FAQ

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1. How to use the custom version framework or the different version of the cluster installation to execute the program?

Specify the required files for the related version of framework and dependent libraries by the submit parameters such as --file,--cacheFile or --cacheArchive. Furthermore, setting the environment variables PYTHONPATH as export PYTHONPATH=./:$PYTHONPATH if necessary.
For example, if there is not the tensorflow module on the node of the cluster, user can set the module by the --cacheArchive. More detail:

• In the local path that tensorflow module installed, such /usr/lib/python2.7/site-packages/tensorflow/
• under the directory of the module, package all files in the directory,like tar -zcvf tensorflow.tgz ./*
• upload the package to hdfs
• add the cacheArchive on the submit script, such --cacheArchive /tmp/tensorflow.tgz#tensorflow
• set the environment variable for program: export PYTHONPATH=./:$PYTHONPATH

2. How to view the progress of the execution?

In order to view the progress of the execution both at the XLearning client and the application web interface, user need to print the progress to standard error as the format of "report:progress:<float type>" in the execution program.

3. What distributed deep learning frameworks XLearning supports, how to distinguish between standalone mode ?

XLearning support the distributed deep learning framworks such as TensorFlow, MXNet, XGBoost, LightGBM.

• TensorFlow: Set the --app-type as TensorFlow, and distinguish stand-alone and distributed mode by the number of ps applied.
• MXNet：Set the --app-type as MXNet, and distinguish stand-alone and distributed mode by the number of ps applied.
• XGBoost： Set the --app-type as distxgboost.
• LightGBM： Set the --app-type as distlightgbm.
• LightLDA： Set the --app-type as lightlda, and distinguish stand-alone and distributed mode by the number of ps applied.
• XFlow： Set the --app-type as XFlow, and distinguish stand-alone and distributed mode by the number of ps applied.

4. How to define the ClusterSpec in the distributed mode of TensorFlow application ?

In the distributed mode of TensorFlow application, ClusterSpec is defined by setting the host and port of ps and worker preliminarily. XLearning implements the automatic construction of the ClusterSpec. User can get the information of ClusterSpec, job_name, task_index from the environment variables TF_CLUSTER_DEF, TF_ROLE, TF_INDEX, such as:

import os
import json
cluster_def = json.loads(os.environ["TF_CLUSTER_DEF"])
cluster = tf.train.ClusterSpec(cluster_def)
job_name = os.environ["TF_ROLE"]
task_index = int(os.environ["TF_INDEX"])

5. Report the error：" java.lang.NoClassDefFoundError: org/apache/hadoop/mapred/JobConf" after submit the application.

Default that the yarn.application.classpath setted in the yarn-site.xml not contains the related lib package about mapreduce，try to add the related lib path, such:

<property>
    <name>yarn.application.classpath</name>    
    <value>$HADOOP_CLIENT_CONF_DIR,$HADOOP_CONF_DIR,$HADOOP_COMMON_HOME/*,$HADOOP_COMMON_HOME/lib/*,$HADOOP_HDFS_HOME/*,$HADOOP_HDFS_HOME/lib/*,$HADOOP_YARN_HOME/*,$HADOOP_YARN_HOME/lib/*,$HADOOP_MAPRED_HOME/*,$HADOOP_MAPRED_HOME/lib/*</value>  
</property>  

6. The mnist data set used in the example.

7. How to set the number of the machines and local port on the distribute LightGBM application ?

User can get the information of the number of machines and local port from the environment variables and write into the configuration file on the distribute LightGBM application (More details in $XLEARNING_HOME/examples/distLightGBM ). Note that it is necessary to copy the configuration file at current directory to avoid the more containers modify the same file when they are at one machine, like :

cp train.conf train_real.conf
chmod 777 train_real.conf
echo "num_machines = $LIGHTGBM_NUM_MACHINE" >> train_real.conf
echo "local_listen_port = $LIGHTGBM_LOCAL_LISTEN_PORT" >> train_real.conf
./LightGBM/lightgbm config=train_real.conf

Also, user need to set the machine list file at the configuration which XLearning named as lightGBMlist.txt generated at the executive directory of each worker, like :

machine_list_file = lightGBMlist.txt

8. How to set the environment varibale TF_CONFIG in the Tensorflow application ?

Example of TF_CONFIG for chief training in the distributed mode of Tensorflow estimator application (More details in $XLEARNING_HOME/examples/tfEstimator):

import os
import json

cluster = json.loads(os.environ["TF_CLUSTER_DEF"])
task_index = int(os.environ["TF_INDEX"])
task_type = os.environ["TF_ROLE"]

# chief: worker 0 as chief, other worker index --
tf_config = dict()
worker_num = len(cluster["wroker"])
if task_type == "ps":
  tf_config["task"] = {"index":task_index, "type":task_type}
elif task_type == "worker":
  if taks_index == 0:
    tf_config["task"] = {"index":0, "type":"chief"}
  else:
	tf_config["task"] = {"index":task_index-1, "type":task_type}
elif task_type == "evaluator":
	  tf_config["task"] = {"index":task_index, "type":task_type}

if worker_num == 1:
  cluster["chief"] = cluster["worker"]
  del cluster["worker"]
else:
  cluster["chief"] = [cluster["worker"][0]]
  del cluster["worker"][0]

tf_config["cluster"] = cluster
os.environ["TF_CONFIG"] = json.dumps(tf_config)

9. How to display the cpu metrics when the hadoop version is lower than 2.6.4 ?

Because of loading the required js files for CPU Metrix functionality is based on the WebApp build method which is not achieved in the hadoop version lower than 2.6.4, there is the other method to display the cpu metrics if necessary.

• add the necessary resources to the package of hadoop-yarn-common-xxx.jar on the cluster, more details:

1. unpackage the hadoop-yarn-common-xxx.jar
2. copy the folder src\main\resources\xlWebApp at the source code of XLearning to the directory webapps/static which generated after unpackaging the hadoop-yarn-common-xxx.jar
3. re-package hadoop-yarn-common-xxx.jar
4. replace the hadoop-yarn-common-xxx.jar on the cluster without restart the nodemanager and resource manager. Also can use the --jars to load the jar when submit the application.

• display the cpu matrix in the XLearning JobHistory [Optional]
  XLearning JobHistory is relay on the jars in the directory $XLEARNING_HOME/lib which generated after unpackaging the XLearning dist. Follow the above method to replace the hadoop-yarn-common-xxx.jar, then restart service.

10. How to set the memory scale ratio when the application retry after failed ?

XLearning1.1 support the application retry and memory auto scaled after failed by setting the configuration:

• xlearning.app.max.attempts
• xlearning.worker.mem.autoscale
• xlearning.ps.mem.autoscale
  Note that the information of AM connected error which reported at the client when application retry can ignore.

11. Report the error：" java.io.IOException: Cannot run program "tensorboard": error=2, No such file or directory" after submit the application.

When the XLearning client submits a job, the --user-path "/root/anaconda2/lib/python2.7/site-packages/tensorboard" is added to specify the tensorboard path.

12. How to get the input file list for each worker container when setting the --conf xlearning.input.strategy or --input-strategy as PLACEHOLDER ?

With the input strategy setted as the PLACEHOLDER, worker containers get the assigned input file list to the program by the way of the environment INPUT_FILE_LIST as json format with the key of the input local path and the value of the list of the hdfs file name. However, there is the error when the length of the environment is too long to execute the user program. In this situation, the content of the environment INPUT_FILE_LIST would be written to the local file inputFileList.txt at the current path. User can get the file list like this:

import os
import json
if os.environ.has_key('INPUT_FILE_LIST') :
  inputfile = json.loads(os.environ["INPUT_FILE_LIST"])
  data_file = inputfile["data"]
else :
  with open("inputFileList.txt") as f:
    fileStr = f.readline()
  inputfile = json.loads(fileStr)

13. How to use the custom module ?

Upload the files using the --files to each container. Load the related path to the system path, such as sys.path.append(os.getcwd()) before calling the module.

14. Recommended to directly operate the hdfs data for input and model output if use the TensorFlow Estimator advanced API.

15. YARN greater than or equal to 2.6 support node label expressions, XLearning support to configure xlearning.am.nodeLabelException、xlearning.worker.nodeLabelExpression、xlearning.ps.nodeLabelExpression to define the scheduled node.

16. TensorFlow support various distribution strategy not limited the ps/worker, but getting the cluster information is still necessary. According to set the --conf xlearning.tf.distribution.strategy=true , XLearning build the cluster information for application which use the advanced api for TensorFlow distribution strategy.

17. The following is the required for MPI job,

1. Unzip the version 3.1.1 of openmpi package to /usr/local as /usr/local/openmpinossh which provided by XLearning under the examples/mpi/;
   2）Configure the install dir to the xlearning-site.xml:

   xlearning.mpi.install.dir /usr/local/openmpinossh/

18. How to execute application in the docker?

1）Set the container type as docker: --conf xlearning.container.type=docker；
2）Assign the docker image, such as --conf xlearning.docker.image=tensorflow/tensorflow:devel-gpu;
3）Define the working dir --conf xlearning.docker.worker.dir=/work;