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