{
  "schemaVersion": 1,
  "lessonId": "deployment-05",
  "contentHash": "c229344c4a2c52d7669643c92b4b25cb997cf90ecb36600645fff53e8c8cbe68",
  "executedAt": "2026-10-20T10:50:41.243511+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "<class 'jax.numpy.float32'> max absolute error 0.0\n<class 'jax.numpy.float16'> max absolute error 0.0010547637939453125\n<class 'jax.numpy.bfloat16'> max absolute error 0.006171703338623047\nW8A32 simulated output error 0.008031368255615234\nW4A32 simulated output error 0.14571428298950195\n<class 'jax.numpy.float32'> max absolute error 0.0\n<class 'jax.numpy.float16'> max absolute error 0.0010547637939453125\n<class 'jax.numpy.bfloat16'> max absolute error 0.006171703338623047\nW8A32 simulated output error 0.008031368255615234\nW4A32 simulated output error 0.14571428298950195\nAffine accumulators: [[5, 15]] output codes: [[2, 7]]\nCorrect / missing zero point / clipped: [[0.625, 0.9375]] [[-0.125, 0.5625]] [[0.52734375, 0.52734375]]\nClipped values: 1 reconstructed: [0.19685039 0.8976378  1.        ]\nAffine accumulators: [[5, 15]] output codes: [[2, 7]]\nCorrect / missing zero point / clipped: [[0.625, 0.9375]] [[-0.125, 0.5625]] [[0.52734375, 0.52734375]]\nSmall-channel max error; shared / per-channel: 0.03 7.874053e-05\nZero channel handled\nW8A8 fixture error 0.00742650032043457\nCode-origin invariance and bias-only zero input verified.\nWeight-plus-scale subtotals: 24 14 11\nPASS: deployment-05\n",
  "notebookCells": [
    {
      "sourceHash": "c84aab026baa98296ddd4ec55a293a08044df3a833fa94f639a35bc2af26f132",
      "outputs": []
    },
    {
      "sourceHash": "7e7d3ab5a6a3750770c20ff70c4632be3710225e5048a98d79012c8b99fc969d",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'jax.numpy.float32'> max absolute error 0.0\n",
            "<class 'jax.numpy.float16'> max absolute error 0.0010547637939453125\n",
            "<class 'jax.numpy.bfloat16'> max absolute error 0.006171703338623047\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "0c46b5f8dd46f047fdc9a8f3c8c3e48626a893688f37987471752284789dd2bd",
      "outputs": []
    },
    {
      "sourceHash": "8d14d47bd3cb8d4e8311d56398900825d3c2e6f460913f1d74de287c6aa71d05",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W8A32 simulated output error 0.008031368255615234\n",
            "W4A32 simulated output error 0.14571428298950195\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "0bfbd482a3d7224b387d2015bc89d18c23655d7e2427e31ec4185b977c78f3cb",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'jax.numpy.float32'> max absolute error 0.0\n",
            "<class 'jax.numpy.float16'> max absolute error 0.0010547637939453125\n",
            "<class 'jax.numpy.bfloat16'> max absolute error 0.006171703338623047\n",
            "W8A32 simulated output error 0.008031368255615234\n",
            "W4A32 simulated output error 0.14571428298950195\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "3792708110eaeb14764a6f9b2713e5b81ac394a05ba621e58074ada0b198d96b",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Affine accumulators: [[5, 15]] output codes: [[2, 7]]\n",
            "Correct / missing zero point / clipped: [[0.625, 0.9375]] [[-0.125, 0.5625]] [[0.52734375, 0.52734375]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "e3825743b20fefc8cd49b0cd0dabfbccaeaa994ad51bc1077862e121ff2aecfb",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clipped values: 1 reconstructed: [0.19685039 0.8976378  1.        ]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "e76e6b30f58e400a5ad3e236fd636208558f87a28a244e3d5faa49befff40e82",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Affine accumulators: [[5, 15]] output codes: [[2, 7]]\n",
            "Correct / missing zero point / clipped: [[0.625, 0.9375]] [[-0.125, 0.5625]] [[0.52734375, 0.52734375]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "cdd0e17b1f1720a3a0b416e570934051b26857a4e0a021871efe089be46f702d",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Small-channel max error; shared / per-channel: 0.03 7.874053e-05\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "05f208bc714918a31d0b4d4ff5f9a5a9ee3a19ddb3c01892ff9a5ff4ed183341",
      "outputs": []
    },
    {
      "sourceHash": "b39b296575538c429a325e1c349e7e05fcb9125069841efacb8144ab15306c4c",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Zero channel handled\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "03d2c187ce66ee839f05f3699c4c3a743d8801d0d1813bcc43d754962ac2e9e8",
      "outputs": []
    },
    {
      "sourceHash": "34c45dc59881d6c1fa37a1ed3948b098b7b0cad44ab45138aa36703397fd176d",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W8A8 fixture error 0.00742650032043457\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "dd318e93db4448409361e13fdf33816a2bd40f0477a14965b6de42bee1735eb1",
      "outputs": []
    },
    {
      "sourceHash": "347231a721184597459781d812f4c5be1b96c29c358fd7d07d72fdddf0e48c73",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Code-origin invariance and bias-only zero input verified.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "fc6668d1b7ca56c4cea0ca7618e16b55a7c92810286bd3bfcad5c17ac2f08cf5",
      "outputs": []
    },
    {
      "sourceHash": "535b5aa610a9749b2305348efcf4cbbc79c245a9a0bf5ba3adc62e5f028abac1",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weight-plus-scale subtotals: 24 14 11\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}