t81_558_class_13_4_tpu.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LSIM-PITWYFa"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jeffheaton/app_deep_learning/blob/main/t81_558_class_13_4_tpu.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDTXd8-Lmp8Q"
      },
      "source": [
        "# T81-558: Applications of Deep Neural Networks\n",
        "**Module 13: Advanced/Other Topics**\n",
        "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
        "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncNrAEpzmp8S"
      },
      "source": [
        "# Module 13 Video Material\n",
        "\n",
        "* Part 13.1: Using Denoising AutoEncoders [[Video]](https://www.youtube.com/watch?v=BBrRD89sTk8&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_13_1_auto_encode.ipynb)\n",
        "* Part 13.2: Anomaly Detection [[Video]](https://www.youtube.com/watch?v=wubZ516TkI8&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_13_2_anomaly.ipynb)\n",
        "* Part 13.3: Model Drift and Retraining [[Video]](https://www.youtube.com/watch?v=F4395B1ySpg&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_13_3_retrain.ipynb)\n",
        "* **Part 13.4: Tensor Processing Units (TPUs)** [[Video]](https://www.youtube.com/watch?v=Cp3xOyxOZNo&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_13_4_tpu.ipynb)\n",
        "* Part 13.5: Future Directions in Artificial Intelligence [[Video]](https://www.youtube.com/watch?v=RjxvEZh73Yc&list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi) [[Notebook]](t81_558_class_13_5_new_tech.ipynb)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lux_6KOXMU94"
      },
      "source": [
        "# Google CoLab Instructions\n",
        "\n",
        "The following code ensures that Google CoLab is running the correct version of TensorFlow."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fU9UhAxTmp8S",
        "outputId": "5caa882a-b8c4-4b37-be65-9e349778fe9a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Note: using Google CoLab\n"
          ]
        }
      ],
      "source": [
        "# Detect Colab if present\n",
        "try:\n",
        "    from google.colab import drive\n",
        "    COLAB = True\n",
        "    print(\"Note: using Google CoLab\")\n",
        "except:\n",
        "    print(\"Note: not using Google CoLab\")\n",
        "    COLAB = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q09yMGGcmp9N"
      },
      "source": [
        "# Part 13.4: Tensor Processing Units (TPUs)\n",
        "\n",
        "This book focuses primarily on NVIDIA Graphics Processing Units (GPUs) for deep learning acceleration. NVIDIA GPUs are not the only option for deep learning acceleration. TensorFlow continues to gain additional support for AMD and Intel GPUs. TPUs are also available from Google cloud platforms to accelerate deep learning. The focus of this book and course is on NVIDIA GPUs because of their wide availability on both local and cloud systems.\n",
        "\n",
        "Though this book focuses on NVIDIA GPUs, we will briefly examine Google Tensor Processing Units (TPUs). These devices are an AI accelerator Application-Specific Integrated Circuit (ASIC) developed by Google. They were designed specifically for neural network machine learning, mainly using Google's TensorFlow software. Google began using TPUs internally in 2015 and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by offering a smaller version of the chip for sale.\n",
        "\n",
        "The full use of a TPU is a complex topic that I only introduced in this part. Supporting TPUs is slightly more complicated than GPUs because specialized coding is needed. Changes are rarely required to adapt CPU code to GPU for most relatively simple mainstream GPU tasks in PyTorch. I will cover the mild code changes needed to utilize in this part. You might also want to refer to the [CoLab PyTorch TPU guide](https://colab.research.google.com/github/pytorch/xla/blob/master/contrib/colab/getting-started.ipynb#scrollTo=OApBOAe1fpH_) provided by Google.\n",
        "\n",
        "We will begin by checking the environment to see if a TPU is present.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "PEtB2fpRSov-"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "assert os.environ['COLAB_TPU_ADDR'], 'Make sure to select TPU from Edit > Notebook settings > Hardware accelerator'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gftO2Vj6v6Du"
      },
      "source": [
        "If a TPU is not detected follow the instructions and select a TPU hardware accelerator. Next, we will install the necessary software into CoLab to allow TPU access."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AOvubSN0SyWj",
        "outputId": "42cedbe3-8be1-4593-d7a5-62e833d46f54"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.15.1) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.15.1) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.15.1) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.0.0) (1.3.0)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.56.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (1.61.0)\n",
            "Requirement already satisfied: protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<4.0.0dev,>=3.19.5 in /usr/local/lib/python3.10/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (3.20.3)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (5.3.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /usr/local/lib/python3.10/dist-packages (from httplib2<1dev,>=0.9.2->google-api-python-client==1.8.0->cloud-tpu-client==0.10) (3.1.1)\n",
            "Building wheels for collected packages: lit\n",
            "  Building wheel for lit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lit: filename=lit-17.0.4-py3-none-any.whl size=93257 sha256=bbe5209534478820e32d3b80e26292fdc1aae242328420c3db65936411dcc80c\n",
            "  Stored in directory: /root/.cache/pip/wheels/be/ae/00/696c57d438bfc7c0e89c4c379083ea08b1c2e54d85a5f7cd7c\n",
            "Successfully built lit\n",
            "Installing collected packages: lit, uritemplate, 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, nvidia-cusolver-cu11, nvidia-cudnn-cu11, google-api-core, google-api-python-client, cloud-tpu-client, torch-xla, triton, torch, torchvision\n",
            "  Attempting uninstall: uritemplate\n",
            "    Found existing installation: uritemplate 4.1.1\n",
            "    Uninstalling uritemplate-4.1.1:\n",
            "      Successfully uninstalled uritemplate-4.1.1\n",
            "  Attempting uninstall: google-api-core\n",
            "    Found existing installation: google-api-core 2.11.1\n",
            "    Uninstalling google-api-core-2.11.1:\n",
            "      Successfully uninstalled google-api-core-2.11.1\n",
            "  Attempting uninstall: google-api-python-client\n",
            "    Found existing installation: google-api-python-client 2.84.0\n",
            "    Uninstalling google-api-python-client-2.84.0:\n",
            "      Successfully uninstalled google-api-python-client-2.84.0\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.1.0\n",
            "    Uninstalling triton-2.1.0:\n",
            "      Successfully uninstalled triton-2.1.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.1.0+cu118\n",
            "    Uninstalling torch-2.1.0+cu118:\n",
            "      Successfully uninstalled torch-2.1.0+cu118\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.16.0+cu118\n",
            "    Uninstalling torchvision-0.16.0+cu118:\n",
            "      Successfully uninstalled torchvision-0.16.0+cu118\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pydrive2 1.6.3 requires google-api-python-client>=1.12.5, but you have google-api-python-client 1.8.0 which is incompatible.\n",
            "earthengine-api 0.1.377 requires google-api-python-client>=1.12.1, but you have google-api-python-client 1.8.0 which is incompatible.\n",
            "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 2.0.0 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.0.0 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed cloud-tpu-client-0.10 google-api-core-1.34.0 google-api-python-client-1.8.0 lit-17.0.4 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 torch-2.0.0 torch-xla-2.0.0.dev20230516+colab torchvision-0.15.1 triton-2.0.0 uritemplate-3.0.1\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "!pip install cloud-tpu-client==0.10 torch==2.0.0 torchvision==0.15.1 https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-2.0-cp310-cp310-linux_x86_64.whl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VOe-ijQrX6Q"
      },
      "source": [
        "Please note, once the above code runs in CoLab, it will request you to restart the environment before continuing. Please follow these instructions, you likely are provided with a \"RESTART RUNTIME\" button.\n",
        "\n",
        "Next, we will query to see if we have TPUs available."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3vfKjYhQkhs",
        "outputId": "1304f5fa-cfe6-4afa-afb9-ba36d548a42c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TPU Cores Available: ['xla:1', 'xla:2', 'xla:3', 'xla:4', 'xla:5', 'xla:6', 'xla:7', 'xla:8']\n"
          ]
        }
      ],
      "source": [
        "import torch_xla.core.xla_model as xm\n",
        "tpu_cores = xm.get_xla_supported_devices()\n",
        "print(\"TPU Cores Available:\", tpu_cores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_hjljzjwGz7"
      },
      "source": [
        "It is typical in Google CoLab to have 8 TPUs; this is roughly analogous to multiple GPUs, and, unfortunately, requires some of the same coordination in code. I will use just a single TPU core, which is plug-and-play compatible with most of the software in this book."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KCY2f6NVFj8",
        "outputId": "ba1f86b9-3fda-43d1-d00f-ff945b698ad8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Device: xla:1\n"
          ]
        }
      ],
      "source": [
        "# Creates a random tensor on xla:1 (a Cloud TPU core)\n",
        "device = xm.xla_device()\n",
        "print(f\"Device: {device}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pcsi1kRvxiGz"
      },
      "source": [
        "Now that the device is set, we can use the TPU core just like we would a GPU or Apple MPS. Overall, I've found CUDA to be the most compatible/capable, followed by TPS, and lastly MPS. Though, I MPS is adding features all the time. We will begin by looking at the simple Neural Network example [provied by PyTorch](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VgLHNlUU7M5",
        "outputId": "72df8f62-6150-4fc6-d47e-9c2250987083"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[ 0.1263, -0.0300, -0.1846,  0.1202, -0.1197,  0.0876, -0.0388,  0.0518,\n",
            "          0.0542,  0.0563]], device='xla:1', grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# Simple example network from\n",
        "class Net(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        # 1 input image channel, 6 output channels, 3x3 square convolution\n",
        "        # kernel\n",
        "        self.conv1 = nn.Conv2d(1, 6, 3)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 3)\n",
        "        # an affine operation: y = Wx + b\n",
        "        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Max pooling over a (2, 2) window\n",
        "        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
        "        # If the size is a square you can only specify a single number\n",
        "        x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
        "        x = x.view(-1, self.num_flat_features(x))\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "    def num_flat_features(self, x):\n",
        "        size = x.size()[1:]  # all dimensions except the batch dimension\n",
        "        num_features = 1\n",
        "        for s in size:\n",
        "            num_features *= s\n",
        "        return num_features\n",
        "\n",
        "\n",
        "# Places network on the default TPU core\n",
        "net = Net().to(device)\n",
        "\n",
        "# Creates random input on the default TPU core\n",
        "input = torch.randn(1, 1, 32, 32, device=device)\n",
        "\n",
        "# Runs network\n",
        "out = net(input)\n",
        "print(out)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0x20nlRGyh6E"
      },
      "source": [
        "This code defines and utilizes a simple convolutional neural network (CNN) using PyTorch, a popular machine learning library in Python. The CNN, named Net, is a subclass of torch.nn.Module, which is the base class for all neural network modules in PyTorch.\n",
        "\n",
        "Key components and operations of the Net class are:\n",
        "\n",
        "1. Initialization (__init__): This method initializes two convolutional layers (conv1 and conv2) and three fully connected (linear) layers (fc1, fc2, and fc3). The convolutional layers are used for extracting features from the input image, and the linear layers are used for classification tasks.\n",
        "\n",
        "2. Forward Propagation (forward): This method defines the forward pass of the network. It applies a sequence of operations on the input tensor x:\n",
        "\n",
        "3. Convolution followed by ReLU activation and max pooling is performed twice, first with conv1 and then with conv2.\n",
        "The output is then flattened (transformed from a multi-dimensional tensor into a 1D tensor).\n",
        "T\n",
        "his flattened tensor is passed through three linear layers with ReLU activation functions (except for the last linear layer).\n",
        "Flattening (num_flat_features): This method calculates the total number of features in a flattened tensor, which is needed to determine the input size for the first linear layer.\n",
        "\n",
        "After defining the Net class, the code creates an instance of this network and moves it to a specific device (dev), which could be a CPU, GPU, or TPU, depending on how dev is defined. It then creates a random input tensor mimicking an image of size 32x32 with 1 channel (e.g., grayscale) and runs this input through the network (net(input)). The output of the network is then printed, which typically represents the class scores for a classification task.\n",
        "\n",
        "This code is a classic example of defining and using a CNN in PyTorch for tasks like image classification.\n",
        "\n",
        "## Mandelbrot Set\n",
        "\n",
        "Earlier in this book, I provided code to calculate the Mandelbrot set on either CPU or CUDA. Currently, the Mac MPS does not support complex numbers and, as a result, cannot run the Mandelbrot Set example. Here, I present the same code from Module 3, with no changes. Setting the device is sufficient to run this example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "QEPLRe7Cu4JI"
      },
      "outputs": [],
      "source": [
        "from io import BytesIO\n",
        "import torch\n",
        "import numpy as np\n",
        "import PIL.Image\n",
        "from IPython.display import Image\n",
        "\n",
        "\n",
        "# Take a NumPy array and render it as a Mandelbrot.\n",
        "def render(a):\n",
        "    a_cyclic = (a * 0.3).reshape(list(a.shape) + [1])\n",
        "    img = np.concatenate(\n",
        "        [\n",
        "            10 + 20 * np.cos(a_cyclic),\n",
        "            30 + 50 * np.sin(a_cyclic),\n",
        "            155 - 80 * np.cos(a_cyclic),\n",
        "        ],\n",
        "        2,\n",
        "    )\n",
        "    img[a == a.max()] = 0\n",
        "    a = img\n",
        "    a = np.uint8(np.clip(a, 0, 255))\n",
        "    f = BytesIO()\n",
        "    return PIL.Image.fromarray(a)\n",
        "\n",
        "\n",
        "# Loop through the render cycles for. Mandlebrot plot.\n",
        "def mandelbrot_helper(grid_c, current_values, counts, cycles):\n",
        "    for i in range(cycles):\n",
        "        # The Mandlebrot formula\n",
        "        temp = current_values * current_values + grid_c\n",
        "        not_diverged = torch.abs(temp) < 4\n",
        "        current_values.copy_(temp)\n",
        "        counts.copy_(torch.add(counts, not_diverged.double()))\n",
        "\n",
        "\n",
        "# Render a Mandelbrot plot at the specified location, zoom, and render cycles.\n",
        "def mandelbrot(render_size, center, zoom, cycles):\n",
        "    f = zoom / render_size[0]\n",
        "\n",
        "    real_start = center[1] - (render_size[1] / 2) * f\n",
        "    real_end = real_start + render_size[1] * f\n",
        "    imag_start = center[0] - (render_size[0] / 2) * f\n",
        "    imag_end = imag_start + render_size[0] * f\n",
        "\n",
        "    real_range = torch.arange(\n",
        "        real_start, real_end, f, dtype=torch.float32, device=device\n",
        "    )\n",
        "    imag_range = torch.arange(\n",
        "        imag_start, imag_end, f, dtype=torch.float32, device=device\n",
        "    )\n",
        "    real, imag = torch.meshgrid(real_range, imag_range, indexing=\"ij\")\n",
        "    grid_c = torch.complex(imag, real)\n",
        "    current_values = torch.clone(grid_c)\n",
        "    counts = torch.Tensor(torch.zeros_like(grid_c, dtype=torch.float32))\n",
        "\n",
        "    mandelbrot_helper(grid_c, current_values, counts, cycles)\n",
        "    return counts.cpu().numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPeFj7h604fb"
      },
      "source": [
        "With the support functions defined, we can now run the Mandelbrot Set example on the TPU."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "3OAONSiGvHYV",
        "outputId": "306574b7-73fe-4ec4-e9b7-7e580f4a0454"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(640, 480)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=640x480>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "# Pytorch does not currently support complex numbers on MPS\n",
        "temp_device = device\n",
        "if device == \"mps\":\n",
        "    device = \"cpu\"\n",
        "\n",
        "counts = mandelbrot(\n",
        "    # render_size=(1920,1080), # HD\n",
        "    render_size=(640, 480),\n",
        "    center=(-0.5, 0),\n",
        "    zoom=4,\n",
        "    cycles=200,\n",
        ")\n",
        "\n",
        "img = render(counts)\n",
        "print(img.size)\n",
        "\n",
        "# restore device\n",
        "device = temp_device\n",
        "\n",
        "# display image\n",
        "img"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "anaconda-cloud": {},
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9 (torch)",
      "language": "python",
      "name": "pytorch"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
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