EMT structure relaxations, but the two jobs are not dependent on one another. In this example, the workflow manager will know that it can run the two jobs separately, and even if Job 1 were to fail, Job 2 would still progress.

graph LR
  A[Input] --> B(Relax) --> D[Output]
  A[Input] --> C(Relax) --> D[Output];

CovalentDaskParslPrefectRedunJobflow

from ase.build import bulk, molecule
from quacc import flow
from quacc.recipes.emt.core import relax_job


# Define workflow
@flow
def workflow(atoms1, atoms2):
    # Define two independent relaxation jobs
    result1 = relax_job(atoms1)
    result2 = relax_job(atoms2)

    return {"result1": result1, "result2": result2}


# Define two Atoms objects
atoms1 = bulk("Cu")
atoms2 = molecule("N2")

# Dispatch the workflow to the Covalent server
dispatch_id = ct.dispatch(workflow)(atoms1, atoms2)

# Fetch the results from the server
result = ct.get_result(dispatch_id, wait=True)
print(result)

from ase.build import bulk, molecule
from quacc.recipes.emt.core import relax_job


# Define workflow
def workflow(atoms1, atoms2):
    # Define two independent relaxation jobs
    result1 = relax_job(atoms1)
    result2 = relax_job(atoms2)

    return [result1, result2]


# Define two Atoms objects
atoms1 = bulk("Cu")
atoms2 = molecule("N2")

# Define two independent relaxation jobs
delayed = workflow(atoms1, atoms2)

# Fetch the results
results = client.gather(client.compute(delayed))
print(results)

from ase.build import bulk, molecule
from quacc.recipes.emt.core import relax_job


# Define workflow
def workflow(atoms1, atoms2):
    # Define two independent relaxation jobs
    result1 = relax_job(atoms1)
    result2 = relax_job(atoms2)

    return {"result1": result1, "result2": result2}


# Define two Atoms objects
atoms1 = bulk("Cu")
atoms2 = molecule("N2")

# Define two independent relaxation jobs
futures = workflow(atoms1, atoms2)

# Fetch the results
result1 = futures["result1"].result()
result2 = futures["result2"].result()
print(result1, result2)

from ase.build import bulk, molecule
from quacc import flow
from quacc.recipes.emt.core import relax_job


# Define workflow
@flow
def workflow(atoms1, atoms2):
    # Define two independent relaxation jobs
    result1 = relax_job(atoms1)
    result2 = relax_job(atoms2)

    return {"result1": result1, "result2": result2}


# Define two Atoms objects
atoms1 = bulk("Cu")
atoms2 = molecule("N2")

# Dispatch the workflow
results = workflow(atoms1, atoms2)

# Fetch the results
result1 = results["result1"]
result2 = results["result2"]
print(result1, result2)

from ase.build import bulk, molecule
from redun import Scheduler
from quacc import flow
from quacc.recipes.emt.core import relax_job

# Instantiate the scheduler
scheduler = Scheduler()


# Define workflow
@flow
def workflow(atoms1, atoms2):
    # Define two independent relaxation jobs
    result1 = relax_job(atoms1)
    result2 = relax_job(atoms2)

    return {"result1": result1, "result2": result2}


# Define two Atoms objects
atoms1 = bulk("Cu")
atoms2 = molecule("N2")

# Dispatch the workflow
result = scheduler.run(workflow(atoms1, atoms2))
print(result)

import jobflow as jf
from ase.build import bulk, molecule
from quacc.recipes.emt.core import relax_job

# Define two Atoms objects
atoms1 = bulk("Cu")
atoms2 = molecule("N2")

# Define two independent relaxation jobs
job1 = relax_job(atoms1)
job2 = relax_job(atoms2)

# Define the workflow
workflow = jf.Flow([job1, job2])

# Run the workflow locally
responses = jf.run_locally(workflow)

# Get the result
result = responses[job2.uuid][1].output
print(result)

Running a User-Constructed Dynamic Workflow

For this example, let's consider a toy scenario where we wish to relax a bulk Cu structure, carve all possible slabs, and then run a new relaxation calculation on each slab (with no static calculation at the end).

graph LR
  A[Input] --> B(Relax) --> C(Make Slabs)
  C(Make Slabs) --> D(Slab Relax) --> H[Output]
  C(Make Slabs) --> E(Slab Relax) --> H[Output]
  C(Make Slabs) --> F(Slab Relax) --> H[Output]
  C(Make Slabs) --> G(Slab Relax) --> H[Output];

CovalentDaskParslPrefectRedunJobflow

import covalent as ct
from ase.build import bulk
from quacc import flow
from quacc.recipes.emt.core import relax_job
from quacc.recipes.emt.slabs import bulk_to_slabs_flow


# Define the workflow
@flow
def workflow(atoms):
    relaxed_bulk = relax_job(atoms)
    relaxed_slabs = bulk_to_slabs_flow(relaxed_bulk["atoms"], run_static=False)

    return relaxed_slabs


# Define the Atoms object
atoms = bulk("Cu")

# Dispatch the workflow and retrieve result
dispatch_id = ct.dispatch(workflow)(atoms)
result = ct.get_result(dispatch_id, wait=True)
print(result)

Selectively Modifying Job Decorators in a Pre-Made Flow

If you want to modify the decorators of select jobs in a pre-made workflow, such as to modify the executor of a given function, you can use the job_decorators keyword argument:

ct.dispatch(bulk_to_slabs_flow)(
    atoms, job_decorators={"static_job": job(executor="local")}
)

As a shorthand, all of the decorators can be modified at once using the "all" key:

ct.dispatch(bulk_to_slabs_flow)(
    atoms, job_decorators={"all": job(executor="local")}
)  # (1)!

1. Alternatively, you can simply modify the @flow decorator itself:

   from quacc import flow
   
   
   @flow(executor="local")
   def bulk_to_slabs_flow_(*args, **kwargs):
       return bulk_to_slabs_flow(*args, **kwargs)
   
   
   ct.dispatch(bulk_to_slabs_flow_)(atoms)
   

from ase.build import bulk
from quacc.recipes.emt.core import relax_job
from quacc.recipes.emt.slabs import bulk_to_slabs_flow


# Define the workflow
def workflow(atoms):
    relaxed_bulk = relax_job(atoms)
    relaxed_slabs = bulk_to_slabs_flow(relaxed_bulk["atoms"], run_static=False)

    return relaxed_slabs


# Define the Atoms object
atoms = bulk("Cu")

# Dispatch the workflow
delayed = workflow(atoms)

# Fetch the results
result = client.compute(delayed).result()
print(result)

Selectively Modifying Job Decorators in a Pre-Made Flow

If you want to modify the decorators of select jobs in a pre-made workflow, such as to modify the allowed executors of a given function, you can use the job_decorators keyword argument:

bulk_to_slabs_flow(atoms, job_decorators={"static_job": job(name="my_custom_name")})

As a shorthand, all of the decorators can be modified at once using the "all" key:

bulk_to_slabs_flow(atoms, job_decorators={"all": job(name="my_custom_name")})

from ase.build import bulk
from quacc.recipes.emt.core import relax_job
from quacc.recipes.emt.slabs import bulk_to_slabs_flow


# Define the workflow
def workflow(atoms):
    relaxed_bulk = relax_job(atoms)
    relaxed_slabs = bulk_to_slabs_flow(relaxed_bulk["atoms"], run_static=False)

    return relaxed_slabs


# Define the Atoms object
atoms = bulk("Cu")

# Dispatch the workflow
future = workflow(atoms)

# Fetch the results
result = future.result()
print(result)

Selectively Modifying Job Decorators in a Pre-Made Flow

If you want to modify the decorators of select jobs in a pre-made workflow, such as to modify the allowed executors of a given function, you can use the job_decorators keyword argument:

bulk_to_slabs_flow(
    atoms, job_decorators={"static_job": job(executors=["MyFavoriteExecutor"])}
)

As a shorthand, all of the decorators can be modified at once using the "all" key:

bulk_to_slabs_flow(atoms, job_decorators={"all": job(executors=["MyFavoriteExecutor"])})

from ase.build import bulk
from quacc import flow
from quacc.recipes.emt.core import relax_job
from quacc.recipes.emt.slabs import bulk_to_slabs_flow


# Define the workflow
@flow
def workflow(atoms):
    relaxed_bulk = relax_job(atoms)
    relaxed_slabs = bulk_to_slabs_flow(relaxed_bulk["atoms"], run_static=False)

    return relaxed_slabs


# Define the Atoms object
atoms = bulk("Cu")

# Dispatch the workflow
results = workflow(atoms)

# print the results
print(results)

Selectively Modifying Job Decorators in a Pre-Made Flow

If you want to modify the decorators of select jobs in a pre-made workflow, such as to modify the number of retries, you can use the job_decorators keyword argument:

bulk_to_slabs_flow(atoms, job_decorators={"static_job": job(retries=2)})

As a shorthand, all of the decorators can be modified at once using the "all" key:

bulk_to_slabs_flow(atoms, job_decorators={"all": job(retries=2)})

from ase.build import bulk
from redun import Scheduler
from quacc import flow
from quacc.recipes.emt.core import relax_job
from quacc.recipes.emt.slabs import bulk_to_slabs_flow

scheduler = Scheduler()


# Define the workflow
@flow
def workflow(atoms):
    relaxed_bulk = relax_job(atoms)
    relaxed_slabs = bulk_to_slabs_flow(relaxed_bulk["atoms"], run_static=False)

    return relaxed_slabs


# Define the Atoms object
atoms = bulk("Cu")

# Run the workflow
result = scheduler.run(workflow(atoms))
print(result)

Selectively Modifying Job Decorators in a Pre-Made Flow

If you want to modify the decorators of select jobs in a pre-made workflow, such as to modify the allowed executors of a given function, you can use the job_decorators keyword argument:

bulk_to_slabs_flow(atoms, job_decorators={"static_job": job(name="my_custom_name")})

As a shorthand, all of the decorators can be modified at once using the "all" key:

bulk_to_slabs_flow(atoms, job_decorators={"all": job(name="my_custom_name")})

Limitations

Due to the difference in how Jobflow handles workflows (particularly dynamic ones) compared to other supported workflow engines, any quacc recipes that have been pre-defined with a @flow decorator (i.e. have _flow in the name) cannot be run directly with Jobflow. Rather, a Jobflow-specific Flow needs to be constructed by the user.