{
  "schemaVersion": 1,
  "lessonId": "recovery-02",
  "contentHash": "6742a4d4033d726195b9cfacf994dd74246ec6fbcd882f42ccd13651b834e4e6",
  "executedAt": "2026-10-20T10:46:37.199821+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "First IDs: [8, 6, 7, 9]\nNext IDs: [0, 5, 1, 2]\nIterator state bytes: 329\nRestored next batch matches\nFirst IDs: [8, 6, 7, 9]\nNext IDs: [0, 5, 1, 2]\nIterator state bytes: 329\nRestored next batch matches\nSame seed restarts; it does not resume\nEach epoch covers all twelve IDs\nNext-epoch IDs replayed: [[2, 0, 11, 1], [5, 7, 6, 8]]\nExpected sampler compatibility rejection\nPASS: recovery-02\n",
  "notebookCells": [
    {
      "sourceHash": "7da851b7ea2d504bd89906a5a2505cb46215450b7fa5d3a462d62e75520cf8d7",
      "outputs": []
    },
    {
      "sourceHash": "2cf3d7d08f0647a420d75d8ae04f754ce632215e9195c8be5b8dc007eb1096bb",
      "outputs": []
    },
    {
      "sourceHash": "872b88e8b77a2fb3540087a435e94e2ec10c4d6f4c672176a4fb368109f92067",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First IDs: [8, 6, 7, 9]\n",
            "Next IDs: [0, 5, 1, 2]\n",
            "Iterator state bytes: 329\n",
            "Restored next batch matches\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "366018d9742d609396f5a4913667296f44a3351076581dcc7d7ef338768bd689",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First IDs: [8, 6, 7, 9]\n",
            "Next IDs: [0, 5, 1, 2]\n",
            "Iterator state bytes: 329\n",
            "Restored next batch matches\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "05c60ff932b6d9e025ec2bd9b0d34eb3a2d8460df1d8e642f48fbef087213d00",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "3c7d55e34140025184a599949b7967eb22a711a7351e3e3437e35d729f4a32f6",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Same seed restarts; it does not resume\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "d803769cb7d8089d700374cb2b79ca6eb43400ade13cc2947a2a89e671b97d6c",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Each epoch covers all twelve IDs\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "d694b696fc0fcc1c34ca8aea9c3c81c35e43181f72bcc6cae089650ee891c7ba",
      "outputs": []
    },
    {
      "sourceHash": "31956b407297daf4ec65928c27df87e88cb7b7fbed1a4cf22c8a907662642410",
      "outputs": []
    },
    {
      "sourceHash": "95f25b826277084f0efc28005068954d1cdd2fff59a0d481dfb2ac72790a5209",
      "outputs": []
    },
    {
      "sourceHash": "95b8f26d3f96af662af9f11deef4e0f12c872eefdc95fd3c3ec7ee0333191bc0",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Next-epoch IDs replayed: [[2, 0, 11, 1], [5, 7, 6, 8]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "a26144645b4da923b5cc243e0291e66224ed298bab8253e06a771174119e1f11",
      "outputs": []
    },
    {
      "sourceHash": "d8b64604137220c2ab7f8d10d71108be82eaad59d85b088e8697d43b8078f307",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected sampler compatibility rejection\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}