如何为TFX服务创建model_config文件？

如何解决如何为TFX服务创建model_config文件？

我保存了一堆自tf.keras.layers.Layer继承的自定义模型实例。我想通过TFX Serving为他们提供服务，这需要我拥有model_config文件。

我想知道如何根据这本书来创建它。现在，我有以下代码，我认为这些代码更多的是关于我自己的Bricolage，而不是我应该做的...

model_server_config = model_server_config_pb2.ModelServerConfig()

    #Create a config to add to the list of served models
    config_list = model_server_config_pb2.ModelConfigList()       
        
    for i in range(0,len(trainable_unit_name)): # add models one by one to the model config.    
        model_name  = name[i]
        base_path = "/models/{}".format(name[i])
        
        one_config = config_list.config.add()
        
        one_config.name           = model_name
        one_config.base_path      = base_path
        one_config.model_platform ="tensorflow"
        
    model_server_config.model_config_list.MergeFrom(config_list)
    with open(C.CONF_FILEPATH,'w+') as f:        
        f.write("model_config_list {" + config_list.__str__() + "}") #manually wrap it around "model_config_list { .." because this is the required format by TFX Serving.

解决方法

https://www.tensorflow.org/tfx/serving/serving_config链接中包含有关Tensorflow Serving Configuration的完整信息。

在此TF Serving Github Repository中可以找到关于如何为model_config_file创建Serving的问题的答案。

有关PB文件的详细信息，请参阅此Stack Overflow Question。

在下面的Github Code中提供以下内容，以防上面的Github Link无效：

syntax = "proto3";

package tensorflow.serving;
option cc_enable_arenas = true;

import "google/protobuf/any.proto";
import "tensorflow_serving/config/logging_config.proto";
import "tensorflow_serving/sources/storage_path/file_system_storage_path_source.proto";

// The type of model.    
enum ModelType {
  MODEL_TYPE_UNSPECIFIED = 0 [deprecated = true];
  TENSORFLOW = 1 [deprecated = true];
  OTHER = 2 [deprecated = true];
};

// Common configuration for loading a model being served.
message ModelConfig {
  // Name of the model.
  string name = 1;
      
  repeated string alias = 9;

  string base_path = 2;

  // Type of model.
  // TODO(b/31336131): DEPRECATED. Please use 'model_platform' instead.
  ModelType model_type = 3 [deprecated = true];

  // Type of model (e.g. "tensorflow").
  //
  // (This cannot be changed once a model is in serving.)
  string model_platform = 4;

  reserved 5;

  // Version policy for the model indicating which version(s) of the model to
  // load and make available for serving simultaneously.
  // The default option is to serve only the latest version of the model.
  //
  // (This can be changed once a model is in serving.)
  FileSystemStoragePathSourceConfig.ServableVersionPolicy model_version_policy =
      7;

  // String labels to associate with versions of the model,allowing inference
  // queries to refer to versions by label instead of number. Multiple labels
  // can map to the same version,but not vice-versa.
  map<string,int64> version_labels = 8;

  // Configures logging requests and responses,to the model.
  //
  // (This can be changed once a model is in serving.)
  LoggingConfig logging_config = 6;
}

// Static list of models to be loaded for serving.
message ModelConfigList {
  repeated ModelConfig config = 1;
}

// ModelServer config.
message ModelServerConfig {
  // ModelServer takes either a static file-based model config list or an Any
  // proto representing custom model config that is fetched dynamically at
  // runtime (through network RPC,custom service,etc.).
  oneof config {
    ModelConfigList model_config_list = 1;
    google.protobuf.Any custom_model_config = 2;
  }
}