reana-serial-python

Automate serial Python workflow execution by providing a Python analysis script and a reana.yaml configuration to process remote datasets fetched from S3, HTTP, or TAP. The workflow runs sequentially and caches results locally as Parquet files with PNG visualizations.

Yes, SHBoost-like analyses are supported by swapping the Python analysis script, target data columns, and output names while maintaining reproducibility through standardized Parquet caching and predictable PNG outputs across multiple experiment iterations.

You need a Python analysis script, a reana.yaml configuration file, and access to remote data sources via S3, HTTP, or TAP. Create a workflow directory, plug in the script, configure reana.yaml, and execute the run to generate cached Parquet and PNG outputs.

Yes, REANA serial workflow caches processed results locally as Parquet files to speed up iteration cycles. This local caching mechanism improves reproducibility and reduces redundant data fetching from remote S3, HTTP, or TAP sources during repeated analysis runs.

A serial REANA workflow provides standardized scaffolding for Python analyses against remote datasets, ensuring reproducibility through Parquet caching and predictable PNG outputs, which simplifies repeated experiment execution where only the script and parameters vary.

Serial REANA workflows execute steps sequentially rather than in parallel, which may increase runtime for large-scale analyses. The workflow requires a Python script and reana.yaml configuration, and outputs are limited to Parquet cache files and PNG visualizations.