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Contributors
Last Commit
Oct. 22, 2018
Created
Sep. 10, 2018

TonY Build Status

TonY is a framework to natively run deep learning jobs on Apache Hadoop. It currently supports TensorFlow and PyTorch. TonY enables running either single node or distributed training as a Hadoop application. This native connector, together with other TonY features, aims to run machine learning jobs reliably and flexibly.

Build

TonY is built using Gradle. To build TonY, run:

./gradlew build

The jar required to run TonY will be located in ./tony-cli/build/libs/.

Usage

TonY is a library, so it is as simple as running a java program. First, copy your artifacts to the machine with Hadoop installed, where you plan on running TonY from. This includes:

Python virtual environment in a zip

$ unzip -Z1 my-venv.zip | head -n 10
Python/
Python/bin/
Python/bin/rst2xml.py
Python/bin/wheel
Python/bin/rst2html5.py
Python/bin/rst2odt.py
Python/bin/rst2s5.py
Python/bin/pip2.7
Python/bin/saved_model_cli
Python/bin/rst2pseudoxml.pyc

TonY jar and tony.xml

$ ls tony/
  tony-cli-0.1.0-SNAPSHOT-all.jar  tony.xml

In the tony directory there’s also a tony.xml which contains all of your TonY job configurations. For example:

$ cat tony/tony.xml
<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.ps.memory</name>
    <value>3g</value>
  </property>
</configuration>

For a full list of configurations, please see the wiki.

Model code

$ ls src/models/ | grep mnist_distributed
  mnist_distributed.py

Then you can launch your job:

$ java -cp "`hadoop classpath --glob`:tony/*:tony" \
            com.linkedin.tony.cli.ClusterSubmitter \
            -executes src/models/mnist_distributed.py \
            -task_params '--input_dir /path/to/hdfs/input --output_dir /path/to/hdfs/output --steps 2500 --batch_size 64' \
            -python_venv my-venv.zip \
            -python_binary_path Python/bin/python \
            -src_dir src \
            -shell_env LD_LIBRARY_PATH=/usr/java/latest/jre/lib/amd64/server

The command line arguments are as follows:

Name Required? Example Meaning
executes yes -executes src/model/mnist.py Location to the entry point of your training code.
src_dir yes -src src/ Specifies the name of the root directory locally which contains all of your python model source code. This directory will be copied to all worker node.
task_params no --input_dir /hdfs/input --output_dir /hdfs/output The command line arguments which will be passed to your entry point
python_venv no --python_venv venv.zip Path to the zipped local Python virtual environment
python_binary_path no --python_binary_path Python/bin/python Used together with python_venv, describes the relative path in your python virtual environment which contains the python binary, or an absolute path to use a python binary already installed on all worker nodes
shell_env no --shel_env LD_LIBRARY_PATH=/usr/local/lib64/ Specifies key-value pairs for environment variables which will be set in your python worker/ps processes.
conf_file no --conf_file tony-local.xml Location of a TonY configuration file.
conf no --conf tony.application.security.enabled=false Override configurations from your configuration file via command line

TonY configurations

There are multiple ways to specify configurations for your TonY job. As above, you can create an XML file called tony.xml and add its parent directory to your java classpath.

Alternatively, you can pass -conf_file <name_of_conf_file> to the java command line if you have a file not named tony.xml containing your configurations. (As before, the parent directory of this file must be added to the java classpath.)

If you wish to override configurations from your configuration file via command line, you can do so by passing -conf <tony.conf.key>=<tony.conf.value> argument pairs on the command line.

Finally, please check tony-default.xml or the wiki for default values of each TonY configuration.

Here is a full example of configuring your TonY application:

$ cat tony/tony.xml
<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>
</configuration>

$ java -cp "`hadoop classpath --glob`:tony/*:tony" com.linkedin.tony.cli.ClusterSubmitter \
            -task_params '--data_dir hdfs://default/data/mnist --working_dir hdfs://default/mnist/working_dir --steps 2500 --batch_size 64' \
            -python_binary_path Python/bin/python \
            -python_venv my-venv.zip \
            -executes src/mnist_distributed.py \
            -shell_env LD_LIBRARY_PATH=/usr/java/latest/jre/lib/amd64/server \
            -conf tony.ps.instances=2 \
            -conf tony.worker.instances=2

CLI configurations have highest priority, so we will get 2 ps instances and 2 worker instances. Then the XML file takes next priority so each worker will get 4g memory and 1 GPU. Finally every other configuration will be default value, e.g. each ps will get 2g memory.

TonY Examples

  1. Distributed MNIST with TensorFlow
  2. Distributed MNIST with PyTorch

FAQ

  1. My tensorflow process hangs with

    2018-09-13 03:02:31.538790: E tensorflow/core/distributed_runtime/master.cc:272] CreateSession failed because worker /job:worker/replica:0/task:0 returned error: Unavailable: OS Error
    INFO:tensorflow:An error was raised while a session was being created. This may be due to a preemption of a connected worker or parameter server. A new session will be created. Error: OS Error
    INFO:tensorflow:Graph was finalized.
    2018-09-13 03:03:33.792490: I tensorflow/core/distributed_runtime/master_session.cc:1150] Start master session ea811198d338cc1d with config: 
    INFO:tensorflow:Waiting for model to be ready.  Ready_for_local_init_op:  Variables not initialized: conv1/Variable, conv1/Variable_1, conv2/Variable, conv2/Variable_1, fc1/Variable, fc1/Variable_1, fc2/Variable, fc2/Variable_1, global_step, adam_optimizer/beta1_power, adam_optimizer/beta2_power, conv1/Variable/Adam, conv1/Variable/Adam_1, conv1/Variable_1/Adam, conv1/Variable_1/Adam_1, conv2/Variable/Adam, conv2/Variable/Adam_1, conv2/Variable_1/Adam, conv2/Variable_1/Adam_1, fc1/Variable/Adam, fc1/Variable/Adam_1, fc1/Variable_1/Adam, fc1/Variable_1/Adam_1, fc2/Variable/Adam, fc2/Variable/Adam_1, fc2/Variable_1/Adam, fc2/Variable_1/Adam_1, ready: None
    

    Why?

    Try adding the path to your libjvm.so shared library to your LD_LIBRARY_PATH environment variable for your workers. See above for an example.

  2. How do I configure arbitrary TensorFlow job types?

    Please see the wiki on TensorFlow task configuration for details.

Latest Releases
Open source release v0.1.3
 Oct. 4 2018
v0.1.2
 Oct. 1 2018
v0.1.1
 Sep. 28 2018
First release
 Sep. 12 2018