notebooks/keras-mnist/run_skil_client.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with existing deployments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'>>> Authenticating SKIL...'\n",
      "'>>> Done!'\n"
     ]
    }
   ],
   "source": [
    "from skil import Skil, Service\n",
    "from skil import get_workspace_by_id\n",
    "from skil import get_experiment_by_id\n",
    "from skil import get_model_by_id\n",
    "from skil import get_deployment_by_id\n",
    "\n",
    "skil_server = Skil(\n",
    "    host='localhost',\n",
    "    port=9008,\n",
    "    user_id='admin',\n",
    "    password='Skymind'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "workspace_id  = '3147de1f-ca0c-4857-a6f0-16ad8aee0426'\n",
    "experiment_id = 'mnist-experiment-01'\n",
    "model_id      = 'mnist-model-01'\n",
    "deployment_id = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "work_space = get_workspace_by_id(skil_server, workspace_id)\n",
    "\n",
    "experiment = get_experiment_by_id(work_space, experiment_id)\n",
    "\n",
    "deployment = get_deployment_by_id(skil_server, deployment_id)\n",
    "\n",
    "model      = get_model_by_id(experiment, model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "service = Service(\n",
    "    skil=skil_server,\n",
    "    model=model,\n",
    "    deployment=deployment,\n",
    "    model_deployment=None\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ycu5M1q7qtllzx8qL9BLSXE29ZurqUbfNjQCeVNXu3l+rlWMtdYIcBDC+1/OmKFbLDvc02Yv+7yzz/mQi6ra5EcArqvpGFK65Yy11gnwCoFlErheRAQAeBLC5xPtQapsBzIsezwPwVhn3JRNet03U4rGW+pP06EY7fwJQD2C1qi4p6Q4UkYi8CuAO5IZ+HwawCMAmAK8DmIDcUP85qnr1hXxVCXTb3I5aO1YONSHy8SKdKIAJQhTABCEKYIIQBTBBiAKYIEQBTBCigP8DpuhJAri1458AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = cv2.imread(\"mnist.jpg\", cv2.IMREAD_GRAYSCALE)\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.imshow(image, cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = np.squeeze(image.reshape(1,-1))\n",
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: [[ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "Target Class: 2\n"
     ]
    }
   ],
   "source": [
    "predicted = service.predict_single(image)\n",
    "print('Predicted:',predicted)\n",
    "print('Target Class:',np.argmax(predicted))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}