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这篇文章主要介绍“如何部署TensorFlow Serving”，在日常操作中，相信很多人在如何部署TensorFlow Serving问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”如何部署TensorFlow Serving”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
准备好 TensorFlow 环境，导入依赖：
import sys # Confirm that we're using Python 3 assert sys.version_info.major == 3, 'Oops, not running Python 3. Use Runtime > Change runtime type'
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import os
import subprocess
print(f'TensorFlow version: {tf.__version__}')
print(f'TensorFlow GPU support: {tf.test.is_built_with_gpu_support()}')
physical_gpus = tf.config.list_physical_devices('GPU')
print(physical_gpus)
for gpu in physical_gpus:
# memory growth must be set before GPUs have been initialized
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(physical_gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")TensorFlow version: 2.4.1 TensorFlow GPU support: True [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 1 Physical GPUs, 1 Logical GPUs
载入 Fashion MNIST 数据集：
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
# scale the values to 0.0 to 1.0
train_images = train_images / 255.0
test_images = test_images / 255.0
# reshape for feeding into the model
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)
test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
print('\ntrain_images.shape: {}, of {}'.format(train_images.shape, train_images.dtype))
print('test_images.shape: {}, of {}'.format(test_images.shape, test_images.dtype))train_images.shape: (60000, 28, 28, 1), of float64 test_images.shape: (10000, 28, 28, 1), of float64
用最简单的 CNN 训练模型，
model = keras.Sequential([
keras.layers.Conv2D(input_shape=(28,28,1), filters=8, kernel_size=3,
strides=2, activation='relu', name='Conv1'),
keras.layers.Flatten(),
keras.layers.Dense(10, name='Dense')
])
model.summary()
testing = False
epochs = 5
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.fit(train_images, train_labels, epochs=epochs)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('\nTest accuracy: {}'.format(test_acc))Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= Conv1 (Conv2D) (None, 13, 13, 8) 80 _________________________________________________________________ flatten (Flatten) (None, 1352) 0 _________________________________________________________________ Dense (Dense) (None, 10) 13530 ================================================================= Total params: 13,610 Trainable params: 13,610 Non-trainable params: 0 _________________________________________________________________ Epoch 1/5 1875/1875 [==============================] - 3s 722us/step - loss: 0.7387 - sparse_categorical_accuracy: 0.7449 Epoch 2/5 1875/1875 [==============================] - 1s 793us/step - loss: 0.4561 - sparse_categorical_accuracy: 0.8408 Epoch 3/5 1875/1875 [==============================] - 1s 720us/step - loss: 0.4097 - sparse_categorical_accuracy: 0.8566 Epoch 4/5 1875/1875 [==============================] - 1s 718us/step - loss: 0.3899 - sparse_categorical_accuracy: 0.8636 Epoch 5/5 1875/1875 [==============================] - 1s 719us/step - loss: 0.3673 - sparse_categorical_accuracy: 0.8701 313/313 [==============================] - 0s 782us/step - loss: 0.3937 - sparse_categorical_accuracy: 0.8630 Test accuracy: 0.8629999756813049
将模型保存成 SavedModel 格式，路径里加上版本号，以便 TensorFlow Serving 时可选择模型版本。
# Fetch the Keras session and save the model
# The signature definition is defined by the input and output tensors,
# and stored with the default serving key
import tempfile
MODEL_DIR = os.path.join(tempfile.gettempdir(), 'tfx')
version = 1
export_path = os.path.join(MODEL_DIR, str(version))
print('export_path = {}\n'.format(export_path))
tf.keras.models.save_model(
model,
export_path,
overwrite=True,
include_optimizer=True,
save_format=None,
signatures=None,
options=None
)
print('\nSaved model:')
!ls -l {export_path}export_path = /tmp/tfx/1 INFO:tensorflow:Assets written to: /tmp/tfx/1/assets Saved model: total 88 drwxr-xr-x 2 john john 4096 Apr 13 15:10 assets -rw-rw-r-- 1 john john 78169 Apr 13 15:12 saved_model.pb drwxr-xr-x 2 john john 4096 Apr 13 15:12 variables
使用 saved_model_cli 工具查看模型的 MetaGraphDefs (the models) 和 SignatureDefs (the methods you can call)，了解信息。
!saved_model_cli show --dir '/tmp/tfx/1' --all
2021-04-13 15:12:29.433576: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0 MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs: signature_def['__saved_model_init_op']: The given SavedModel SignatureDef contains the following input(s): The given SavedModel SignatureDef contains the following output(s): outputs['__saved_model_init_op'] tensor_info: dtype: DT_INVALID shape: unknown_rank name: NoOp Method name is: signature_def['serving_default']: The given SavedModel SignatureDef contains the following input(s): inputs['Conv1_input'] tensor_info: dtype: DT_FLOAT shape: (-1, 28, 28, 1) name: serving_default_Conv1_input:0 The given SavedModel SignatureDef contains the following output(s): outputs['Dense'] tensor_info: dtype: DT_FLOAT shape: (-1, 10) name: StatefulPartitionedCall:0 Method name is: tensorflow/serving/predict Defined Functions: Function Name: '__call__' Option #1 Callable with: Argument #1 Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input') Argument #2 DType: bool Value: False Argument #3 DType: NoneType Value: None Option #2 Callable with: Argument #1 inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs') Argument #2 DType: bool Value: False Argument #3 DType: NoneType Value: None Option #3 Callable with: Argument #1 inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs') Argument #2 DType: bool Value: True Argument #3 DType: NoneType Value: None Option #4 Callable with: Argument #1 Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input') Argument #2 DType: bool Value: True Argument #3 DType: NoneType Value: None ...
echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list && \ curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add - sudo apt update sudo apt install tensorflow-model-server
开启 TensorFlow Serving ，提供 REST API ：
rest_api_port: REST 请求端口。
model_name: REST 请求 URL ，自定义的名称。
model_base_path: 模型所在目录。
nohup tensorflow_model_server \ --rest_api_port=8501 \ --model_name=fashion_model \ --model_base_path="/tmp/tfx" >server.log 2>&1 &
$ tail server.log
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-04-13 15:12:10.706648: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.
2021-04-13 15:12:10.726722: I external/org_tensorflow/tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2599990000 Hz
2021-04-13 15:12:10.756506: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: /tmp/tfx/1
2021-04-13 15:12:10.759935: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 110653 microseconds.
2021-04-13 15:12:10.760277: I tensorflow_serving/servables/tensorflow/saved_model_warmup_util.cc:59] No warmup data file found at /tmp/tfx/1/assets.extra/tf_serving_warmup_requests
2021-04-13 15:12:10.760486: I tensorflow_serving/core/loader_harness.cc:87] Successfully loaded servable version {name: fashion_model version: 1}
2021-04-13 15:12:10.763938: I tensorflow_serving/model_servers/server.cc:371] Running gRPC ModelServer at 0.0.0.0:8500 ...
[evhttp_server.cc : 238] NET_LOG: Entering the event loop ...
2021-04-13 15:12:10.765308: I tensorflow_serving/model_servers/server.cc:391] Exporting HTTP/REST API at:localhost:8501 ...随机显示一张测试图：
def show(idx, title):
plt.figure()
plt.imshow(test_images[idx].reshape(28,28))
plt.axis('off')
plt.title('\n\n{}'.format(title), fontdict={'size': 16})
import random
rando = random.randint(0,len(test_images)-1)
show(rando, 'An Example Image: {}'.format(class_names[test_labels[rando]]))
创建 JSON 对象，给到三张要预测的图：
import json
data = json.dumps({"signature_name": "serving_default", "instances": test_images[0:3].tolist()})
print('Data: {} ... {}'.format(data[:50], data[len(data)-52:]))Data: {"signature_name": "serving_default", "instances": ... [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]]]}最新模型版本进行预测：
!pip install -q requests
import requests
headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/fashion_model:predict', data=data, headers=headers)
predictions = json.loads(json_response.text)['predictions']
show(0, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(
class_names[np.argmax(predictions[0])], np.argmax(predictions[0]), class_names[test_labels[0]], test_labels[0]))
指定模型版本进行预测：
headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/fashion_model/versions/1:predict', data=data, headers=headers)
predictions = json.loads(json_response.text)['predictions']
for i in range(0,3):
show(i, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(
class_names[np.argmax(predictions[i])], np.argmax(predictions[i]), class_names[test_labels[i]], test_labels[i]))
到此，关于“如何部署TensorFlow Serving”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注亿速云网站，小编会继续努力为大家带来更多实用的文章！
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