Fixes. End-to-End Pipeline Example on Azure

pipelines/azurepipeline/code/deploy/deploy.sh

@@ -17,4 +17,4 @@ while getopts "m:n:i:d:s:p:u:r:w:t:b:" option;
     esac
 done
 az login --service-principal --username ${SERVICE_PRINCIPAL_ID} --password ${SERVICE_PRINCIPAL_PASSWORD} -t $TENANT_ID
-az ml model deploy -n $MODEL_NAME -m ${MODEL}:1 --ic $INFERENCE_CONFIG --pi ${BASE_PATH}/myprofileresult.json --dc $DEPLOYMENTCONFIG -w $WORKSPACE -g $RESOURCE_GROUP --overwrite -v
+az ml model deploy -n $MODEL_NAME -m ${MODEL}:1 --ic $INFERENCE_CONFIG --dc $DEPLOYMENTCONFIG -w $WORKSPACE -g $RESOURCE_GROUP --overwrite -v

pipelines/azurepipeline/code/deploy/score.py

@@ -11,83 +11,84 @@
 
 
 def init():
-  if Model.get_model_path('tacosandburritos'):
-    model_path = Model.get_model_path('tacosandburritos')
-  else:
-    model_path = '/model/latest.h5'
+    global model
+    if Model.get_model_path('tacosandburritos'):
+        model_path = Model.get_model_path('tacosandburritos')
+    else:
+        model_path = '/model/latest.h5'
 
-  print('Attempting to load model')
-  model = tf.keras.models.load_model(model_path)
-  model.summary()
-  print('Done!')
+    print('Attempting to load model')
+    model = tf.keras.models.load_model(model_path)
+    model.summary()
+    print('Done!')
 
-  print('Initialized model "{}" at {}'.format(model_path, datetime.datetime.now()))
-  return model
+    print('Initialized model "{}" at {}'.format(
+        model_path, datetime.datetime.now()))
 
 
-def run(raw_data, model):
-  prev_time = time.time()
+def run(raw_data):
+    prev_time = time.time()
 
-  post = json.loads(raw_data)
-  img_path = post['image']
+    post = json.loads(raw_data)
+    img_path = post['image']
 
-  current_time = time.time()
+    current_time = time.time()
 
-  tensor = process_image(img_path, 160)
-  t = tf.reshape(tensor, [-1, 160, 160, 3])
-  o = model.predict(t, steps=1)  # [0][0]
-  print(o)
-  o = o[0][0]
-  inference_time = datetime.timedelta(seconds=current_time - prev_time)
-  payload = {
-    'time': inference_time.total_seconds(),
-    'prediction': 'burrito' if o > 0.5 else 'tacos',
-    'scores': str(o)
-  }
+    tensor = process_image(img_path, 160)
+    t = tf.reshape(tensor, [-1, 160, 160, 3])
+    o = model.predict(t, steps=1)  # [0][0]
+    print(o)
+    o = o[0][0]
+    inference_time = datetime.timedelta(seconds=current_time - prev_time)
+    payload = {
+        'time': inference_time.total_seconds(),
+        'prediction': 'burrito' if o > 0.5 else 'tacos',
+        'scores': str(o)
+    }
 
-  print('Input ({}), Prediction ({})'.format(post['image'], payload))
+    print('Input ({}), Prediction ({})'.format(post['image'], payload))
 
-  return payload
+    return payload
 
 
 def process_image(path, image_size):
-  # Extract image (from web or path)
-  if path.startswith('http'):
-    response = requests.get(path)
-    img = np.array(Image.open(BytesIO(response.content)))
-  else:
-    img = np.array(Image.open(path))
+    # Extract image (from web or path)
+    if path.startswith('http'):
+        response = requests.get(path)
+        img = np.array(Image.open(BytesIO(response.content)))
+    else:
+        img = np.array(Image.open(path))
 
-  img_tensor = tf.convert_to_tensor(img, dtype=tf.float32)
-  # tf.image.decode_jpeg(img_raw, channels=3)
-  img_final = tf.image.resize(img_tensor, [image_size, image_size]) / 255
-  return img_final
+    img_tensor = tf.convert_to_tensor(img, dtype=tf.float32)
+    # tf.image.decode_jpeg(img_raw, channels=3)
+    img_final = tf.image.resize(img_tensor, [image_size, image_size]) / 255
+    return img_final
 
 
 def info(msg, char="#", width=75):
-  print("")
-  print(char * width)
-  print(char + "   %0*s" % ((-1 * width) + 5, msg) + char)
-  print(char * width)
+    print("")
+    print(char * width)
+    print(char + "   %0*s" % ((-1 * width) + 5, msg) + char)
+    print(char * width)
 
 
 if __name__ == "__main__":
-  images = {
-    'tacos': 'https://c1.staticflickr.com/5/4022/4401140214_f489c708f0_b.jpg',
-    'burrito': 'https://www.exploreveg.org/files/2015/05/sofritas-burrito.jpeg'
-  }
+    images = {
+        'tacos': 'https://c1.staticflickr.com/5/4022/4401140214_f489c708f0_b.jpg',  # noqa: E501
+        'burrito': 'https://www.exploreveg.org/files/2015/05/sofritas-burrito.jpeg'  # noqa: E501
+    }
 
-  my_model = init()
+    init()
 
-  for k, v in images.items():
-    print('{} => {}'.format(k, v))
+    for k, v in images.items():
+        print('{} => {}'.format(k, v))
 
-  info('Taco Test')
-  taco = json.dumps({'image': images['tacos']})
-  print(taco)
-  run(taco, my_model)
+    info('Taco Test')
+    taco = json.dumps({'image': images['tacos']})
+    print(taco)
+    run(taco)
 
-  info('Burrito Test')
-  burrito = json.dumps({'image': images['burrito']})
-  print(burrito)
-  run(burrito, my_model)
+    info('Burrito Test')
+    burrito = json.dumps({'image': images['burrito']})
+    print(burrito)
+    run(burrito)

pipelines/azurepipeline/code/pipeline.py

@@ -33,7 +33,7 @@ def tacosandburritos_train(
   # preprocess data
   operations['preprocess'] = dsl.ContainerOp(
     name='preprocess',
-    image='insert your image here',
+    image='insert image name:tag',
     command=['python'],
     arguments=[
       '/scripts/data.py',
@@ -48,7 +48,7 @@ def tacosandburritos_train(
   # train
   operations['training'] = dsl.ContainerOp(
     name='training',
-    image='insert your image here',
+    image='insert image name:tag',
     command=['python'],
     arguments=[
       '/scripts/train.py',
@@ -67,7 +67,7 @@ def tacosandburritos_train(
   # register model
   operations['register'] = dsl.ContainerOp(
     name='register',
-    image='insert your image here',
+    image='insert image name:tag',
     command=['python'],
     arguments=[
       '/scripts/register.py',
@@ -84,30 +84,9 @@ def tacosandburritos_train(
   )
   operations['register'].after(operations['training'])
 
-  operations['profile'] = dsl.ContainerOp(
-    name='profile',
-    image='insert your image here',
-    command=['sh'],
-    arguments=[
-      '/scripts/profile.sh',
-      '-n', profile_name,
-      '-m', model_name,
-      '-i', '/scripts/inferenceconfig.json',
-      '-d', '{"image":"https://www.exploreveg.org/files/2015/05/sofritas-burrito.jpeg"}',
-      '-t', tenant_id,
-      '-r', resource_group,
-      '-w', workspace,
-      '-s', service_principal_id,
-      '-p', service_principal_password,
-      '-u', subscription_id,
-      '-b', persistent_volume_path
-    ]
-  )
-  operations['profile'].after(operations['register'])
-
   operations['deploy'] = dsl.ContainerOp(
     name='deploy',
-    image='insert your image here',
+    image='insert image name:tag',
     command=['sh'],
     arguments=[
       '/scripts/deploy.sh',
@@ -124,7 +103,7 @@ def tacosandburritos_train(
       '-b', persistent_volume_path
     ]
   )
-  operations['deploy'].after(operations['profile'])
+  operations['deploy'].after(operations['register'])
   for _, op_1 in operations.items():
     op_1.container.set_image_pull_policy("Always")
     op_1.add_volume(

pipelines/azurepipeline/code/preprocess/Dockerfile

@@ -1,4 +1,4 @@
-FROM tensorflow/tensorflow:2.0.0a0-gpu-py3
+FROM tensorflow/tensorflow:2.0.0a0-py3
 
 # pip install
 COPY requirements.txt /scripts/requirements.txt