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container-collection
Advanced tools
Collection of tasks for running containerized models. Designed to be used both in Prefect workflows and as modular, useful pieces of code.
The collection can be installed using:
pip install container-collection
We recommend using Poetry to manage and install dependencies. To install into your Poetry project, use:
poetry add container-collection
All tasks in this collection are wrapped in a Prefect @task
decorator, and can be used directly in a Prefect @flow
.
Running tasks within a Prefect flow enables you to take advantage of features such as automatically retrying failed tasks, monitoring workflow states, running tasks concurrently, deploying and scheduling flows, and more.
from prefect import flow
from container_collection.<module_name> import <task_name>
@flow
def run_flow():
<task_name>()
if __name__ == "__main__":
run_flow()
See cell-abm-pipeline for examples of using tasks from different collections to build a pipeline for simulating and analyzing agent-based model data.
Not all use cases require a full workflow.
Tasks in this collection can be used without the Prefect @task
decorator by simply importing directly from the module:
from container_collection.<module_name>.<task_name> import <task_name>
def main():
<task_name>()
if __name__ == "__main__":
main()
or using the .fn()
method:
from container_collection.<module_name> import <task_name>
def main():
<task_name>.fn()
if __name__ == "__main__":
main()
FAQs
Collection of tasks for running containerized models.
We found that container-collection demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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