提供 TF 服务模型

要运行 ModelServer，请执行以下命令：

$ tensorflow_model_server --model_name=mnist --model_base_path=/home/armando/models/mnist

服务器开始在端口 8500 上提供模型：

I tensorflow_serving/model_servers/main.cc:147] Building single TensorFlow model file config: model_name: mnist model_base_path: /home/armando/models/mnist
I tensorflow_serving/model_servers/server_core.cc:441] Adding/updating models.
I tensorflow_serving/model_servers/server_core.cc:492] (Re-)adding model: mnist
I tensorflow_serving/core/basic_manager.cc:705] Successfully reserved resources to load servable {name: mnist version: 1}
I tensorflow_serving/core/loader_harness.cc:66] Approving load for servable version {name: mnist version: 1}
I tensorflow_serving/core/loader_harness.cc:74] Loading servable version {name: mnist version: 1}
I external/org_tensorflow/tensorflow/contrib/session_bundle/bundle_shim.cc:360] Attempting to load native SavedModelBundle in bundle-shim from: /home/armando/models/mnist/1
I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:236] Loading SavedModel from: /home/armando/models/mnist/1
I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:155] Restoring SavedModel bundle.
I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:190] Running LegacyInitOp on SavedModel bundle.
I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:284] Loading SavedModel: success. Took 29853 microseconds.
I tensorflow_serving/core/loader_harness.cc:86] Successfully loaded servable version {name: mnist version: 1}
E1121 ev_epoll1_linux.c:1051] grpc epoll fd: 3
I tensorflow_serving/model_servers/main.cc:288] Running ModelServer at 0.0.0.0:8500 ...

要通过调用模型对图像进行分类来测试服务器，请按照笔记本ch-11c_TF_Serving_MNIST进行操作。

笔记本电脑的前两个单元提供了服务仓库中 TensorFlow 官方示例的测试客户端功能。我们修改了示例以发送'input'并在函数签名中接收'output'以调用 ModelServer。

使用以下代码调用笔记本的第三个单元中的测试客户端函数：

error_rate = do_inference(hostport='0.0.0.0:8500', 
                          work_dir='/home/armando/datasets/mnist',
                          concurrency=1, 
                          num_tests=100)
print('\nInference error rate: %s%%' % (error_rate * 100))

我们得到差不多 7%的错误率！ （您可能会得到不同的值）：

Extracting /home/armando/datasets/mnist/train-images-idx3-ubyte.gz
Extracting /home/armando/datasets/mnist/train-labels-idx1-ubyte.gz
Extracting /home/armando/datasets/mnist/t10k-images-idx3-ubyte.gz
Extracting /home/armando/datasets/mnist/t10k-labels-idx1-ubyte.gz

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Inference error rate: 7.0%