Pypiper is a lightweight Python toolkit designed for building robust, restartable command-line pipelines. It simplifies the process of creating complex data processing workflows by handling logging, error recovery, and status tracking. The current version is 0.15.1, and it maintains an active release cadence with regular updates.
pip install pypiperVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to initialize a `PipelineManager`, define stages using `pm.run()` with shell commands, specify target files for restartability, and report a simple result. It creates an output directory, generates a file, processes it, and then reports a simple metric.
Upgrade your Python environment to 3.10 or newer. Pypiper requires Python >=3.10.
Review your `PipelineManager` initialization and `report_result`/`report_object` calls. Update parameter names and ensure `message_raw` values conform to `pipestat`'s `value_dict` expectation.
If you rely on stages skipping when target files exist, explicitly set `force_overwrite=False` in your `PipelineManager` constructor or in individual `pm.run()` calls.
Ensure that the `target` file specified for `pm.run()` is indeed created or modified successfully by the executed command. Use `pypiper.check_file_existance(target)` or `pypiper.file_checksum(target)` in debugging.