werkit 0.7.0

Toolkit for encapsulating Python-based computation into deployable and distributable tasks

werkit

Toolkit for encapsulating Python-based computation into deployable and distributable tasks.

Provides code that helps package things up:

• Serializing results
• Handling and serializing errors
• Deploying task workers using Redis, RQ and the Fargate CLI

They're particularly useful for providing repsonse consistency across different revisions of a service or different services.

Installation

pip install werkit

Usage

from werkit import Manager

def myfunc(param, verbose=False, handle_exceptions=True):
    with Manager(handle_exceptions=handle_exceptions, verbose=verbose) as manager:
        manager.result = do_some_computation()
    return manager.serialized_result

Parallel computation

Werkit supports parallel computation using Redis and RQ.

You must install the dependencies separately:

pip install redis rq

Requesting work

from mylib import myfunc
from werkit.parallel import invoke_for_each


items = {'a': ..., 'b': ...}
job_ids = invoke_for_each(myfunc, items, connection=Redis.from_url(...))

Performing work

pip install redis rq
rq worker --burst werkit-default --url rediss://...

Note: mylib.myfunc must be importable.

Using CloudManager

In place of the low-level API you can make your calls using CloudManager:

#!/usr/bin/env python


import click
from werkit.parallel import Config, CloudManager, invoke_for_each

manager = CloudManager(
    config=Config(
        local_repository="my-project",
        ecr_repository="123456789012.dkr.ecr.us-east-1.amazonaws.com/my-project",
        ecs_task_name="my-project",
        task_args=[
            "--cpu",
            "1024",
            "--memory",
            "2048",
            "--task-role",
            "arn:aws:iam::123456789012:role/...",
            "--security-group-id",
            "sg-...",
            "--subnet-id",
            "subnet-...",
        ],
        default_task_count=5,
    )
)


@click.group()
def cli():
    pass


@cli.command()
def login():
    manager.login()


@cli.command()
@click.argument("tag")
def build_and_push(tag):
    manager.build_and_push()


@cli.command()
def enqueue():
    from myproject import myfunc

    items = {"key1": "value1", "key2": "value2"}

    invoke_for_each(
        measure_body,
        items,
        clean=True,
        connection=manager.redis_connection,
    )


@cli.command()
@click.option(
    "--count",
    default=manager.config.default_task_count,
    type=int,
    help="Number of tasks to run",
)
@click.argument("tag")
def run(count, tag):
    manager.run(tag=tag, count=count)


@cli.command()
def dashboard():
    manager.dashboard()


@cli.command()
def ps():
    manager.ps()


@cli.command()
def get_results():
    print(manager.get_results())


@cli.command()
def clean():
    manager.clean()


if __name__ == "__main__":
    cli()

Getting results

from redis import Redis
from werkit.parallel import get_results


get_results(wait_until_done=True, connection=Redis.from_url(...))

Monitoring

You can monitor your queues using RQ Dashboard or one of the other methods outlined here.

Parallel computation on AWS lambda

Werkit also implements a parallel map on AWS lambda.

Werkit comes with a default lambda handler, that accepts an event of the form {"input":[a, b, ...],"extra_args":[c, d, ...]}. Werkit invokes a lambda function in parallel for every item in input, with an event of the form {"input": a, "extra_args":[c, d, ...]}.

The werkit default handler is configurable via the following environmnent variables:

• LAMBDA_WORKER_FUNCTION_NAME: Name of the lambda worker function to invoke
• LAMBDA_WORKER_TIMEOUT: How long to wait in seconds for the lambda worker function to return before returning a TimeoutError

Contribute

• Issue Tracker: https://github.com/metabolize/werkit/issues
• Source Code: https://github.com/metabolize/werkit

Pull requests welcome!

Support

If you are having issues, please let us know.

License

The project is licensed under the MIT License.