For installation, see the installation instructions.

Quick Tutorial: AWS Cluster

This is a quick tutorial to run a distributed Poseidon task on an AWS cluster. In this tutorial, we will use CIFAR-10, which is a common benchmark in machine learning for image recognition using convolutional neural networks (CNN). More detailed instructions on how to get started are available at: https://www.tensorflow.org/versions/r0.10/tutorials/deep_cnn/.

Data

The dataset will download automatically when you run the training code with the default options. You can also put your dataset in a distributed file system.

Training

The executable for running Poseidon tasks is psd_run. Running psd_run -h should print this:

usage: psd_run [-h] [-c,--cluster_config CLUSTER_CONFIG]
               [-o,--out OUTPUT_FOLDER]
               cmd

psd_run runs Poseidon for distributed machine learning on a GPU cluster. The following are its command line arguments.

positional arguments:
  cmd

optional arguments:
  -h, --help            show this help message and exit
  -c,--cluster_config CLUSTER_CONFIG
                        configuration file for cluster environment: specify workers in each machine. See example:
                        {
                          "virtualenv": "/home/ubuntu/sandbox",
                          "username": "ubuntu",
                          "pem_file": "/path/to/pem-file.pem",
                          "master_node": "localhost",
                          "worker_nodes": [
                            "192.168.1.11",
                            "192.168.1.15"
                          ],
                          "server_nodes": [
                            "192.168.1.70",
                            "192.168.1.80"
                          ]
                        }
  -o,--out OUTPUT_FOLDER
                        output log folder

Setup

We must create a config.json to specify our cluster configurations. If running a single node on Ubuntu within an AWS instance (with no virtualenv), the configurations are very simple:

{
  "pem_file": "cluster-key.pem",
  "worker_nodes": [
    "127.0.0.1"
  ],
  "server_nodes": [
    "127.0.0.1"
  ]
}

Note: replace cluster-key.pem with a path to your AWS pem file.

Execution

We can now launch Posiedon with the following command. The script, cifar10_train.py is an example model script included with the Poseidon installation.

# The model is in the Poseidon install directory. This line gets the Poseidon home.
POSEIDON_HOME=`python -c 'import os; import tensorflow; print os.path.dirname(tensorflow.__file__)'`

psd_run -c config.json "python $POSEIDON_HOME/models/image/cifar10/cifar10_train.py --max_steps 1000"

Note that the above script for cifarNet is included in the Poseidon release. If you wish to view the model, it is located in $POSEIDON_HOME/models/image/cifar10.

Poseidon Logs

After running Poseidon, you can check the execution log poseidon_run.log in the same path you ran psd_run. There are also output log files for debugging and monitoring purpose created in poseidon_log_$TIMESTAMP_SUFFIX folder.

Evaluating

Poseidon’s evaluating procedure is the same as TensorFlow’s. Please follow the tutorial here.