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Roope Astala
529
pipeline/06.pipeline-batch-scoring.ipynb
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529
pipeline/06.pipeline-batch-scoring.ipynb
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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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"Copyright (c) Microsoft Corporation. All rights reserved.\n",
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"\n",
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"Licensed under the MIT License."
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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 os\n",
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"from azureml.core import Workspace, Run, Experiment\n",
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"\n",
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"ws = Workspace.from_config()\n",
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"print('Workspace name: ' + ws.name, \n",
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" 'Azure region: ' + ws.location, \n",
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" 'Subscription id: ' + ws.subscription_id, \n",
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" 'Resource group: ' + ws.resource_group, sep = '\\n')\n",
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"\n",
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"# Also create a Project and attach to Workspace\n",
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"project_folder = \"sample_projects\"\n",
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"run_history_name = project_folder\n",
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"\n",
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"if not os.path.isdir(project_folder):\n",
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" os.mkdir(project_folder)"
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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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"from azureml.core.compute import BatchAiCompute, ComputeTarget\n",
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"from azureml.core.datastore import Datastore\n",
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"from azureml.data.data_reference import DataReference\n",
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"from azureml.pipeline.core import Pipeline, PipelineData\n",
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"from azureml.pipeline.steps import PythonScriptStep\n",
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"from azureml.core.runconfig import CondaDependencies, RunConfiguration"
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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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"## Create and attach Compute targets\n",
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"Use the below code to create and attach Compute targets. "
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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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"# Batch AI compute\n",
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"cluster_name = \"gpu_cluster\"\n",
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"try:\n",
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" cluster = BatchAiCompute(ws, cluster_name)\n",
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" print(\"found existing cluster.\")\n",
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"except:\n",
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" print(\"creating new cluster\")\n",
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" provisioning_config = BatchAiCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n",
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" autoscale_enabled = True,\n",
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" cluster_min_nodes = 0, \n",
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" cluster_max_nodes = 1)\n",
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"\n",
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" # create the cluster\n",
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" cluster = ComputeTarget.create(ws, cluster_name, provisioning_config)\n",
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" cluster.wait_for_completion(show_output=True)"
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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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"# Python scripts to run"
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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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"Python scripts that run the batch scoring. `batchai_score.py` takes input images in `dataset_path`, pretrained models in `model_dir` and outputs a `results-label.txt` to `output_dir`."
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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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"%%writefile $project_folder/batchai_score.py\n",
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"import os\n",
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"import argparse\n",
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"import datetime,time\n",
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"import tensorflow as tf\n",
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"from math import ceil\n",
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"import numpy as np\n",
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"import shutil\n",
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"from tensorflow.contrib.slim.python.slim.nets import inception_v3\n",
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"\n",
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"slim = tf.contrib.slim\n",
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"\n",
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"parser = argparse.ArgumentParser(description=\"Start a tensorflow model serving\")\n",
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"parser.add_argument('--model_dir', dest=\"model_dir\", required=True)\n",
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"parser.add_argument('--dataset_path', dest=\"dataset_path\", required=True)\n",
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"parser.add_argument('--output_dir', dest=\"output_dir\", required=True)\n",
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"parser.add_argument('--batch_size', dest=\"batch_size\", type=int, required=True)\n",
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"\n",
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"args = parser.parse_args()\n",
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"\n",
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"image_size = 299\n",
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"num_channel = 3\n",
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"\n",
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"# create output directory if it does not exist\n",
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"os.makedirs(args.output_dir, exist_ok=True)\n",
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"\n",
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"def get_class_label_dict(label_file):\n",
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" label = []\n",
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" proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()\n",
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" for l in proto_as_ascii_lines:\n",
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" label.append(l.rstrip())\n",
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" return label\n",
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"\n",
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"\n",
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"class DataIterator:\n",
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" def __init__(self, data_dir):\n",
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" self.file_paths = []\n",
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" image_list = os.listdir(data_dir)\n",
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" total_size = len(image_list)\n",
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" self.file_paths = [data_dir + '/' + file_name.rstrip() for file_name in image_list ]\n",
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"\n",
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" self.labels = [1 for file_name in self.file_paths]\n",
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"\n",
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" @property\n",
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" def size(self):\n",
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" return len(self.labels)\n",
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"\n",
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" def input_pipeline(self, batch_size):\n",
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" images_tensor = tf.convert_to_tensor(self.file_paths, dtype=tf.string)\n",
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" labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)\n",
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" input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], shuffle=False)\n",
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" labels = input_queue[1]\n",
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" images_content = tf.read_file(input_queue[0])\n",
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"\n",
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" image_reader = tf.image.decode_jpeg(images_content, channels=num_channel, name=\"jpeg_reader\")\n",
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" float_caster = tf.cast(image_reader, tf.float32)\n",
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" new_size = tf.constant([image_size, image_size], dtype=tf.int32)\n",
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" images = tf.image.resize_images(float_caster, new_size)\n",
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" images = tf.divide(tf.subtract(images, [0]), [255])\n",
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"\n",
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" image_batch, label_batch = tf.train.batch([images, labels], batch_size=batch_size, capacity=5 * batch_size)\n",
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" return image_batch\n",
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"\n",
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"def main(_):\n",
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" start_time = datetime.datetime.now()\n",
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" label_file_name = os.path.join(args.model_dir, \"labels.txt\")\n",
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" label_dict = get_class_label_dict(label_file_name)\n",
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" classes_num = len(label_dict)\n",
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" test_feeder = DataIterator(data_dir=args.dataset_path)\n",
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" total_size = len(test_feeder.labels)\n",
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" count = 0\n",
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" with tf.Session() as sess:\n",
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" test_images = test_feeder.input_pipeline(batch_size=args.batch_size)\n",
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" with slim.arg_scope(inception_v3.inception_v3_arg_scope()):\n",
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" input_images = tf.placeholder(tf.float32, [args.batch_size, image_size, image_size, num_channel])\n",
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" logits, _ = inception_v3.inception_v3(input_images,\n",
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" num_classes=classes_num,\n",
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" is_training=False)\n",
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" probabilities = tf.argmax(logits, 1)\n",
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"\n",
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" sess.run(tf.global_variables_initializer())\n",
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" sess.run(tf.local_variables_initializer())\n",
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" coord = tf.train.Coordinator()\n",
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" threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
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" saver = tf.train.Saver()\n",
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" model_path = os.path.join(args.model_dir, \"inception_v3.ckpt\")\n",
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" saver.restore(sess, model_path)\n",
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" out_filename = os.path.join(args.output_dir, \"result-labels.txt\")\n",
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" with open(out_filename, \"w\") as result_file:\n",
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" i = 0\n",
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" while count < total_size and not coord.should_stop():\n",
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" test_images_batch = sess.run(test_images)\n",
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" file_names_batch = test_feeder.file_paths[i*args.batch_size: min(test_feeder.size, (i+1)*args.batch_size)]\n",
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" results = sess.run(probabilities, feed_dict={input_images: test_images_batch})\n",
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" new_add = min(args.batch_size, total_size-count)\n",
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" count += new_add\n",
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" i += 1\n",
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" for j in range(new_add):\n",
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" result_file.write(os.path.basename(file_names_batch[j]) + \": \" + label_dict[results[j]] + \"\\n\")\n",
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" result_file.flush()\n",
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" coord.request_stop()\n",
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" coord.join(threads)\n",
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" \n",
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" # copy the file to artifacts\n",
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" shutil.copy(out_filename, \"./outputs/\")\n",
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" # Move the processed data out of the blob so that the next run can process the data.\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" tf.app.run()"
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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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"account_name = \"pipelinedata\"\n",
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"sample_data = Datastore.register_azure_blob_container(ws, \"sampledata\", \"sampledata\", \n",
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" account_name=account_name, \n",
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" overwrite=True)"
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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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"# Output datastore"
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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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"We write the outputs to the default datastore"
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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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"default_ds = \"workspaceblobstore\""
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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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"# Specify where the data is stored or will be written to"
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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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"from azureml.core.conda_dependencies import CondaDependencies\n",
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"from azureml.data.data_reference import DataReference\n",
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"from azureml.pipeline.core import Pipeline, PipelineData\n",
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"from azureml.core import Datastore\n",
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"from azureml.core import Experiment"
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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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"input_images = DataReference(datastore=sample_data, \n",
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" data_reference_name=\"input_images\",\n",
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" path_on_datastore=\"batchscoring/images\",\n",
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" mode=\"download\"\n",
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" )\n",
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"model_dir = DataReference(datastore=sample_data, \n",
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" data_reference_name=\"input_model\",\n",
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" path_on_datastore=\"batchscoring/models\",\n",
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" mode=\"download\" \n",
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" )\n",
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"output_dir = PipelineData(name=\"scores\", \n",
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" datastore_name=default_ds, \n",
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" output_path_on_compute=\"batchscoring/results\")"
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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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"# Specify environment to run the 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.4.0\", \"azureml-defaults\"])\n",
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"\n",
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"# Runconfig\n",
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"batchai_run_config = RunConfiguration(conda_dependencies=cd)\n",
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"batchai_run_config.environment.docker.enabled = True\n",
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"batchai_run_config.environment.docker.gpu_support = True\n",
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"batchai_run_config.environment.docker.base_image = \"microsoft/mmlspark:gpu-0.12\"\n",
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"batchai_run_config.environment.spark.precache_packages = False"
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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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"# Steps to run"
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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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"step = PythonScriptStep(\n",
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" name=\"batch ai scoring\",\n",
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" script_name=\"batchai_score.py\",\n",
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" arguments=[\"--dataset_path\", input_images, \"--model_dir\", model_dir, \"--output_dir\", output_dir, \"--batch_size\", 20],\n",
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" target=cluster,\n",
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" inputs=[input_images, model_dir],\n",
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" outputs=[output_dir],\n",
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" runconfig=batchai_run_config,\n",
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||||
" source_directory=project_folder,\n",
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||||
" allow_reuse=False\n",
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||||
")"
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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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"pipeline = Pipeline(workspace=ws, steps=[step])\n",
|
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"pipeline_run = Experiment(ws, 'batch_scoring').submit(pipeline)"
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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": [
|
||||
"# Monitor run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.train.widgets import RunDetails\n",
|
||||
"RunDetails(pipeline_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline_run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"node_run = list(pipeline_run.get_children())[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"node_run.download_file(\"./outputs/result-labels.txt\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Display few results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"df = pd.read_csv(\"result-labels.txt\", delimiter=\":\", header=None)\n",
|
||||
"df.columns = [\"Filename\", \"Prediction\"]\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create template and rerun the pipeline using a REST call"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create template"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"template = pipeline_run.create_template(name=\"batch score\", description=\"scores images kept in container sampledata\",\n",
|
||||
" version=\"1.0\")\n",
|
||||
"template_id = template.template_id"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Rerun using REST call"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Get AAD token"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.authentication import AzureCliAuthentication\n",
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"cli_auth = AzureCliAuthentication()\n",
|
||||
"aad_token = cli_auth.get_authentication_header()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Hit the REST endpoint"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core import Template\n",
|
||||
"\n",
|
||||
"rest_endpoint = Template.get_template_endpoint(template_id, ws)\n",
|
||||
"response = requests.post(rest_endpoint, headers=aad_token, json={})\n",
|
||||
"run_id = response.json()[\"Id\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Monitor the template run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core.run import PipelineRun\n",
|
||||
"template_run = PipelineRun(ws.experiments()[\"batch_scoring\"], run_id)\n",
|
||||
"\n",
|
||||
"RunDetails(template_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user