minor change to notebook 02
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fboylu
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Различия файлов скрыты, потому что одна или несколько строк слишком длинны
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Develop Model Driver\n",
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"\n",
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"In this notebook, we will develop the API that will call our model. This module initializes the model, transforms the input so that it is in the appropriate format and defines the scoring method that will produce the predictions. The API will expect the input to be in JSON format. Once a request is received, the API will convert the json encoded request body into the image format. There are two main functions in the API: init() and run(). The init() function loads the model and returns a scoring function. The run() function process the images and uses the first function to score them.\n",
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"\n",
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" Note: Always make sure you don't have any lingering notebooks running (Shutdown previous notebooks). Otherwise it may cause GPU memory issue."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core import Workspace\n",
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"from azureml.core.compute import AksCompute, ComputeTarget\n",
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"from azureml.core.webservice import Webservice, AksWebservice\n",
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"from azureml.core.image import Image\n",
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"from azureml.core.model import Model\n",
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"from dotenv import set_key, get_key, find_dotenv\n",
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"import logging\n",
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"from testing_utilities import img_url_to_json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import keras\n",
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"import tensorflow\n",
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"print(\"Keras: \", keras.__version__)\n",
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"print(\"Tensorflow: \", tensorflow.__version__)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"env_path = find_dotenv(raise_error_if_not_found=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"_MODEL_NAME = get_key(env_path, 'model_name')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Write and save driver script"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Overwriting driver.py\n"
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]
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}
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],
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"source": [
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"%%writefile driver.py\n",
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"\n",
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"import tensorflow as tf\n",
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"from resnet152 import ResNet152\n",
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"from keras.preprocessing import image\n",
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"from keras.applications.imagenet_utils import preprocess_input, decode_predictions\n",
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"\n",
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"from azureml.core.model import Model\n",
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"\n",
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"import numpy as np\n",
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"import timeit as t\n",
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"import base64\n",
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"import json\n",
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"from PIL import Image, ImageOps\n",
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"from io import BytesIO\n",
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"import logging\n",
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"\n",
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"number_results = 3\n",
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"logger = logging.getLogger(\"model_driver\")\n",
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"\n",
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"def _base64img_to_numpy(base64_img_string):\n",
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" decoded_img = base64.b64decode(base64_img_string)\n",
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" img_buffer = BytesIO(decoded_img)\n",
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" imageData = Image.open(img_buffer).convert(\"RGB\")\n",
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" img = ImageOps.fit(imageData, (224, 224), Image.ANTIALIAS)\n",
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" img = image.img_to_array(img)\n",
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" return img\n",
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"\n",
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"def create_scoring_func():\n",
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" \"\"\" Initialize ResNet 152 Model \n",
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" \"\"\" \n",
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" start = t.default_timer()\n",
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" # model = ResNet152(weights='imagenet')\n",
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" model_name = 'resnet_model'\n",
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" model_path=Model.get_model_path(model_name)\n",
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" model = ResNet152()\n",
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" model.load_weights(model_path)\n",
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" end = t.default_timer()\n",
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" \n",
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" loadTimeMsg = \"Model loading time: {0} ms\".format(round((end-start)*1000, 2))\n",
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" logger.info(loadTimeMsg)\n",
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" \n",
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" def call_model(img_array):\n",
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" img_array = np.expand_dims(img_array, axis=0)\n",
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" img_array = preprocess_input(img_array)\n",
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" preds = model.predict(img_array)\n",
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" preds = decode_predictions(preds, top=number_results)[0] \n",
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" return preds\n",
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" \n",
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" return call_model \n",
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"\n",
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"\n",
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"def tuple_float_to_str(x):\n",
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" \"\"\"tuple x = ('n02127052', 'lynx', 0.9816483) convert the 3rd element type numpy.float32 to string\n",
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" return: ('n02127052', 'lynx', '0.9816483')\n",
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" \"\"\"\n",
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" a= list(x)\n",
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" a[2]= str(a[2])\n",
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" b= tuple(a)\n",
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" return b\n",
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" \n",
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"def get_model_api():\n",
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" logger = logging.getLogger(\"model_driver\")\n",
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" scoring_func = create_scoring_func()\n",
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" \n",
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" def process_and_score(inputString):\n",
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" \"\"\" Classify the input using the loaded model\n",
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" \"\"\"\n",
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" start = t.default_timer()\n",
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"\n",
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" base64Dict = json.loads(inputString) \n",
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" k, v = next(iter(base64Dict.items())) \n",
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" img_array = _base64img_to_numpy(base64Img)\n",
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" preds = scoring_func(img_array)\n",
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" \n",
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" # convert the 3rd element type of preds items from numpy.float32 to string\n",
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" for i, s in enumerate(preds):\n",
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" preds[i] = tuple_float_to_str(s)\n",
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"\n",
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" responses = {img_file_name: preds}\n",
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" \n",
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" end = t.default_timer()\n",
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" \n",
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" logger.info(\"Predictions: {0}\".format(responses))\n",
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" logger.info(\"Predictions took {0} ms\".format(round((end-start)*1000, 2)))\n",
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" return (responses, \"Computed in {0} ms\".format(round((end-start)*1000, 2)))\n",
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" return process_and_score\n",
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"\n",
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"def init():\n",
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" \"\"\" Initialise the model and scoring function\n",
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" \"\"\"\n",
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" global process_and_score\n",
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" process_and_score = get_model_api()\n",
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" \n",
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"def run(raw_data):\n",
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" \"\"\" Make a prediction based on the data passed in using the preloaded model\n",
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" \"\"\"\n",
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" return process_and_score(json.dumps(json.loads(raw_data)['input']))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Test the driver¶ \n",
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"We test the driver by passing data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"logging.basicConfig(level=logging.DEBUG)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"%run driver.py"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's load the workspace."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ws = Workspace.from_config()\n",
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"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep=\"\\n\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_path = Model.get_model_path(_MODEL_NAME, _workspace=ws)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"IMAGEURL = \"https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Lynx_lynx_poing.jpg/220px-Lynx_lynx_poing.jpg\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Always make sure you don't have any lingering notebooks running. Otherwise it may cause GPU memory issue.\n",
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"init()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"jsonimg = img_url_to_json(IMAGEURL)\n",
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"resp = run(jsonimg)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Clear GPU memory\n",
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"from keras import backend as K\n",
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"K.clear_session()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Next, we will [build a docker image with this modle driver and other supporting files](03_BuildImage.ipynb)."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
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@ -0,0 +1,131 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Tear it all down\n",
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"Once you are done with your cluster you can use the following two commands to destroy it all."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core import Workspace\n",
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"from azureml.core.compute import AksCompute, ComputeTarget\n",
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"from dotenv import set_key, get_key, find_dotenv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ws = Workspace.from_config()\n",
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"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep=\"\\n\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"env_path = find_dotenv(raise_error_if_not_found=True)\n",
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"resource_group = get_key(env_path, 'resource_group')\n",
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"aks_name = get_key(env_path, 'aks_name')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Once you are done with your cluster you can use the following command to delete the AKS cluster. This step may take a few minutes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"aks_target = AksCompute(name=aks_name,workspace=ws)\n",
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"aks_aml_name = aks_target.cluster_resource_id.rsplit(\"/\")[-1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!az aks delete -n $aks_aml_name -g $resource_group -y"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Finally, you should delete the resource group. This also deletes the AKS cluster and can be used instead of the above command if the resource group is only used for this purpose."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!az group delete --name $resource_group -y"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[31mThe scope '/subscriptions/edf507a2-6235-46c5-b560-fd463ba2e771/resourcegroups/yanzbaimm' cannot perform delete operation because following scope(s) are locked: '/subscriptions/edf507a2-6235-46c5-b560-fd463ba2e771/resourceGroups/yanzbaimm'. Please remove the lock and try again.\u001b[0m\r\n"
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]
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}
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],
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"source": [
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"!az group delete --name yanzbaimm -y"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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||||
}
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],
|
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"metadata": {
|
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
|
||||
"name": "python3"
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},
|
||||
"language_info": {
|
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -149,8 +149,7 @@
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" start = t.default_timer()\n",
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"\n",
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" base64Dict = json.loads(inputString) \n",
|
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" for k, v in base64Dict.items():\n",
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" img_file_name, base64Img = k, v \n",
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" k, v = next(iter(base64Dict.items())) \n",
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" img_array = _base64img_to_numpy(base64Img)\n",
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" preds = scoring_func(img_array)\n",
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" \n",
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@ -288,14 +287,14 @@
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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"pygments_lexer": "ipython2",
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"version": "2.7.14"
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}
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},
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"nbformat": 4,
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|||
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