Result-Dependent Loops#

To iterate over the result from an electron, put the iteration (loop) logic inside another electron.


Often the output of one task is a collection that you want to iterate over using another task. In Covalent terms, this means you want to use an electron to produce an iterable, then process the iterable with another electron. In these cases, perform the iteration inside an electron.

When a lattice is dispatched, the Covalent server executes the lattice in order to build the transport graph. The transport graph is then analyzed to parallelize the execution of electrons on their assigned executors.

If the server encounters a loop over the output of an electron, it cannot infer the structure on which the loop depends (the size and composition of the iterable) and is prevented from building the transport graph.

Putting the loop inside an electron defers resolution of the loop to when the electron is dispatched, and ensures that it takes place on the electron’s executor.

Note: This pattern applies only when the iterator is produced by an electron. Iterating on fixed values in a lattice as described here does not require electron execution to evaluate the iterator and build the graph.

Best Practice#

Compute dynamically generated iterators inside an electron. Electrons’ execution is deferred during the graph build phase, so their output cannot be used to build the transport graph and analyze the execution for parallelization. Instead, the electron is added to the transport graph and the loop is computed within the electron when it is executed.

For result-dependent computations that might be too complex to encapsulate in a single electron, use a sublattice.


Contrast the two examples below.

Example 1: Incorrect#

This example demonstrates the incorrect approach: looping over a computed iterator in the lattice but not within an electron.

import covalent as ct
import random

# Technique 1: Fails because the transport graph cannot be defined

def task():
    return random.sample(range(10, 30), 5)

def task_2(x):
    return x ** 2

def workflow_1():
    random_list = task()

    res = []
    for rn in random_list:

    return rn

id = ct.dispatch(workflow_1)()
res = ct.get_result(id, wait=True)
print("Status: ", res.status, "\n", res.error) # (Selected output)
Status:  FAILED
 The following tasks failed:
16: :task()[5]
19: :task()[6]
22: :task()[7]
25: :task()[8]
28: :task()[9]
31: :task()[10]
34: :task()[11]
37: :task()[12]
40: :task()[13]
43: :task()[14]
46: :task()[15]
49: :task()[16]
52: :task()[17]

Example 2: Improved#

The iterator is passed to the second electron, which loops over it internally and returns the results in a list. In this case the loop is executed entirely at electron execution time, in the electron’s executor.

import covalent as ct
import random

# Technique 2: Iterator is contained in an electron

def task_1():
    return random.sample(range(10, 30), 5)

# Method (2):
def task_2_new(x_list):

    squares = []
    for x in x_list:
        squares.append(x ** 2)

    return squares

def workflow_2():
    random_list = task_1()
    return task_2_new(random_list)

id = ct.dispatch(workflow_2)()
res = ct.get_result(id, wait=True)

Lattice Result
result: [256, 100, 625, 196, 676]
input args: []
input kwargs: {}
error: None

start_time: 2023-03-13 20:26:42.797010
end_time: 2023-03-13 20:26:42.985698

results_dir: /Users/dave/.local/share/covalent/data
dispatch_id: a03a79f3-748f-488d-8a7e-aebe78a4a26e

Node Outputs
task_1(0): [16, 10, 25, 14, 26]
task_2_new(1): [256, 100, 625, 196, 676]

See Also#

Result-Dependent If-Else

Dynamic Workflows