Add distributed MNIST example

.gitignore

@@ -22,3 +22,4 @@ build/
 dependency-reduced-pom.xml
 out/
 target
+tony-final.xml

tony-core/src/main/java/com/linkedin/tony/TonyClient.java

@@ -235,7 +235,7 @@ private boolean init(String[] args) throws ParseException {
 
     tonyConf.addResource(Constants.TONY_DEFAULT_XML);
     if (cliParser.hasOption("conf_file")) {
-      tonyConf.addResource(cliParser.getOptionValue("conf_file"));
+      tonyConf.addResource(new Path(cliParser.getOptionValue("conf_file")));
     } else {
       tonyConf.addResource(Constants.TONY_XML);
     }

tony-examples/README.md

@@ -0,0 +1,61 @@
+### Running Examples
+
+To run the examples here, you need to:
+
+* build a Python virtual environment with TensorFlow 1.9.0 installed
+* install Hadoop 3.1.1+
+
+If you don't have security enabled, you'll also need to provide a custom config file with security turned off.
+
+
+### Building a Python virtual environment with TensorFlow
+
+TonY requires a Python virtual environment zip with TensorFlow and any needed Python libraries already installed.
+
+```
+wget https://files.pythonhosted.org/packages/33/bc/fa0b5347139cd9564f0d44ebd2b147ac97c36b2403943dbee8a25fd74012/virtualenv-16.0.0.tar.gz
+tar xf virtualenv-16.0.0.tar.gz
+
+# Make sure to install using Python 3, as TensorFlow only provides Python 3 artifacts
+python virtualenv-16.0.0/virtualenv.py venv
+. venv/bin/activate
+pip install tensorflow==1.9.0
+zip -r venv.zip venv
+```
+
+
+### Installing Hadoop
+
+TonY only requires YARN, not HDFS. Please see the [open-source documentation](https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/SingleCluster.html) on how to set YARN up.
+
+
+### Disabling security
+
+If your Hadoop cluster is not running with security enabled (e.g.: for local testing), you can disable security by creating a config file as follows:
+
+```
+<configuration>
+  <property>
+    <name>tony.application.insecure-mode</name>
+    <value>true</value>
+  </property>
+</configuration>
+```
+
+For the instructions below, we assume this file is named `tony-test.xml`.
+
+
+### Running an example
+
+Once you've installed Hadoop and built your Python virtual environment zip, you can run an example as follows:
+
+```
+gradlew :tony-cli:build
+
+java -cp `hadoop classpath`:/path/to/TonY/tony-cli/build/libs/tony-cli-x.x.x-all.jar com.linkedin.tony.cli.ClusterSubmitter \
+--python_venv=/path/to/venv.zip \
+--src_dir=/path/to/TonY/tony-examples/mnist \
+--executes=/path/to/TonY/tony-examples/mnist/mnist_distributed.py \
+--conf_file=/path/to/tony-test.xml \
+--python_binary_path=venv/bin/python
+```

tony-examples/mnist/mnist_distributed.py

@@ -0,0 +1,246 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A deep MNIST classifier using convolutional layers.
+
+This example was adapted from
+https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/mnist_deep.py.
+
+Each worker reads the full MNIST dataset and asynchronously trains a CNN with dropout and using the Adam optimizer,
+updating the model parameters on shared parameter servers.
+
+The current training accuracy is printed out after every 100 steps.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.examples.tutorials.mnist import input_data
+from threading import Thread
+
+import json
+import logging
+import os
+import sys
+import tensorboard.main as tb_main
+import tensorflow as tf
+
+# Environment variable containing port to launch TensorBoard on, set by TonY.
+TB_PORT_ENV_VAR = 'TB_PORT'
+
+# Input/output directories
+tf.flags.DEFINE_string('data_dir', '/tmp/tensorflow/mnist/input_data',
+                           'Directory for storing input data')
+tf.flags.DEFINE_string('working_dir', '/tmp/tensorflow/mnist/working_dir',
+                           'Directory under which events and output will be stored (in separate subdirectories).')
+
+# Training parameters
+tf.flags.DEFINE_integer("steps", 2500, "The number of training steps to execute.")
+tf.flags.DEFINE_integer("batch_size", 64, "The batch size per step.")
+
+FLAGS = tf.flags.FLAGS
+
+
+def deepnn(x):
+    """deepnn builds the graph for a deep net for classifying digits.
+
+    Args:
+      x: an input tensor with the dimensions (N_examples, 784), where 784 is the
+      number of pixels in a standard MNIST image.
+
+    Returns:
+      A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
+      equal to the logits of classifying the digit into one of 10 classes (the
+      digits 0-9). keep_prob is a scalar placeholder for the probability of
+      dropout.
+    """
+    # Reshape to use within a convolutional neural net.
+    # Last dimension is for "features" - there is only one here, since images are
+    # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
+    with tf.name_scope('reshape'):
+        x_image = tf.reshape(x, [-1, 28, 28, 1])
+
+    # First convolutional layer - maps one grayscale image to 32 feature maps.
+    with tf.name_scope('conv1'):
+        W_conv1 = weight_variable([5, 5, 1, 32])
+        b_conv1 = bias_variable([32])
+        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+
+    # Pooling layer - downsamples by 2X.
+    with tf.name_scope('pool1'):
+        h_pool1 = max_pool_2x2(h_conv1)
+
+    # Second convolutional layer -- maps 32 feature maps to 64.
+    with tf.name_scope('conv2'):
+        W_conv2 = weight_variable([5, 5, 32, 64])
+        b_conv2 = bias_variable([64])
+        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+
+    # Second pooling layer.
+    with tf.name_scope('pool2'):
+        h_pool2 = max_pool_2x2(h_conv2)
+
+    # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
+    # is down to 7x7x64 feature maps -- maps this to 1024 features.
+    with tf.name_scope('fc1'):
+        W_fc1 = weight_variable([7 * 7 * 64, 1024])
+        b_fc1 = bias_variable([1024])
+
+        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
+        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+
+    # Dropout - controls the complexity of the model, prevents co-adaptation of
+    # features.
+    with tf.name_scope('dropout'):
+        keep_prob = tf.placeholder(tf.float32)
+        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+
+    # Map the 1024 features to 10 classes, one for each digit
+    with tf.name_scope('fc2'):
+        W_fc2 = weight_variable([1024, 10])
+        b_fc2 = bias_variable([10])
+
+        y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
+    return y_conv, keep_prob
+
+
+def conv2d(x, W):
+    """conv2d returns a 2d convolution layer with full stride."""
+    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+
+
+def max_pool_2x2(x):
+    """max_pool_2x2 downsamples a feature map by 2X."""
+    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
+                          strides=[1, 2, 2, 1], padding='SAME')
+
+
+def weight_variable(shape):
+    """weight_variable generates a weight variable of a given shape."""
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+
+def bias_variable(shape):
+    """bias_variable generates a bias variable of a given shape."""
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+
+def create_model():
+    """Creates our model and returns the target nodes to be run or populated"""
+    # Create the model
+    x = tf.placeholder(tf.float32, [None, 784])
+
+    # Define loss and optimizer
+    y_ = tf.placeholder(tf.int64, [None])
+
+    # Build the graph for the deep net
+    y_conv, keep_prob = deepnn(x)
+
+    with tf.name_scope('loss'):
+        cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y_conv)
+        cross_entropy = tf.reduce_mean(cross_entropy)
+
+    global_step = tf.train.get_or_create_global_step()
+    with tf.name_scope('adam_optimizer'):
+        train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy, global_step=global_step)
+
+    with tf.name_scope('accuracy'):
+        correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)
+        correct_prediction = tf.cast(correct_prediction, tf.float32)
+    accuracy = tf.reduce_mean(correct_prediction)
+
+    tf.summary.scalar('cross_entropy_loss', cross_entropy)
+    tf.summary.scalar('accuracy', accuracy)
+
+    merged = tf.summary.merge_all()
+
+    return x, y_, keep_prob, global_step, train_step, accuracy, merged
+
+
+def start_tensorboard(checkpoint_dir):
+    FLAGS.logdir = checkpoint_dir
+    if TB_PORT_ENV_VAR in os.environ:
+        FLAGS.port = os.environ[TB_PORT_ENV_VAR]
+
+    tb_thread = Thread(target=tb_main.run_main)
+    tb_thread.daemon = True
+
+    logging.info("Starting TensorBoard with --logdir=" + checkpoint_dir + " in daemon thread...")
+    tb_thread.start()
+
+
+def main(_):
+    logging.getLogger().setLevel(logging.INFO)
+
+    cluster_spec_str = os.environ["CLUSTER_SPEC"]
+    cluster_spec = json.loads(cluster_spec_str)
+    ps_hosts = cluster_spec['ps']
+    worker_hosts = cluster_spec['worker']
+
+    # Create a cluster from the parameter server and worker hosts.
+    cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
+
+    # Create and start a server for the local task.
+    job_name = os.environ["JOB_NAME"]
+    task_index = int(os.environ["TASK_INDEX"])
+    server = tf.train.Server(cluster, job_name=job_name, task_index=task_index)
+
+    if job_name == "ps":
+        server.join()
+    elif job_name == "worker":
+        # Create our model graph. Assigns ops to the local worker by default.
+        with tf.device(tf.train.replica_device_setter(
+                worker_device="/job:worker/task:%d" % task_index,
+                cluster=cluster)):
+            features, labels, keep_prob, global_step, train_step, accuracy, merged = create_model()
+
+        if task_index is 0:  # chief worker
+            tf.gfile.MakeDirs(FLAGS.working_dir)
+            start_tensorboard(FLAGS.working_dir)
+
+        # The StopAtStepHook handles stopping after running given steps.
+        hooks = [tf.train.StopAtStepHook(num_steps=FLAGS.steps)]
+
+        with tf.train.MonitoredTrainingSession(master=server.target,
+                                               is_chief=(task_index == 0),
+                                               checkpoint_dir=FLAGS.working_dir,
+                                               hooks=hooks) as sess:
+            # Import data
+            logging.info('Extracting and loading input data...')
+            mnist = input_data.read_data_sets(FLAGS.data_dir)
+
+            # Train
+            logging.info('Starting training')
+            i = 0
+            while not sess.should_stop():
+                batch = mnist.train.next_batch(FLAGS.batch_size)
+                if i % 100 == 0:
+                    step, _, train_accuracy = sess.run([global_step, train_step, accuracy],
+                                                       feed_dict={features: batch[0], labels: batch[1], keep_prob: 1.0})
+                    logging.info('Step %d, training accuracy: %g' % (step, train_accuracy))
+                else:
+                    sess.run([global_step, train_step],
+                             feed_dict={features: batch[0], labels: batch[1], keep_prob: 0.5})
+                i += 1
+
+        logging.info('Done training!')
+        sys.exit()
+
+
+if __name__ == '__main__':
+    tf.app.run()