{
  "schemaVersion": 1,
  "lessonId": "tpu-05",
  "contentHash": "09cbfe2aa061d9ab3329236f15e12a6bbbdb04609765c10fc42a27e3e277a585",
  "executedAt": "2026-10-20T10:58:54.082998+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "CPU route: python public/exercises/distributed-02.py\nCPU-only route remains excluded from TPU Pod execution\nCorrectly rejected CPU-only lesson for TPU Pod execution\nSlice catalog global chips: {'v5litepod-4': 4, 'v5litepod-8': 8, 'v5litepod-16': 16, 'v6e-16': 16}\nCPU route: python public/exercises/distributed-02.py\nv5litepod-16 global chips: 16 Idle chips without --worker=all: 12\nTotal HBM GiB by slice: {'v5litepod-4': 64, 'v5litepod-8': 128, 'v5litepod-16': 256, 'v6e-16': 512}\nCPU route: python public/exercises/distributed-02.py\nRejected unsafe route: {}\nRejected unsafe route: {'hosts': 4, 'execution_class': 'cpu-only'}\nMulti-host Pod slices: ['v5litepod-16', 'v6e-16']\nPer-host batch: 32 Global batch: 128\nPASS: tpu-05\n",
  "notebookCells": [
    {
      "sourceHash": "77f3da7cffc7d81ed999279bb91040fbfef93d7613ce716ae197201c6dbd645a",
      "outputs": []
    },
    {
      "sourceHash": "062a7faa73ee22818a117f5335542dd4af4aa8aa1cf27a0264537c68303df9b9",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU route: python public/exercises/distributed-02.py\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "36543657f21d86e4542c1f101d199f5a031bfb041ab61cc173b3d236432e816b",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU-only route remains excluded from TPU Pod execution\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "9703e166736326061d9159209199afd371e181b2e56296b93bf5741f1624f2f0",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correctly rejected CPU-only lesson for TPU Pod execution\n",
            "Slice catalog global chips: {'v5litepod-4': 4, 'v5litepod-8': 8, 'v5litepod-16': 16, 'v6e-16': 16}\n",
            "CPU route: python public/exercises/distributed-02.py\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "a137f12e40acf73c77cafa7edd3baf002b770e913af9ceeeca4ad7ca847bce99",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "a6214094278c09de83d3df570a349f11a3ca54609a67b02ba592a4cbb0b9a220",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "v5litepod-16 global chips: 16 Idle chips without --worker=all: 12\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "d3666a4304e4911e4f164dec7deae4aee333530625652ea762f82b88e8913a48",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total HBM GiB by slice: {'v5litepod-4': 64, 'v5litepod-8': 128, 'v5litepod-16': 256, 'v6e-16': 512}\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "62a941fa163ed0db0f94279196fa8dc589dc5eb7b19205b5afcad5f7ec334e3a",
      "outputs": []
    },
    {
      "sourceHash": "38dfa73b09558d9a6a05394935106f0bb8e0802b812072171cbb41e3b4dd9b03",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU route: python public/exercises/distributed-02.py\n",
            "Rejected unsafe route: {}\n",
            "Rejected unsafe route: {'hosts': 4, 'execution_class': 'cpu-only'}\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "c5dfa2f8e9bbc4d41b643fb9220e6c3b8a73778b0b054318706b06064477343e",
      "outputs": []
    },
    {
      "sourceHash": "e547bb2ab83193b1645ad2a2abfc900ce57b930842c032bd956f8ea9b81fa68d",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Multi-host Pod slices: ['v5litepod-16', 'v6e-16']\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "3e6997ebb4ad915f6610805168b78bc4ec84f62caf1475ae18f7b1ae35df183e",
      "outputs": []
    },
    {
      "sourceHash": "bc4a13058469a83f518bf27cf7f2297580a53000267f0a90c88be934ea46a01b",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Per-host batch: 32 Global batch: 128\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}