提供 TF 服務模型 · 精通 TensorFlow 1.x

# 提供 TF 服務模型要運行 ModelServer，請執行以下命令： ```py $ tensorflow_model_server --model_name=mnist --model_base_path=/home/armando/models/mnist ``` 服務器開始在端口 8500 上提供模型： ```py 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 ... ``` 要通過調用模型對圖像進行分類來測試服務器，請按照筆記本`ch-11c_TF_Serving_MNIST`進行操作。筆記本電腦的前兩個單元提供了服務倉庫中 TensorFlow 官方示例的測試客戶端功能。我們修改了示例以發送`'input'`并在函數簽名中接收`'output'`以調用 ModelServer。使用以下代碼調用筆記本的第三個單元中的測試客戶端函數： ```py 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)) ``` 我們得到差不多 7%的錯誤率！（您可能會得到不同的值）： ```py 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 .................................................. .................................................. Inference error rate: 7.0% ```