a stand alone machine learning suite which can easy to integrate with angel ps

<dependency>
    <groupId>com.tencent.angel</groupId>
    <artifactId>angel-mlcore</artifactId>
    <version>0.1.1</version>
</dependency>

The core of mlcore is computation graph, which performs the forward and backward calculation and computes gradient automatically. The abstraction of variable and optimizer makes mlcore can run in everywhere include single node, Angel, Spark and so on. here is the architecture of mlcore:

here is the runtime architecture of mlcore:

• pull parameters from local or parameter server (PS)
• perform the forward calculation
• perform the backward calculation to calculate gradient
• push gradient to local or PS
• finally, update parameter in local or PS

The variable is a vector or matrix with slots and updater. The updater is used to update the value of variable and slots are the auxiliary data of updater. The number of slots is decided by the type of updater. Usually, the shape of value is the same as that of slot.

The variable and updater are interfaces in mlcore. Different distributed systems can implement their own variables and updaters. In this way, mlcore is easy to embed into other distributed systems.

The basic operation of variable

• create: create a variable in PS or local
• init: initial a variable in PS or local
• load: load data from disk to initial a variable in PS or local
• pull: pull the value of a variable from PS or local
• push: push gradient of a variable to PS or local
• update: update a variable in PS or local, the slot attached will also updated if necessary.
• saveWithSlot/saveWithoutSlot/checkpoint: save a variable in PS or local. as mentioned about, variable usually with slots, you can choose to save slots or not. note: checkpoint is the same as saveWithSlot
• release: release a variable in PS or local

The status and life cycle of a variable:

The top abstraction of updater:

trait Updater extends Serializable {
  val numSlot: Int

  def update[T](variable: Variable, epoch: Int, batchSize: Int): Future[T]
}

The computation graph in mlcore is coarse grain, the basic operator is layer. The coarse grain computation graph has a smooth learning curve. Consequently, it is user friendly.