ihuman_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6AcK2BLwCRG"
      },
      "source": [
        "# iHuman: Instant Animatable Digital Humans From Monocular Videos [ECCV 2024]![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tbj4By3B1bUpWdQ-WPbynd733d13bjR8?usp=sharing)\n",
        "\n",
        "### This is official colab implementation of the paper: \"iHuman: Instant Animatable Digital Humans From Monocular Videos\".\n",
        "\n",
        "Learn more on our project page: [iHuman](https://pramishp.github.io/iHuman/index.html)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AqW8vzkJZOGj"
      },
      "source": [
        "#### You must select a run time with GPU eg. T4 and add the following two directories as shortcut to your google drive at \"My Drive\"\n",
        "\n",
        "Go to Runtime -> Change runtime type and select GPU (e.g., T4).\n",
        "\n",
        "- Get the prebuilt wheels: https://drive.google.com/drive/folders/1tNCO1sfMlKS5X2aWEEtAniF-eYSn_66h?usp=sharing\n",
        "\n",
        "- Get the dataset, models and other assests: https://drive.google.com/drive/folders/1Ux98nBmlGbtvjyiu_k_aZYASXCH4Qy5P?usp=drive_link\n",
        "\n",
        "![image.png](data:image/png;base64,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        "\n",
        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)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucC5AjMcuxtb"
      },
      "source": [
        "## Mount the drive to load the assests in colab\n",
        "\n",
        "Please provide access to read and write in the google drive\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-B6l0X_eY02T",
        "outputId": "24dc23fa-112d-4668-8fbe-67438f5b182d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-PVQHZymu5le"
      },
      "source": [
        "## Download code from github and install conda\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Uc5o7fyXU6g",
        "outputId": "deec0d19-b80d-4278-e7ca-2e1c0c791c48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "⏬ Downloading https://github.com/conda-forge/miniforge/releases/download/23.11.0-0/Mambaforge-23.11.0-0-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:11\n",
            "🔁 Restarting kernel...\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/pramishp/ihuman.git\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCDYyI5-Y2qp"
      },
      "source": [
        "## Creating a new conda env for the project may take upto 15mins\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tTT9otwjXq5x",
        "outputId": "5b1d2793-fa14-4424-d727-46a47116e2f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  79% 0.7871838091425487/1 [00:08<00:01,  6.67s/it]\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  52% 0.5240777320724572/1 [00:08<00:04,  9.81s/it]\u001b[A\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  13% 0.12670434858759363/1 [00:08<00:36, 42.12s/it]\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  80% 0.8023142344586073/1 [00:08<00:01,  6.70s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  13% 0.12909521046847203/1 [00:08<00:36, 42.39s/it]\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  82% 0.8225997161876591/1 [00:08<00:01,  6.13s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  13% 0.1314634101514274/1 [00:08<00:37, 43.08s/it] \n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  84% 0.83900216841082/1 [00:08<00:01,  6.36s/it]  \u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  13% 0.1339902452198439/1 [00:08<00:36, 42.00s/it]\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  85% 0.8548020815727218/1 [00:08<00:01,  7.03s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  14% 0.13638110710072232/1 [00:08<00:41, 47.61s/it]\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  87% 0.8693299678275213/1 [00:09<00:00,  7.15s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  14% 0.13882862447640826/1 [00:09<00:39, 45.58s/it]\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  88% 0.8841256492206582/1 [00:09<00:00,  7.04s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  14% 0.14193334559186171/1 [00:09<00:35, 40.87s/it]\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  90% 0.9010636917204936/1 [00:09<00:00,  6.75s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  14% 0.14459615384781632/1 [00:09<00:34, 40.35s/it]\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  92% 0.9160602194673835/1 [00:09<00:00,  6.75s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  15% 0.14712298891623285/1 [00:09<00:34, 40.18s/it]\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  93% 0.9327304668288815/1 [00:09<00:00,  6.52s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  15% 0.14987644596387953/1 [00:09<00:33, 38.99s/it]\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  15% 0.15247126762606508/1 [00:09<00:34, 40.63s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  63% 0.6341274062805308/1 [00:09<00:03, 10.27s/it]\u001b[A\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  96% 0.9636608053068418/1 [00:09<00:00,  6.95s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  16% 0.1553493667622884/1 [00:09<00:33, 40.08s/it] \n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  98% 0.9782556403462257/1 [00:09<00:00,  9.97s/it]\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  16% 0.15786487073174338/1 [00:10<00:57, 67.93s/it]\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | :  99% 0.9901725240022364/1 [00:10<00:00, 20.54s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :   0% 0.00011179919674215067/1 [00:11<27:49:12, 100163.54s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  66% 0.6646677345135007/1 [00:11<00:16, 48.52s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :   0% 0.0031303775087802186/1 [00:11<42:35, 2563.99s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :   0% 0.0001518791001574902/1 [00:11<20:48:22, 74914.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :   0% 0.004556373004724707/1 [00:11<29:40, 1789.00s/it]     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  16% 0.15984781305000745/1 [00:11<03:27, 246.97s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :   1% 0.008161341362177/1 [00:11<12:23, 749.84s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | : 100% 0.9990767123519523/1 [00:11<00:00, 44.65s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :   1% 0.008808987809134432/1 [00:11<13:09, 796.45s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :   1% 0.009838329313309259/1 [00:11<09:04, 549.53s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :   1% 0.011542811611969257/1 [00:11<08:54, 541.13s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :   1% 0.011403518067699368/1 [00:11<06:50, 415.58s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :   1% 0.01458039361511906/1 [00:11<05:56, 361.61s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  16% 0.16127553151915758/1 [00:12<03:33, 254.75s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :   3% 0.028396995972506268/1 [00:12<01:19, 81.96s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :   4% 0.041311115242837336/1 [00:12<01:06, 69.43s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  16% 0.16360973790522843/1 [00:12<02:38, 189.75s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :   4% 0.043378088335954454/1 [00:12<00:41, 43.83s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :   7% 0.07001626517260298/1 [00:12<00:30, 32.76s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  17% 0.16592128209337628/1 [00:12<02:00, 144.30s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :   6% 0.06037156624076136/1 [00:12<00:25, 26.73s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  10% 0.10115148070488848/1 [00:12<00:17, 19.10s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  17% 0.1687087324379075/1 [00:12<01:28, 106.91s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :   8% 0.07602345378466245/1 [00:12<00:17, 19.16s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  13% 0.13304609173796142/1 [00:12<00:10, 12.66s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  17% 0.17122423640736248/1 [00:12<01:11, 86.14s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  10% 0.09670630518196033/1 [00:12<00:12, 13.37s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  16% 0.15795426416378983/1 [00:12<00:08, 10.14s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  17% 0.17362642938720238/1 [00:12<01:00, 73.70s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  12% 0.11504137344767303/1 [00:12<00:09, 10.67s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  18% 0.18195116198867328/1 [00:12<00:06,  8.35s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  18% 0.1760286223670423/1 [00:12<00:52, 64.14s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  14% 0.13617142163193952/1 [00:12<00:07,  8.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  21% 0.20974503731749397/1 [00:12<00:05,  7.05s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  18% 0.17827217996142108/1 [00:12<00:52, 63.31s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  15% 0.1529413011432621/1 [00:12<00:06,  8.20s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  24% 0.2360201216447398/1 [00:12<00:04,  6.11s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  73% 0.7344402728871131/1 [00:12<00:04, 17.01s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  18% 0.18060638634749193/1 [00:12<00:47, 58.13s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  26% 0.26001701946962325/1 [00:12<00:04,  5.62s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  18% 0.1827026396553711/1 [00:13<00:47, 57.61s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  20% 0.1979963774303488/1 [00:13<00:05,  6.38s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  28% 0.2837101590941917/1 [00:13<00:03,  5.21s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  19% 0.18502551494248046/1 [00:13<00:43, 53.68s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  22% 0.21610784730257723/1 [00:13<00:04,  6.28s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  31% 0.3074032987187602/1 [00:13<00:03,  5.01s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  19% 0.1873370591306283/1 [00:13<00:41, 50.90s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  23% 0.23377212038783704/1 [00:13<00:04,  6.17s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  33% 0.3320077129442736/1 [00:13<00:03,  4.72s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  19% 0.18946730573539197/1 [00:13<00:42, 52.04s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  25% 0.2513245942763547/1 [00:13<00:04,  6.18s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  36% 0.3591940718724643/1 [00:13<00:02,  4.46s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  78% 0.7815569223538412/1 [00:13<00:02, 12.30s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  19% 0.1915069035484636/1 [00:13<00:41, 51.60s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  39% 0.3910886829055373/1 [00:13<00:02,  4.00s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  79% 0.7902013076390676/1 [00:13<00:02, 12.78s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  19% 0.19371646784595783/1 [00:13<00:40, 49.69s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  42% 0.4176675254330981/1 [00:13<00:02,  4.14s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  80% 0.7983707266998309/1 [00:13<00:02, 13.08s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  20% 0.19582405225279853/1 [00:13<00:39, 49.13s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  44% 0.44303133515939896/1 [00:13<00:02,  4.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  20% 0.19790897446171618/1 [00:13<00:41, 52.13s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  35% 0.3462421123104406/1 [00:13<00:03,  4.95s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  48% 0.4793304400970391/1 [00:13<00:01,  3.74s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  20% 0.19988058568101874/1 [00:13<00:41, 51.93s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  37% 0.3732975179220411/1 [00:13<00:02,  4.57s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  51% 0.5069724363257023/1 [00:13<00:01,  3.75s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  82% 0.8248263454024192/1 [00:13<00:02, 12.80s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  20% 0.2018408658013598/1 [00:14<00:47, 59.90s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  53% 0.5343106743540506/1 [00:14<00:01,  4.02s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  83% 0.8330907577080751/1 [00:14<00:02, 12.63s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  20% 0.20393711910923895/1 [00:14<00:45, 56.68s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  56% 0.5625601869833438/1 [00:14<00:01,  3.90s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  21% 0.2059993791202336/1 [00:14<00:43, 54.24s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  59% 0.5886833922104321/1 [00:14<00:01,  3.91s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  44% 0.4360168672943876/1 [00:14<00:02,  5.32s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | :  21% 0.2082429367146124/1 [00:14<00:41, 52.13s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  62% 0.6152622347379928/1 [00:14<00:01,  3.88s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  46% 0.4588239034297863/1 [00:14<00:02,  5.05s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  86% 0.8591189068086467/1 [00:14<00:01, 12.77s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  21% 0.21020321683495344/1 [00:14<00:42, 53.34s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  48% 0.47894775884337343/1 [00:14<00:02,  5.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  87% 0.8682382583183359/1 [00:14<00:01, 12.31s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  21% 0.21270738970544692/1 [00:14<00:39, 50.19s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  50% 0.49840081907650763/1 [00:14<00:02,  5.27s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  88% 0.8775000996953641/1 [00:14<00:01, 12.06s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  70% 0.7048709038309121/1 [00:14<00:01,  3.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  21% 0.21472432532059552/1 [00:14<00:40, 51.04s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  89% 0.8910841337150055/1 [00:14<00:01, 10.32s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  22% 0.21747778236824222/1 [00:14<00:35, 45.97s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  73% 0.7322091418592603/1 [00:14<00:01,  3.88s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  90% 0.900868437938943/1 [00:14<00:01, 10.68s/it] \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  56% 0.5575425941531054/1 [00:14<00:02,  5.81s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  22% 0.21967601556677496/1 [00:14<00:42, 54.57s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  91% 0.9103202658057564/1 [00:14<00:01, 11.58s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  78% 0.7817217285106021/1 [00:15<00:00,  4.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  22% 0.221613633489193/1 [00:15<00:44, 56.80s/it]  \n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  92% 0.9191071409583216/1 [00:15<00:00, 12.22s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  81% 0.8087562083386354/1 [00:15<00:00,  4.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  22% 0.2238798532814948/1 [00:15<00:43, 55.58s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  93% 0.9274190498864238/1 [00:15<00:00, 12.69s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  84% 0.8411064566721808/1 [00:15<00:00,  3.84s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  23% 0.2257381535111823/1 [00:15<00:45, 59.30s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  94% 0.935398482457402/1 [00:15<00:00, 13.85s/it] \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  87% 0.8676852991997416/1 [00:15<00:00,  4.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  23% 0.22747181165229316/1 [00:15<00:48, 62.59s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  94% 0.9427604589365782/1 [00:15<00:00, 15.03s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | :  90% 0.8950235372280898/1 [00:15<00:00,  4.08s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  23% 0.22910348990275045/1 [00:15<00:48, 62.51s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  95% 0.9514523408442508/1 [00:15<00:00, 13.99s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  67% 0.6749317507323636/1 [00:15<00:01,  5.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  23% 0.23113175661686056/1 [00:15<00:45, 58.64s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  96% 0.9588143173234271/1 [00:15<00:00, 14.01s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  70% 0.69606179891663/1 [00:15<00:01,  5.22s/it]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  23% 0.23336398311227782/1 [00:15<00:41, 54.40s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  97% 0.9683136418126868/1 [00:15<00:00, 12.85s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  72% 0.7176390438878651/1 [00:15<00:01,  5.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  24% 0.2358228315869253/1 [00:15<00:38, 50.04s/it] \n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  98% 0.977100516965252/1 [00:15<00:00, 12.44s/it] \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  24% 0.23789642269688144/1 [00:15<00:37, 49.51s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | :  99% 0.9897346185359674/1 [00:15<00:00, 11.02s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  24% 0.24064987974452812/1 [00:16<00:34, 45.02s/it]\n",
            "libcublas-12.1.0.26  | 329.0 MB  | : 100% 0.9995664193823511/1 [00:16<00:00, 10.81s/it]\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  24% 0.2432900258025597/1 [00:16<00:32, 43.37s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  25% 0.24572621207928416/1 [00:16<00:32, 42.68s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  25% 0.24808308066327803/1 [00:16<00:36, 48.78s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  25% 0.25045128034623343/1 [00:16<00:35, 46.89s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  25% 0.25264951354476617/1 [00:16<00:36, 49.49s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  26% 0.25530099070175927/1 [00:16<00:34, 46.10s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | :  94% 0.9413492365689086/1 [00:16<00:00,  4.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  26% 0.257521886098215/1 [00:16<00:38, 51.72s/it]  \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  28% 0.27984415105238775/1 [00:17<00:31, 43.57s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  28% 0.28264293249588046/1 [00:17<00:29, 40.99s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  29% 0.2885011106589806/1 [00:18<00:26, 37.72s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  29% 0.29117525001389677/1 [00:18<00:28, 40.69s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  10% 0.09649160033740423/1 [00:18<01:20, 89.05s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   0% 0.00019718423464384747/1 [00:18<25:53:06, 93205.36s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  29% 0.29367942288439025/1 [00:18<00:32, 46.09s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   3% 0.03273258295087868/1 [00:18<06:23, 396.81s/it]        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  30% 0.29593431157773054/1 [00:18<00:33, 47.61s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :   7% 0.06940885059463431/1 [00:18<02:23, 154.13s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  30% 0.29808722038041724/1 [00:18<00:33, 47.84s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  11% 0.11101472410448612/1 [00:18<01:09, 78.34s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  30% 0.30061405544883374/1 [00:18<00:32, 46.07s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  15% 0.14887409715610483/1 [00:18<00:41, 48.65s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  30% 0.302823619746328/1 [00:18<00:34, 48.85s/it]  \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  19% 0.18633910173843585/1 [00:18<00:26, 32.21s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  30% 0.30490854195524564/1 [00:19<00:34, 49.39s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  22% 0.22321255361683534/1 [00:19<00:17, 22.66s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  31% 0.3077979721904304/1 [00:19<00:31, 45.07s/it] \n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  26% 0.2626494005456048/1 [00:19<00:11, 15.86s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  31% 0.31004152978480926/1 [00:19<00:32, 47.12s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  30% 0.30129751053579895/1 [00:19<00:08, 11.60s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  31% 0.3127270002386869/1 [00:19<00:30, 43.94s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  34% 0.33955125205670533/1 [00:19<00:05,  8.83s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  32% 0.31532182190087243/1 [00:19<00:28, 42.28s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  39% 0.385297994494078/1 [00:19<00:04,  6.52s/it]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  32% 0.3177126837817508/1 [00:19<00:29, 43.65s/it] \n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  43% 0.43065036846216287/1 [00:19<00:02,  5.13s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  32% 0.32002422796989866/1 [00:19<00:32, 47.66s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  47% 0.47127032079879544/1 [00:19<00:02,  4.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  32% 0.32230177886116196/1 [00:19<00:32, 48.11s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  51% 0.5128761943086473/1 [00:19<00:01,  4.02s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  32% 0.3244093632680026/1 [00:19<00:34, 51.63s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  55% 0.5513271200641975/1 [00:19<00:01,  3.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  33% 0.32660759646653537/1 [00:20<00:33, 49.87s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  60% 0.5962851255629947/1 [00:20<00:01,  3.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  59% 0.5894412752077345/1 [00:20<00:01,  3.68s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  33% 0.328647194279607/1 [00:20<00:36, 54.69s/it]  \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  62% 0.6183728588344356/1 [00:20<00:01,  3.68s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  33% 0.331117373853216/1 [00:20<00:33, 50.66s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  33% 0.33382550650501663/1 [00:20<00:30, 46.04s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  74% 0.7374690375679895/1 [00:20<00:00,  2.69s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  70% 0.7016703853201025/1 [00:20<00:00,  3.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  34% 0.3360577330004339/1 [00:20<00:31, 48.01s/it] \n",
            "\n",
            "\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  34% 0.3381879796051976/1 [00:20<00:32, 49.01s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  82% 0.824821653515214/1 [00:20<00:00,  2.67s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  77% 0.7673228250883858/1 [00:20<00:00,  3.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  34% 0.34132669401753557/1 [00:20<00:28, 43.03s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  87% 0.868793737840792/1 [00:20<00:00,  2.55s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  80% 0.7973671619314985/1 [00:20<00:00,  3.78s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  34% 0.34369489370049094/1 [00:20<00:29, 45.23s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | :  91% 0.9096108744120683/1 [00:20<00:00,  2.84s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  82% 0.8247090981061831/1 [00:20<00:00,  3.92s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  10% 0.10162595564432231/1 [00:20<01:48, 120.42s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  35% 0.34594978239383123/1 [00:20<00:32, 49.64s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  85% 0.8526868932616742/1 [00:20<00:00,  3.88s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "qt-main-5.15.2       | 53.7 MB   | :  15% 0.14646950054181695/1 [00:20<00:59, 70.02s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  35% 0.34802337350378737/1 [00:21<00:34, 53.04s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  88% 0.8789160762199472/1 [00:21<00:00,  4.09s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  35% 0.3506748506607805/1 [00:21<00:31, 48.62s/it] \n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  91% 0.9084835188274549/1 [00:21<00:00,  3.88s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  35% 0.35283909056242874/1 [00:21<00:31, 48.32s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  94% 0.9405943973581892/1 [00:21<00:00,  3.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  35% 0.35499199936511544/1 [00:21<00:31, 48.30s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | :  97% 0.9741359585957382/1 [00:21<00:00,  3.60s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  36% 0.35716757036572516/1 [00:21<00:30, 47.74s/it]\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "\n",
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            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  40% 0.4001577598256903/1 [00:23<00:34, 58.09s/it] \n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  40% 0.40189141796680117/1 [00:23<00:36, 60.34s/it]\n",
            "\n",
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            "\n",
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            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  41% 0.4053134098531769/1 [00:24<00:35, 59.56s/it]\n",
            "\n",
            "\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  41% 0.40756829854651716/1 [00:24<00:33, 57.19s/it]\n",
            "\n",
            "\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  41% 0.40932461888555105/1 [00:24<00:35, 59.40s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | :  37% 0.37047331337238726/1 [00:24<00:13, 20.97s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   0% 0.00046810108526368127/1 [00:24<14:38:47, 52752.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libllvm14-14.0.6     | 33.4 MB   | :   2% 0.01872404341054725/1 [00:24<15:18, 935.91s/it]        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "icu-73.1             | 25.9 MB   | :   0% 0.0006024446146419119/1 [00:24<11:28:05, 41310.72s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  41% 0.41101295263081594/1 [00:25<01:42, 173.47s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   4% 0.03604378356530346/1 [00:25<06:38, 413.63s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "icu-73.1             | 25.9 MB   | :   4% 0.04397845686885957/1 [00:25<05:24, 339.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   5% 0.045873906355840766/1 [00:25<04:35, 288.55s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :   0% 0.000657242526624159/1 [00:25<10:42:49, 38595.05s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :   2% 0.015773820638979818/1 [00:25<18:48, 1147.05s/it]    \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   5% 0.053831624805323344/1 [00:25<03:24, 216.63s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :   6% 0.05662979377633971/1 [00:25<03:40, 233.46s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   6% 0.06085314108427856/1 [00:25<02:36, 166.57s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :   3% 0.029575913698087156/1 [00:25<08:18, 513.81s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :   7% 0.06687135222525221/1 [00:25<02:41, 173.26s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   7% 0.0674065562779701/1 [00:25<02:01, 129.82s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | :  44% 0.4401585168441328/1 [00:25<00:11, 21.25s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :   4% 0.0400917941240737/1 [00:25<05:11, 325.02s/it]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :   8% 0.0759080214448809/1 [00:25<02:02, 132.76s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   7% 0.07349187038639796/1 [00:25<01:34, 101.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :   8% 0.08373980143522575/1 [00:25<01:35, 104.13s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :   5% 0.049293189496811926/1 [00:25<03:33, 224.89s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   8% 0.07957718449482581/1 [00:25<01:13, 79.89s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :   9% 0.0915715814255706/1 [00:26<01:13, 81.19s/it]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  41% 0.41225937351658193/1 [00:26<03:04, 313.86s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   9% 0.08519439751798999/1 [00:26<00:58, 64.02s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  10% 0.09940336141591546/1 [00:26<00:56, 63.08s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :   7% 0.06638149518904006/1 [00:26<01:49, 117.18s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | :   9% 0.09081161054115416/1 [00:26<00:47, 51.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  11% 0.10723514140626031/1 [00:26<00:44, 49.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :   7% 0.07492564803515413/1 [00:26<01:19, 86.00s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  41% 0.41317719253246415/1 [00:26<03:01, 310.13s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | :  49% 0.48779642430221876/1 [00:26<00:09, 18.83s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  18% 0.17591382747543827/1 [00:26<00:10, 12.21s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :  14% 0.1426216282774425/1 [00:26<00:17, 20.74s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  41% 0.4148768573766905/1 [00:26<02:11, 225.47s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | :  57% 0.5665367672081459/1 [00:26<00:05, 12.48s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  26% 0.26326829659851547/1 [00:26<00:04,  5.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :  28% 0.2773563462353951/1 [00:26<00:05,  7.05s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  42% 0.4168824618928776/1 [00:26<01:34, 161.88s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | :  65% 0.6531511444046658/1 [00:26<00:02,  8.40s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  36% 0.3596594349412214/1 [00:26<00:02,  3.53s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :  38% 0.3798861803887639/1 [00:26<00:02,  4.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  42% 0.41934131036752503/1 [00:26<01:06, 114.96s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | :  72% 0.7236237513054705/1 [00:26<00:01,  6.36s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  45% 0.4470139040642986/1 [00:26<00:01,  2.62s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :  49% 0.49490362254799175/1 [00:26<00:01,  3.04s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  42% 0.42099565081590534/1 [00:26<00:58, 100.23s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  54% 0.5446099316362883/1 [00:26<00:00,  2.01s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | :  79% 0.7874034290592715/1 [00:26<00:01,  5.08s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :  59% 0.5856030912221257/1 [00:26<00:01,  2.44s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  42% 0.42277463335286225/1 [00:26<00:50, 87.75s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  63% 0.6337717346032913/1 [00:26<00:00,  1.72s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | :  88% 0.876773718257499/1 [00:26<00:00,  3.67s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :  70% 0.6979915632748569/1 [00:26<00:00,  1.89s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  42% 0.42466692687943425/1 [00:26<00:45, 78.93s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  75% 0.745223988312045/1 [00:26<00:00,  1.41s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | :  94% 0.9448841148711259/1 [00:27<00:00,  3.19s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :  79% 0.7900055170022392/1 [00:27<00:00,  1.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  43% 0.4263552606246991/1 [00:27<00:42, 74.32s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  83% 0.8349882358936899/1 [00:27<00:00,  1.37s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.19        | 23.8 MB   | :  88% 0.880047743149749/1 [00:27<00:00,  1.55s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  43% 0.4290407310785767/1 [00:27<00:34, 61.14s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "icu-73.1             | 25.9 MB   | :  92% 0.9211378157874832/1 [00:27<00:00,  1.32s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
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            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  43% 0.43127295757399403/1 [00:27<00:31, 56.17s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  43% 0.4332672309912196/1 [00:27<00:31, 55.72s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  45% 0.44558413556237986/1 [00:27<00:23, 41.63s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  45% 0.4480316529380658/1 [00:28<00:23, 42.65s/it] \n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "intel-openmp-2023.1. | 17.2 MB   | :   0% 0.0009096082871621815/1 [00:28<8:33:11, 30819.57s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  45% 0.45056981910544386/1 [00:28<00:22, 41.67s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  19% 0.1910177403040581/1 [00:28<01:23, 103.41s/it]       \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  45% 0.4529946742832068/1 [00:28<00:23, 42.89s/it] \n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "libnvjitlink-12.1.10 | 16.9 MB   | :   0% 0.0009221176422649559/1 [00:28<8:30:04, 30633.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  38% 0.3802162640337919/1 [00:28<00:26, 43.19s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  46% 0.45535154286720064/1 [00:28<00:23, 43.03s/it]\n",
            "\n",
            "\n",
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            "\n",
            "libnvjitlink-12.1.10 | 16.9 MB   | :  17% 0.1742802343880767/1 [00:28<01:34, 114.22s/it]       \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  54% 0.5439457557229845/1 [00:28<00:11, 25.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "cuda-cupti-12.1.105  | 15.4 MB   | :   0% 0.001016089426288399/1 [00:28<7:45:09, 27937.62s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  46% 0.4583542840920005/1 [00:28<00:21, 39.68s/it] \n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "libnvjitlink-12.1.10 | 16.9 MB   | :  35% 0.3476383511338884/1 [00:28<00:31, 47.54s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  71% 0.7076752474121772/1 [00:28<00:04, 16.07s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "cuda-cupti-12.1.105  | 15.4 MB   | :  11% 0.1097376580391471/1 [00:28<02:42, 182.50s/it]      \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  46% 0.4608924502593785/1 [00:28<00:25, 47.03s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "libnvjitlink-12.1.10 | 16.9 MB   | :  51% 0.5099310561725207/1 [00:28<00:13, 26.94s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  87% 0.8686759142398833/1 [00:28<00:01, 10.75s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "cuda-cupti-12.1.105  | 15.4 MB   | :  40% 0.39627487625247565/1 [00:28<00:23, 39.00s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "cuda-nvrtc-12.1.105  | 19.7 MB   | :  82% 0.8214513073172236/1 [00:28<00:01,  8.83s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  46% 0.4631360078537573/1 [00:28<00:29, 55.81s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "cuda-cupti-12.1.105  | 15.4 MB   | :  53% 0.5334469488014095/1 [00:28<00:11, 25.60s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "cuda-nvrtc-12.1.105  | 19.7 MB   | :  98% 0.9782954334973686/1 [00:28<00:00,  5.97s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  47% 0.46508495687513685/1 [00:28<00:29, 56.07s/it]\n",
            "\n",
            "\n",
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            "\n",
            "cuda-cupti-12.1.105  | 15.4 MB   | :  70% 0.7021177935652838/1 [00:28<00:04, 16.07s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  47% 0.46736250776640015/1 [00:29<00:28, 53.26s/it]\n",
            "\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  47% 0.47223488031984906/1 [00:29<00:24, 47.28s/it]\n",
            "\n",
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            "pytorch-2.4.1        | 1.35 GB   | :  48% 0.4751809660498414/1 [00:29<00:22, 42.66s/it] \n",
            "\n",
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            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :  27% 0.27242851622175623/1 [00:29<00:55, 75.62s/it]       \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  48% 0.4775944901286428/1 [00:29<00:22, 43.84s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :  53% 0.5320368669742533/1 [00:29<00:15, 32.25s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  48% 0.47992869651471365/1 [00:29<00:23, 45.50s/it]\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libclang13-14.0.6    | 9.8 MB    | :  77% 0.7708124488392044/1 [00:29<00:04, 18.55s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  48% 0.4821722541090924/1 [00:29<00:44, 85.24s/it] \n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  67% 0.6675377020204182/1 [00:37<00:11, 35.18s/it]\n",
            "\n",
            "\n",
            "pytorch-2.4.1        | 1.35 GB   | :  99% 0.9940999740911075/1 [00:43<00:00, 16.90s/it]\n",
            "\n",
            "\n",
            "\n",
            "libcusparse-12.0.2.5 | 163.0 MB  | : 100% 1.0/1 [01:09<00:00,  5.19s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libcufft-11.0.2.4    | 102.9 MB  | : 100% 1.0/1 [01:29<00:00,  3.99s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "torchtriton-3.0.0    | 233.4 MB  | : 100% 1.0/1 [01:36<00:00, 44.65s/it]               \u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "mysql-5.7.20         | 79.2 MB   | : 100% 1.0/1 [01:54<00:00,  2.82s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libnpp-12.0.2.50     | 139.8 MB  | : 100% 1.0/1 [01:59<00:00,  4.70s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "qt-main-5.15.2       | 53.7 MB   | : 100% 1.0/1 [02:04<00:00,  2.47s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libcusolver-11.4.4.5 | 98.3 MB   | : 100% 1.0/1 [02:31<00:00,  3.60s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "python-3.8.19        | 23.8 MB   | : 100% 1.0/1 [02:32<00:00, 375.24s/it]             \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "python-3.8.19        | 23.8 MB   | : 100% 1.0/1 [02:32<00:00, 375.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "icu-73.1             | 25.9 MB   | : 100% 1.0/1 [02:33<00:00,  1.32s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "libcurand-10.3.7.77  | 39.7 MB   | : 100% 1.0/1 [02:34<00:00,  3.19s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "libllvm14-14.0.6     | 33.4 MB   | : 100% 1.0/1 [02:35<00:00,  1.29s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "intel-openmp-2023.1. | 17.2 MB   | : 100% 1.0/1 [02:36<00:00, 10.75s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "cuda-nvrtc-12.1.105  | 19.7 MB   | : 100% 1.0/1 [02:41<00:00,  5.97s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libnvjitlink-12.1.10 | 16.9 MB   | : 100% 1.0/1 [02:45<00:00,  8.48s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "cuda-cupti-12.1.105  | 15.4 MB   | : 100% 1.0/1 [02:48<00:00, 304.17s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "cuda-cupti-12.1.105  | 15.4 MB   | : 100% 1.0/1 [02:48<00:00, 304.17s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libclang13-14.0.6    | 9.8 MB    | : 100% 1.0/1 [02:53<00:00, 12.36s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pytorch-2.4.1        | 1.35 GB   | : 100% 1.0/1 [06:28<00:00, 16.90s/it]               \n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                         \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                         \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
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            "\u001b[A\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
            "\n",
            "\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
            "\n",
            "\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
            "\n",
            "\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Executing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Installing pip dependencies: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- 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\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ Ran pip subprocess with arguments:\n",
            "['/usr/local/envs/ihuman/bin/python', '-m', 'pip', 'install', '-U', '-r', '/content/ihuman/condaenv.odkb6579.requirements.txt', '--exists-action=b']\n",
            "Pip subprocess output:\n",
            "Collecting absl-py==2.1.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 1))\n",
            "  Downloading absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\n",
            "Collecting addict==2.4.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 2))\n",
            "  Downloading addict-2.4.0-py3-none-any.whl.metadata (1.0 kB)\n",
            "Collecting aiohttp==3.9.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 3))\n",
            "  Downloading aiohttp-3.9.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.4 kB)\n",
            "Collecting aiosignal==1.3.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 4))\n",
            "  Downloading aiosignal-1.3.1-py3-none-any.whl.metadata (4.0 kB)\n",
            "Collecting aitviewer==1.13.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 5))\n",
            "  Downloading aitviewer-1.13.0-py3-none-any.whl.metadata (6.4 kB)\n",
            "Collecting alembic==1.13.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 6))\n",
            "  Downloading alembic-1.13.2-py3-none-any.whl.metadata (7.4 kB)\n",
            "Collecting antlr4-python3-runtime==4.9.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 7))\n",
            "  Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Collecting asttokens==2.4.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 8))\n",
            "  Downloading asttokens-2.4.1-py2.py3-none-any.whl.metadata (5.2 kB)\n",
            "Collecting async-timeout==4.0.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 9))\n",
            "  Downloading async_timeout-4.0.3-py3-none-any.whl.metadata (4.2 kB)\n",
            "Collecting attrs==23.2.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 10))\n",
            "  Downloading attrs-23.2.0-py3-none-any.whl.metadata (9.5 kB)\n",
            "Collecting autopage==0.5.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 11))\n",
            "  Downloading autopage-0.5.2-py3-none-any.whl.metadata (7.9 kB)\n",
            "Collecting beautifulsoup4==4.12.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 12))\n",
            "  Downloading beautifulsoup4-4.12.3-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting blinker==1.7.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 13))\n",
            "  Downloading blinker-1.7.0-py3-none-any.whl.metadata (1.9 kB)\n",
            "Collecting cachetools==5.3.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 14))\n",
            "  Downloading cachetools-5.3.3-py3-none-any.whl.metadata (5.3 kB)\n",
            "Collecting certifi==2022.12.7 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 15))\n",
            "  Downloading certifi-2022.12.7-py3-none-any.whl.metadata (2.9 kB)\n",
            "Collecting cffi==1.16.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 16))\n",
            "  Downloading cffi-1.16.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting charset-normalizer==2.1.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 17))\n",
            "  Downloading charset_normalizer-2.1.1-py3-none-any.whl.metadata (11 kB)\n",
            "Collecting click==8.1.7 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 18))\n",
            "  Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB)\n",
            "Collecting cliff==4.7.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 19))\n",
            "  Downloading cliff-4.7.0-py3-none-any.whl.metadata (2.1 kB)\n",
            "Collecting cmaes==0.10.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 20))\n",
            "  Downloading cmaes-0.10.0-py3-none-any.whl.metadata (19 kB)\n",
            "Collecting cmake==3.25.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 21))\n",
            "  Downloading cmake-3.25.0-py2.py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.8 kB)\n",
            "Collecting cmd2==2.4.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 22))\n",
            "  Downloading cmd2-2.4.3-py3-none-any.whl.metadata (12 kB)\n",
            "Collecting colorlog==6.8.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 23))\n",
            "  Downloading colorlog-6.8.2-py3-none-any.whl.metadata (10 kB)\n",
            "Collecting commonmark==0.9.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 24))\n",
            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl.metadata (5.7 kB)\n",
            "Collecting configargparse==1.7 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 25))\n",
            "  Downloading ConfigArgParse-1.7-py3-none-any.whl.metadata (23 kB)\n",
            "Collecting contourpy==1.1.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 26))\n",
            "  Downloading contourpy-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.9 kB)\n",
            "Collecting cuda-python==12.3.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 27))\n",
            "  Downloading cuda_python-12.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting cycler==0.12.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 28))\n",
            "  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting cython==3.0.10 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 29))\n",
            "  Downloading Cython-3.0.10-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.2 kB)\n",
            "Collecting dash==2.16.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 30))\n",
            "  Downloading dash-2.16.1-py3-none-any.whl.metadata (10 kB)\n",
            "Collecting dash-core-components==2.0.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 31))\n",
            "  Downloading dash_core_components-2.0.0-py3-none-any.whl.metadata (2.9 kB)\n",
            "Collecting dash-html-components==2.0.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 32))\n",
            "  Downloading dash_html_components-2.0.0-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting fastjsonschema==2.19.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 33))\n",
            "  Downloading fastjsonschema-2.19.1-py3-none-any.whl.metadata (2.1 kB)\n",
            "Collecting ffmpeg==1.4 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 34))\n",
            "  Downloading ffmpeg-1.4.tar.gz (5.1 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Collecting filelock==3.9.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 35))\n",
            "  Downloading filelock-3.9.0-py3-none-any.whl.metadata (2.3 kB)\n",
            "Collecting filterpy==1.4.5 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 36))\n",
            "  Downloading filterpy-1.4.5.zip (177 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Collecting flask==3.0.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 37))\n",
            "  Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB)\n",
            "Collecting flatbuffers==24.3.25 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 38))\n",
            "  Downloading flatbuffers-24.3.25-py2.py3-none-any.whl.metadata (850 bytes)\n",
            "Collecting fonttools==4.51.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 39))\n",
            "  Downloading fonttools-4.51.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (159 kB)\n",
            "Collecting frozenlist==1.4.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 40))\n",
            "  Downloading frozenlist-1.4.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting fsspec==2024.3.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 41))\n",
            "  Downloading fsspec-2024.3.1-py3-none-any.whl.metadata (6.8 kB)\n",
            "Collecting fvcore==0.1.5.post20221221 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 42))\n",
            "  Downloading fvcore-0.1.5.post20221221.tar.gz (50 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Collecting gdown==5.2.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 43))\n",
            "  Downloading gdown-5.2.0-py3-none-any.whl.metadata (5.8 kB)\n",
            "Collecting glcontext==3.0.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 44))\n",
            "  Downloading glcontext-3.0.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.0 kB)\n",
            "Collecting google-auth==2.29.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 45))\n",
            "  Downloading google_auth-2.29.0-py2.py3-none-any.whl.metadata (4.7 kB)\n",
            "Collecting google-auth-oauthlib==1.0.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 46))\n",
            "  Downloading google_auth_oauthlib-1.0.0-py2.py3-none-any.whl.metadata (2.7 kB)\n",
            "Collecting greenlet==3.0.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 47))\n",
            "  Downloading greenlet-3.0.3-cp38-cp38-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (3.8 kB)\n",
            "Collecting grpcio==1.62.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 48))\n",
            "  Downloading grpcio-1.62.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.0 kB)\n",
            "Collecting h5py==3.10.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 49))\n",
            "  Downloading h5py-3.10.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)\n",
            "Collecting hydra-core==1.3.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 50))\n",
            "  Downloading hydra_core-1.3.2-py3-none-any.whl.metadata (5.5 kB)\n",
            "Collecting hydra-optuna-sweeper==1.2.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 51))\n",
            "  Downloading hydra_optuna_sweeper-1.2.0-py3-none-any.whl.metadata (1.0 kB)\n",
            "Collecting idna==3.4 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 52))\n",
            "  Downloading idna-3.4-py3-none-any.whl.metadata (9.8 kB)\n",
            "Collecting imageio==2.34.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 53))\n",
            "  Downloading imageio-2.34.0-py3-none-any.whl.metadata (4.9 kB)\n",
            "Collecting imgui==2.0.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 54))\n",
            "  Downloading imgui-2.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.0 kB)\n",
            "Collecting importlib-metadata==7.1.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 55))\n",
            "  Downloading importlib_metadata-7.1.0-py3-none-any.whl.metadata (4.7 kB)\n",
            "Collecting importlib-resources==6.4.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 56))\n",
            "  Downloading importlib_resources-6.4.0-py3-none-any.whl.metadata (3.9 kB)\n",
            "Collecting iopath==0.1.10 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 57))\n",
            "  Downloading iopath-0.1.10.tar.gz (42 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Collecting ipython==8.12.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 58))\n",
            "  Downloading ipython-8.12.3-py3-none-any.whl.metadata (5.7 kB)\n",
            "Collecting ipywidgets==8.1.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 59))\n",
            "  Downloading ipywidgets-8.1.2-py3-none-any.whl.metadata (2.4 kB)\n",
            "Collecting itsdangerous==2.1.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 60))\n",
            "  Downloading itsdangerous-2.1.2-py3-none-any.whl.metadata (2.9 kB)\n",
            "Collecting jax==0.4.13 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 61))\n",
            "  Downloading jax-0.4.13.tar.gz (1.3 MB)\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.3/1.3 MB 58.5 MB/s eta 0:00:00\n",
            "  Installing build dependencies: started\n",
            "  Installing build dependencies: finished with status 'done'\n",
            "  Getting requirements to build wheel: started\n",
            "  Getting requirements to build wheel: finished with status 'done'\n",
            "  Preparing metadata (pyproject.toml): started\n",
            "  Preparing metadata (pyproject.toml): finished with status 'done'\n",
            "Collecting jaxlib==0.4.13 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 62))\n",
            "  Downloading jaxlib-0.4.13-cp38-cp38-manylinux2014_x86_64.whl.metadata (2.1 kB)\n",
            "Collecting jinja2==3.1.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 63))\n",
            "  Downloading Jinja2-3.1.2-py3-none-any.whl.metadata (3.5 kB)\n",
            "Collecting joblib==1.4.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 64))\n",
            "  Downloading joblib-1.4.0-py3-none-any.whl.metadata (5.4 kB)\n",
            "Collecting jsonschema==4.21.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 65))\n",
            "  Downloading jsonschema-4.21.1-py3-none-any.whl.metadata (7.8 kB)\n",
            "Collecting jsonschema-specifications==2023.12.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 66))\n",
            "  Downloading jsonschema_specifications-2023.12.1-py3-none-any.whl.metadata (3.0 kB)\n",
            "Collecting jupyterlab-widgets==3.0.10 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 67))\n",
            "  Downloading jupyterlab_widgets-3.0.10-py3-none-any.whl.metadata (4.1 kB)\n",
            "Collecting kiwisolver==1.4.5 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 68))\n",
            "  Downloading kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (6.4 kB)\n",
            "Collecting lazy-loader==0.4 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 69))\n",
            "  Downloading lazy_loader-0.4-py3-none-any.whl.metadata (7.6 kB)\n",
            "Collecting lightning-utilities==0.11.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 70))\n",
            "  Downloading lightning_utilities-0.11.2-py3-none-any.whl.metadata (4.7 kB)\n",
            "Collecting lit==15.0.7 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 71))\n",
            "  Downloading lit-15.0.7.tar.gz (132 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Collecting mako==1.3.5 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 72))\n",
            "  Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB)\n",
            "Collecting markdown==3.6 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 73))\n",
            "  Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB)\n",
            "Collecting matplotlib==3.7.5 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 74))\n",
            "  Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB)\n",
            "Collecting matplotlib-inline==0.1.6 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 75))\n",
            "  Downloading matplotlib_inline-0.1.6-py3-none-any.whl.metadata (2.8 kB)\n",
            "Collecting mediapipe==0.10.11 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 76))\n",
            "  Downloading mediapipe-0.10.11-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.7 kB)\n",
            "Collecting ml-dtypes==0.2.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 77))\n",
            "  Downloading ml_dtypes-0.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
            "Collecting moderngl==5.12.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 78))\n",
            "  Downloading moderngl-5.12.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.8 kB)\n",
            "Collecting moderngl-window==2.4.6 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 79))\n",
            "  Downloading moderngl_window-2.4.6-py3-none-any.whl.metadata (8.8 kB)\n",
            "Collecting multidict==6.0.5 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 80))\n",
            "  Downloading multidict-6.0.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\n",
            "Collecting multipledispatch==1.0.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 81))\n",
            "  Downloading multipledispatch-1.0.0-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting nbformat==5.10.4 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 82))\n",
            "  Downloading nbformat-5.10.4-py3-none-any.whl.metadata (3.6 kB)\n",
            "Collecting networkx==3.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 83))\n",
            "  Downloading networkx-3.0-py3-none-any.whl.metadata (5.1 kB)\n",
            "Collecting norfair==2.2.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 84))\n",
            "  Downloading norfair-2.2.0-py3-none-any.whl.metadata (17 kB)\n",
            "Collecting numpy==1.24.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 85))\n",
            "  Downloading numpy-1.24.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)\n",
            "Collecting nvidia-cublas-cu11==11.11.3.6 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 86))\n",
            "  Downloading nvidia_cublas_cu11-11.11.3.6-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-cupti-cu11==11.8.87 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 87))\n",
            "  Downloading nvidia_cuda_cupti_cu11-11.8.87-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cuda-nvrtc-cu11==11.8.89 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 88))\n",
            "  Downloading nvidia_cuda_nvrtc_cu11-11.8.89-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-runtime-cu11==11.8.89 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 89))\n",
            "  Downloading nvidia_cuda_runtime_cu11-11.8.89-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cudnn-cu11==8.7.0.84 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 90))\n",
            "  Downloading nvidia_cudnn_cu11-8.7.0.84-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cufft-cu11==10.9.0.58 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 91))\n",
            "  Downloading nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-curand-cu11==10.3.0.86 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 92))\n",
            "  Downloading nvidia_curand_cu11-10.3.0.86-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cusolver-cu11==11.4.1.48 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 93))\n",
            "  Downloading nvidia_cusolver_cu11-11.4.1.48-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cusparse-cu11==11.7.5.86 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 94))\n",
            "  Downloading nvidia_cusparse_cu11-11.7.5.86-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-nccl-cu11==2.20.5 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 95))\n",
            "  Downloading nvidia_nccl_cu11-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
            "Collecting nvidia-nvtx-cu11==11.8.86 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 96))\n",
            "  Downloading nvidia_nvtx_cu11-11.8.86-py3-none-manylinux2014_x86_64.whl.metadata (1.7 kB)\n",
            "Collecting oauthlib==3.2.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 97))\n",
            "  Downloading oauthlib-3.2.2-py3-none-any.whl.metadata (7.5 kB)\n",
            "Collecting omegaconf==2.3.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 98))\n",
            "  Downloading omegaconf-2.3.0-py3-none-any.whl.metadata (3.9 kB)\n",
            "Collecting open3d==0.18.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 99))\n",
            "  Downloading open3d-0.18.0-cp38-cp38-manylinux_2_27_x86_64.whl.metadata (4.2 kB)\n",
            "Collecting opencv-contrib-python==4.9.0.80 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 100))\n",
            "  Downloading opencv_contrib_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
            "Collecting opencv-contrib-python-headless==4.9.0.80 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 101))\n",
            "  Downloading opencv_contrib_python_headless-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
            "Collecting opencv-python==4.10.0.84 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 102))\n",
            "  Downloading opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
            "Collecting opencv-python-headless==4.10.0.84 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 103))\n",
            "  Downloading opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
            "Collecting opt-einsum==3.3.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 104))\n",
            "  Downloading opt_einsum-3.3.0-py3-none-any.whl.metadata (6.5 kB)\n",
            "Collecting optuna==2.10.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 105))\n",
            "  Downloading optuna-2.10.1-py3-none-any.whl.metadata (15 kB)\n",
            "Collecting packaging==24.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 106))\n",
            "  Downloading packaging-24.0-py3-none-any.whl.metadata (3.2 kB)\n",
            "Collecting pandas==2.0.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 107))\n",
            "  Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)\n",
            "Collecting pbr==6.0.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 108))\n",
            "  Downloading pbr-6.0.0-py2.py3-none-any.whl.metadata (1.3 kB)\n",
            "Collecting pdbr==0.8.9 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 109))\n",
            "  Downloading pdbr-0.8.9-py3-none-any.whl.metadata (8.6 kB)\n",
            "Collecting pillow==10.2.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 110))\n",
            "  Downloading pillow-10.2.0-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (9.7 kB)\n",
            "Requirement already satisfied: pip==24.3.1 in /usr/local/envs/ihuman/lib/python3.8/site-packages (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 111)) (24.3.1)\n",
            "Collecting pkgutil-resolve-name==1.3.10 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 112))\n",
            "  Downloading pkgutil_resolve_name-1.3.10-py3-none-any.whl.metadata (624 bytes)\n",
            "Collecting platformdirs==4.2.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 113))\n",
            "  Downloading platformdirs-4.2.0-py3-none-any.whl.metadata (11 kB)\n",
            "Collecting plotly==5.20.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 114))\n",
            "  Downloading plotly-5.20.0-py3-none-any.whl.metadata (7.0 kB)\n",
            "Collecting portalocker==2.8.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 115))\n",
            "  Downloading portalocker-2.8.2-py3-none-any.whl.metadata (8.5 kB)\n",
            "Collecting prettytable==3.10.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 116))\n",
            "  Downloading prettytable-3.10.0-py3-none-any.whl.metadata (30 kB)\n",
            "Collecting prompt-toolkit==3.0.43 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 117))\n",
            "  Downloading prompt_toolkit-3.0.43-py3-none-any.whl.metadata (6.5 kB)\n",
            "Collecting protobuf==3.20.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 118))\n",
            "  Downloading protobuf-3.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (679 bytes)\n",
            "Collecting pure-eval==0.2.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 119))\n",
            "  Downloading pure_eval-0.2.2-py3-none-any.whl.metadata (6.2 kB)\n",
            "Collecting pyasn1==0.6.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 120))\n",
            "  Downloading pyasn1-0.6.0-py2.py3-none-any.whl.metadata (8.3 kB)\n",
            "Collecting pyasn1-modules==0.4.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 121))\n",
            "  Downloading pyasn1_modules-0.4.0-py3-none-any.whl.metadata (3.4 kB)\n",
            "Collecting pycparser==2.22 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 122))\n",
            "  Downloading pycparser-2.22-py3-none-any.whl.metadata (943 bytes)\n",
            "Collecting pyglet==2.0.15 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 123))\n",
            "  Downloading pyglet-2.0.15-py3-none-any.whl.metadata (7.8 kB)\n",
            "Collecting pyglm==2.7.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 124))\n",
            "  Downloading pyglm-2.7.3.tar.gz (483 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Collecting pygments==2.17.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 125))\n",
            "  Downloading pygments-2.17.2-py3-none-any.whl.metadata (2.6 kB)\n",
            "Collecting pyopengl==3.1.7 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 126))\n",
            "  Downloading PyOpenGL-3.1.7-py3-none-any.whl.metadata (3.2 kB)\n",
            "Collecting pyparsing==3.1.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 127))\n",
            "  Downloading pyparsing-3.1.2-py3-none-any.whl.metadata (5.1 kB)\n",
            "Collecting pyperclip==1.9.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 128))\n",
            "  Downloading pyperclip-1.9.0.tar.gz (20 kB)\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Collecting pyqt5==5.15.11 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 129))\n",
            "  Downloading PyQt5-5.15.11-cp38-abi3-manylinux_2_17_x86_64.whl.metadata (2.1 kB)\n",
            "Collecting pyqt5-qt5==5.15.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 130))\n",
            "  Downloading PyQt5_Qt5-5.15.2-py3-none-manylinux2014_x86_64.whl.metadata (535 bytes)\n",
            "Collecting pyqt5-sip==12.15.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 131))\n",
            "  Downloading PyQt5_sip-12.15.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (421 bytes)\n",
            "Collecting pyquaternion==0.9.9 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 132))\n",
            "  Downloading pyquaternion-0.9.9-py3-none-any.whl.metadata (1.4 kB)\n",
            "Collecting pyrr==0.10.3 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 133))\n",
            "  Downloading pyrr-0.10.3-py3-none-any.whl.metadata (832 bytes)\n",
            "Requirement already satisfied: pysocks==1.7.1 in /usr/local/envs/ihuman/lib/python3.8/site-packages (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 134)) (1.7.1)\n",
            "Collecting python-dateutil==2.9.0.post0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 135))\n",
            "  Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)\n",
            "Collecting pytorch-lightning==2.1.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 136))\n",
            "  Downloading pytorch_lightning-2.1.2-py3-none-any.whl.metadata (21 kB)\n",
            "Collecting pytz==2024.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 137))\n",
            "  Downloading pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB)\n",
            "Collecting pywavelets==1.4.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 138))\n",
            "  Downloading PyWavelets-1.4.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB)\n",
            "Collecting pyyaml==6.0.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 139))\n",
            "  Downloading PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.1 kB)\n",
            "Collecting referencing==0.34.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 140))\n",
            "  Downloading referencing-0.34.0-py3-none-any.whl.metadata (2.8 kB)\n",
            "Collecting requests==2.28.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 141))\n",
            "  Downloading requests-2.28.1-py3-none-any.whl.metadata (4.6 kB)\n",
            "Collecting requests-oauthlib==2.0.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 142))\n",
            "  Downloading requests_oauthlib-2.0.0-py2.py3-none-any.whl.metadata (11 kB)\n",
            "Collecting retrying==1.3.4 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 143))\n",
            "  Downloading retrying-1.3.4-py3-none-any.whl.metadata (6.9 kB)\n",
            "Collecting rich==12.6.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 144))\n",
            "  Downloading rich-12.6.0-py3-none-any.whl.metadata (18 kB)\n",
            "Collecting roma==1.4.4 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 145))\n",
            "  Downloading roma-1.4.4-py3-none-any.whl.metadata (4.7 kB)\n",
            "Collecting rpds-py==0.18.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 146))\n",
            "  Downloading rpds_py-0.18.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n",
            "Collecting rsa==4.9 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 147))\n",
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            "Collecting rtree==1.2.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 148))\n",
            "  Downloading Rtree-1.2.0-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.1 kB)\n",
            "Collecting ruamel-yaml==0.18.6 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 149))\n",
            "  Downloading ruamel.yaml-0.18.6-py3-none-any.whl.metadata (23 kB)\n",
            "Collecting ruamel-yaml-clib==0.2.8 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 150))\n",
            "  Downloading ruamel.yaml.clib-0.2.8-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (2.2 kB)\n",
            "Collecting scikit-image==0.21.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 151))\n",
            "  Downloading scikit_image-0.21.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n",
            "Collecting scikit-learn==1.3.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 152))\n",
            "  Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
            "Collecting scikit-video==1.1.11 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 153))\n",
            "  Downloading scikit_video-1.1.11-py2.py3-none-any.whl.metadata (1.1 kB)\n",
            "Collecting scipy==1.10.1 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 154))\n",
            "  Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB)\n",
            "Collecting segment-anything==1.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 155))\n",
            "  Downloading segment_anything-1.0-py3-none-any.whl.metadata (487 bytes)\n",
            "Requirement already satisfied: setuptools==75.3.0 in /usr/local/envs/ihuman/lib/python3.8/site-packages (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 156)) (75.3.0)\n",
            "Collecting smplx==0.1.28 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 157))\n",
            "  Downloading smplx-0.1.28-py3-none-any.whl.metadata (10 kB)\n",
            "Collecting sounddevice==0.4.6 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 158))\n",
            "  Downloading sounddevice-0.4.6-py3-none-any.whl.metadata (1.4 kB)\n",
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            "  Downloading stevedore-5.2.0-py3-none-any.whl.metadata (2.3 kB)\n",
            "Collecting sympy==1.12 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 163))\n",
            "  Downloading sympy-1.12-py3-none-any.whl.metadata (12 kB)\n",
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            "  Downloading tabulate-0.9.0-py3-none-any.whl.metadata (34 kB)\n",
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            "Collecting tensorboard==2.14.0 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 166))\n",
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            "  Downloading termcolor-2.4.0-py3-none-any.whl.metadata (6.1 kB)\n",
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            "  Downloading threadpoolctl-3.4.0-py3-none-any.whl.metadata (13 kB)\n",
            "Collecting tifffile==2023.7.10 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 170))\n",
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            "Collecting werkzeug==3.0.2 (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 181))\n",
            "  Downloading werkzeug-3.0.2-py3-none-any.whl.metadata (4.1 kB)\n",
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            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Requirement already satisfied: wheel==0.45.1 in /usr/local/envs/ihuman/lib/python3.8/site-packages (from -r /content/ihuman/condaenv.odkb6579.requirements.txt (line 183)) (0.45.1)\n",
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            "Collecting dash-table==5.0.0 (from dash==2.16.1->-r /content/ihuman/condaenv.odkb6579.requirements.txt (line 30))\n",
            "  Downloading dash_table-5.0.0-py3-none-any.whl.metadata (2.4 kB)\n",
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            "Collecting torchmetrics>=0.7.0 (from pytorch-lightning==2.1.2->-r /content/ihuman/condaenv.odkb6579.requirements.txt (line 136))\n",
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            "Downloading absl_py-2.1.0-py3-none-any.whl (133 kB)\n",
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            "Building wheels for collected packages: antlr4-python3-runtime, ffmpeg, filterpy, fvcore, iopath, jax, lit, pyglm, pyperclip, wget\n",
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            "  Stored in directory: /root/.cache/pip/wheels/b1/a3/c2/6df046c09459b73cc9bb6c4401b0be6c47048baf9a1617c485\n",
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            "  Stored in directory: /root/.cache/pip/wheels/30/33/46/5ab7eca55b9490dddbf3441c68a29535996270ef1ce8b9b6d7\n",
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            "  Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31529 sha256=ba93c241825c7bf283ba513f2c3fd97059254ef83143a68a58536e4d76471117\n",
            "  Stored in directory: /root/.cache/pip/wheels/89/3e/24/0f349c0b2eeb6965903035f3b00dbb5c9bea437b4a2f18d82c\n",
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            "  Stored in directory: /root/.cache/pip/wheels/0e/47/23/ef2e2c441fcfda594f1a69c7f977bd94cff3bc39601af6a918\n",
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            "  Stored in directory: /root/.cache/pip/wheels/87/11/e2/0d12ece1a042e119f3f96d7b596c76f0c01af0c55cdf0d749d\n",
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            "  Created wheel for pyperclip: filename=pyperclip-1.9.0-py3-none-any.whl size=11004 sha256=09e291194f7079378e6282421641f7e5cbf0b99975419b52e562495ca5e5c172\n",
            "  Stored in directory: /root/.cache/pip/wheels/6d/a5/02/e801d8db207899213bc1e564f861696b03cb9598c2817b2b62\n",
            "  Building wheel for wget (setup.py): started\n",
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            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9655 sha256=6bcd24c04f0a13d2751c503e4f4c10eba3192fba1dd24c7e0caef7d319030330\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/a8/c3/3cf2c14a1837a4e04bd98631724e81f33f462d86a1d895fae0\n",
            "Successfully built antlr4-python3-runtime ffmpeg filterpy fvcore iopath jax lit pyglm pyperclip wget\n",
            "Installing collected packages: wget, segment-anything, pytz, pyqt5-qt5, pyperclip, pyopengl, pyglm, pure-eval, multipledispatch, lit, imgui, glcontext, flatbuffers, ffmpeg, fastjsonschema, dash-table, dash-html-components, dash-core-components, cuda-python, commonmark, cmake, antlr4-python3-runtime, addict, zipp, widgetsnbextension, werkzeug, websockets, usd-core, urllib3, ujson, tzdata, typing-extensions, traitlets, tqdm, threadpoolctl, termcolor, tensorboard-data-server, tenacity, tabulate, sympy, soupsieve, ruamel-yaml-clib, rtree, rpds-py, roma, retrying, pyyaml, python-dateutil, pyqt5-sip, pyparsing, pygments, pyglet, pycparser, pyasn1, protobuf, prompt-toolkit, prettytable, portalocker, platformdirs, pkgutil-resolve-name, pillow, pbr, packaging, oauthlib, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, numpy, networkx, multidict, moderngl, mako, kiwisolver, jupyterlab-widgets, joblib, jinja2, itsdangerous, idna, grpcio, greenlet, fsspec, frozenlist, fonttools, filelock, cython, cycler, configargparse, colorlog, click, charset-normalizer, certifi, cachetools, blinker, autopage, attrs, async-timeout, asttokens, absl-py, yarl, yacs, triton, trimesh, tifffile, stevedore, stack-data, sqlalchemy, scipy, ruamel-yaml, rsa, rich, requests, referencing, pywavelets, pyrr, pyquaternion, pyqt5, pyasn1-modules, plotly, pandas, opt-einsum, opencv-python-headless, opencv-python, opencv-contrib-python-headless, opencv-contrib-python, omegaconf, nvidia-cusolver-cu11, nvidia-cudnn-cu11, ml-dtypes, matplotlib-inline, lightning-utilities, lazy-loader, iopath, importlib-resources, importlib-metadata, imageio, h5py, contourpy, cmd2, cmaes, cffi, beautifulsoup4, aiosignal, torchmetrics, sounddevice, smplx, scikit-video, scikit-learn, scikit-image, requests-oauthlib, pdbr, moderngl-window, matplotlib, markdown, jsonschema-specifications, jaxlib, jax, ipython, hydra-core, google-auth, fvcore, flask, cliff, alembic, aiohttp, optuna, mediapipe, jsonschema, ipywidgets, google-auth-oauthlib, gdown, filterpy, dash, aitviewer, tensorboard, pytorch-lightning, norfair, nbformat, hydra-optuna-sweeper, open3d\n",
            "  Attempting uninstall: pure-eval\n",
            "    Found existing installation: pure_eval 0.2.3\n",
            "    Uninstalling pure_eval-0.2.3:\n",
            "      Successfully uninstalled pure_eval-0.2.3\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.11.0\n",
            "    Uninstalling typing_extensions-4.11.0:\n",
            "      Successfully uninstalled typing_extensions-4.11.0\n",
            "  Attempting uninstall: traitlets\n",
            "    Found existing installation: traitlets 5.14.3\n",
            "    Uninstalling traitlets-5.14.3:\n",
            "      Successfully uninstalled traitlets-5.14.3\n",
            "  Attempting uninstall: sympy\n",
            "    Found existing installation: sympy 1.13.3\n",
            "    Uninstalling sympy-1.13.3:\n",
            "      Successfully uninstalled sympy-1.13.3\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 6.0\n",
            "    Uninstalling PyYAML-6.0:\n",
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            "  Attempting uninstall: python-dateutil\n",
            "    Found existing installation: python-dateutil 2.9.0\n",
            "    Uninstalling python-dateutil-2.9.0:\n",
            "      Successfully uninstalled python-dateutil-2.9.0\n",
            "  Attempting uninstall: pyqt5-sip\n",
            "    Found existing installation: PyQt5-sip 12.9.0\n",
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            "      Successfully uninstalled PyQt5-sip-12.9.0\n",
            "  Attempting uninstall: pygments\n",
            "    Found existing installation: Pygments 2.18.0\n",
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            "  Attempting uninstall: prompt-toolkit\n",
            "    Found existing installation: prompt_toolkit 3.0.48\n",
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            "    Found existing installation: numpy 1.24.3\n",
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            "  Attempting uninstall: networkx\n",
            "    Found existing installation: networkx 3.1\n",
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            "  Attempting uninstall: jinja2\n",
            "    Found existing installation: Jinja2 3.1.4\n",
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            "      Successfully uninstalled Jinja2-3.1.4\n",
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            "    Found existing installation: filelock 3.16.1\n",
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            "  Attempting uninstall: asttokens\n",
            "    Found existing installation: asttokens 3.0.0\n",
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            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 3.0.0\n",
            "    Uninstalling triton-3.0.0:\n",
            "      Successfully uninstalled triton-3.0.0\n",
            "  Attempting uninstall: stack-data\n",
            "    Found existing installation: stack-data 0.6.2\n",
            "    Uninstalling stack-data-0.6.2:\n",
            "      Successfully uninstalled stack-data-0.6.2\n",
            "  Attempting uninstall: pyqt5\n",
            "    Found existing installation: PyQt5 5.15.4\n",
            "    Uninstalling PyQt5-5.15.4:\n",
            "      Successfully uninstalled PyQt5-5.15.4\n",
            "  Attempting uninstall: matplotlib-inline\n",
            "    Found existing installation: matplotlib-inline 0.1.7\n",
            "    Uninstalling matplotlib-inline-0.1.7:\n",
            "      Successfully uninstalled matplotlib-inline-0.1.7\n",
            "  Attempting uninstall: ipython\n",
            "    Found existing installation: ipython 8.12.2\n",
            "    Uninstalling ipython-8.12.2:\n",
            "      Successfully uninstalled ipython-8.12.2\n",
            "Successfully installed absl-py-2.1.0 addict-2.4.0 aiohttp-3.9.3 aiosignal-1.3.1 aitviewer-1.13.0 alembic-1.13.2 antlr4-python3-runtime-4.9.3 asttokens-2.4.1 async-timeout-4.0.3 attrs-23.2.0 autopage-0.5.2 beautifulsoup4-4.12.3 blinker-1.7.0 cachetools-5.3.3 certifi-2022.12.7 cffi-1.16.0 charset-normalizer-2.1.1 click-8.1.7 cliff-4.7.0 cmaes-0.10.0 cmake-3.25.0 cmd2-2.4.3 colorlog-6.8.2 commonmark-0.9.1 configargparse-1.7 contourpy-1.1.1 cuda-python-12.3.0 cycler-0.12.1 cython-3.0.10 dash-2.16.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 fastjsonschema-2.19.1 ffmpeg-1.4 filelock-3.9.0 filterpy-1.4.5 flask-3.0.3 flatbuffers-24.3.25 fonttools-4.51.0 frozenlist-1.4.1 fsspec-2024.3.1 fvcore-0.1.5.post20221221 gdown-5.2.0 glcontext-3.0.0 google-auth-2.29.0 google-auth-oauthlib-1.0.0 greenlet-3.0.3 grpcio-1.62.1 h5py-3.10.0 hydra-core-1.3.2 hydra-optuna-sweeper-1.2.0 idna-3.4 imageio-2.34.0 imgui-2.0.0 importlib-metadata-7.1.0 importlib-resources-6.4.0 iopath-0.1.10 ipython-8.12.3 ipywidgets-8.1.2 itsdangerous-2.1.2 jax-0.4.13 jaxlib-0.4.13 jinja2-3.1.2 joblib-1.4.0 jsonschema-4.21.1 jsonschema-specifications-2023.12.1 jupyterlab-widgets-3.0.10 kiwisolver-1.4.5 lazy-loader-0.4 lightning-utilities-0.11.2 lit-15.0.7 mako-1.3.5 markdown-3.6 matplotlib-3.7.5 matplotlib-inline-0.1.6 mediapipe-0.10.11 ml-dtypes-0.2.0 moderngl-5.12.0 moderngl-window-2.4.6 multidict-6.0.5 multipledispatch-1.0.0 nbformat-5.10.4 networkx-3.0 norfair-2.2.0 numpy-1.24.1 nvidia-cublas-cu11-11.11.3.6 nvidia-cuda-cupti-cu11-11.8.87 nvidia-cuda-nvrtc-cu11-11.8.89 nvidia-cuda-runtime-cu11-11.8.89 nvidia-cudnn-cu11-8.7.0.84 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.3.0.86 nvidia-cusolver-cu11-11.4.1.48 nvidia-cusparse-cu11-11.7.5.86 nvidia-nccl-cu11-2.20.5 nvidia-nvtx-cu11-11.8.86 oauthlib-3.2.2 omegaconf-2.3.0 open3d-0.18.0 opencv-contrib-python-4.9.0.80 opencv-contrib-python-headless-4.9.0.80 opencv-python-4.10.0.84 opencv-python-headless-4.10.0.84 opt-einsum-3.3.0 optuna-2.10.1 packaging-24.0 pandas-2.0.3 pbr-6.0.0 pdbr-0.8.9 pillow-10.2.0 pkgutil-resolve-name-1.3.10 platformdirs-4.2.0 plotly-5.20.0 portalocker-2.8.2 prettytable-3.10.0 prompt-toolkit-3.0.43 protobuf-3.20.3 pure-eval-0.2.2 pyasn1-0.6.0 pyasn1-modules-0.4.0 pycparser-2.22 pyglet-2.0.15 pyglm-2.7.3 pygments-2.17.2 pyopengl-3.1.7 pyparsing-3.1.2 pyperclip-1.9.0 pyqt5-5.15.11 pyqt5-qt5-5.15.2 pyqt5-sip-12.15.0 pyquaternion-0.9.9 pyrr-0.10.3 python-dateutil-2.9.0.post0 pytorch-lightning-2.1.2 pytz-2024.1 pywavelets-1.4.1 pyyaml-6.0.1 referencing-0.34.0 requests-2.28.1 requests-oauthlib-2.0.0 retrying-1.3.4 rich-12.6.0 roma-1.4.4 rpds-py-0.18.0 rsa-4.9 rtree-1.2.0 ruamel-yaml-0.18.6 ruamel-yaml-clib-0.2.8 scikit-image-0.21.0 scikit-learn-1.3.2 scikit-video-1.1.11 scipy-1.10.1 segment-anything-1.0 smplx-0.1.28 sounddevice-0.4.6 soupsieve-2.5 sqlalchemy-2.0.31 stack-data-0.6.3 stevedore-5.2.0 sympy-1.12 tabulate-0.9.0 tenacity-8.2.3 tensorboard-2.14.0 tensorboard-data-server-0.7.2 termcolor-2.4.0 threadpoolctl-3.4.0 tifffile-2023.7.10 torchmetrics-1.5.2 tqdm-4.66.2 traitlets-5.14.2 trimesh-3.23.5 triton-2.3.1 typing-extensions-4.8.0 tzdata-2024.1 ujson-5.10.0 urllib3-1.26.13 usd-core-24.3 websockets-12.0 werkzeug-3.0.2 wget-3.2 widgetsnbextension-4.0.10 yacs-0.1.8 yarl-1.9.4 zipp-3.18.1\n",
            "\n",
            "\b\b| \b\bdone\n",
            "#\n",
            "# To activate this environment, use\n",
            "#\n",
            "#     $ conda activate ihuman\n",
            "#\n",
            "# To deactivate an active environment, use\n",
            "#\n",
            "#     $ conda deactivate\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!conda env create -f /content/ihuman/environment-cuda12_1.yaml"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TrnbPqorArG"
      },
      "source": [
        "## Installing following libraries may take about 3 min\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hM1spdaUXvbY",
        "outputId": "c3b485a1-9a5f-49fa-deb7-d5b0e835f1a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Channels:\n",
            " - pytorch\n",
            " - nvidia\n",
            " - conda-forge\n",
            " - defaults\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "    current version: 23.11.0\n",
            "    latest version: 24.11.2\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c conda-forge conda\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local/envs/ihuman\n",
            "\n",
            "  added / updated specs:\n",
            "    - pytorch\n",
            "    - pytorch-cuda=12.1\n",
            "    - torchaudio\n",
            "    - torchvision\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    certifi-2024.8.30          |     pyhd8ed1ab_0         160 KB  conda-forge\n",
            "    cffi-1.15.0                |   py38h3931269_0         226 KB  conda-forge\n",
            "    charset-normalizer-3.4.0   |     pyhd8ed1ab_0          46 KB  conda-forge\n",
            "    ffmpeg-4.3                 |       hf484d3e_0         9.9 MB  pytorch\n",
            "    h2-4.1.0                   |     pyhd8ed1ab_0          46 KB  conda-forge\n",
            "    hpack-4.0.0                |     pyh9f0ad1d_0          25 KB  conda-forge\n",
            "    hyperframe-6.0.1           |     pyhd8ed1ab_0          14 KB  conda-forge\n",
            "    idna-3.10                  |     pyhd8ed1ab_0          49 KB  conda-forge\n",
            "    pillow-10.4.0              |   py38h5eee18b_0         795 KB\n",
            "    pycparser-2.22             |     pyhd8ed1ab_0         103 KB  conda-forge\n",
            "    requests-2.32.3            |     pyhd8ed1ab_0          57 KB  conda-forge\n",
            "    torchaudio-2.4.1           |       py38_cu121         6.2 MB  pytorch\n",
            "    torchvision-0.20.0         |       py38_cu121        13.3 MB  pytorch\n",
            "    urllib3-2.2.3              |     pyhd8ed1ab_0          96 KB  conda-forge\n",
            "    zstandard-0.23.0           |   py38h2c38b39_0         432 KB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        31.4 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  certifi            conda-forge/noarch::certifi-2024.8.30-pyhd8ed1ab_0 \n",
            "  cffi               conda-forge/linux-64::cffi-1.15.0-py38h3931269_0 \n",
            "  charset-normalizer conda-forge/noarch::charset-normalizer-3.4.0-pyhd8ed1ab_0 \n",
            "  ffmpeg             pytorch/linux-64::ffmpeg-4.3-hf484d3e_0 \n",
            "  h2                 conda-forge/noarch::h2-4.1.0-pyhd8ed1ab_0 \n",
            "  hpack              conda-forge/noarch::hpack-4.0.0-pyh9f0ad1d_0 \n",
            "  hyperframe         conda-forge/noarch::hyperframe-6.0.1-pyhd8ed1ab_0 \n",
            "  idna               conda-forge/noarch::idna-3.10-pyhd8ed1ab_0 \n",
            "  pillow             pkgs/main/linux-64::pillow-10.4.0-py38h5eee18b_0 \n",
            "  pycparser          conda-forge/noarch::pycparser-2.22-pyhd8ed1ab_0 \n",
            "  requests           conda-forge/noarch::requests-2.32.3-pyhd8ed1ab_0 \n",
            "  torchaudio         pytorch/linux-64::torchaudio-2.4.1-py38_cu121 \n",
            "  torchvision        pytorch/linux-64::torchvision-0.20.0-py38_cu121 \n",
            "  urllib3            conda-forge/noarch::urllib3-2.2.3-pyhd8ed1ab_0 \n",
            "  zstandard          pkgs/main/linux-64::zstandard-0.23.0-py38h2c38b39_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "torchvision-0.20.0   | 13.3 MB   | :   0% 0/1 [00:00<?, ?it/s]\n",
            "ffmpeg-4.3           | 9.9 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "torchaudio-2.4.1     | 6.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "pillow-10.4.0        | 795 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "zstandard-0.23.0     | 432 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "cffi-1.15.0          | 226 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "certifi-2024.8.30    | 160 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pycparser-2.22       | 103 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "urllib3-2.2.3        | 96 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "requests-2.32.3      | 57 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "idna-3.10            | 49 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "charset-normalizer-3 | 46 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "h2-4.1.0             | 46 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "hpack-4.0.0          | 25 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "torchvision-0.20.0   | 13.3 MB   | :   0% 0.0011778283588344963/1 [00:00<01:43, 103.87s/it]\n",
            "ffmpeg-4.3           | 9.9 MB    | :   0% 0.0015713236502815128/1 [00:00<01:24, 84.31s/it]\u001b[A\n",
            "\n",
            "torchvision-0.20.0   | 13.3 MB   | :  30% 0.2979905747851276/1 [00:00<00:00,  1.60it/s]    \n",
            "ffmpeg-4.3           | 9.9 MB    | :  33% 0.3268353192585547/1 [00:00<00:00,  1.69it/s]   \u001b[A\n",
            "\n",
            "torchvision-0.20.0   | 13.3 MB   | :  60% 0.6006924630055931/1 [00:00<00:00,  2.20it/s]\n",
            "ffmpeg-4.3           | 9.9 MB    | :  74% 0.7416647629328741/1 [00:00<00:00,  2.73it/s]\u001b[A\n",
            "\n",
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            "cffi-1.15.0          | 226 KB    | :   7% 0.07066150276452779/1 [00:00<00:05,  6.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "pillow-10.4.0        | 795 KB    | : 100% 1.0/1 [00:00<00:00,  2.09it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "certifi-2024.8.30    | 160 KB    | :  10% 0.10005373980165128/1 [00:00<00:04,  4.98s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "urllib3-2.2.3        | 96 KB     | :  17% 0.1670541212936906/1 [00:00<00:02,  3.26s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pycparser-2.22       | 103 KB    | :  16% 0.15589259548231174/1 [00:00<00:02,  3.54s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "charset-normalizer-3 | 46 KB     | :  35% 0.3462822843133111/1 [00:00<00:01,  1.68s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "requests-2.32.3      | 57 KB     | :  28% 0.27859207617752085/1 [00:00<00:01,  2.20s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "h2-4.1.0             | 46 KB     | :  35% 0.35042990974034305/1 [00:00<00:01,  1.78s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "cffi-1.15.0          | 226 KB    | : 100% 1.0/1 [00:00<00:00,  1.88it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "cffi-1.15.0          | 226 KB    | : 100% 1.0/1 [00:00<00:00,  1.88it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "idna-3.10            | 49 KB     | :  33% 0.32875173064189256/1 [00:00<00:01,  2.00s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "hpack-4.0.0          | 25 KB     | :  65% 0.6465411783276114/1 [00:00<00:00,  1.01s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "hyperframe-6.0.1     | 14 KB     | : 100% 1.0/1 [00:00<00:00,  1.52it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "zstandard-0.23.0     | 432 KB    | :   4% 0.03705521416891514/1 [00:00<00:17, 18.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "certifi-2024.8.30    | 160 KB    | : 100% 1.0/1 [00:00<00:00,  1.80it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "certifi-2024.8.30    | 160 KB    | : 100% 1.0/1 [00:00<00:00,  1.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "zstandard-0.23.0     | 432 KB    | :  15% 0.14822085667566057/1 [00:00<00:03,  4.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "zstandard-0.23.0     | 432 KB    | :  30% 0.29644171335132113/1 [00:00<00:01,  2.20s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "zstandard-0.23.0     | 432 KB    | :  56% 0.5558282125337272/1 [00:01<00:00,  1.17s/it] \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "zstandard-0.23.0     | 432 KB    | :  74% 0.7411042833783029/1 [00:01<00:00,  1.07it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "torchaudio-2.4.1     | 6.2 MB    | : 100% 1.0/1 [00:02<00:00,  2.56s/it]               \u001b[A\u001b[A\n",
            "\n",
            "torchaudio-2.4.1     | 6.2 MB    | : 100% 1.0/1 [00:02<00:00,  2.56s/it]\u001b[A\u001b[A\n",
            "\n",
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            "urllib3-2.2.3        | 96 KB     | : 100% 1.0/1 [00:02<00:00,  2.17s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "urllib3-2.2.3        | 96 KB     | : 100% 1.0/1 [00:02<00:00,  2.17s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pycparser-2.22       | 103 KB    | : 100% 1.0/1 [00:02<00:00,  2.26s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pycparser-2.22       | 103 KB    | : 100% 1.0/1 [00:02<00:00,  2.26s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "\n",
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            "charset-normalizer-3 | 46 KB     | : 100% 1.0/1 [00:02<00:00,  2.42s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "requests-2.32.3      | 57 KB     | : 100% 1.0/1 [00:02<00:00,  2.38s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "requests-2.32.3      | 57 KB     | : 100% 1.0/1 [00:02<00:00,  2.38s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "h2-4.1.0             | 46 KB     | : 100% 1.0/1 [00:02<00:00,  2.46s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "h2-4.1.0             | 46 KB     | : 100% 1.0/1 [00:02<00:00,  2.46s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "ffmpeg-4.3           | 9.9 MB    | : 100% 1.0/1 [00:02<00:00,  2.73it/s]               \u001b[A\n",
            "\n",
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            "idna-3.10            | 49 KB     | : 100% 1.0/1 [00:02<00:00,  2.98s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "idna-3.10            | 49 KB     | : 100% 1.0/1 [00:02<00:00,  2.98s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "hpack-4.0.0          | 25 KB     | : 100% 1.0/1 [00:02<00:00,  3.35s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "hpack-4.0.0          | 25 KB     | : 100% 1.0/1 [00:02<00:00,  3.35s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "hyperframe-6.0.1     | 14 KB     | : 100% 1.0/1 [00:02<00:00,  1.52it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "zstandard-0.23.0     | 432 KB    | : 100% 1.0/1 [00:02<00:00,  3.51s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: / \b\bdone\n",
            "Verifying transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Channels:\n",
            " - iopath\n",
            " - conda-forge\n",
            " - defaults\n",
            " - nvidia\n",
            " - pytorch\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "    current version: 23.11.0\n",
            "    latest version: 24.11.2\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c conda-forge conda\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local/envs/ihuman\n",
            "\n",
            "  added / updated specs:\n",
            "    - iopath\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    iopath-0.1.9               |             py38          41 KB  iopath\n",
            "    portalocker-2.10.1         |   py38h578d9bd_0          36 KB  conda-forge\n",
            "    tqdm-4.67.1                |     pyhd8ed1ab_0          87 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         165 KB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  colorama           conda-forge/noarch::colorama-0.4.6-pyhd8ed1ab_0 \n",
            "  iopath             iopath/linux-64::iopath-0.1.9-py38 \n",
            "  portalocker        conda-forge/linux-64::portalocker-2.10.1-py38h578d9bd_0 \n",
            "  tqdm               conda-forge/noarch::tqdm-4.67.1-pyhd8ed1ab_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "tqdm-4.67.1          | 87 KB     | :   0% 0/1 [00:00<?, ?it/s]\n",
            "iopath-0.1.9         | 41 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "portalocker-2.10.1   | 36 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "portalocker-2.10.1   | 36 KB     | :  44% 0.4397326820365549/1 [00:00<00:00,  4.38it/s]\u001b[A\u001b[A\n",
            "\n",
            "tqdm-4.67.1          | 87 KB     | : 100% 1.0/1 [00:00<00:00,  6.27it/s]\n",
            "iopath-0.1.9         | 41 KB     | : 100% 1.0/1 [00:01<00:00,  1.54s/it]\u001b[A\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\n",
            "Preparing transaction: - \b\bdone\n",
            "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Channels:\n",
            " - pytorch3d\n",
            " - conda-forge\n",
            " - defaults\n",
            " - nvidia\n",
            " - pytorch\n",
            " - iopath\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "    current version: 23.11.0\n",
            "    latest version: 24.11.2\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c conda-forge conda\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local/envs/ihuman\n",
            "\n",
            "  added / updated specs:\n",
            "    - pytorch3d\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    pytorch3d-0.7.8            |py38_cu121_pyt241        27.5 MB  pytorch3d\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        27.5 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  pytorch3d          pytorch3d/linux-64::pytorch3d-0.7.8-py38_cu121_pyt241 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "                                                                        \n",
            "Preparing transaction: | \b\bdone\n",
            "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Executing transaction: \\ \b\bdone\n"
          ]
        }
      ],
      "source": [
        "!source activate ihuman && conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia\n",
        "!source activate ihuman && conda install -c iopath iopath\n",
        "!source activate ihuman && conda install pytorch3d -c pytorch3d"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IIdTY8A2veV5"
      },
      "source": [
        "## Install remaining libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RgkVDAPKeL2J",
        "outputId": "4d80d7c0-e316-4ffc-fd24-4aab7651a2f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in links: /content/drive/MyDrive/wheels\n",
            "Processing /content/drive/MyDrive/wheels/diff_gaussian_rasterization-0.0.0-cp38-cp38-linux_x86_64.whl\n",
            "Installing collected packages: diff-gaussian-rasterization\n",
            "Successfully installed diff-gaussian-rasterization-0.0.0\n"
          ]
        }
      ],
      "source": [
        "!source activate ihuman && /usr/local/envs/ihuman/bin/pip install --no-index --find-links=/content/drive/MyDrive/wheels /content/drive/MyDrive/wheels/diff_gaussian_rasterization-0.0.0-cp38-cp38-linux_x86_64.whl\n",
        "!source activate ihuman && /usr/local/envs/ihuman/bin/pip install /content/drive/MyDrive/wheels/simple_knn-0.0.0-cp38-cp38-linux_x86_64.whl\n",
        "!source activate ihuman && /usr/local/envs/ihuman/bin/pip install /content/drive/MyDrive/wheels/tinycudann-1.7-cp38-cp38-linux_x86_64.whl\n",
        "!source activate ihuman && /usr/local/envs/ihuman/bin/pip install git+https://github.com/mattloper/chumpy@9b045ff5d6588a24a0bab52c83f032e2ba433e17"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGwqjCoZvjVs"
      },
      "source": [
        "## Get all the assests including dataset and models into the required locations\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1reWg_gNoZ3j",
        "outputId": "d68eeccc-ece4-4dda-ebec-68787d6088d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/MyDrive/ihuman/male-3-casual.zip\n",
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            "  inflating: ./temp/male-3-casual/normals/image_0674.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0675.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0676.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0677.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0678.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0679.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0680.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0681.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0682.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0683.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0684.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0685.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0686.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0687.png  \n",
            "  inflating: ./temp/male-3-casual/normals/image_0688.png  \n",
            "   creating: ./temp/male-3-casual/poses/\n",
            "  inflating: ./temp/male-3-casual/poses/anim_nerf_test.npz  \n",
            "  inflating: ./temp/male-3-casual/poses/anim_nerf_train.npz  \n",
            "  inflating: ./temp/male-3-casual/poses/anim_nerf_val.npz  \n",
            "Archive:  /content/drive/MyDrive/ihuman/SMPL_python_v.1.1.0.zip\n",
            "   creating: ./temp_smpl/SMPL_python_v.1.1.0/\n",
            "   creating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/\n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/.DS_Store  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/__init__.py  \n",
            "   creating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/models/\n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/models/basicmodel_f_lbs_10_207_0_v1.1.0.pkl  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/models/basicmodel_neutral_lbs_10_207_0_v1.1.0.pkl  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/models/basicmodel_m_lbs_10_207_0_v1.1.0.pkl  \n",
            "   creating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/\n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/__init__.py  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/lbs.py  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/LICENSE.txt  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/posemapper.py  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/README.txt  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/serialization.py  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/verts.py  \n",
            "   creating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/hello_world/\n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/hello_world/hello_smpl.py  \n",
            "  inflating: ./temp_smpl/SMPL_python_v.1.1.0/smpl/smpl_webuser/hello_world/render_smpl.py  \n",
            "Archive:  /content/drive/MyDrive/ihuman/small.zip\n",
            "   creating: ./temp/small/\n",
            "  inflating: ./temp/small/J_regressor.txt  \n",
            "  inflating: ./temp/small/weights.txt  \n",
            "  inflating: ./temp/small/uv_coords.txt  \n",
            "  inflating: ./temp/small/vertices.txt  \n",
            "  inflating: ./temp/small/kintree_table.txt  \n",
            "  inflating: ./temp/small/uv_map2face.txt  \n",
            "  inflating: ./temp/small/mesh2smpl_idx.txt  \n",
            "  inflating: ./temp/small/joint_locations.txt  \n",
            "  inflating: ./temp/small/triangles.txt  \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/drive/MyDrive/ihuman/male-3-casual.zip -d ./temp/\n",
        "!mkdir -p /content/ihuman/data/peoplesnapshot/\n",
        "!mv ./temp/* /content/ihuman/data/peoplesnapshot/\n",
        "!unzip /content/drive/MyDrive/ihuman/SMPL_python_v.1.1.0.zip -d ./temp_smpl/\n",
        "!mkdir -p /content/ihuman/data/smplx_models/smpl/\n",
        "!mv ./temp_smpl/SMPL_python_v.1.1.0/smpl/models/*.pkl /content/ihuman/data/smplx_models/smpl/\n",
        "!mkdir -p /content/ihuman/data/smpl/models\n",
        "!cp /content/ihuman/data/smplx_models/smpl/basicmodel_f_lbs_10_207_0_v1.1.0.pkl /content/ihuman/data/smpl/models/SMPL_FEMALE.pkl\n",
        "!cp /content/ihuman/data/smplx_models/smpl/basicmodel_m_lbs_10_207_0_v1.1.0.pkl /content/ihuman/data/smpl/models/SMPL_MALE.pkl\n",
        "!cp /content/ihuman/data/smplx_models/smpl/basicmodel_neutral_lbs_10_207_0_v1.1.0.pkl /content/ihuman/data/smpl/models/SMPL_NEUTRAL.pkl\n",
        "!unzip /content/drive/MyDrive/ihuman/small.zip -d ./temp/\n",
        "!mkdir -p /content/ihuman/data/smpl/\n",
        "!mv ./temp/* /content/ihuman/data/smpl/\n",
        "!mkdir -p /content/ihuman/data/animation/\n",
        "!cp -r /content/drive/MyDrive/ihuman/animation/* /content/ihuman/data/animation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jD_LU07ovtOv"
      },
      "source": [
        "## Training\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLaFWkPApdsB",
        "outputId": "b638d82f-84fe-424f-a158-bce46c5d0f4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/ihuman\n",
            "/content/ihuman/animatableGaussian/model/nerf_model.py:33: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
            "  @custom_fwd(cast_inputs=torch.float32)\n",
            "/content/ihuman/animatableGaussian/model/nerf_model.py:51: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
            "  @custom_fwd(cast_inputs=torch.float32)\n",
            "Seed set to 0\n",
            "loading trainset...\n",
            "100% 19/19 [00:01<00:00, 14.09it/s]\n",
            "100% 19/19 [00:01<00:00, 11.01it/s]\n",
            "[train] Loading from ../../data/peoplesnapshot/male-3-casual/poses/anim_nerf_train.npz\n",
            "loading valset...\n",
            "100% 1/1 [00:00<00:00,  7.95it/s]\n",
            "100% 1/1 [00:00<00:00,  9.41it/s]\n",
            "[val] Loading from ../../data/peoplesnapshot/male-3-casual/poses/anim_nerf_val.npz\n",
            "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /root/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n",
            "100% 233M/233M [00:01<00:00, 160MB/s]\n",
            "/usr/local/envs/ihuman/lib/python3.8/site-packages/torchmetrics/functional/image/lpips.py:325: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  self.load_state_dict(torch.load(model_path, map_location=\"cpu\"), strict=False)\n",
            "self.headless=True\n",
            "GPU available: True (cuda), used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n",
            "Missing logger folder: ./logs/lightning_logs\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name        | Type           | Params\n",
            "-----------------------------------------------\n",
            "0 | model       | SMPLModel      | 14.3 M\n",
            "1 | evaluator   | EvaluatorRecon | 2.5 M \n",
            "2 | robustifier | GMoF           | 0     \n",
            "-----------------------------------------------\n",
            "14.3 M    Trainable params\n",
            "2.5 M     Non-trainable params\n",
            "16.8 M    Total params\n",
            "67.197    Total estimated model params size (MB)\n",
            "Sanity Checking: |          | 0/? [00:00<?, ?it/s]/usr/local/envs/ihuman/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.\n",
            "/usr/local/envs/ihuman/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.\n",
            "Epoch 0:   0% 0/19 [00:00<?, ?it/s] /usr/local/envs/ihuman/lib/python3.8/site-packages/pytorch3d/ops/laplacian_matrices.py:51: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated.  Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at /opt/conda/conda-bld/pytorch_1724789259345/work/torch/csrc/utils/tensor_new.cpp:641.)\n",
            "  A = torch.sparse.FloatTensor(idx, ones, (V, V))\n",
            "Epoch 14: 100% 19/19 [00:02<00:00,  7.04it/s, v_num=0, train_loss=0.0211, gaussian_loss=0.00839, mesh_loss=0.0364, normal_loss=0.0123]`Trainer.fit` stopped: `max_epochs=15` reached.\n",
            "Epoch 14: 100% 19/19 [00:02<00:00,  7.03it/s, v_num=0, train_loss=0.0211, gaussian_loss=0.00839, mesh_loss=0.0364, normal_loss=0.0123]\n",
            "loading testset...\n",
            "100% 55/55 [00:05<00:00, 10.27it/s]\n",
            "100% 55/55 [00:03<00:00, 15.05it/s]\n",
            "[test] Loading from ../../data/peoplesnapshot/male-3-casual/poses/anim_nerf_test.npz\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "/usr/local/envs/ihuman/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.\n",
            "Testing DataLoader 0: 100% 55/55 [00:31<00:00,  1.72it/s]\n",
            "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
            "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
            "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
            "│\u001b[36m \u001b[0m\u001b[36m       test/lpips        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m  0.019897041842341423   \u001b[0m\u001b[35m \u001b[0m│\n",
            "│\u001b[36m \u001b[0m\u001b[36m        test/psnr        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   27.822582244873047    \u001b[0m\u001b[35m \u001b[0m│\n",
            "│\u001b[36m \u001b[0m\u001b[36m      test/rgb_loss      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m  0.0016750454669818282  \u001b[0m\u001b[35m \u001b[0m│\n",
            "│\u001b[36m \u001b[0m\u001b[36m        test/ssim        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9659939408302307    \u001b[0m\u001b[35m \u001b[0m│\n",
            "└───────────────────────────┴───────────────────────────┘\n",
            "{'test/psnr': 27.822582244873047, 'test/ssim': 0.9659939408302307, 'test/lpips': 0.019897041842341423, 'test/rgb_loss': 0.0016750454669818282}\n"
          ]
        }
      ],
      "source": [
        "%cd /content/ihuman/\n",
        "!source activate ihuman && /usr/local/envs/ihuman/bin/python /content/ihuman/train.py\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KaJWqoFvwce"
      },
      "source": [
        "## Animating\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8sGuZvgqJ73",
        "outputId": "e1da7c69-abaf-48d1-d342-250db27eb289"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/ihuman\n",
            "/content/ihuman/animatableGaussian/model/nerf_model.py:33: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
            "  @custom_fwd(cast_inputs=torch.float32)\n",
            "/content/ihuman/animatableGaussian/model/nerf_model.py:51: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
            "  @custom_fwd(cast_inputs=torch.float32)\n",
            "Seed set to 0\n",
            "/usr/local/envs/ihuman/lib/python3.8/site-packages/lightning_fabric/utilities/cloud_io.py:55: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "/usr/local/envs/ihuman/lib/python3.8/site-packages/torchmetrics/functional/image/lpips.py:325: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  self.load_state_dict(torch.load(model_path, map_location=\"cpu\"), strict=False)\n",
            "self.headless=True\n",
            "103it [00:06, 16.45it/s]\n"
          ]
        }
      ],
      "source": [
        "%cd /content/ihuman\n",
        "!source activate ihuman && /usr/local/envs/ihuman/bin/python /content/ihuman/animate.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUoRuGjMqWiE"
      },
      "source": [
        "## To download the output\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4mXoYDEDqTlH"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "!zip -r outputs.zip /content/ihuman/outputs\n",
        "files.download('outputs.zip')\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}