Server

An in-process TensorFlow server, for use in distributed training.

A Server instance encapsulates a set of devices and a Session target that can participate in distributed training. A server belongs to a cluster (specified by a ClusterSpec), and corresponds to a particular task in a named job. The server can communicate with any other server in the same cluster. The server will not serve any requests until start() is invoked. The server will stop serving requests once stop() or close() is invoked. Be aware that close() method stops the server if it is running.

WARNING: A Server owns resources that must be explicitly freed by invoking close().

Instances of a Server are thread-safe.

Using example:

import org.tensorflow.Server;
 import org.tensorflow.distruntime.ClusterDef;
 import org.tensorflow.distruntime.JobDef;
 import org.tensorflow.distruntime.ServerDef;

 ClusterDef clusterDef = ClusterDef.newBuilder()
   .addJob(JobDef.newBuilder()
   .setName("worker")
   .putTasks(0, "localhost:4321")
   .build()
 ).build();

 ServerDef serverDef = ServerDef.newBuilder()
   .setCluster(clusterDef)
   .setJobName("worker")
   .setTaskIndex(0)
   .setProtocol("grpc")
 .build();

 try (Server srv = new Server(serverDef)) {
   srv.start();
   srv.join();
 
 }