agi-app-polars-execution
agi-app-polars-execution publishes the execution_polars_project AGILAB app
as a self-contained PyPI payload. It mirrors the Pandas execution example with a
Polars worker path.
Purpose
Use this package to compare AGILAB execution behavior on a deterministic tabular workload while using Polars for the processing step. It is useful when you want native dataframe performance without changing the surrounding AGILAB manager/worker contract.
Installed Project
The distribution name is agi-app-polars-execution; the AGILAB project name is
execution_polars_project. The package exposes both execution_polars and
execution_polars_project through the agilab.apps entry point group, so
AgiEnv(app="execution_polars_project") works without a monorepo checkout.
Install
pip install agi-app-polars-execution
Most users get this package through agi-apps, agilab[ui], or
agilab[examples]; direct installation is useful when validating one app
package in isolation.
Run In AGILAB
Select execution_polars_project, open ORCHESTRATE, then run INSTALL and
EXECUTE. Start locally, then compare the output with
execution_pandas_project if you want an engine-level contrast.
Expected Inputs
The default run creates its own deterministic CSV workload under shared storage. No external dataset, cloud account, notebook, or API key is required.
Expected Outputs
Workers write processed CSV or Parquet outputs and the reducer writes a summary with row counts, source files, engine labels, score metrics, and execution metadata.
Change One Thing
Change the partition count or output format, then compare the reducer summary against a Pandas run. The point is to change the engine while keeping the workflow contract stable.
Scope
This is a synthetic execution-path example. It is useful for validating Polars worker behavior, not for demonstrating a domain analytics product.
1 maintainer
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