{
  "schemaVersion": 1,
  "lessonId": "deployment-07",
  "contentHash": "93a1f10568b65730bcac178c53015434c5c5d86e436c7219598fee4a97ea04ba",
  "executedAt": "2026-10-20T10:50:42.914381+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Fresh-process valid requests: 2 rejected boundaries: 3\nThis companion tests the process/artifact contract. Run the Docker lab for container evidence.\nFresh-process valid requests: 2 rejected boundaries: 3\nThis companion tests the process/artifact contract. Run the Docker lab for container evidence.\nIndependent three-input predictions: 0.5, 2.5, 5.0\nMatching digest did not bypass model validation.\nChanged artifact requires a new recorded digest.\nNonfinite input rejected before output.\nNew artifact loads, but the old behavioral expectation no longer passes.\nPASS: deployment-07\n",
  "notebookCells": [
    {
      "sourceHash": "6ea075edc4df071aae23d8d4c130f7681d9e468ab96c82e2bc448328a7057113",
      "outputs": []
    },
    {
      "sourceHash": "cfe19e727b044197aa8669d67c83947a57fb04a98931b60d561362c246997f3b",
      "outputs": []
    },
    {
      "sourceHash": "5a9b7ee1f1ae83f3f533753dc40d776b061a4247f3becddbdf10240e2fe1eac3",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fresh-process valid requests: 2 rejected boundaries: 3\n",
            "This companion tests the process/artifact contract. Run the Docker lab for container evidence.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "0e94c76deb07ebc7780cb4354f69763463b7264d206c1bbcc4c10ecc3ccc58af",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fresh-process valid requests: 2 rejected boundaries: 3\n",
            "This companion tests the process/artifact contract. Run the Docker lab for container evidence.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "6a94d74ce81970a6637525628b2a5b586e7b85c096e7d240900646ff96de7409",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "2f0ae7545ddc4e0c1b02637389c6ce44d798b63c0e1709c8901d22e1877f9d7e",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Independent three-input predictions: 0.5, 2.5, 5.0\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "e9eea2880f21de0880f46df02491f3f93d6c91c72adce7f45cbd6185f67a5652",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matching digest did not bypass model validation.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "8eee7b0abeb726b029ddf9d620e33cc5073301ffd70bef852b41a01bad6eb875",
      "outputs": []
    },
    {
      "sourceHash": "a05c0caf91836a9832c09905596ae27347381e2fb30a25524374cd6511b4b69a",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Changed artifact requires a new recorded digest.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "b08508c70f6ad4b495f75a2236697bd4d3f2350b77988730de6b8e1d589043b5",
      "outputs": []
    },
    {
      "sourceHash": "a8e1e92726adf2fe6e345f01dc0456a4990eb2b760120a20a5200a98ac62e17f",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nonfinite input rejected before output.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "d43643411d7af606e4a9f41681d94f3b22648dae3fef0f4fc1deb774631427e9",
      "outputs": []
    },
    {
      "sourceHash": "351309a5b0231442e0685d7f5744f07784ae079e7794410435bc1bc9e2744d4d",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New artifact loads, but the old behavioral expectation no longer passes.\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}