{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "a6c4301acabc",
      "metadata": {},
      "source": "# Predictor identity and operation usage\n\nThis executable example uses synthetic input and a default no-network fixture. In explicit live mode it fits the qualified classifier; created synthetic datasets/predictors are retained. It never deletes state. Usage shows workload and lifecycle events, with no monetary rates or inferred prices."
    },
    {
      "cell_type": "code",
      "id": "49eece00ca6b",
      "metadata": {},
      "source": "from support import notebook_client, prepare\nclient = notebook_client()\ncolumns, query, predictor = prepare(client)",
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "id": "c172161c6baf",
      "metadata": {},
      "source": "Inspect the immutable fitted identity and reopen it. Metadata export returns pinned server provenance only; names, deletion plans and recipes remain later routes; a predictor ID is not an exported executable model."
    },
    {
      "cell_type": "code",
      "id": "b70603a31ba8",
      "metadata": {},
      "source": "metadata = client.predictor(predictor[\"id\"])\nassert metadata[\"dataset_id\"] == predictor[\"dataset_id\"]\nassert metadata[\"model_version\"] == predictor[\"model_version\"]\nassert metadata[\"configuration\"] == predictor[\"configuration\"]\nassert any(p[\"id\"] == predictor[\"id\"] for p in client.predictors())\nclient.predict(predictor[\"id\"], columns=columns, rows=query.tolist(), probabilities=False)",
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "id": "2be6a56a278d",
      "metadata": {},
      "source": "One logical fit can have queued/running/finished usage records. Count distinct operation IDs, not every event as another fit; preserve attempt/status details. Row/column metadata describes workloads, not charges."
    },
    {
      "cell_type": "code",
      "id": "eff618c11f0f",
      "metadata": {},
      "source": "events = [e for e in client.usage() if e.get(\"dataset_id\") == predictor[\"dataset_id\"]]\nfit_events = [e for e in events if e[\"kind\"] == \"fit\"]\nassert fit_events\nassert len({e[\"operation_id\"] for e in fit_events}) == 1\nassert any(e[\"status\"] == \"succeeded\" for e in fit_events)\nassert all(e[\"rows\"] >= 0 and e[\"columns\"] >= 0 for e in events)\nassert all(\"predictions\" not in e and \"probabilities\" not in e for e in events)\nassert {\"fit\", \"predict\"} <= {e[\"kind\"] for e in events}\nclient.close()",
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "id": "92c4718760ea",
      "metadata": {},
      "source": "Cache eviction and process restart are independent operational checks. Reopening metadata here does not force or prove them. Datasets/predictors persist until explicit authorized deletion; seven-day temporary result access expiry is distinct from physical storage cleanup."
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}