mirror of
https://github.com/wassname/ray.git
synced 2026-07-09 09:03:42 +08:00
[doc] remove redundant PS example (#6634)
This commit is contained in:
Richard Liaw
@@ -1,80 +0,0 @@
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import argparse
|
||||
import time
|
||||
|
||||
import ray
|
||||
import model
|
||||
|
||||
parser = argparse.ArgumentParser(description="Run the asynchronous parameter "
|
||||
"server example.")
|
||||
parser.add_argument("--num-workers", default=4, type=int,
|
||||
help="The number of workers to use.")
|
||||
parser.add_argument("--redis-address", default=None, type=str,
|
||||
help="The Redis address of the cluster.")
|
||||
|
||||
|
||||
@ray.remote
|
||||
class ParameterServer(object):
|
||||
def __init__(self, keys, values):
|
||||
# These values will be mutated, so we must create a copy that is not
|
||||
# backed by the object store.
|
||||
values = [value.copy() for value in values]
|
||||
self.weights = dict(zip(keys, values))
|
||||
|
||||
def push(self, keys, values):
|
||||
for key, value in zip(keys, values):
|
||||
self.weights[key] += value
|
||||
|
||||
def pull(self, keys):
|
||||
return [self.weights[key] for key in keys]
|
||||
|
||||
|
||||
@ray.remote
|
||||
def worker_task(ps, worker_index, batch_size=50):
|
||||
# Download MNIST.
|
||||
mnist = model.download_mnist_retry(seed=worker_index)
|
||||
|
||||
# Initialize the model.
|
||||
net = model.SimpleCNN()
|
||||
keys = net.get_weights()[0]
|
||||
|
||||
while True:
|
||||
# Get the current weights from the parameter server.
|
||||
weights = ray.get(ps.pull.remote(keys))
|
||||
net.set_weights(keys, weights)
|
||||
|
||||
# Compute an update and push it to the parameter server.
|
||||
xs, ys = mnist.train.next_batch(batch_size)
|
||||
gradients = net.compute_update(xs, ys)
|
||||
ps.push.remote(keys, gradients)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = parser.parse_args()
|
||||
|
||||
ray.init(redis_address=args.redis_address)
|
||||
|
||||
# Create a parameter server with some random weights.
|
||||
net = model.SimpleCNN()
|
||||
all_keys, all_values = net.get_weights()
|
||||
ps = ParameterServer.remote(all_keys, all_values)
|
||||
|
||||
# Start some training tasks.
|
||||
worker_tasks = [worker_task.remote(ps, i) for i in range(args.num_workers)]
|
||||
|
||||
# Download MNIST.
|
||||
mnist = model.download_mnist_retry()
|
||||
|
||||
i = 0
|
||||
while True:
|
||||
# Get and evaluate the current model.
|
||||
current_weights = ray.get(ps.pull.remote(all_keys))
|
||||
net.set_weights(all_keys, current_weights)
|
||||
test_xs, test_ys = mnist.test.next_batch(1000)
|
||||
accuracy = net.compute_accuracy(test_xs, test_ys)
|
||||
print("Iteration {}: accuracy is {}".format(i, accuracy))
|
||||
i += 1
|
||||
time.sleep(1)
|
||||
@@ -1,203 +0,0 @@
|
||||
# Most of the tensorflow code is adapted from Tensorflow's tutorial on using
|
||||
# CNNs to train MNIST
|
||||
# https://www.tensorflow.org/get_started/mnist/pros#build-a-multilayer-convolutional-network. # noqa: E501
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import time
|
||||
|
||||
import tensorflow as tf
|
||||
from tensorflow.examples.tutorials.mnist import input_data
|
||||
|
||||
import ray
|
||||
import ray.experimental.tf_utils
|
||||
|
||||
|
||||
def download_mnist_retry(seed=0, max_num_retries=20):
|
||||
for _ in range(max_num_retries):
|
||||
try:
|
||||
return input_data.read_data_sets(
|
||||
"MNIST_data", one_hot=True, seed=seed)
|
||||
except tf.errors.AlreadyExistsError:
|
||||
time.sleep(1)
|
||||
raise Exception("Failed to download MNIST.")
|
||||
|
||||
|
||||
class SimpleCNN(object):
|
||||
def __init__(self, learning_rate=1e-4):
|
||||
with tf.Graph().as_default():
|
||||
|
||||
# Create the model
|
||||
self.x = tf.placeholder(tf.float32, [None, 784])
|
||||
|
||||
# Define loss and optimizer
|
||||
self.y_ = tf.placeholder(tf.float32, [None, 10])
|
||||
|
||||
# Build the graph for the deep net
|
||||
self.y_conv, self.keep_prob = deepnn(self.x)
|
||||
|
||||
with tf.name_scope("loss"):
|
||||
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
|
||||
labels=self.y_, logits=self.y_conv)
|
||||
self.cross_entropy = tf.reduce_mean(cross_entropy)
|
||||
|
||||
with tf.name_scope("adam_optimizer"):
|
||||
self.optimizer = tf.train.AdamOptimizer(learning_rate)
|
||||
self.train_step = self.optimizer.minimize(self.cross_entropy)
|
||||
|
||||
with tf.name_scope("accuracy"):
|
||||
correct_prediction = tf.equal(
|
||||
tf.argmax(self.y_conv, 1), tf.argmax(self.y_, 1))
|
||||
correct_prediction = tf.cast(correct_prediction, tf.float32)
|
||||
self.accuracy = tf.reduce_mean(correct_prediction)
|
||||
|
||||
self.sess = tf.Session(
|
||||
config=tf.ConfigProto(
|
||||
intra_op_parallelism_threads=1,
|
||||
inter_op_parallelism_threads=1))
|
||||
self.sess.run(tf.global_variables_initializer())
|
||||
|
||||
# Helper values.
|
||||
|
||||
self.variables = ray.experimental.tf_utils.TensorFlowVariables(
|
||||
self.cross_entropy, self.sess)
|
||||
|
||||
self.grads = self.optimizer.compute_gradients(self.cross_entropy)
|
||||
self.grads_placeholder = [(tf.placeholder(
|
||||
"float", shape=grad[1].get_shape()), grad[1])
|
||||
for grad in self.grads]
|
||||
self.apply_grads_placeholder = self.optimizer.apply_gradients(
|
||||
self.grads_placeholder)
|
||||
|
||||
def compute_update(self, x, y):
|
||||
# TODO(rkn): Computing the weights before and after the training step
|
||||
# and taking the diff is awful.
|
||||
weights = self.get_weights()[1]
|
||||
self.sess.run(
|
||||
self.train_step,
|
||||
feed_dict={
|
||||
self.x: x,
|
||||
self.y_: y,
|
||||
self.keep_prob: 0.5
|
||||
})
|
||||
new_weights = self.get_weights()[1]
|
||||
return [x - y for x, y in zip(new_weights, weights)]
|
||||
|
||||
def compute_gradients(self, x, y):
|
||||
return self.sess.run(
|
||||
[grad[0] for grad in self.grads],
|
||||
feed_dict={
|
||||
self.x: x,
|
||||
self.y_: y,
|
||||
self.keep_prob: 0.5
|
||||
})
|
||||
|
||||
def apply_gradients(self, gradients):
|
||||
feed_dict = {}
|
||||
for i in range(len(self.grads_placeholder)):
|
||||
feed_dict[self.grads_placeholder[i][0]] = gradients[i]
|
||||
self.sess.run(self.apply_grads_placeholder, feed_dict=feed_dict)
|
||||
|
||||
def compute_accuracy(self, x, y):
|
||||
return self.sess.run(
|
||||
self.accuracy,
|
||||
feed_dict={
|
||||
self.x: x,
|
||||
self.y_: y,
|
||||
self.keep_prob: 1.0
|
||||
})
|
||||
|
||||
def set_weights(self, variable_names, weights):
|
||||
self.variables.set_weights(dict(zip(variable_names, weights)))
|
||||
|
||||
def get_weights(self):
|
||||
weights = self.variables.get_weights()
|
||||
return list(weights.keys()), list(weights.values())
|
||||
|
||||
|
||||
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)
|
||||
@@ -1,18 +0,0 @@
|
||||
# This file is generated by `ray project create`.
|
||||
|
||||
# A unique identifier for the head node and workers of this cluster.
|
||||
cluster_name: ray-example-parameter-server
|
||||
|
||||
# The maximum number of workers nodes to launch in addition to the head
|
||||
# node. This takes precedence over min_workers. min_workers defaults to 0.
|
||||
max_workers: 1
|
||||
|
||||
# Cloud-provider specific configuration.
|
||||
provider:
|
||||
type: aws
|
||||
region: us-west-2
|
||||
availability_zone: us-west-2a
|
||||
|
||||
# How Ray will authenticate with newly launched nodes.
|
||||
auth:
|
||||
ssh_user: ubuntu
|
||||
@@ -1,41 +0,0 @@
|
||||
# This file is generated by `ray project create`.
|
||||
|
||||
name: ray-example-parameter-server
|
||||
|
||||
description: "A simple parameter server example implemented with ray actors"
|
||||
tags: ["ray-example", "parameter-server", "machine-learning"]
|
||||
documentation: https://ray.readthedocs.io/en/latest/auto_examples/plot_parameter_server.html
|
||||
|
||||
cluster:
|
||||
config: ray-project/cluster.yaml
|
||||
|
||||
environment:
|
||||
requirements: ray-project/requirements.txt
|
||||
|
||||
commands:
|
||||
- name: run-sync
|
||||
command: python sync_parameter_server.py --num-workers {{num-workers}}
|
||||
help: "Start the synchronous parameter server."
|
||||
params:
|
||||
- name: num-workers
|
||||
help: "Number of workers"
|
||||
default: 4
|
||||
type: int
|
||||
config:
|
||||
tmux: true
|
||||
|
||||
- name: run-async
|
||||
command: python async_parameter_server.py --num-workers {{num-workers}}
|
||||
help: "Start the asynchronous parameter server."
|
||||
params:
|
||||
- name: num-workers
|
||||
help: "Number of workers"
|
||||
default: 4
|
||||
type: int
|
||||
config:
|
||||
tmux: true
|
||||
|
||||
output_files: [
|
||||
# Save the logs from the latest run in snapshots.
|
||||
"/tmp/ray/session_latest/logs"
|
||||
]
|
||||
@@ -1,4 +0,0 @@
|
||||
ray[debug,rllib]
|
||||
torch
|
||||
torchvision
|
||||
filelock
|
||||
@@ -1,76 +0,0 @@
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import argparse
|
||||
import numpy as np
|
||||
|
||||
import ray
|
||||
import model
|
||||
|
||||
parser = argparse.ArgumentParser(description="Run the synchronous parameter "
|
||||
"server example.")
|
||||
parser.add_argument("--num-workers", default=4, type=int,
|
||||
help="The number of workers to use.")
|
||||
parser.add_argument("--redis-address", default=None, type=str,
|
||||
help="The Redis address of the cluster.")
|
||||
|
||||
|
||||
@ray.remote
|
||||
class ParameterServer(object):
|
||||
def __init__(self, learning_rate):
|
||||
self.net = model.SimpleCNN(learning_rate=learning_rate)
|
||||
|
||||
def apply_gradients(self, *gradients):
|
||||
self.net.apply_gradients(np.mean(gradients, axis=0))
|
||||
return self.net.variables.get_flat()
|
||||
|
||||
def get_weights(self):
|
||||
return self.net.variables.get_flat()
|
||||
|
||||
|
||||
@ray.remote
|
||||
class Worker(object):
|
||||
def __init__(self, worker_index, batch_size=50):
|
||||
self.worker_index = worker_index
|
||||
self.batch_size = batch_size
|
||||
self.mnist = model.download_mnist_retry(seed=worker_index)
|
||||
self.net = model.SimpleCNN()
|
||||
|
||||
def compute_gradients(self, weights):
|
||||
self.net.variables.set_flat(weights)
|
||||
xs, ys = self.mnist.train.next_batch(self.batch_size)
|
||||
return self.net.compute_gradients(xs, ys)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = parser.parse_args()
|
||||
|
||||
ray.init(redis_address=args.redis_address)
|
||||
|
||||
# Create a parameter server.
|
||||
net = model.SimpleCNN()
|
||||
ps = ParameterServer.remote(1e-4 * args.num_workers)
|
||||
|
||||
# Create workers.
|
||||
workers = [Worker.remote(worker_index)
|
||||
for worker_index in range(args.num_workers)]
|
||||
|
||||
# Download MNIST.
|
||||
mnist = model.download_mnist_retry()
|
||||
|
||||
i = 0
|
||||
current_weights = ps.get_weights.remote()
|
||||
while True:
|
||||
# Compute and apply gradients.
|
||||
gradients = [worker.compute_gradients.remote(current_weights)
|
||||
for worker in workers]
|
||||
current_weights = ps.apply_gradients.remote(*gradients)
|
||||
|
||||
if i % 10 == 0:
|
||||
# Evaluate the current model.
|
||||
net.variables.set_flat(ray.get(current_weights))
|
||||
test_xs, test_ys = mnist.test.next_batch(1000)
|
||||
accuracy = net.compute_accuracy(test_xs, test_ys)
|
||||
print("Iteration {}: accuracy is {}".format(i, accuracy))
|
||||
i += 1
|
||||
Reference in New Issue
Block a user