prefect-memory-profiling
Readme
A collection of Prefect Tasks for memory profiling in Prefect 1.0.
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.
Install prefect-memory-profiling with pip:
pip install prefect-memory-profiling
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)
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.
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
FAQs
A collection of Prefect Tasks for different varieties of profiling
We found that prefect-memory-profiling 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.
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