MLRunner

MLRunner is the entry point to Angel algorithms. It defines the standard process of starting up an Angel application, and encapsulates AngelClient.

Purpose

• Starts Angel PS, loads and stores model, and starts the default implementations of processes such as Task
• In general, tha application can directly use the functions above

Core Methods

1. train

   • Definition: train(conf: Configuration)
   • Purpose: starts up Angel app's model training process
   • Parameters: conf: configures Angel job and algorithm settings
   • Return value: none

2. train (default implementation)

   • Definition: train(conf: Configuration, model: MLModel, taskClass: Class[_ <: BaseTask[_, _, _]]): Unit

   • Purpose: starts Angel app's model training process. This method encapsulates specific Angel PS/worker starting-up and model-loading/saving processes; can be referenced by subclasses directly

   • Parameters

     • conf: configures Angel job and algorithm
     • model: MLModel, the machine-learning model/algorithm
     • taskClass: Class[_ <: TrainTask[_, _, _]] Task class in the algorithm's process

   • Return value: none

3. incTrain

   • Definition: incTrain(conf: Configuration)
   • Purpose: updates an existing model using incremental training
   • Parameters: conf: configures Angel job and algorithm
   • Return value: none

4. predict

   • Definition: predict(conf: Configuration)
   • Purpose: starts Angel app and computes model prediction
   • Parameters: conf: configures Angel job
   • Return value: none

5. predict (default implementation)

   • Definition: predict(conf: Configuration, model: MLModel, taskClass: Class[_ <: PredictTask[_, _, _]]): Unit
   • Purpose: starts Angel app and updates an existing model using incremental training. This method encapsulates specific Angel PS/worker starting-up and model-loading/saving processes; can be referenced by subclasses directly
   • Parameters: conf: configures Angel job
   • Return value: none