TensorFlow Serving

最新推荐文章于 2025-04-18 14:53:17 发布

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最新推荐文章于 2025-04-18 14:53:17 发布

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本文链接：https://blog.youkuaiyun.com/winorlose2000/article/details/115735089

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本文介绍了如何使用TensorFlow训练一个简单的CNN模型，然后通过Docker部署到TensorFlow Serving，提供了从数据预处理到创建模型、保存、验证及最后通过REST API进行预测的完整流程。

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TensorFlow Serving 可以快速部署 Tensorflow 模型，上线 gRPC 或 REST API。

官方推荐 Docker 部署，也给了训练到部署的完整教程：Servers: TFX for TensorFlow Serving。本文只是遵照教程进行的练习，有助于了解 TensorFlow 训练到部署的整个过程。

准备环境

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

创建模型

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

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