hf_vision_model_tfserving.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/sayakpaul/deploy-hf-tf-vision-models/blob/main/hf_vision_model_tfserving.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z2bwRLGzGQlk"
   },
   "source": [
    "This notebook shows how to deploy a vision model in TensorFlow from 🤗 Transformers with TensorFlow Serving. It uses [this blog post](https://huggingface.co/blog/tf-serving) as a reference. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pGC5JtQbGktj"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-kgm_ksCPTkJ",
    "outputId": "d9de5a1d-066a-4f6e-865d-64a0dfa72357"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K     |████████████████████████████████| 4.4 MB 4.4 MB/s \n",
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      "\u001b[K     |████████████████████████████████| 596 kB 39.9 MB/s \n",
      "\u001b[K     |████████████████████████████████| 101 kB 9.9 MB/s \n",
      "\u001b[K     |████████████████████████████████| 511.7 MB 5.5 kB/s \n",
      "\u001b[K     |████████████████████████████████| 438 kB 46.6 MB/s \n",
      "\u001b[K     |████████████████████████████████| 5.8 MB 33.5 MB/s \n",
      "\u001b[K     |████████████████████████████████| 1.6 MB 41.1 MB/s \n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install -q transformers\n",
    "!pip install -q tensorflow_serving_api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wo-b-LuuGnEK"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1cuyr4mcP5Su"
   },
   "outputs": [],
   "source": [
    "from transformers import ViTFeatureExtractor, TFViTForImageClassification\n",
    "import tensorflow as tf\n",
    "import tempfile\n",
    "import requests\n",
    "import base64\n",
    "import json\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bGCF3EyL92cT",
    "outputId": "492d4032-58ad-4c01-de73-dfce7f750092"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.20.1\n"
     ]
    }
   ],
   "source": [
    "import transformers\n",
    "\n",
    "print(transformers.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jfKdDDqCGpHo"
   },
   "source": [
    "## Save the ViT model and investigate its inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223,
     "referenced_widgets": [
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    },
    "id": "eqOIWKJfPnDk",
    "outputId": "29ac45f0-82f4-4f66-c268-dbec8fc391fa"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6ec5d13fc6d4296b8f76bbba303701d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/68.0k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d853522fb35f4894b935b360a4afb1b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/330M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFViTForImageClassification.\n",
      "\n",
      "All the layers of TFViTForImageClassification were initialized from the model checkpoint at google/vit-base-patch16-224.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTForImageClassification for predictions without further training.\n",
      "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, layernorm_layer_call_fn while saving (showing 5 of 421). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: resnet/saved_model/1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: resnet/saved_model/1/assets\n"
     ]
    }
   ],
   "source": [
    "# the saved_model parameter is a flag to create a saved model version of the model\n",
    "temp_model_dir = \"vit\"\n",
    "model = TFViTForImageClassification.from_pretrained(\"google/vit-base-patch16-224\")\n",
    "model.save_pretrained(temp_model_dir, saved_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fv9VDVBzTzZr",
    "outputId": "2e1bbc98-e54f-48a3-903d-3df375782986"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The given SavedModel SignatureDef contains the following input(s):\n",
      "  inputs['pixel_values'] tensor_info:\n",
      "      dtype: DT_FLOAT\n",
      "      shape: (-1, -1, -1, -1)\n",
      "      name: serving_default_pixel_values:0\n",
      "The given SavedModel SignatureDef contains the following output(s):\n",
      "  outputs['logits'] tensor_info:\n",
      "      dtype: DT_FLOAT\n",
      "      shape: (-1, 1000)\n",
      "      name: StatefulPartitionedCall:0\n",
      "Method name is: tensorflow/serving/predict\n"
     ]
    }
   ],
   "source": [
    "!saved_model_cli show --dir {temp_model_dir}/saved_model/1 --tag_set serve --signature_def serving_default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0567_tMkGvVx"
   },
   "source": [
    "## Save the model embedding pre-processing and post-processing ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1H-LEF8FP2G8",
    "outputId": "41c5c91f-45e3-4fa0-96fe-9e596334593d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ViTFeatureExtractor {\n",
       "  \"do_normalize\": true,\n",
       "  \"do_resize\": true,\n",
       "  \"feature_extractor_type\": \"ViTFeatureExtractor\",\n",
       "  \"image_mean\": [\n",
       "    0.5,\n",
       "    0.5,\n",
       "    0.5\n",
       "  ],\n",
       "  \"image_std\": [\n",
       "    0.5,\n",
       "    0.5,\n",
       "    0.5\n",
       "  ],\n",
       "  \"resample\": 2,\n",
       "  \"size\": 224\n",
       "}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_extractor = ViTFeatureExtractor()\n",
    "feature_extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RvsoVW_0Qb2k"
   },
   "outputs": [],
   "source": [
    "CONCRETE_INPUT = \"pixel_values\"\n",
    "SIZE = feature_extractor.size\n",
    "INPUT_SHAPE = (SIZE, SIZE, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2YeDe1M5Q5AP"
   },
   "outputs": [],
   "source": [
    "def normalize_img(\n",
    "    img, mean=feature_extractor.image_mean, std=feature_extractor.image_std\n",
    "):\n",
    "    # Scale to the value range of [0, 1] first and then normalize.\n",
    "    img = img / 255\n",
    "    mean = tf.constant(mean)\n",
    "    std = tf.constant(std)\n",
    "    return (img - mean) / std\n",
    "\n",
    "\n",
    "def preprocess(string_input):\n",
    "    decoded_input = tf.io.decode_base64(string_input)\n",
    "    decoded = tf.io.decode_jpeg(decoded_input, channels=3)\n",
    "    resized = tf.image.resize(decoded, size=(SIZE, SIZE))\n",
    "    normalized = normalize_img(resized)\n",
    "    normalized = tf.transpose(\n",
    "        normalized, (2, 0, 1)\n",
    "    )  # Since HF models are channel-first.\n",
    "    return normalized\n",
    "\n",
    "\n",
    "@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n",
    "def preprocess_fn(string_input):\n",
    "    decoded_images = tf.map_fn(\n",
    "        preprocess, string_input, dtype=tf.float32, back_prop=False\n",
    "    )\n",
    "    return {CONCRETE_INPUT: decoded_images}\n",
    "\n",
    "\n",
    "def model_exporter(model: tf.keras.Model):\n",
    "    m_call = tf.function(model.call).get_concrete_function(\n",
    "        tf.TensorSpec(\n",
    "            shape=[None, 3, SIZE, SIZE], dtype=tf.float32, name=CONCRETE_INPUT\n",
    "        )\n",
    "    )\n",
    "\n",
    "    @tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n",
    "    def serving_fn(string_input):\n",
    "        labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)\n",
    "        images = preprocess_fn(string_input)\n",
    "\n",
    "        predictions = m_call(**images)\n",
    "        indices = tf.argmax(predictions.logits, axis=1)\n",
    "        pred_source = tf.gather(params=labels, indices=indices)\n",
    "        probs = tf.nn.softmax(predictions.logits, axis=1)\n",
    "        pred_confidence = tf.reduce_max(probs, axis=1)\n",
    "        return {\"label\": pred_source, \"confidence\": pred_confidence}\n",
    "\n",
    "    return serving_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J9Xdj_OiF6TG"
   },
   "source": [
    "**Notes on making the model accept string inputs**:\n",
    "\n",
    "When dealing with images via REST or gRPC requests the size of the request payload can easily spiral up depending on the resolution of the images being passed. This is why, it is good practice to compress them reliably and then prepare the request payload."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7IGRj3kAS7oZ",
    "outputId": "fbb4464a-d48a-4006-fdcb-8ee5920fe9c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:629: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use fn_output_signature instead\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:629: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use fn_output_signature instead\n",
      "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, layernorm_layer_call_fn while saving (showing 5 of 421). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/1/assets\n"
     ]
    }
   ],
   "source": [
    "MODEL_DIR = tempfile.gettempdir()\n",
    "VERSION = 1\n",
    "\n",
    "tf.saved_model.save(\n",
    "    model,\n",
    "    os.path.join(MODEL_DIR, str(VERSION)),\n",
    "    signatures={\"serving_default\": model_exporter(model)},\n",
    ")\n",
    "os.environ[\"MODEL_DIR\"] = MODEL_DIR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_TlzGe8XG1gx"
   },
   "source": [
    "Investigate the `SavedModel` once again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QXXz4BDkY2A_",
    "outputId": "15db1f42-93fe-4e8d-e5ee-ea237164da09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The given SavedModel SignatureDef contains the following input(s):\n",
      "  inputs['string_input'] tensor_info:\n",
      "      dtype: DT_STRING\n",
      "      shape: (-1)\n",
      "      name: serving_default_string_input:0\n",
      "The given SavedModel SignatureDef contains the following output(s):\n",
      "  outputs['confidence'] tensor_info:\n",
      "      dtype: DT_FLOAT\n",
      "      shape: (-1)\n",
      "      name: StatefulPartitionedCall:0\n",
      "  outputs['label'] tensor_info:\n",
      "      dtype: DT_STRING\n",
      "      shape: (-1)\n",
      "      name: StatefulPartitionedCall:1\n",
      "Method name is: tensorflow/serving/predict\n"
     ]
    }
   ],
   "source": [
    "!saved_model_cli show --dir {MODEL_DIR}/1 --tag_set serve --signature_def serving_default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MJU-LJuwG5eC"
   },
   "source": [
    "## Install TensorFlow Model Server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XYgQ9ufJZTV1",
    "outputId": "0ab1d58e-79f8-4828-8f6c-f471229cd7b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-07-15 04:11:11--  http://storage.googleapis.com/tensorflow-serving-apt/pool/tensorflow-model-server-universal-2.8.0/t/tensorflow-model-server-universal/tensorflow-model-server-universal_2.8.0_all.deb\n",
      "Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.8.128, 74.125.23.128, 74.125.203.128, ...\n",
      "Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.8.128|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 335421916 (320M) [application/x-debian-package]\n",
      "Saving to: ‘tensorflow-model-server-universal_2.8.0_all.deb’\n",
      "\n",
      "tensorflow-model-se 100%[===================>] 319.88M  68.7MB/s    in 4.7s    \n",
      "\n",
      "2022-07-15 04:11:16 (68.7 MB/s) - ‘tensorflow-model-server-universal_2.8.0_all.deb’ saved [335421916/335421916]\n",
      "\n",
      "Selecting previously unselected package tensorflow-model-server-universal.\n",
      "(Reading database ... 155653 files and directories currently installed.)\n",
      "Preparing to unpack tensorflow-model-server-universal_2.8.0_all.deb ...\n",
      "Unpacking tensorflow-model-server-universal (2.8.0) ...\n",
      "Setting up tensorflow-model-server-universal (2.8.0) ...\n"
     ]
    }
   ],
   "source": [
    "# Deviates from the original installation instructions.\n",
    "# https://issuemode.com/issues/tensorflow/serving/92945160\n",
    "!wget 'http://storage.googleapis.com/tensorflow-serving-apt/pool/tensorflow-model-server-universal-2.8.0/t/tensorflow-model-server-universal/tensorflow-model-server-universal_2.8.0_all.deb'\n",
    "!dpkg -i tensorflow-model-server-universal_2.8.0_all.deb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k5m_7XZjG8Qo"
   },
   "source": [
    "## Deploy the model \n",
    "\n",
    "By default TF Serving exposes two APIs: REST and gRPC. We will see how to infer with both. Each has their own pros and cons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "be609R85hiik",
    "outputId": "148b3fcb-6243-4d69-e163-3c30c6cbaaa2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting job # 0 in a separate thread.\n"
     ]
    }
   ],
   "source": [
    "%%bash --bg \n",
    "nohup tensorflow_model_server \\\n",
    "  --rest_api_port=8501 \\\n",
    "  --model_name=vit \\\n",
    "  --model_base_path=$MODEL_DIR >server.log 2>&1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1r9O1Q_CjJnI",
    "outputId": "40ab765e-6017-436e-e94f-7bff927e6c44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[warn] getaddrinfo: address family for nodename not supported\n",
      "[evhttp_server.cc : 245] NET_LOG: Entering the event loop ...\n"
     ]
    }
   ],
   "source": [
    "!cat server.log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S1wSLcPwkAuK",
    "outputId": "d379d947-e6d6-45af-8ec3-b1c72ec112da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node        8 root   21u  IPv6  26436      0t0  TCP *:8080 (LISTEN)\n",
      "colab-fil  30 root    5u  IPv6  26409      0t0  TCP *:3453 (LISTEN)\n",
      "colab-fil  30 root    6u  IPv4  26410      0t0  TCP *:3453 (LISTEN)\n",
      "jupyter-n  43 root    6u  IPv4  27130      0t0  TCP 172.28.0.2:9000 (LISTEN)\n",
      "python3    60 root   15u  IPv4  30327      0t0  TCP 127.0.0.1:46129 (LISTEN)\n",
      "python3    60 root   18u  IPv4  30331      0t0  TCP 127.0.0.1:58207 (LISTEN)\n",
      "python3    60 root   21u  IPv4  30335      0t0  TCP 127.0.0.1:44103 (LISTEN)\n",
      "python3    60 root   24u  IPv4  30339      0t0  TCP 127.0.0.1:53393 (LISTEN)\n",
      "python3    60 root   30u  IPv4  30345      0t0  TCP 127.0.0.1:46873 (LISTEN)\n",
      "python3    60 root   43u  IPv4  31046      0t0  TCP 127.0.0.1:59625 (LISTEN)\n",
      "python3    80 root    3u  IPv4  31602      0t0  TCP 127.0.0.1:20352 (LISTEN)\n",
      "python3    80 root    4u  IPv4  31603      0t0  TCP 127.0.0.1:34417 (LISTEN)\n",
      "python3    80 root    9u  IPv4  32828      0t0  TCP 127.0.0.1:36819 (LISTEN)\n",
      "tensorflo 259 root    5u  IPv4  93837      0t0  TCP *:8500 (LISTEN)\n",
      "tensorflo 259 root   12u  IPv4  92983      0t0  TCP *:8501 (LISTEN)\n"
     ]
    }
   ],
   "source": [
    "!sudo lsof -i -P -n | grep LISTEN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aXJLcpoYke-d"
   },
   "source": [
    "## REST API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JhG4DRPakf5D",
    "outputId": "d23f844d-9da1-4f2f-c5e2-820f23994460"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://images.cocodataset.org/val2017/000000039769.jpg\n",
      "173131/173131 [==============================] - 1s 3us/step\n",
      "Data: {\"signature_name\": \"serving_default\", \"instances\": ... TRmYgEHbbrYWv0A6b4o2n1HZgYLq91nP-o7O2pcNa6r__2Q==\"]}\n"
     ]
    }
   ],
   "source": [
    "image_path = tf.keras.utils.get_file(\n",
    "    \"image.jpg\", \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n",
    ")\n",
    "bytes_inputs = tf.io.read_file(image_path)\n",
    "b64str = base64.urlsafe_b64encode(bytes_inputs.numpy()).decode(\"utf-8\")\n",
    "\n",
    "data = json.dumps({\"signature_name\": \"serving_default\", \"instances\": [b64str]})\n",
    "print(\"Data: {} ... {}\".format(data[:50], data[len(data) - 52 :]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vrqZA1Qylv_D",
    "outputId": "7b87bb1d-3f52-4a3b-e0b2-6d564616a0a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [{'label': 'Egyptian cat', 'confidence': 0.896659195}]}\n"
     ]
    }
   ],
   "source": [
    "headers = {\"content-type\": \"application/json\"}\n",
    "json_response = requests.post(\n",
    "    \"http://localhost:8501/v1/models/vit:predict\", data=data, headers=headers\n",
    ")\n",
    "print(json.loads(json_response.text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_-yy3u3kmNQO"
   },
   "source": [
    "## gRPC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "f7gAQSo6EdKi"
   },
   "outputs": [],
   "source": [
    "import grpc\n",
    "from tensorflow_serving.apis import predict_pb2\n",
    "from tensorflow_serving.apis import prediction_service_pb2_grpc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qlArzjKvEZ3N"
   },
   "outputs": [],
   "source": [
    "channel = grpc.insecure_channel(\"localhost:8500\")\n",
    "stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VOU72ENGE1Xq",
    "outputId": "3362cc36-3e70-4d90-9689-ced9e1dd1187"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Serving function input: string_input\n"
     ]
    }
   ],
   "source": [
    "loaded = tf.saved_model.load(f\"{MODEL_DIR}/{VERSION}\")\n",
    "serving_input = list(\n",
    "    loaded.signatures[\"serving_default\"].structured_input_signature[1].keys()\n",
    ")[0]\n",
    "print(\"Serving function input:\", serving_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jBloKw6REfTq"
   },
   "outputs": [],
   "source": [
    "request = predict_pb2.PredictRequest()\n",
    "request.model_spec.name = \"vit\"\n",
    "request.model_spec.signature_name = \"serving_default\"\n",
    "request.inputs[serving_input].CopyFrom(tf.make_tensor_proto([b64str]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mMRC_qp8FNtT",
    "outputId": "a6452d17-07af-46b0-ba14-450690ffa523"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outputs {\n",
       "  key: \"confidence\"\n",
       "  value {\n",
       "    dtype: DT_FLOAT\n",
       "    tensor_shape {\n",
       "      dim {\n",
       "        size: 1\n",
       "      }\n",
       "    }\n",
       "    float_val: 0.8966591954231262\n",
       "  }\n",
       "}\n",
       "outputs {\n",
       "  key: \"label\"\n",
       "  value {\n",
       "    dtype: DT_STRING\n",
       "    tensor_shape {\n",
       "      dim {\n",
       "        size: 1\n",
       "      }\n",
       "    }\n",
       "    string_val: \"Egyptian cat\"\n",
       "  }\n",
       "}\n",
       "model_spec {\n",
       "  name: \"resnet\"\n",
       "  version {\n",
       "    value: 1\n",
       "  }\n",
       "  signature_name: \"serving_default\"\n",
       "}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpc_predictions = stub.Predict(request, 10.0)  # 10 secs timeout\n",
    "print(grpc_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lEpEU508Fj3G",
    "outputId": "5d635b49-33ed-449b-fd65-4223274f53f5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([b'Egyptian cat'], [0.8966591954231262])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpc_predictions.outputs[\"label\"].string_val, grpc_predictions.outputs[\n",
    "    \"confidence\"\n",
    "].float_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YYIcV9BsHGy-"
   },
   "source": [
    "## Next steps\n",
    "\n",
    "* Deploy the SavedModel to Vertex AI \n",
    "* Deploy with TF Serving + Kubernetes (via GKE)"
   ]
  }
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