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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2MDUUcFa7O2u"
+      },
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-ai-edge/model-explorer/blob/main/example_colabs/model_explorer_demo.ipynb) \n",
+        "\n",
+        "# Install Model Explorer and dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": true,
+        "id": "me12eMftdu4H"
+      },
+      "outputs": [],
+      "source": [
+        "# Install tflite & model-explorer dependencies.\n",
+        "!pip install tflite\n",
+        "!pip install --no-deps ai-edge-model-explorer model-explorer-adapter\n",
+        "\n",
+        "# Install kagglehub to load a model.\n",
+        "!pip install kagglehub --no-deps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Im5BQ2Qz7na_"
+      },
+      "source": [
+        "# Download MobileNet v3 from Kaggle"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xaSxh0rQf4Fj"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO: Workaround until Eric's fixes\n",
+        "import kagglehub\n",
+        "\n",
+        "# Download latest version\n",
+        "# TODO: Update this to use mobilebert or selected model.\n",
+        "path = kagglehub.model_download(\"google/mobilenet-v3/tfLite/large-075-224-classification\")\n",
+        "model_path = f\"{path}/1.tflite\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mWm4BE_I70hi"
+      },
+      "source": [
+        "# Run the model with random test data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6cuAg62OvfTK"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "# Load the TFLite model and allocate tensors.\n",
+        "interpreter = tf.lite.Interpreter(model_path=model_path)\n",
+        "interpreter.allocate_tensors()\n",
+        "\n",
+        "# Get input and output tensors.\n",
+        "input_details = interpreter.get_input_details()\n",
+        "output_details = interpreter.get_output_details()\n",
+        "\n",
+        "# Generate random input data.\n",
+        "for input_detail in input_details:\n",
+        "  input_shape = input_detail['shape']\n",
+        "  input_data = np.array(np.random.random_sample(input_shape), dtype=input_detail['dtype'])\n",
+        "  interpreter.set_tensor(input_detail['index'], input_data)\n",
+        "\n",
+        "# Test the model on random input data.\n",
+        "interpreter.invoke()\n",
+        "\n",
+        "# The function `get_tensor()` returns a copy of the tensor data.\n",
+        "# Use `tensor()` in order to get a pointer to the tensor.\n",
+        "for output_detail in output_details:\n",
+        "  print(f\"Output for {output_detail['name']}\")\n",
+        "  output_data = interpreter.get_tensor(output_detail['index'])\n",
+        "  print(output_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HhwvzBUIO9SZ"
+      },
+      "source": [
+        "# Download the TF benchmark utility and run it"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": true,
+        "id": "2FLsz9OsC6w_"
+      },
+      "outputs": [],
+      "source": [
+        "%env MODEL_PATH=$model_path\n",
+        "\n",
+        "!mkdir -p /tmp/data\n",
+        "%cd /tmp/data\n",
+        "\n",
+        "# Download the tflite model benchmark binary.\n",
+        "!wget -nc https://storage.googleapis.com/tensorflow-nightly-public/prod/tensorflow/release/lite/tools/nightly/latest/linux_x86-64_benchmark_model\n",
+        "!chmod +x /tmp/data/linux_x86-64_benchmark_model\n",
+        "\n",
+        "# Run the benchmark locally only using CPU kernels.\n",
+        "# TODO: Update this once the feature is live.\n",
+        "# !./linux_x86-64_benchmark_model --graph=$MODEL_PATH --use_xnnpack=false --num_threads=4\n",
+        "\n",
+        "# # Run the benchmark locally with XNNPACK kernels enabled.\n",
+        "# # TODO: Update this once the feature is live.\n",
+        "# !./linux_x86-64_benchmark_model --graph=$MODEL_PATH --use_xnnpack=true --num_threads=4\n",
+        "\n",
+        "# TODO: Remove the below once the feature is live.\n",
+        "!wget -nc https://storage.googleapis.com/tfweb/model-explorer-demo/mv3-cpu-op-profile.json\n",
+        "!wget -nc https://storage.googleapis.com/tfweb/model-explorer-demo/mv3-xnnpack-op-profile.json\n",
+        "CPU_PROFILING_JSON_PATH=\"/tmp/data/mv3-cpu-op-profile.json\"\n",
+        "XNNPACK_PROFILING_JSON_PATH=\"/tmp/data/mv3-xnnpack-op-profile.json\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oCgFNFP4WAzE"
+      },
+      "source": [
+        "# Visualize the model with the per op latency overlaid"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6SVyHrthM_hz"
+      },
+      "outputs": [],
+      "source": [
+        "import model_explorer\n",
+        "\n",
+        "config = model_explorer.config()\n",
+        "(config\n",
+        " .add_model_from_path(model_path)\n",
+        " .add_node_data_from_path(CPU_PROFILING_JSON_PATH)\n",
+        " .add_node_data_from_path(XNNPACK_PROFILING_JSON_PATH))\n",
+        "\n",
+        "model_explorer.visualize_from_config(config)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}