change batch_predict to predict

python/examples/bert/benchmark_batch.py

@@ -57,8 +57,7 @@ def single_func(idx, resource):
                         os.getpid(),
                         int(round(b_start * 1000000)),
                         int(round(b_end * 1000000))))
-                result = client.batch_predict(
-                    feed_batch=feed_batch, fetch=fetch)
+                result = client.predict(feed_batch=feed_batch, fetch=fetch)
             else:
                 print("unsupport batch size {}".format(args.batch_size))
 

python/examples/criteo_ctr/benchmark_batch.py

@@ -55,8 +55,7 @@ def single_func(idx, resource):
                     for i in range(1, 27):
                         feed_dict["sparse_{}".format(i - 1)] = data[0][i]
                     feed_batch.append(feed_dict)
-                result = client.batch_predict(
-                    feed_batch=feed_batch, fetch=fetch)
+                result = client.predict(feed_batch=feed_batch, fetch=fetch)
             else:
                 print("unsupport batch size {}".format(args.batch_size))
 

python/examples/imagenet/benchmark_batch.py

@@ -50,8 +50,7 @@ def single_func(idx, resource):
                     img = reader.process_image(img_list[i])
                     img = img.reshape(-1)
                     feed_batch.append({"image": img})
-                result = client.batch_predict(
-                    feed_batch=feed_batch, fetch=fetch)
+                result = client.predict(feed_batch=feed_batch, fetch=fetch)
             else:
                 print("unsupport batch size {}".format(args.batch_size))
 

python/examples/imdb/benchmark_batch.py

@@ -42,7 +42,7 @@ def single_func(idx, resource):
                 for bi in range(args.batch_size):
                     word_ids, label = imdb_dataset.get_words_and_label(line)
                     feed_batch.append({"words": word_ids})
-                result = client.batch_predict(
+                result = client.predict(
                     feed_batch=feed_batch, fetch=["prediction"])
             else:
                 print("unsupport batch size {}".format(args.batch_size))