contrib/notebooks/qsv_benchmark.ipynb

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      "source": [
        " [![qsv - Benchmarks](https://img.shields.io/badge/qsv-Benchmarks-5b69c2)](https://https://github.com/jqnatividad/qsv/tree/master/scripts/results)\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/drive/1fq4A7hYe0ofwJECp26-uqbPYHGhstK4S#scrollTo=XNbl9l4ili2C\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/>\n",
        "</a>\n",
        "#qsv Benchmarks Results\n"
      ],
      "metadata": {
        "id": "MQyBEPdKtB2I"
      }
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    {
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      "source": [
        "\n",
        "\n",
        "\n",
        "These benchmarks results are compiled on my main development machine - an Apple Mac Mini 2023 model with an M2 Pro chip with 12 CPU cores and 32GB of RAM; a 1TB SSD primary drive and a 1TB Samsung SSD 970 Evo plus external drive running MacOS Sonoma Public Beta.\n",
        "It uses the prebuilt, CPU optimized qsv binary variant in aarch64-apple-darwin.zip found at https://github.com/jqnatividad/qsv/releases/latest.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "3fhSVPjqtdyK"
      }
    },
    {
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      "execution_count": null,
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        "outputId": "e4b45f91-a54b-4595-e6aa-131f16ff494e",
        "cellView": "form"
      },
      "outputs": [
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            "text/plain": [
              "<google.colab.data_table.DataTable object>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "# @title Results\n",
        "import pandas as pd\n",
        "from google.colab import data_table\n",
        "data_table.enable_dataframe_formatter()\n",
        "benchmark=pd.read_csv(r\"/content/benchmark_result.csv\")\n",
        "benchmark\n",
        "data_table.DataTable(benchmark, include_index=False, num_rows_per_page=50)\n"
      ]
    }
  ]
}