建立MLOps模型API

在本系列文章中，我们将引导您完成将CI/CD应用于AI任务的过程。最后，您将获得一个满足GoogleMLOps成熟度模型第2级要求的功能管道。我们假设您对Python，深度学习，Docker，DevOps和Flask有所了解。

在上一篇文章中，我们讨论了MLCI/CD管道中的单元测试步骤。在这一篇中，我们将构建模型API以支持预测服务。

下图显示了我们在项目流程中的位置。

代码文件的结构如下：

本文中的大多数代码与上一篇代码几乎相同，因此我们仅关注它们之间的区别。

在此存储库中找到完整的代码，因为下面显示的摘录是精简版本。

task.py

协调容器中程序执行的task.py文件如下所示：

import tensorflow as tf
from tensorflow.keras.models import load_modelimport jsonpickleimport data_utils, email_notificationsimport sysimport os
from google.cloud import storageimport datetimeimport numpy as npimport jsonpickleimport cv2
from flask import flash,Flask,Response,request,jsonifyimport threadingimport requestsimport time

# IMPORTANT
# If you're running this container locally and you want to access the API via local browser, use http://172.17.0.2:5000/

# Starting flask app
app = Flask(__name__)

# general variables declaration
model_name = 'best_model.hdf5'bucket_name = 'automatictrainingcicd-aiplatform'global model

@app.before_first_request
def before_first_request():
 def initialize_job():  if len(tf.config.experimental.list_physical_devices('GPU')) > 0:
   tf.config.set_soft_device_placement(True)
   tf.debugging.set_log_device_placement(True)
  global model
  # Checking if there's any model saved at testing on GCS
  model_gcs = data_utils.previous_model(bucket_name,model_name)
  # If any model exists at prod, load it, test it on data and use it on the API  if model_gcs[0] == True:
   model_gcs = data_utils.load_model(bucket_name,model_name)   if model_gcs[0] == True:    try:
     model = load_model(model_name)
    except Exception as e:
     email_notifications.exception('Something went wrong trying to production model. Exception: '+str(e))
     sys.exit(1)   else:
    email_notifications.exception('Something went wrong when trying to load production model. Exception: '+str(model_gcs[1]))
    sys.exit(1)  if model_gcs[0] == False:
   email_notifications.send_update('There are no artifacts at model registry. Check GCP for more information.')
   sys.exit(1)  if model_gcs[0] == None:
   email_notifications.exception('Something went wrong when trying to check if production model exists. Exception: '+model_gcs[1]+'. Aborting execution.')
   sys.exit(1)
 thread = threading.Thread(target=initialize_job)
 thread.start()

@app.route('/init', methods=['GET','POST'])
def init():
 message = {'message': 'API initialized.'}
 response = jsonpickle.encode(message) return Response(response=response, status=200, mimetype="application/json")

@app.route('/', methods=['POST'])
def index(): if request.method=='POST':  try:
   #Converting string that contains image to uint8
   image = np.fromstring(request.data,np.uint8)
   image = image.reshape((128,128,3))
   image = [image]
   image = np.array(image)
   image = image.astype(np.float16)
   result = model.predict(image)
   result = np.argmax(result)
   message = {'message': '{}'.format(str(result))}
   json_response = jsonify(message)   return json_response

  except Exception as e:
   message = {'message': 'Error'}
   json_response = jsonify(message)
   email_notifications.exception('Something went wrong when trying to make prediction via Production API. Exception: '+str(e)+'. Aborting execution.')   return json_response else:
  message = {'message': 'Error. Please use this API in a proper manner.'}
  json_response = jsonify(message)  return json_response

def self_initialize():
 def initialization():
  global started
  started = False  while started == False:   try:
    server_response = requests.get('http://127.0.0.1:5000/init')    if server_response.status_code == 200:
     print('API has started successfully, quitting initialization job.')
     started = True
   except:
    print('API has not started. Still attempting to initialize it.')
   time.sleep(3)
 thread = threading.Thread(target=initialization)
 thread.start()
if __name__ == '__main__':
 self_initialize()
 app.run(host='0.0.0.0',debug=True,threaded=True)123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109复制代码类型：[cpp]

data_utils.py

该data_utils.py文件不同于其以前的版本仅在加载从生产注册表模型中的一部分。不同之处在于：

status=storage.Blob(bucket=bucket,name='{}/{}'.format('testing',model_filename)).exists(storage_client)经过status=storage.Blob(bucket=bucket,name='{}/{}'.format('production',model_filename)).exists(storage_client)

blob1=bucket.blob('{}/{}'.format('testing',model_filename))byblob1=bucket.blob('{}/{}'.format('production',model_filename))

Docker文件

在我们的Dockerfile中，替换

运行gitclone   https://github.com/sergiovirahonda/AutomaticTraining-UnitTesting.git

和

运行gitclone    https://github.com/sergiovirahonda/AutomaticTraining-PredictionAPI.git

在本地构建和运行容器后，应该可以通过POST请求在http://172.17.0.2:5000/上获得功能齐全的预测服务。

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