{
  "schemaVersion": 1,
  "lessonId": "performance-01",
  "contentHash": "4b0835048c71e4db833b4b8988c5210075885e908194780b4db2d0cad29c33ed",
  "executedAt": "2026-10-20T10:47:33.645730+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "{\n  \"backend\": \"TFRT_CPU_0\",\n  \"jax\": \"0.9.2\",\n  \"numpy\": \"2.4.4\",\n  \"python\": \"3.14.3\",\n  \"input_shape\": [\n    64,\n    32\n  ],\n  \"weight_shape\": [\n    32,\n    16\n  ],\n  \"dtype\": \"float32\",\n  \"boundary\": \"input already placed; call plus output synchronization\",\n  \"first_call_seconds\": 0.051288416143506765,\n  \"warm_samples_seconds\": [\n    5.9833284467458725e-05,\n    2.5249551981687546e-05,\n    1.200009137392044e-05,\n    1.429067924618721e-05,\n    1.00000761449337e-05,\n    8.584000170230865e-06,\n    8.875038474798203e-06\n  ],\n  \"warm_median_seconds\": 1.200009137392044e-05,\n  \"max_abs_error\": 2.384185791015625e-07\n}\n{\n  \"backend\": \"TFRT_CPU_0\",\n  \"jax\": \"0.9.2\",\n  \"numpy\": \"2.4.4\",\n  \"python\": \"3.14.3\",\n  \"input_shape\": [\n    64,\n    32\n  ],\n  \"weight_shape\": [\n    32,\n    16\n  ],\n  \"dtype\": \"float32\",\n  \"boundary\": \"input already placed; call plus output synchronization\",\n  \"first_call_seconds\": 0.02849070867523551,\n  \"warm_samples_seconds\": [\n    0.00011466676369309425,\n    0.0001077083870768547,\n    4.1958410292863846e-05,\n    3.5458244383335114e-05,\n    1.2999866157770157e-05,\n    1.1667143553495407e-05,\n    1.3041310012340546e-05\n  ],\n  \"warm_median_seconds\": 3.5458244383335114e-05,\n  \"max_abs_error\": 2.384185791015625e-07\n}\nHandle-only seconds: 1.1832918971776962e-05\nComplete-call samples: [1.8625054508447647e-05, 1.2416858226060867e-05, 1.2083910405635834e-05]\nStructured samples: [6.495881825685501e-05, 3.074994310736656e-05, 3.1624920666217804e-05]\nChanged workload: (96, 32) [6.645871326327324e-05, 5.883257836103439e-05, 5.4459087550640106e-05, 2.1249987185001373e-05, 1.1249911040067673e-05]\nEnergy shape and samples: (64,) [5.758274346590042e-05, 4.183407872915268e-05, 2.150004729628563e-05, 1.8625054508447647e-05, 1.8666032701730728e-05]\n{'boundary': 'placed inputs; warm call plus output wait', 'samples': [4.9999915063381195e-05, 5.924981087446213e-05, 5.816575139760971e-05, 5.9042125940322876e-05, 6.141699850559235e-05]}\nPASS: performance-01\n",
  "notebookCells": [
    {
      "sourceHash": "845033583e57e7ad45f1511c809afe6b29d52f36baec2f09513d0a7b23d51ad5",
      "outputs": []
    },
    {
      "sourceHash": "18d6cb86157abb5475e8661972e2abeeb749bb0733268a0342984d0f788004fe",
      "outputs": []
    },
    {
      "sourceHash": "6419e7a42831afe51608d0120aa475e2907f83e3aac722ff6f3cc08b919230b6",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"backend\": \"TFRT_CPU_0\",\n",
            "  \"jax\": \"0.9.2\",\n",
            "  \"numpy\": \"2.4.4\",\n",
            "  \"python\": \"3.14.3\",\n",
            "  \"input_shape\": [\n",
            "    64,\n",
            "    32\n",
            "  ],\n",
            "  \"weight_shape\": [\n",
            "    32,\n",
            "    16\n",
            "  ],\n",
            "  \"dtype\": \"float32\",\n",
            "  \"boundary\": \"input already placed; call plus output synchronization\",\n",
            "  \"first_call_seconds\": 0.04347733408212662,\n",
            "  \"warm_samples_seconds\": [\n",
            "    4.7209206968545914e-05,\n",
            "    2.0790845155715942e-05,\n",
            "    1.1125113815069199e-05,\n",
            "    1.00000761449337e-05,\n",
            "    1.1083204299211502e-05,\n",
            "    1.0166317224502563e-05,\n",
            "    1.2207776308059692e-05\n",
            "  ],\n",
            "  \"warm_median_seconds\": 1.1125113815069199e-05,\n",
            "  \"max_abs_error\": 2.384185791015625e-07\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "9c2270e6f95ab1e2ed159863d3d4fef29b8ffdbde1811413ebe767e4678beee4",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"backend\": \"TFRT_CPU_0\",\n",
            "  \"jax\": \"0.9.2\",\n",
            "  \"numpy\": \"2.4.4\",\n",
            "  \"python\": \"3.14.3\",\n",
            "  \"input_shape\": [\n",
            "    64,\n",
            "    32\n",
            "  ],\n",
            "  \"weight_shape\": [\n",
            "    32,\n",
            "    16\n",
            "  ],\n",
            "  \"dtype\": \"float32\",\n",
            "  \"boundary\": \"input already placed; call plus output synchronization\",\n",
            "  \"first_call_seconds\": 0.024726249743252993,\n",
            "  \"warm_samples_seconds\": [\n",
            "    6.283307448029518e-05,\n",
            "    1.804158091545105e-05,\n",
            "    1.6083940863609314e-05,\n",
            "    2.2875145077705383e-05,\n",
            "    1.1666212230920792e-05,\n",
            "    8.500181138515472e-06,\n",
            "    9.083189070224762e-06\n",
            "  ],\n",
            "  \"warm_median_seconds\": 1.6083940863609314e-05,\n",
            "  \"max_abs_error\": 2.384185791015625e-07\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "eb13ce29780d7a91ea388129aab33f5c1fc7dc74180a229c2f429b325e42c8ad",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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JRU5ErkJggg==",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "e63a974f74b56a4396113ab5f8719a9cb5475ecb57cb407ebf4af7cf19b5026c",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Handle-only seconds: 6.066588684916496e-05\n",
            "Complete-call samples: [2.679135650396347e-05, 1.458311453461647e-05, 1.1457595974206924e-05]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "34c19a017e37ccfba6dd17781e8550de0b62aa769dcbad7b63bd58cd37c11eaf",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Structured samples: [7.479172199964523e-05, 6.062490865588188e-05, 6.416719406843185e-05]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "067bac0c45fbeb65b80f34aed2c35c21b26d39015595437a9c7c569c4aaae081",
      "outputs": []
    },
    {
      "sourceHash": "fe0b97e7aa9e1138804d8b5e015962d12105d40b5c395268ddfc190a3c6f650c",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Changed workload: (96, 32) [7.87912867963314e-05, 5.900021642446518e-05, 5.745794624090195e-05, 2.141715958714485e-05, 1.5249941498041153e-05]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "c6add1ba61bf3c17b1eee02f0ac4a4c57a369e31b6dce0df8cc9f42ee56fbe4f",
      "outputs": []
    },
    {
      "sourceHash": "5d1887dcb8dd16ddeaa44d181a0b63c6a8560d04ac326e83320a0b4c91d293dd",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Energy shape and samples: (64,) [8.345814421772957e-05, 1.962529495358467e-05, 1.3082753866910934e-05, 1.3208016753196716e-05, 1.1874828487634659e-05]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "91b0d9aa3c43b4cf5ea81dfb95c5fdd02bf41803e871550f440a5987952158a9",
      "outputs": []
    },
    {
      "sourceHash": "3b102d7c3090e72b57ceff1317c03d72b43fe05d789fc07b99869ed2ac158346",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'boundary': 'placed inputs; warm call plus output wait', 'samples': [2.916669473052025e-05, 1.895800232887268e-05, 1.1624768376350403e-05, 1.2250151485204697e-05, 8.333008736371994e-06]}\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}