模型上线

train/predict_tfserving.py

@@ -10,6 +10,8 @@ import time
 with open('/Users/edz/software/Recommend/train_samples.csv', 'r') as f:
 
 
+
+    for _ in range(50):
         count = 0
         examples = []
         for line in f:
@@ -73,11 +75,17 @@ with open('/Users/edz/software/Recommend/train_samples.csv', 'r') as f:
             examples.append({'b64': base64.b64encode(tf_serialized)})
 
             count += 1
-        if count == 1000:
+            if count == 500:
                 break
         start = time.time()
-    res = requests.post("http://localhost:8501/v1/models/wide_deep:predict",
-                        json={"inputs": {"examples": examples},
-                              "signature_name": "predict"})
-    print(res.text)
+        # res = requests.post("http://localhost:8501/v1/models/wide_deep:predict",
+        #                     json={"inputs": {"examples": examples},
+        #                           "signature_name": "predict"})
+        # res = requests.post("http://tensorserving-sk.paas-develop.env/v1/models/service:predict",
+        #                     json={"inputs": {"inputs": examples},
+        #                           "signature_name": "regression"})
+        res = requests.post("http://localhost:8000/v1/models/service:predict",
+                            json={"inputs": {"inputs": examples},
+                                  "signature_name": "regression"})
+        # print(res.text)
         print(time.time() - start)

train/predict_tfserving_grpc.py

@@ -17,7 +17,8 @@ from tensorflow_serving.apis import predict_pb2
 from tensorflow_serving.apis import prediction_service_pb2_grpc
 import grpc
 
-tf.app.flags.DEFINE_string('server', 'localhost:8502', 'PredictionService host:port')
+# tf.app.flags.DEFINE_string('server', 'localhost:8502', 'PredictionService host:port')
+tf.app.flags.DEFINE_string('server', 'tensorserving-sk.paas-develop.env:8090', 'PredictionService host:port')
 FLAGS = tf.app.flags.FLAGS
 
 def prediction():
@@ -25,9 +26,9 @@ def prediction():
     channel = grpc.insecure_channel(FLAGS.server, options = options)
     stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
     request = predict_pb2.PredictRequest()
-    request.model_spec.name = 'wide_deep' #对应上图第一个方框
+    request.model_spec.name = 'service' #对应上图第一个方框
     request.model_spec.signature_name = 'regression' #对应上图第二个方框with open('/Users/edz/software/Recommend/train_samples.csv', 'r') as f:
-    for _ in range(20):
+    for _ in range(1):
         with open('/Users/edz/software/Recommend/train_samples.csv', 'r') as f:
 
             count = 0
@@ -92,7 +93,7 @@ def prediction():
                 examples.append(tf_serialized)
 
                 count += 1
-                if count == 1000:
+                if count == 1:
                     break
         start = time.time()
         # request.inputs['examples'].CopyFrom(tf.make_tensor_proto(examples))  # in对应上图第三个方框，为模型的输入Name

train/train_service_sk_tf2_distibute.py

@@ -238,7 +238,7 @@ early_stopping = tf.estimator.experimental.stop_if_no_increase_hook(wideAndDeepM
 
 hooks = [early_stopping]
 
-train_spec = tf.estimator.TrainSpec(input_fn = lambda: input_fn(DATA_DIR + 'train_samples.csv', 100, True, 2048), hooks = hooks)
+train_spec = tf.estimator.TrainSpec(input_fn = lambda: input_fn(DATA_DIR + 'train_samples.csv', 1, True, 2048), hooks = hooks)
 
 serving_feature_spec = tf.feature_column.make_parse_example_spec(
   linear_feature_columns + dnn_feature_columns)