{
  "schemaVersion": 1,
  "lessonId": "distributed-02",
  "contentHash": "1c8eea315a4b010f2d42b9d63799ef7d0db1b4f49a05b7582498d773c890dae2",
  "executedAt": "2026-10-20T10:47:46.399473+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 4,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Initial loss: 4.875\nGradient: [-5.25 -0.75] updated weights: [0.52500004 0.075     ]\nUpdated loss: 2.6784372329711914\nInitial loss: 4.875\nGradient: [-5.25 -0.75] updated weights: [0.52500004 0.075     ]\nUpdated loss: 2.6784372329711914\nPermutation preserves the global update\nTen-step weights: [ 1.704636  -0.7047408]\nSummed local means are four times too large; weighted aggregation repaired\nSummed local means are four times too large; weighted aggregation repaired\nPASS: distributed-02\n",
  "notebookCells": [
    {
      "sourceHash": "538eb5b487dd2eff1115f4f2758cb069da750dd853180a765c6d2a3f1bd8c54b",
      "outputs": []
    },
    {
      "sourceHash": "056ef5517ad588649b40488bdf38b54beb948c16bd0baac0d9bfaa99f6f380ac",
      "outputs": []
    },
    {
      "sourceHash": "d5647275de5e627a6e2a5e10ffc8bfc8f6830a87039186b0c41423095311e6e2",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial loss: 4.875\n",
            "Gradient: [-5.25 -0.75] updated weights: [0.52500004 0.075     ]\n",
            "Updated loss: 2.6784372329711914\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "6397d6a6420dc76e594cbda80c82a5cb5f335a57512bf0ef169efbafae2c0f6f",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial loss: 4.875\n",
            "Gradient: [-5.25 -0.75] updated weights: [0.52500004 0.075     ]\n",
            "Updated loss: 2.6784372329711914\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "330521089b855dbc962c9d4722ca19d95049dc7adb23e46464d3956a4e0337e6",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "bbecc1df29a741bd1bbd6a5d8782f2cd23c4cf039b935211f97c85d141775b59",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Permutation preserves the global update\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "3b19e43a7004e2c540e9e46bf8f8a9ad21a907a0522f346abb21876e5f84f309",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ten-step weights: [ 1.704636  -0.7047408]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "202321108b1d58519af424b7aa3dabfd3c38fde52deee2e74275bbb51525be2c",
      "outputs": []
    },
    {
      "sourceHash": "e1c5a64457dbca38078430facbd4ebaf44ee4aa830d42b3663cec454ff902d81",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summed local means are four times too large; weighted aggregation repaired\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "3e04a08f448f494e1876791ae4429d593d73353a317700af7a0a875d16db4898",
      "outputs": []
    },
    {
      "sourceHash": "d4b1be302b9281c42a39e696d8851ab1253fd8b6b0684d50504b2bca8e3fbea7",
      "outputs": []
    },
    {
      "sourceHash": "9503c9c0f49ac1182560648074e83923ab2702350f2e30b4cf626e30e97ad601",
      "outputs": []
    },
    {
      "sourceHash": "bcfbfb665e7ef2a74dae696c4e0a20f4c24409a0863c4e29840527da44541055",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summed local means are four times too large; weighted aggregation repaired\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}