Re-executing a Workflow

Use the Covalent redispatch command to re-execute a workflow with:

• New input parameters

• Updated or replacement task definitions

Optionally, you can reuse previous results as well.

Prerequisites

Run the Covalent server.

Procedure

Construct a workflow.

import covalent as ct

@ct.electron
def task_0(a):
    return a


@ct.electron
def task_1(a, b):
    return a + b


@ct.electron
def task_2(a, b):
    return a / b

# For demonstrating redispatch
@ct.electron
def task_2_redefined(a, b):
    return a * b


@ct.lattice
def workflow(a, b):
    res_0 = task_0(a)
    res_1 = task_1(res_0, b)
    res_2 = task_2(res_1, b)
    return res_2

2. Dispatch the workflow.

dispatch_id = ct.dispatch(workflow)(1, 2) # Input parameters are a=1, b=2
print(f"Dispatch id: {dispatch_id}")
result = ct.get_result(dispatch_id, wait=True)
print(f"Workflow execution status: {result.status}")
print(f"Workflow output: {result.result}")

Dispatch id: f6553e74-86dc-4ed7-9f38-6c3c71b6ebb7
Workflow execution status: COMPLETED
Workflow output: 1.5

3. Use the Covalent redispatch function to redispatch the workflow, replacing one or more of the tasks. (One task is replaced in these demos. In practice you can re-execute any number of tasks.)

Three cases are illustrated below:

1. Using the previous input parameters.
2. Reusing (where possible) previous task results.
3. Using new input parameters.

1. Using the previous input parameters

Leave the input parameters empty to dispatch the workflow with the previous input parameters.

redispatch_id = ct.redispatch(
    dispatch_id,
    replace_electrons={"task_2": task_2_redefined}
)()
print(f"Redispatch id: {redispatch_id}")
result = ct.get_result(redispatch_id, wait=True)
print(f"Redispatched workflow execution status: {result.status}")
print(f"Redispatched workflow output: {result.result}")

Redispatch id: 3c3be52a-4ee7-4b17-9732-c12334530839
Redispatched workflow execution status: COMPLETED
Redispatched workflow output: 6

2: Reusing the previous task results

Set the keyword argument reuse_previous_results to True to reuse computed results wherever possible. Thereuse_previous_results flag defaults to False, so you must explicitly set it to True to reuse results.

Warning: When the workflow involves stochastic results (values that are randomly generated or computed from randomly generated values), do not set reuse_previous_results to True.

redispatch_id = ct.redispatch(
    dispatch_id,
    replace_electrons={"task_2": task_2_redefined},
    reuse_previous_results=True
)()
print(f"Redispatch id: {redispatch_id}")
result = ct.get_result(redispatch_id, wait=True)
print(f"Redispatched workflow execution status: {result.status}")
print(f"Redispatched workflow output: {result.result}")

Redispatch id: 52230138-e415-44c8-bd4c-f4986beb0601
Redispatched workflow execution status: COMPLETED
Redispatched workflow output: 6

The following code compares the start and end times times of the reused results to demonstrate that the tasks were not re-run.

# If previous results were reused to compute res_0 and res_1, execution time of the tasks should be 0
# and the following two statements should be `True`.

print(result.get_node_result(0)["start_time"] == result.get_node_result(0)["end_time"])
print(result.get_node_result(2)["start_time"] == result.get_node_result(2)["end_time"])

True
True

3. Using new input parameters

To evaluate the workflow with new input parameters, pass a new set of parameters in the ct.dispatch command.

redispatch_id = ct.redispatch(
    dispatch_id,
    replace_electrons={"task_2": task_2_redefined},
    reuse_previous_results=True
)(1, 4)  # Input parameters are a=1 (same as before), b=4 (different)

print(f"Redispatch id: {redispatch_id}")
result = ct.get_result(redispatch_id, wait=True)
print(f"Redispatched workflow execution status: {result.status}")
print(f"Redispatched workflow output: {result.result}")

Redispatch id: 57ab8b01-e6ee-454e-bd45-9122d750df33
Redispatched workflow execution status: COMPLETED
Redispatched workflow output: 20

With reuse_previous_results set to True, only results that depend on the new input parameters are recomputed. In this case res_0 is reused; res_1 is recomputed since one of its arguments, b, is changed.

Note that res_0 is reused based on a comparison of its arguments: Covalent doesn’t just recompute the results because the arguments were resubmitted. Instead, it sees that the resubmitted argument a is identical and thus correctly concludes that there is no need to recompute res_0.

# res_0 was reused; res_1 was recomputed since the value of b changed.

print(result.get_node_result(0)["start_time"] == result.get_node_result(0)["end_time"])
print(result.get_node_result(2)["start_time"] == result.get_node_result(2)["end_time"])

True
False