2026.10.10.1

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 Deploy scheduler & workers and RUN. 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.