aiocells 0.1.18

A package for synchronous and asynchronous dependency graph computation

aiocells is a package that provides tools for synchronous and asynchronous execution of nodes in a dependency graph.

Contents:

1. Examples
2. Development Installation

Examples

Hello world

Here is the code for the first demo.

#!/usr/bin/env python3

import aiocells


def hello_world():
    print("Hello, world!")


def main():
    graph = aiocells.DependencyGraph()

    # The node can be any callable, in this case a function.
    graph.add_node(hello_world)
    aiocells.compute_sequential(graph)

This is synchronous graph computation. There is only one node in the graph. It is a function that prints a message. Synchronous nodes must be callable.

Defining ordering constraints

Here is demo 4. It shows how edges between nodes are defined:

#!/usr/bin/env python3

import time

import aiocells


def main():
    graph = aiocells.DependencyGraph()

    # 'add_node' always returns the node that has just been added, in this
    # case the lambda functions. We will use this below to define precedence
    # relationships
    print_sleeping = graph.add_node(lambda: print("Sleeping..."))
    sleep = graph.add_node(lambda: time.sleep(2))
    print_woke_up = graph.add_node(lambda: print("Woke up!"))

    print("Define the precedence relationships...")
    graph.add_precedence(print_sleeping, sleep)
    graph.add_precedence(sleep, print_woke_up)

    # Now, after we've defined the precedence relationships, we use the
    # simplest computer to compute the graph. The nodes will be called in
    # an order that is consistent with the precedence relationships.
    # Specifically, the nodes are executed in topological order.
    aiocells.compute_sequential(graph)

In this case, there are three nodes. After the nodes are added, we define precedence relationships between them. When the graph is computed, it is done so in a way that honours the precedence relationships.

Asynchronous nodes

Below is the code for demo_5. Note the use of asyncio.sleep, functools.partial and aiocells.async_compute_sequential.

#!/usr/bin/env python3

import asyncio
from functools import partial

import aiocells

# This example demonstrates graph nodes that are coroutines. We use
# a different computer; one that know how to deal with coroutines.


def main():
    graph = aiocells.DependencyGraph()

    # First, we add a lambda function
    before_sleep = graph.add_node(lambda: print("Sleeping..."))

    # Second, we create a coroutine function using functools.partial. This
    # is the closest we can get to a lambda for an async function
    sleep_2 = partial(asyncio.sleep, 2)

    # Finally, another lambda function
    wake_up = graph.add_node(lambda: print("Woke up!"))

    # Here, 'sleep' will implicitly be added to the graph because it is
    # part of the precedence relationship
    graph.add_precedence(before_sleep, sleep_2)
    graph.add_precedence(sleep_2, wake_up)

    # Here, we use the `async_compute_sequential`, which, like
    # `compute_sequential`, call the nodes in a topologically correct sequence.
    # However, whereas `compute_sequential` only supports vanilla callables,
    # `async_compute_sequential` additionally supports coroutine functions,
    # as defined by `inspect.iscoroutinefunction`. However, the execution is
    # still sequential. Each coroutine function is executed using 'await' and
    # must complete before the next node is executed. The function
    # `async_compute_sequential` is a coroutine and must be awaited.  Here,
    # we simply pass it to `asyncio.run`.
    asyncio.run(aiocells.async_compute_sequential(graph))

Concurrent computation

demo 6 is a an example of graph that could be computed concurrently but is not due to the use if async_compute_sequential.

import asyncio
from functools import partial

import aiocells


def create_graph(stopwatch):

    graph = aiocells.DependencyGraph()

    # The method to start the stopwatch
    start_stopwatch = stopwatch.start

    # Two sleeps. Note that they are asyncio.sleep
    sleep_1 = partial(asyncio.sleep, 1)
    sleep_2 = partial(asyncio.sleep, 2)

    # The method to stop the stopwatch
    stop_stopwatch = stopwatch.stop

    # Start the stopwatch before the first sleep
    graph.add_precedence(start_stopwatch, sleep_1)
    # Stop the stopwatch after the first sleep
    graph.add_precedence(sleep_1, stop_stopwatch)

    # Start the stopwatch before the second sleep
    graph.add_precedence(start_stopwatch, sleep_2)
    # Stop the stopwatch after the second sleep
    graph.add_precedence(sleep_2, stop_stopwatch)

    # Note that there is no precedence relationship between the two
    # sleeps.
    return graph


def main():

    stopwatch = aiocells.Stopwatch()
    graph = create_graph(stopwatch)
    # Even though the graph is a diamond (the sleeps do no depend on each
    # other and _could_ be executed concurrenty, `async_compute_sequential`
    # does not support concurrent execution. Thus, the execution time is
    # about 3 seconds, the sum of the two sleeps.
    print("Two async sleeps computed sequentially.")
    print("Total time should take about 3 seconds...")
    asyncio.run(aiocells.async_compute_sequential(graph))
    print("Computation with `async_compute_sequential` took"
          f" {stopwatch.elapsed_time()}")

demo_7 is the same graph as above but computed concurrently with async_compute_concurrent.

#!/usr/bin/env python3

import asyncio

import aiocells
import aiocells.demo_6 as demo_6


def main():
    stopwatch = aiocells.Stopwatch()
    graph = demo_6.create_graph(stopwatch)

    # Here, we run the same graph as the previous demo but we use
    # 'async_compute_concurrent' which will run the two sleeps concurrently.
    # Thus, the execution time will be around 2 seconds, the maximum of
    # the two sleeps.

    print("Running previous demo's graph concurrently.")
    print("Total execution time should be about 2 seconds...")
    asyncio.run(aiocells.async_compute_concurrent(graph))
    print("Computation with `async_compute_concurrent` took"
          f" {stopwatch.elapsed_time()}")

Development Installation

There is a Makefile in the repository. The default target will initialise a virtual environment, install dependencies into that environment and then test the code. It requires Python 3.8, virtualenv and pip to be installed. If those are missing, it will print suggestions on how to address the problem.

$ make

Activating the virtual environment and running the demos

The default make target will generate a file called activate_aiocells. To activate the virtual environment:

$ source activate_aiocells

Once you've done that, you should have the following command available:

$ aiocells demo-1

Tab completion

activate_aiocells will enable tab completion for aiocells:

$ aiocells <TAB>

Editable installation

The package will be installed in the virutal environment using pip --editable. This means that modifications to the code will be immediately available.

To test this, try modifying src/aiocells/demo_1.py to print a different message. You should be able to immediately run the demo and see the new message:

$ aiocells demo-1