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Basic parameter server example. (#1198)
* Basic parameter server example. * Consolidate files. * Whitespace. * Add documentation.
This commit is contained in:
Robert Nishihara
committed by
Philipp Moritz
Philipp Moritz
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Parameter Server
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================
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This document walks through how to implement a simple parameter server example
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using actors. To run the application, first install some dependencies.
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.. code-block:: bash
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pip install tensorflow
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You can view the `code for this example`_.
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.. _`code for this example`: https://github.com/ray-project/ray/tree/master/examples/parameter_server
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The example can be run as follows.
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.. code-block:: bash
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python ray/examples/parameter_server/parameter_server.py --num-workers=4
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Note that this examples uses distributed actor handles, which are still
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considered experimental.
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The parameter server itself is implemented as an actor, which exposes the
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methods ``push`` and ``pull``.
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.. code-block:: python
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@ray.remote
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class ParameterServer(object):
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def __init__(self, keys, values):
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values = [value.copy() for value in values]
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self.weights = dict(zip(keys, values))
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def push(self, keys, values):
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for key, value in zip(keys, values):
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self.weights[key] += value
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def pull(self, keys):
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return [self.weights[key] for key in keys]
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We then define a worker task, which take a parameter server as an argument and
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submits tasks to it. The structure of the code looks as follows.
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.. code-block:: python
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@ray.remote
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def worker_task(ps):
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while True:
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# Get the latest weights from the parameter server.
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weights = ray.get(ps.pull.remote(keys))
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# Compute an update.
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...
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# Push the update to the parameter server.
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ps.push.remote(keys, update)
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Then we can create a parameter server and initiate training as follows.
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.. code-block:: python
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ps = ParameterServer.remote(keys, initial_values)
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worker_tasks = [worker_task.remote(ps) for _ in range(4)]
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@@ -52,6 +52,7 @@ Example Program
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example-hyperopt.rst
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example-rl-pong.rst
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example-policy-gradient.rst
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example-parameter-server.rst
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example-resnet.rst
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example-a3c.rst
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example-lbfgs.rst
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# Most of the tensorflow code is adapted from Tensorflow's tutorial on using
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# CNNs to train MNIST
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# https://www.tensorflow.org/get_started/mnist/pros#build-a-multilayer-convolutional-network. # noqa: E501
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import ray
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import tensorflow as tf
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class SimpleCNN(object):
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def __init__(self):
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with tf.Graph().as_default():
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# Create the model
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self.x = tf.placeholder(tf.float32, [None, 784])
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# Define loss and optimizer
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self.y_ = tf.placeholder(tf.float32, [None, 10])
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# Build the graph for the deep net
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self.y_conv, self.keep_prob = deepnn(self.x)
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with tf.name_scope('loss'):
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cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
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labels=self.y_, logits=self.y_conv)
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self.cross_entropy = tf.reduce_mean(cross_entropy)
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with tf.name_scope('adam_optimizer'):
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self.optimizer = tf.train.AdamOptimizer(1e-4)
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self.train_step = self.optimizer.minimize(
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self.cross_entropy)
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with tf.name_scope('accuracy'):
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correct_prediction = tf.equal(tf.argmax(self.y_conv, 1),
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tf.argmax(self.y_, 1))
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correct_prediction = tf.cast(correct_prediction, tf.float32)
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self.accuracy = tf.reduce_mean(correct_prediction)
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self.sess = tf.Session(config=tf.ConfigProto(
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intra_op_parallelism_threads=1,
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inter_op_parallelism_threads=1))
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self.sess.run(tf.global_variables_initializer())
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# Helper values.
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self.variables = ray.experimental.TensorFlowVariables(
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self.cross_entropy, self.sess)
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self.grads = self.optimizer.compute_gradients(
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self.cross_entropy)
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def compute_update(self, x, y):
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# TODO(rkn): Computing the weights before and after the training step
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# and taking the diff is awful.
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weights = self.get_weights()[1]
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self.sess.run(self.train_step, feed_dict={self.x: x,
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self.y_: y,
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self.keep_prob: 0.5})
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new_weights = self.get_weights()[1]
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return [x - y for x, y in zip(new_weights, weights)]
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def compute_gradients(self, x, y):
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return self.sess.run([grad[0] for grad in self.grads],
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feed_dict={self.x: x,
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self.y_: y,
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self.keep_prob: 0.5})
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def compute_accuracy(self, x, y):
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return self.sess.run(self.accuracy,
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feed_dict={self.x: x,
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self.y_: y,
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self.keep_prob: 1.0})
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def set_weights(self, variable_names, weights):
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self.variables.set_weights(dict(zip(variable_names, weights)))
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def get_weights(self):
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weights = self.variables.get_weights()
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return list(weights.keys()), list(weights.values())
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def deepnn(x):
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"""deepnn builds the graph for a deep net for classifying digits.
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Args:
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x: an input tensor with the dimensions (N_examples, 784), where 784 is
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the number of pixels in a standard MNIST image.
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Returns:
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A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
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values equal to the logits of classifying the digit into one of 10
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classes (the digits 0-9). keep_prob is a scalar placeholder for the
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probability of dropout.
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"""
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# Reshape to use within a convolutional neural net.
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# Last dimension is for "features" - there is only one here, since images
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# are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
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with tf.name_scope('reshape'):
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x_image = tf.reshape(x, [-1, 28, 28, 1])
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# First convolutional layer - maps one grayscale image to 32 feature maps.
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with tf.name_scope('conv1'):
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W_conv1 = weight_variable([5, 5, 1, 32])
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b_conv1 = bias_variable([32])
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h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
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# Pooling layer - downsamples by 2X.
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with tf.name_scope('pool1'):
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h_pool1 = max_pool_2x2(h_conv1)
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# Second convolutional layer -- maps 32 feature maps to 64.
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with tf.name_scope('conv2'):
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W_conv2 = weight_variable([5, 5, 32, 64])
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b_conv2 = bias_variable([64])
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h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
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# Second pooling layer.
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with tf.name_scope('pool2'):
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h_pool2 = max_pool_2x2(h_conv2)
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# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
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# is down to 7x7x64 feature maps -- maps this to 1024 features.
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with tf.name_scope('fc1'):
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W_fc1 = weight_variable([7 * 7 * 64, 1024])
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b_fc1 = bias_variable([1024])
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h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
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h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
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# Dropout - controls the complexity of the model, prevents co-adaptation of
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# features.
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with tf.name_scope('dropout'):
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keep_prob = tf.placeholder(tf.float32)
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h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
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# Map the 1024 features to 10 classes, one for each digit
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with tf.name_scope('fc2'):
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W_fc2 = weight_variable([1024, 10])
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b_fc2 = bias_variable([10])
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y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
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return y_conv, keep_prob
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def conv2d(x, W):
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"""conv2d returns a 2d convolution layer with full stride."""
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return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
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def max_pool_2x2(x):
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"""max_pool_2x2 downsamples a feature map by 2X."""
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return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
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strides=[1, 2, 2, 1], padding='SAME')
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def weight_variable(shape):
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"""weight_variable generates a weight variable of a given shape."""
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initial = tf.truncated_normal(shape, stddev=0.1)
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return tf.Variable(initial)
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def bias_variable(shape):
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"""bias_variable generates a bias variable of a given shape."""
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initial = tf.constant(0.1, shape=shape)
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return tf.Variable(initial)
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@@ -0,0 +1,83 @@
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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from tensorflow.examples.tutorials.mnist import input_data
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import time
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import ray
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import model
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parser = argparse.ArgumentParser(description="Run the parameter server "
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"example.")
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parser.add_argument("--num-workers", default=4, type=int,
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help="The number of workers to use.")
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parser.add_argument("--redis-address", default=None, type=str,
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help="The Redis address of the cluster.")
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@ray.remote
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class ParameterServer(object):
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def __init__(self, keys, values):
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# These values will be mutated, so we must create a copy that is not
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# backed by the object store.
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values = [value.copy() for value in values]
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self.weights = dict(zip(keys, values))
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def push(self, keys, values):
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for key, value in zip(keys, values):
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self.weights[key] += value
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def pull(self, keys):
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return [self.weights[key] for key in keys]
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@ray.remote
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def worker_task(ps):
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# Download MNIST.
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mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
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batch_size = 50
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# Initialize the model.
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net = model.SimpleCNN()
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keys = net.get_weights()[0]
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while True:
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# Get the current weights from the parameter server.
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weights = ray.get(ps.pull.remote(keys))
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net.set_weights(keys, weights)
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# Compute an update and push it to the parameter server.
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xs, ys = mnist.train.next_batch(batch_size)
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gradients = net.compute_update(xs, ys)
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ps.push.remote(keys, gradients)
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if __name__ == '__main__':
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args = parser.parse_args()
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ray.init(redis_address=args.redis_address)
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# Create a parameter server with some random weights.
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net = model.SimpleCNN()
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all_keys, all_values = net.get_weights()
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ps = ParameterServer.remote(all_keys, all_values)
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# Start some training tasks.
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worker_tasks = [worker_task.remote(ps) for _ in range(args.num_workers)]
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# Download MNIST.
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mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
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i = 0
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while True:
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# Get and evaluate the current model.
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current_weights = ray.get(ps.pull.remote(all_keys))
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net.set_weights(all_keys, current_weights)
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test_xs, test_ys = mnist.test.next_batch(1000)
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accuracy = net.compute_accuracy(test_xs, test_ys)
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print("Iteration {}: accuracy is {}".format(i, accuracy))
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i += 1
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time.sleep(1)
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