{
  "schemaVersion": 1,
  "lessonId": "tpu-01",
  "contentHash": "a67833264e71f5170b34b3db66b014a849e8aa6e5840de5def2a404bee7aae74",
  "executedAt": "2026-10-20T10:52:22.243997+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Verified runtime receipt: {\"contract_ok\": true, \"device_count\": 1, \"device_kinds\": [\"cpu\"], \"expected_backend\": \"cpu\", \"expected_sum_of_squares\": 140.0, \"jax\": \"0.9.2\", \"local_device_count\": 1, \"observed_backend\": \"cpu\", \"pmap_sum_of_squares\": 140.0, \"python\": \"3.14.3\", \"receipt_sha256\": \"4ddc0cbb323fe3bd\"}\nSingle-host TPU slice HBM totals (GiB): {'v5litepod-1': 16, 'v5litepod-4': 64, 'v5litepod-8': 128, 'v6e-4': 128}\nVerified runtime receipt: {\"contract_ok\": true, \"device_count\": 1, \"device_kinds\": [\"cpu\"], \"expected_backend\": \"cpu\", \"expected_sum_of_squares\": 140.0, \"jax\": \"0.9.2\", \"local_device_count\": 1, \"observed_backend\": \"cpu\", \"pmap_sum_of_squares\": 140.0, \"python\": \"3.14.3\", \"receipt_sha256\": \"4ddc0cbb323fe3bd\"}\nSingle-host TPU slice HBM totals (GiB): {'v5litepod-1': 16, 'v5litepod-4': 64, 'v5litepod-8': 128, 'v6e-4': 128}\nMismatch contract_ok: False observed: cpu devices: 1\nRequires --worker=all and jax.distributed.initialize(): {'v5litepod-1': False, 'v5litepod-4': False, 'v5litepod-8': False, 'v5litepod-16': True}\nChecked receipt sha256: 4ddc0cbb323fe3bd v5litepod-4 chips: 4 HBM GiB: 64\nSlices fitting 48 GiB: ['v5litepod-4', 'v5litepod-8', 'v6e-4']\nExpected 4-device pmap sum of squares: 10416.0\nPASS: tpu-01\n",
  "notebookCells": [
    {
      "sourceHash": "fdd3c4cb6b30527172f224177160a2f53a19ba5fe8f3c64e826c2721fe40671e",
      "outputs": []
    },
    {
      "sourceHash": "39fb2db355f5800f11534960e7336cd4c9472f37e2f0adfd47e35bbe0c94eca4",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Verified runtime receipt: {\"contract_ok\": true, \"device_count\": 1, \"device_kinds\": [\"cpu\"], \"expected_backend\": \"cpu\", \"expected_sum_of_squares\": 140.0, \"jax\": \"0.9.2\", \"local_device_count\": 1, \"observed_backend\": \"cpu\", \"pmap_sum_of_squares\": 140.0, \"python\": \"3.14.3\", \"receipt_sha256\": \"4ddc0cbb323fe3bd\"}\n",
            "Single-host TPU slice HBM totals (GiB): {'v5litepod-1': 16, 'v5litepod-4': 64, 'v5litepod-8': 128, 'v6e-4': 128}\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "c67ad94209f2275a8667d4db75a6112e421598f15c9878b71d464b9c982d8ef2",
      "outputs": []
    },
    {
      "sourceHash": "d91df2454f5b88c159dbe4dab8e0c42a528df96f690e67b3bc791fe26b853f02",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Verified runtime receipt: {\"contract_ok\": true, \"device_count\": 1, \"device_kinds\": [\"cpu\"], \"expected_backend\": \"cpu\", \"expected_sum_of_squares\": 140.0, \"jax\": \"0.9.2\", \"local_device_count\": 1, \"observed_backend\": \"cpu\", \"pmap_sum_of_squares\": 140.0, \"python\": \"3.14.3\", \"receipt_sha256\": \"4ddc0cbb323fe3bd\"}\n",
            "Single-host TPU slice HBM totals (GiB): {'v5litepod-1': 16, 'v5litepod-4': 64, 'v5litepod-8': 128, 'v6e-4': 128}\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "06f45a84adc951a7493a2a841fe6fc64b115074659ca183a6ef169ab9f56d32f",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "9fc647bc974149b02acef90a207c96abe615ffaad7343b6a8eb4f9078a690865",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mismatch contract_ok: False observed: cpu devices: 1\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "6ae788619155630307d01f4e6dd40f7f4089705538cde3b157bcda446db3ab19",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requires --worker=all and jax.distributed.initialize(): {'v5litepod-1': False, 'v5litepod-4': False, 'v5litepod-8': False, 'v5litepod-16': True}\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "a8ae46c36e19294e0f0c15dd681d321b59b6a1e5c32802008177c2edde8ef710",
      "outputs": []
    },
    {
      "sourceHash": "3691a62c24fd4f631dffe36eeab012d376818233e8631ee2f70f512ee42ef003",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checked receipt sha256: 4ddc0cbb323fe3bd v5litepod-4 chips: 4 HBM GiB: 64\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "bac64dede2019172f1a9507e1050c37a0fcbf55374c9e5e61e16b7f6ba1169fd",
      "outputs": []
    },
    {
      "sourceHash": "8560f2373f21461323bfe12b5eec7ae8a1d3a4ee5467e90febc6afdbe504a9d3",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Slices fitting 48 GiB: ['v5litepod-4', 'v5litepod-8', 'v6e-4']\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "1671397498d9345743e78246feaf4b46899268b2844f00faed7ce95c4b0f4861",
      "outputs": []
    },
    {
      "sourceHash": "adc5509c4d7ea623eae040e45ce249fa17e92b036ae043a0054caa1e29cf6371",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected 4-device pmap sum of squares: 10416.0\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}