simple_test_hello_world.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hello World Example of ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook you will serve your first TensorFlow model with TensorFlow Serving. We will start by building a very simple model to infer the relationship:\n",
    "\n",
    "$$\n",
    "y = 3x + 1 \n",
    "$$\n",
    "\n",
    "[source](https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='sgd', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n",
    "ys = np.array([-2.0, 1.0, 4.0, 7.0, 10.0, 13.0], dtype=float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0107\n",
      "Epoch 105/500\n",
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      "Epoch 385/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.1236e-05\n",
      "Epoch 386/500\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 3.0595e-05\n",
      "Epoch 387/500\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 2.9967e-05\n",
      "Epoch 388/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.9352e-05\n",
      "Epoch 389/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.8749e-05\n",
      "Epoch 390/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.8159e-05\n",
      "Epoch 391/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.7580e-05\n",
      "Epoch 392/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.7015e-05\n",
      "Epoch 393/500\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 2.6460e-05\n",
      "Epoch 394/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.5917e-05\n",
      "Epoch 395/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.5385e-05\n",
      "Epoch 396/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.4863e-05\n",
      "Epoch 397/500\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 2.4353e-05\n",
      "Epoch 398/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.3852e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 399/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.3362e-05\n",
      "Epoch 400/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.2883e-05\n",
      "Epoch 401/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.2412e-05\n",
      "Epoch 402/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.1952e-05\n",
      "Epoch 403/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.1501e-05\n",
      "Epoch 404/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.1059e-05\n",
      "Epoch 405/500\n",
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      "1/1 [==============================] - 0s 2ms/step - loss: 1.9788e-05\n",
      "Epoch 408/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.9382e-05\n",
      "Epoch 409/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.8984e-05\n",
      "Epoch 410/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.8594e-05\n",
      "Epoch 411/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.8211e-05\n",
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      "1/1 [==============================] - 0s 3ms/step - loss: 1.7837e-05\n",
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      "1/1 [==============================] - 0s 2ms/step - loss: 1.7470e-05\n",
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      "1/1 [==============================] - 0s 5ms/step - loss: 1.7111e-05\n",
      "Epoch 415/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.6759e-05\n",
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      "1/1 [==============================] - 0s 3ms/step - loss: 1.6414e-05\n",
      "Epoch 417/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 1.6077e-05\n",
      "Epoch 418/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.5747e-05\n",
      "Epoch 419/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.5424e-05\n",
      "Epoch 420/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.5106e-05\n",
      "Epoch 421/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.4796e-05\n",
      "Epoch 422/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.4493e-05\n",
      "Epoch 423/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 1.4194e-05\n",
      "Epoch 424/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.3903e-05\n",
      "Epoch 425/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.3617e-05\n",
      "Epoch 426/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.3338e-05\n",
      "Epoch 427/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.3063e-05\n",
      "Epoch 428/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.2795e-05\n",
      "Epoch 429/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.2533e-05\n",
      "Epoch 430/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.2276e-05\n",
      "Epoch 431/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.2024e-05\n",
      "Epoch 432/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1776e-05\n",
      "Epoch 433/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1534e-05\n",
      "Epoch 434/500\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.1297e-05\n",
      "Epoch 435/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.1065e-05\n",
      "Epoch 436/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0837e-05\n",
      "Epoch 437/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0615e-05\n",
      "Epoch 438/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0396e-05\n",
      "Epoch 439/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.0183e-05\n",
      "Epoch 440/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 9.9733e-06\n",
      "Epoch 441/500\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 9.7693e-06\n",
      "Epoch 442/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.5683e-06\n",
      "Epoch 443/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.3722e-06\n",
      "Epoch 444/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.1798e-06\n",
      "Epoch 445/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.9909e-06\n",
      "Epoch 446/500\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 8.8061e-06\n",
      "Epoch 447/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.6258e-06\n",
      "Epoch 448/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.4486e-06\n",
      "Epoch 449/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.2747e-06\n",
      "Epoch 450/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1052e-06\n",
      "Epoch 451/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.9382e-06\n",
      "Epoch 452/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.7753e-06\n",
      "Epoch 453/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.6154e-06\n",
      "Epoch 454/500\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.4593e-06\n",
      "Epoch 455/500\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.3060e-06\n",
      "Epoch 456/500\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 7.1554e-06\n",
      "Epoch 457/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.0089e-06\n",
      "Epoch 458/500\n",
      "1/1 [==============================] - 0s 940us/step - loss: 6.8645e-06\n",
      "Epoch 459/500\n",
      "1/1 [==============================] - 0s 20ms/step - loss: 6.7236e-06\n",
      "Epoch 460/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.5858e-06\n",
      "Epoch 461/500\n",
      "1/1 [==============================] - 0s 19ms/step - loss: 6.4510e-06\n",
      "Epoch 462/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6.3185e-06\n",
      "Epoch 463/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.1886e-06\n",
      "Epoch 464/500\n",
      "1/1 [==============================] - 0s 22ms/step - loss: 6.0619e-06\n",
      "Epoch 465/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.9371e-06\n",
      "Epoch 466/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5.8151e-06\n",
      "Epoch 467/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5.6955e-06\n",
      "Epoch 468/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 5.5787e-06\n",
      "Epoch 469/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 5.4639e-06\n",
      "Epoch 470/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 5.3517e-06\n",
      "Epoch 471/500\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5.2420e-06\n",
      "Epoch 472/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5.1344e-06\n",
      "Epoch 473/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.0287e-06\n",
      "Epoch 474/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 4.9254e-06\n",
      "Epoch 475/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 4.8243e-06\n",
      "Epoch 476/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.7251e-06\n",
      "Epoch 477/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.6283e-06\n",
      "Epoch 478/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.5331e-06\n",
      "Epoch 479/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.4401e-06\n",
      "Epoch 480/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.3487e-06\n",
      "Epoch 481/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.2593e-06\n",
      "Epoch 482/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 4.1718e-06\n",
      "Epoch 483/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 4.0863e-06\n",
      "Epoch 484/500\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 4.0023e-06\n",
      "Epoch 485/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.9199e-06\n",
      "Epoch 486/500\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 3.8393e-06\n",
      "Epoch 487/500\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 3.7605e-06\n",
      "Epoch 488/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 3.6830e-06\n",
      "Epoch 489/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 3.6075e-06\n",
      "Epoch 490/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.5336e-06\n",
      "Epoch 491/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.4608e-06\n",
      "Epoch 492/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.3901e-06\n",
      "Epoch 493/500\n",
      "1/1 [==============================] - ETA: 0s - loss: 3.3203e-0 - 0s 5ms/step - loss: 3.3203e-06\n",
      "Epoch 494/500\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 3.2522e-06\n",
      "Epoch 495/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 3.1852e-06\n",
      "Epoch 496/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 2ms/step - loss: 3.1200e-06\n",
      "Epoch 497/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.0557e-06\n",
      "Epoch 498/500\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.9927e-06\n",
      "Epoch 499/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.9314e-06\n",
      "Epoch 500/500\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.8712e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f8358451f98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xs, ys, epochs=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[30.995054]]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict([10.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/mohadese/.virtualenvs/shenasa/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "WARNING:tensorflow:From /home/mohadese/.virtualenvs/shenasa/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "INFO:tensorflow:Assets written to: helloworld/1/assets\n",
      "\n",
      "export_path = helloworld/1\n",
      "total 48\n",
      "drwxr-xr-x 2 mohadese mohadese  4096 Aug 15 09:13 assets\n",
      "-rw-rw-r-- 1 mohadese mohadese 37442 Aug 15 09:13 saved_model.pb\n",
      "drwxr-xr-x 2 mohadese mohadese  4096 Aug 15 09:13 variables\n"
     ]
    }
   ],
   "source": [
    "MODEL_DIR = 'helloworld'\n",
    "\n",
    "version = 1\n",
    "\n",
    "export_path = os.path.join(MODEL_DIR, str(version))\n",
    "\n",
    "tf.keras.models.save_model(model, export_path)\n",
    "\n",
    "print('\\nexport_path = {}'.format(export_path))\n",
    "!ls -l {export_path}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Install Tensorflow Serving](https://www.tensorflow.org/tfx/serving/setup)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add TensorFlow Serving distribution URI as a package source.\n",
    "\n",
    "Before we can install TensorFlow Serving, we need to add the `tensorflow-model-server` package to the list of packages that Aptitude knows about. Note that we're running as root.\n",
    "\n",
    "**Note**: This package doesn't use docker.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!echo \"deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal\" | tee /etc/apt/sources.list.d/tensorflow-serving.list && \\\n",
    "curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | apt-key add -\n",
    "!apt update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!apt-get install tensorflow-model-server"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the TensorFlow Model Server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"MODEL_DIR\"] = os.path.join(os.getcwd(), 'helloworld')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --bg \n",
    "nohup tensorflow_model_server \\\n",
    "  --rest_api_port=8501 \\\n",
    "  --model_name=helloworld \\\n",
    "  --model_base_path=/home/mohadese/workspace/tfserving/helloworld/ >server.log 2>&1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u00002020-08-15 09:22:44.737406: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n",
      "2020-08-15 09:22:45.737448: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n",
      "2020-08-15 09:22:46.737663: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n",
      "2020-08-15 09:22:47.737643: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n",
      "2020-08-15 09:22:48.737858: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n",
      "2020-08-15 09:22:49.737920: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n",
      "2020-08-15 09:22:50.737893: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n",
      "2020-08-15 09:22:51.738028: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n",
      "2020-08-15 09:22:52.738064: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n",
      "2020-08-15 09:22:53.738135: E tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:362] FileSystemStoragePathSource encountered a filesystem access error: Could not find base path /home/mohadese/worksapce/tfserving/helloworld for servable helloworld\r\n"
     ]
    }
   ],
   "source": [
    "!tail server.log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference With Rest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "\"model_spec\":{\n",
      " \"name\": \"helloworld\",\n",
      " \"signature_name\": \"\",\n",
      " \"version\": \"1\"\n",
      "}\n",
      ",\n",
      "\"metadata\": {\"signature_def\": {\n",
      " \"signature_def\": {\n",
      "  \"serving_default\": {\n",
      "   \"inputs\": {\n",
      "    \"dense_input\": {\n",
      "     \"dtype\": \"DT_FLOAT\",\n",
      "     \"tensor_shape\": {\n",
      "      \"dim\": [\n",
      "       {\n",
      "        \"size\": \"-1\",\n",
      "        \"name\": \"\"\n",
      "       },\n",
      "       {\n",
      "        \"size\": \"1\",\n",
      "        \"name\": \"\"\n",
      "       }\n",
      "      ],\n",
      "      \"unknown_rank\": false\n",
      "     },\n",
      "     \"name\": \"serving_default_dense_input:0\"\n",
      "    }\n",
      "   },\n",
      "   \"outputs\": {\n",
      "    \"dense\": {\n",
      "     \"dtype\": \"DT_FLOAT\",\n",
      "     \"tensor_shape\": {\n",
      "      \"dim\": [\n",
      "       {\n",
      "        \"size\": \"-1\",\n",
      "        \"name\": \"\"\n",
      "       },\n",
      "       {\n",
      "        \"size\": \"1\",\n",
      "        \"name\": \"\"\n",
      "       }\n",
      "      ],\n",
      "      \"unknown_rank\": false\n",
      "     },\n",
      "     \"name\": \"StatefulPartitionedCall:0\"\n",
      "    }\n",
      "   },\n",
      "   \"method_name\": \"tensorflow/serving/predict\"\n",
      "  },\n",
      "  \"__saved_model_init_op\": {\n",
      "   \"inputs\": {},\n",
      "   \"outputs\": {\n",
      "    \"__saved_model_init_op\": {\n",
      "     \"dtype\": \"DT_INVALID\",\n",
      "     \"tensor_shape\": {\n",
      "      \"dim\": [],\n",
      "      \"unknown_rank\": true\n",
      "     },\n",
      "     \"name\": \"NoOp\"\n",
      "    }\n",
      "   },\n",
      "   \"method_name\": \"\"\n",
      "  }\n",
      " }\n",
      "}\n",
      "}\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "json_response = requests.get('http://localhost:8501/v1/models/helloworld/metadata')\n",
    "print(json_response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"signature_name\": \"serving_default\", \"inputs\": [[9.0], [10.0]]}\n"
     ]
    }
   ],
   "source": [
    "xs = np.array([[9.0], [10.0]])\n",
    "data = json.dumps({\"signature_name\": \"serving_default\", \"inputs\": xs.tolist()})\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "\"model_spec\":{\n",
      " \"name\": \"helloworld\",\n",
      " \"signature_name\": \"\",\n",
      " \"version\": \"1\"\n",
      "}\n",
      ",\n",
      "\"metadata\": {\"signature_def\": {\n",
      " \"signature_def\": {\n",
      "  \"serving_default\": {\n",
      "   \"inputs\": {\n",
      "    \"dense_input\": {\n",
      "     \"dtype\": \"DT_FLOAT\",\n",
      "     \"tensor_shape\": {\n",
      "      \"dim\": [\n",
      "       {\n",
      "        \"size\": \"-1\",\n",
      "        \"name\": \"\"\n",
      "       },\n",
      "       {\n",
      "        \"size\": \"1\",\n",
      "        \"name\": \"\"\n",
      "       }\n",
      "      ],\n",
      "      \"unknown_rank\": false\n",
      "     },\n",
      "     \"name\": \"serving_default_dense_input:0\"\n",
      "    }\n",
      "   },\n",
      "   \"outputs\": {\n",
      "    \"dense\": {\n",
      "     \"dtype\": \"DT_FLOAT\",\n",
      "     \"tensor_shape\": {\n",
      "      \"dim\": [\n",
      "       {\n",
      "        \"size\": \"-1\",\n",
      "        \"name\": \"\"\n",
      "       },\n",
      "       {\n",
      "        \"size\": \"1\",\n",
      "        \"name\": \"\"\n",
      "       }\n",
      "      ],\n",
      "      \"unknown_rank\": false\n",
      "     },\n",
      "     \"name\": \"StatefulPartitionedCall:0\"\n",
      "    }\n",
      "   },\n",
      "   \"method_name\": \"tensorflow/serving/predict\"\n",
      "  },\n",
      "  \"__saved_model_init_op\": {\n",
      "   \"inputs\": {},\n",
      "   \"outputs\": {\n",
      "    \"__saved_model_init_op\": {\n",
      "     \"dtype\": \"DT_INVALID\",\n",
      "     \"tensor_shape\": {\n",
      "      \"dim\": [],\n",
      "      \"unknown_rank\": true\n",
      "     },\n",
      "     \"name\": \"NoOp\"\n",
      "    }\n",
      "   },\n",
      "   \"method_name\": \"\"\n",
      "  }\n",
      " }\n",
      "}\n",
      "}\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "json_response = requests.get('http://localhost:8501/v1/models/helloworld/metadata')\n",
    "print(json_response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"outputs\": [\n",
      "        [\n",
      "            27.9957714\n",
      "        ],\n",
      "        [\n",
      "            30.9950542\n",
      "        ]\n",
      "    ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "headers = {\"content-type\": \"application/json\"}\n",
    "json_response = requests.post('http://localhost:8501/v1/models/helloworld:predict', data=data, headers=headers)\n",
    "\n",
    "print(json_response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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