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

Project description

werkit

version python versions license build code style

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.

Contribute

Pull requests welcome!

Support

If you are having issues, please let us know.

License

The project is licensed under the MIT License.

Project details


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