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name: plexe description: "Route Plexe model-building, retraining, and dashboard workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Plexe

Use this skill for the Plexe repository: the automated model-building pipeline, retraining flow, package artifacts, and the Streamlit dashboard that inspects saved runs.

What this skill covers

• Build an ML model from a natural-language intent and a dataset.
• Resume or steer a checkpointed run with feedback or model filters.
• Retrain an existing packaged model on fresh data.
• Inspect experiment workdirs and launch the dashboard for saved runs.
• Understand Plexe's config, storage, Spark, and packaging surfaces.

Quick install

• Core runtime: pip install plexe
• Full local workflow: pip install "plexe[pyspark,aws,catboost,lightgbm,pytorch]" streamlit plotly
• Python support: 3.10 through 3.12

Main entry points

Minimal verification after install: python -m plexe.main --help.

• python -m plexe.main --train-dataset-uri ... --intent "..."
• from plexe.main import main
• python -m plexe.viz --work-dir ./workdir

Route map

model-building

Use this route for the main Plexe workflow:

• CLI and Python API usage
• checkpointed model building and resume flows
• retraining packaged models
• Spark local or Databricks execution
• S3-backed artifact handling
• model package contents and inference artifacts
• config, dataset, and validation questions
• workflow failures, optional dependency gaps, and packaging issues

Read the bundled references when you need the concrete CLI flags, API signatures, phase breakdown, data formats, or troubleshooting details.

dashboard

Use this route for dashboard and artifact inspection:

• python -m plexe.viz
• workdir discovery and checkpoint browsing
• dashboard tabs and model package inspection
• interpreting saved experiment metadata
• diagnosing malformed or incomplete run directories

Read the bundled dashboard reference when you need the workdir layout or tab-by-tab behavior.

Shared helpers

• references/repo-provenance.md records the source snapshot.
• references/repo-routing-metadata.json supports router import.
• references/troubleshooting.md covers cross-cutting install, backend, and provider issues.
• scripts/check_env.py smoke-checks the installation.
• scripts/inspect_workdir.py summarizes saved Plexe runs.

When to choose this skill

Choose Plexe when the request mentions any of these signals:

• plexe.main, plexe.viz, python -m plexe.main, or python -m plexe.viz
• build_model, retrain_model, WorkflowIntegration, StandaloneIntegration
• work_dir/model, model.tar.gz, checkpoints, or .build/reports
• spark-mode local, spark-mode databricks, or S3-backed artifact storage
• model search, baseline building, packaging, or dashboard inspection

If you only need a filesystem summary of a saved run, start with the dashboard route. If you need concrete workflow steps or flags, start with model-building.