semvec/pypi-packages huggingface dataset:
container-collection 1.1.0
Collection of tasks for running containerized models.
License: BSD-3-Clause
Requires Python: <4.0,>=3.9

[![Build status](https://bagherilab.github.io/container-collection/_badges/build.svg)](https://github.com/bagherilab/container-collection/actions?query=workflow%3Abuild)
[![Lint status](https://bagherilab.github.io/container-collection/_badges/lint.svg)](https://github.com/bagherilab/container-collection/actions?query=workflow%3Alint)
[![Documentation](https://bagherilab.github.io/container-collection/_badges/documentation.svg)](https://bagherilab.github.io/container-collection/)
[![Coverage](https://bagherilab.github.io/container-collection/_badges/coverage.svg)](https://bagherilab.github.io/container-collection/_coverage/)
[![Code style](https://bagherilab.github.io/container-collection/_badges/style.svg)](https://github.com/psf/black)
[![Version](https://bagherilab.github.io/container-collection/_badges/version.svg)](https://pypi.org/project/container-collection/)
[![License](https://bagherilab.github.io/container-collection/_badges/license.svg)](https://github.com/bagherilab/container-collection/blob/main/LICENSE)

Collection of tasks for running containerized models.
Designed to be used both in [Prefect](https://docs.prefect.io/latest/) workflows and as modular, useful pieces of code.

# Installation

The collection can be installed using:

```bash
pip install container-collection
```

We recommend using [Poetry](https://python-poetry.org/) to manage and install dependencies.
To install into your Poetry project, use:

```bash
poetry add container-collection
```

# Usage

## Prefect workflows

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](https://docs.prefect.io/latest/) 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.

```python
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](https://github.com/allen-cell-animated/cell-abm-pipeline) for examples of using tasks from different collections to build a pipeline for simulating and analyzing agent-based model data.

## Individual tasks

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:

```python
from container_collection.<module_name>.<task_name> import <task_name>

def main():
    <task_name>()

if __name__ == "__main__":
    main()
```

or using the `.fn()` method:

```python
from container_collection.<module_name> import <task_name>

def main():
    <task_name>.fn()

if __name__ == "__main__":
    main()
```
arcade-collection 0.16.0
Collection of tasks for working with ARCADE.
License: BSD-3-Clause
Requires Python: <4.0,>=3.9

[![Build status](https://bagherilab.github.io/arcade-collection/_badges/build.svg)](https://github.com/bagherilab/arcade-collection/actions?query=workflow%3Abuild)
[![Lint status](https://bagherilab.github.io/arcade-collection/_badges/lint.svg)](https://github.com/bagherilab/arcade-collection/actions?query=workflow%3Alint)
[![Documentation](https://bagherilab.github.io/arcade-collection/_badges/documentation.svg)](https://bagherilab.github.io/arcade-collection/)
[![Coverage](https://bagherilab.github.io/arcade-collection/_badges/coverage.svg)](https://bagherilab.github.io/arcade-collection/_coverage/)
[![Code style](https://bagherilab.github.io/arcade-collection/_badges/style.svg)](https://github.com/psf/black)
[![Version](https://bagherilab.github.io/arcade-collection/_badges/version.svg)](https://pypi.org/project/arcade-collection/)
[![License](https://bagherilab.github.io/arcade-collection/_badges/license.svg)](https://github.com/bagherilab/arcade-collection/blob/main/LICENSE)

Collection of tasks for working with ARCADE.
Designed to be used both in [Prefect](https://docs.prefect.io/latest/) workflows and as modular, useful pieces of code.

# Installation

The collection can be installed using:

```bash
pip install arcade-collection
```

We recommend using [Poetry](https://python-poetry.org/) to manage and install dependencies.
To install into your Poetry project, use:

```bash
poetry add arcade-collection
```

# Usage

## Prefect workflows

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](https://docs.prefect.io/latest/) 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.

```python
from prefect import flow
from arcade_collection.<module_name> import <task_name>

@flow
def run_flow():
    <task_name>()

if __name__ == "__main__":
    run_flow()
```

See [cell-abm-pipeline](https://github.com/allen-cell-animated/cell-abm-pipeline) for examples of using tasks from different collections to build a pipeline for simulating and analyzing agent-based model data.

## Individual tasks

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:

```python
from arcade_collection.<module_name>.<task_name> import <task_name>

def main():
    <task_name>()

if __name__ == "__main__":
    main()
```

or using the `.fn()` method:

```python
from arcade_collection.<module_name> import <task_name>

def main():
    <task_name>.fn()

if __name__ == "__main__":
    main()
```