docs/en/source/quickstart/quickstart_in_one_minute.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da8de4e0",
   "metadata": {},
   "source": [
    "# Implementing an Image Classification App in One Minute\n",
    "\n",
    "In this tutorial, constructing a LeNet5 model, downloading dataset, training, starting the server and making predictions of the model using TinyMS 0.3.1 API will be demonstrated. \n",
    "\n",
    "## Prerequisite\n",
    " - Ubuntu: `18.04`\n",
    " - docker: `v18.06.1-ce`\n",
    " - Jupyter: Using `tinyms 0.3.1-Jupyter`, you can refer to [Quick Install tinyms](https://tinyms.readthedocs.io/en/latest/quickstart/install.html) to deploy the environment.\n",
    " \n",
    "## Introduction\n",
    "\n",
    "TinyMS is a high-level API which is designed for amateur of deep learning. It minimizes the number of actions of users required to construct, train, evaluate and serve a model. TinyMS also provides tutorials and documentations for developers.\n",
    "\n",
    "This tutorial consists of six parts, `constructing the model`, `downloading dataset`, `training`, `define servable json`, `starting server` and `making predictions` in which the server will be run in a sub process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d1d65cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[WARNING] ME(12716:140477914756928,MainProcess):2021-03-19-15:58:38.621.652 [mindspore/ops/operations/array_ops.py:2302] WARN_DEPRECATED: The usage of Pack is deprecated. Please use Stack.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: 'ControlDepend' is deprecated from version 1.1 and will be removed in a future version, use 'Depend' instead.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "from PIL import Image\n",
    "\n",
    "from tinyms import context\n",
    "from tinyms.data import MnistDataset, download_dataset\n",
    "from tinyms.vision import mnist_transform, ImageViewer\n",
    "from tinyms.model import Model, lenet5\n",
    "import tinyms.optimizers as opt\n",
    "from tinyms.serving import Server, Client\n",
    "from tinyms.metrics import Accuracy\n",
    "from tinyms.losses import SoftmaxCrossEntropyWithLogits\n",
    "from tinyms.callbacks import ModelCheckpoint, CheckpointConfig, LossMonitor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a89f55ba",
   "metadata": {},
   "source": [
    "### 1. Construct the model\n",
    "\n",
    "TinyMS encapsulates init and construct of the LeNet5 model, the line of the code is reduced to construct the LeNet5 model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a44a69d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build the network\n",
    "net = lenet5(class_num=10)\n",
    "model = Model(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d77f6fca",
   "metadata": {},
   "source": [
    "### 2. Download dataset\n",
    "\n",
    "The MNIST dataset will be downloaded if `mnist` folder didn't exist at the `/home/jovyan` folder. If `mnist` folder already exists, this step will not be performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1184b13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************Dataset already exists.**************\n"
     ]
    }
   ],
   "source": [
    "# download the dataset\n",
    "mnist_path = '/home/jovyan/mnist'\n",
    "if not os.path.exists(mnist_path):\n",
    "    download_dataset('mnist', '/home/jovyan')\n",
    "    print('************Download complete*************')\n",
    "else:\n",
    "    print('************Dataset already exists.**************')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac6beff",
   "metadata": {},
   "source": [
    "### 3. Train the model & evaluation\n",
    "\n",
    "The dataset for both training and evaluation will be defined here, and the parameters for training also set in this block. A trained ckpt file will be saved to `/home/jovyan/tinyms/serving/lenet5` folder for later use, meanwhile the evaluation will be performed and the `Accuracy` can be checked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "074e3cc6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# check lenet folder exists or not\n",
    "ckpt_folder = '/home/jovyan/tinyms/serving/lenet5'\n",
    "ckpt_path = '/home/jovyan/tinyms/serving/lenet5/lenet5.ckpt'\n",
    "if not os.path.exists(ckpt_folder):\n",
    "    !mkdir -p  /home/jovyan/tinyms/serving/lenet5\n",
    "else:\n",
    "    print('lenet5 ckpt folder already exists')\n",
    "\n",
    "# set environment parameters\n",
    "device_target = \"CPU\"\n",
    "context.set_context(mode=context.GRAPH_MODE, device_target=device_target)  \n",
    "dataset_sink_mode = False\n",
    "\n",
    "# define the training and evaluation dataset\n",
    "train_dataset = MnistDataset(os.path.join(mnist_path, \"train\"), shuffle=True)\n",
    "train_dataset = mnist_transform.apply_ds(train_dataset)\n",
    "eval_dataset = MnistDataset(os.path.join(mnist_path, \"test\"), shuffle=True)\n",
    "eval_dataset = mnist_transform.apply_ds(eval_dataset)\n",
    "\n",
    "# parameters for training\n",
    "lr = 0.01\n",
    "momentum = 0.9\n",
    "epoch_size = 1\n",
    "batch_size = 32\n",
    "\n",
    "# define the loss function\n",
    "net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n",
    "\n",
    "# define the optimizer\n",
    "net_opt = opt.Momentum(net.trainable_params(), lr, momentum)\n",
    "net_metrics={\"Accuracy\": Accuracy()}\n",
    "model.compile(loss_fn=net_loss, optimizer=net_opt, metrics=net_metrics)\n",
    "\n",
    "print('************************Start training*************************')\n",
    "ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\", config=CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10))\n",
    "model.train(epoch_size, train_dataset, callbacks=[ckpoint_cb, LossMonitor()],dataset_sink_mode=dataset_sink_mode)\n",
    "print('************************Finished training*************************')\n",
    "model.save_checkpoint(ckpt_path)\n",
    "\n",
    "\n",
    "model.load_checkpoint(ckpt_path)\n",
    "print('************************Start evaluation*************************')\n",
    "acc = model.eval(eval_dataset, dataset_sink_mode=dataset_sink_mode)\n",
    "print(\"============== Accuracy:{} ==============\".format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb49771a",
   "metadata": {},
   "source": [
    "### 4. Define servable.json\n",
    "\n",
    "Define lenet5 servable json file: servable json file defines the servable name, model name, model format and classification quantity for subsequent reasoning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "873f7074",
   "metadata": {},
   "outputs": [],
   "source": [
    "servable_json = [{'name': 'lenet5', \n",
    "                  'description': 'This servable hosts a lenet5 model predicting numbers', \n",
    "                  'model': {\n",
    "                      \"name\": \"lenet5\", \n",
    "                      \"format\": \"ckpt\", \n",
    "                      \"class_num\": 10}}]\n",
    "os.chdir(\"/home/jovyan/tinyms/serving\")\n",
    "json_data = json.dumps(servable_json, indent=4)\n",
    "\n",
    "with open('servable.json', 'w') as json_file:\n",
    "    json_file.write(json_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40642891",
   "metadata": {},
   "source": [
    "### 5. Start server\n",
    "\n",
    "#### 5.1 Introduction\n",
    "TinyMS Serving is a C/S(client/server) structure. TinyMS using [Flask](https://flask.palletsprojects.com/en/1.1.x/) which is a micro web framework written in python as the C/S communication tool. In order to serve a model, user must start server first. If successfully started, the server will be run in a subprocess and listening to POST requests from 127.0.0.1 port 5000 sent by client and handle the requests using MindSpore backend which constructs the model, run the prediction and send the result back to the client.\n",
    "\n",
    "#### 5.2 Start server\n",
    "\n",
    "Run the following code block to start the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95f5d4eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app 'tinyms.serving.server.server' (lazy loading)\n",
      "\n",
      " * Environment: production\n",
      "\n",
      "   WARNING: This is a development server. Do not use it in a production deployment.\n",
      "\n",
      "   Use a production WSGI server instead.\n",
      "\n",
      " * Debug mode: off\n",
      "\n"
     ]
    }
   ],
   "source": [
    "server = Server(serving_path=\"/home/jovyan/tinyms/serving/\")\n",
    "server.start_server()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e591504",
   "metadata": {},
   "source": [
    "The above prompt indicates that the server is started and running."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8462d47",
   "metadata": {},
   "source": [
    "After the server is started, we need to go to the menu bar and click `File`=>`new Notebook` to create a new jupyter file, and then continue to complete the operation of the client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56694ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "from tinyms.vision import ImageViewer\n",
    "from tinyms.serving import Server, Client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8fe1d4",
   "metadata": {},
   "source": [
    "### 6. Make predictions\n",
    "\n",
    "#### 6.1 Upload the pic\n",
    "\n",
    "A picture of a single digit number is required to be the input. The picture we use in this tutorial can be found [HERE](https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/tinyms-test-pics/numbers/7.png), then save the picture to the `/home/jovyan` folder, and rename it to `7.png` (or any other name you like).\n",
    "\n",
    "Or run the following code to download the pic for this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "259825d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-01-26 18:42:14--  https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/tinyms-test-pics/numbers/7.png\n",
      "Resolving ascend-tutorials.obs.cn-north-4.myhuaweicloud.com (ascend-tutorials.obs.cn-north-4.myhuaweicloud.com)... 49.4.112.113, 49.4.112.90, 121.36.121.44\n",
      "Connecting to ascend-tutorials.obs.cn-north-4.myhuaweicloud.com (ascend-tutorials.obs.cn-north-4.myhuaweicloud.com)|49.4.112.113|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 34970 (34K) [image/png]\n",
      "Saving to: ‘/home/jovyan/7.png’\n",
      "\n",
      "7.png               100%[===================>]  34.15K  --.-KB/s    in 0.07s   \n",
      "\n",
      "2022-01-26 18:42:14 (470 KB/s) - ‘/home/jovyan/7.png’ saved [34970/34970]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists('/home/jovyan/7.png'):\n",
    "    !wget -P /home/jovyan https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/tinyms-test-pics/numbers/7.png\n",
    "else:\n",
    "    print('7.png already exists')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f73ae7f",
   "metadata": {},
   "source": [
    "#### 6.2 List servables\n",
    "\n",
    "Use `list_servables` function to check what model is being served right now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7190450e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'description': 'This servable hosts a lenet5 model predicting numbers',\n",
       "  'model': {'class_num': 10, 'format': 'ckpt', 'name': 'lenet5'},\n",
       "  'name': 'lenet5'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client=Client()\n",
    "client.list_servables()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae03e9c",
   "metadata": {},
   "source": [
    "If the output `description` shows it is a `lenet5` model, then we can continue to next step to send our request."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "503ff741",
   "metadata": {},
   "source": [
    "#### 6.3 Sending request and get the result\n",
    "\n",
    "Run `predict` function to send the request, select between `TOP1_CLASS` and `TOP5_CLASS`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf8ea6d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP1: 7, score: 0.9997\n"
     ]
    }
   ],
   "source": [
    "# Set picture path and output policy（You can choose between Top1 and top5）\n",
    "image_path = \"/home/jovyan/7.png\"\n",
    "strategy = \"TOP1_CLASS\"\n",
    "\n",
    "# predict(image_path, servable_name, dataset='mnist', strategy='TOP1_CLASS')\n",
    "img_viewer = ImageViewer(Image.open(image_path), image_path)\n",
    "img_viewer.show()\n",
    "print(client.predict(image_path,'lenet5', 'mnist', strategy))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68161ff6",
   "metadata": {},
   "source": [
    "If user can see the output similar to this:  \n",
    "```\n",
    "TOP1: 7, score: 0.99934917688369750977\n",
    "```\n",
    "that means the prediction is successfully performed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1e7856",
   "metadata": {},
   "source": [
    "## Shutdown server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41b50a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Server shutting down...'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "server.shutdown()"
   ]
  }
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