TonY Configurations

Application Properties:

Name	Default	Meaning
tony.other.namenodes		Namenode URIs to get delegation tokens from.
tony.yarn.queue	default	Default queue to submit to YARN.
tony.application.name	TensorFlowApplication	Name of your YARN application.
tony.application.node-label		YARN partition which this application should run in.
tony.application.single-node	false	Whether this is single node training or not.
tony.application.enable-preprocess	false	Whether the AM should invoke the user's python script or not.
tony.application.timeout	0	Max runtime of the application before killing it, in milliseconds.

Task Properties:

Name	Default	Meaning
tony.task.executor.jvm.opts	-Xmx1536m	JVM opts for each TaskExecutor.
tony.task.registration-timeout-sec	300	Timeout, in seconds, for AM to resubmit unregistered tasks (or fail if no retries configured).
tony.task.registration-retry-count	0	How many times we should resubmit unregistered tasks after the timeout interval.
tony.task.heartbeat-interval	1000	Frequency, in milliseconds, for which TaskExecutors should heartbeat with AM.
tony.task.max-missed-heartbeats	25	How many missed heartbeats before declaring a TaskExecutor dead.

AM Configuration

Name	Default	Meaning
tony.am.retry-count	0	How many times a failed AM should retry.
tony.am.memory	2g	AM memory size, requested as a string (e.g. '2g' or '2048m').
tony.am.vcores	1	Number of AM vcores to use.
tony.am.gpus	0	Number of AM GPUs to use. (In general, should only be applicable in single node mode.)

Task Configuration

Name	Default	Meaning
tony.X.instances	1	Number of tasks for TensorFlowJob "X", default 1 if X=ps or X=worker, 0 otherwise.
tony.X.memory	2g	Memory size per task in TensorFlow job "X", requested as a string (e.g. '2g' or '2048m').
tony.X.vcores	1	Number of vcores per task in TensorFlow job "X".
tony.X.gpus	0	Number of GPUs per task in TensorFlow job "X".

TonY determines which TensorFlow job types to allocate based on configurations of the form "tony.X.instances". For each job "X", it will also search for resource requests corresponding to this TensorFlow job.

For example, you can configure a ps, worker, and chief job via:

<configuration>
  <property>
    <name>tony.worker.instances</name>
    <value>4</value>
  </property>
  <property>
    <name>tony.worker.memory</name>
    <value>4g</value>
  </property>
  <property>
    <name>tony.worker.gpus</name>
    <value>1</value>
  </property>
  <property>
    <name>tony.worker.instances</name>
    <value>4</value>
  </property>
  <property>
    <name>tony.worker.memory</name>
    <value>4g</value>
  </property>
  <property>
    <name>tony.ps.memory</name>
    <value>3g</value>
  </property>
  <property>
    <name>tony.chief.instances</name>
    <value>1</value>
  </property>
  <property>
    <name>tony.chief.memory</name>
    <value>6g</value>
  </property>
  <property>
    <name>tony.chief.gpus</name>
    <value>1</value>
  </property>
</configuration>

Note that TonY will configure default one ps and one worker and no other TensorFlow jobs (in this case, there will be four workers allocated since this is explicitly configured, and one ps since "tony.ps.instances" is omitted). Furthermore TonY will also configure one chief task since "tony.chief.instances" is configured to 1, and this task will have 6 GB and 1 GPU allocated for it.

Others

Name	Default	Meaning
tony.application.security.enabled	true	Whether this application is running in a Kerberized grid. Setting this to true will fetch tokens from the cluster as well as between the client and AM.
tony.application.hdfs-conf-path		Path to HDFS configuration, to be passed as an environment variable to the python training scripts.
tony.application.yarn-conf-path		Path to YARN configuration, to be passed as an environment variable to the python training scripts.