prefect-memory-profiling 1.0.0

A collection of Prefect Tasks for different varieties of profiling

prefect-memory-profiling

Welcome!

A collection of Prefect Tasks for memory profiling in Prefect 1.0.

Getting Started

Python setup

Requires an installation of Python 3.7+.

We recommend using a Python virtual environment manager such as pipenv, conda or virtualenv.

For more information about how to use Prefect, please refer to the Prefect documentation.

Installation

Install prefect-memory-profiling with pip:

pip install prefect-memory-profiling

Write and run a flow

from prefect import Flow
from prefect_memory_profiling.tasks import profiled_task

@profiled_task(name='My Profiled Task', stream=open('logfile.txt', 'a+'))
def resource_intensive_task(n: int = 100):
    a = [n**n for n in range(2*n)]
    b = [n**n for n in range(4*n)]
    c = [n**n for n in range(n**2)]
                
    return sum(a + b + c)

with Flow('Memory Profiled Flow') as flow:
    resource_intensive_task()
    
if __name__ == "__main__":
    flow.run(run_on_schedule=False)

Resources

If you encounter any bugs while using prefect-memory-profiling, feel free to open an issue in the prefect-memory-profiling repository.

If you have any questions or issues while using prefect-memory-profiling, you can find help in either the Prefect Discourse forum or the Prefect Slack community.

Development

If you'd like to install a version of prefect-memory-profiling for development, clone the repository and perform an editable install with pip:

git clone https://github.com/zzstoatzz/prefect-memory-profiling.git

cd prefect-memory-profiling/

pip install -e ".[dev]"

# Install linting pre-commit hooks
pre-commit install