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[autoscaler] [tune] More doc fixes (#1560)
* Fri Feb 16 13:53:50 PST 2018 * Sat Feb 17 15:32:08 PST 2018 * Sat Feb 17 15:44:59 PST 2018 * fix * Sun Feb 18 14:46:24 PST 2018 * Sun Feb 18 14:46:37 PST 2018 * Sun Feb 18 14:55:52 PST 2018 * Sun Feb 18 15:14:32 PST 2018 * Wed Feb 21 17:34:17 PST 2018 * Sun Feb 25 17:51:17 PST 2018 * Sun Feb 25 22:18:40 PST 2018 * Wed Feb 28 13:19:05 PST 2018 * Wed Feb 28 13:22:13 PST 2018 * Wed Feb 28 13:33:29 PST 2018 * Wed Feb 28 13:35:33 PST 2018 * add ex * Fri Mar 2 12:50:17 PST 2018 * Fri Mar 2 12:54:31 PST 2018
This commit is contained in:
Eric Liang
@@ -13,6 +13,7 @@ max_workers: 2
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# usage. For example, if a cluster of 10 nodes is 100% busy and
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# target_utilization is 0.8, it would resize the cluster to 13. This fraction
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# can be decreased to increase the aggressiveness of upscaling.
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# This value must be less than 1.0 for scaling to happen.
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target_utilization_fraction: 0.8
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# If a node is idle for this many minutes, it will be removed.
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@@ -80,6 +81,10 @@ file_mounts: {
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# List of shell commands to run to set up nodes.
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setup_commands:
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# Consider uncommenting these if you run into dpkg locking issues
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# - sudo pkill -9 apt-get || true
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# - sudo pkill -9 dpkg || true
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# - sudo dpkg --configure -a
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# Install basics.
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- sudo apt-get update
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- sudo apt-get install -y cmake pkg-config build-essential autoconf curl libtool unzip python
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@@ -20,6 +20,7 @@ docker:
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# usage. For example, if a cluster of 10 nodes is 100% busy and
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# target_utilization is 0.8, it would resize the cluster to 13. This fraction
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# can be decreased to increase the aggressiveness of upscaling.
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# This value must be less than 1.0 for scaling to happen.
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target_utilization_fraction: 0.8
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# If a node is idle for this many minutes, it will be removed.
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@@ -84,7 +85,11 @@ setup_commands:
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# Note: if you're developing Ray, you probably want to create an AMI that
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# has your Ray repo pre-cloned. Then, you can replace the pip installs
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# below with a git checkout <your_sha> (and possibly a recompile).
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- most_recent() { echo pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/$(aws s3 ls s3://ray-wheels --recursive | grep $1 | sort -r | head -n 1 | awk '{print $4}'); } && $( most_recent "cp36-cp36m-manylinux1" ) || $( most_recent "cp35-cp35m-manylinux1" )
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- source activate tensorflow_p36 && most_recent() { echo pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/$(aws s3 ls s3://ray-wheels --recursive | grep $1 | sort -r | head -n 1 | awk '{print $4}'); } && $( most_recent "cp36-cp36m-manylinux1" ) || $( most_recent "cp35-cp35m-manylinux1" )
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# Consider uncommenting these if you also want to run apt-get commands during setup
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# - sudo pkill -9 apt-get || true
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# - sudo pkill -9 dpkg || true
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# - sudo dpkg --configure -a
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# Custom commands that will be run on the head node after common setup.
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head_setup_commands:
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+238
@@ -0,0 +1,238 @@
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#!/usr/bin/env python
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#
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""A deep MNIST classifier using convolutional layers.
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See extensive documentation at
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https://www.tensorflow.org/get_started/mnist/pros
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"""
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# Disable linter warnings to maintain consistency with tutorial.
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# pylint: disable=invalid-name
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# pylint: disable=g-bad-import-order
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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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import time
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import ray
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from ray.tune import grid_search, run_experiments, register_trainable, \
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Trainable, TrainingResult
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from ray.tune.hyperband import HyperBandScheduler
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from tensorflow.examples.tutorials.mnist import input_data
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import tensorflow as tf
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import numpy as np
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activation_fn = None # e.g. tf.nn.relu
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def setupCNN(x):
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"""setupCNN 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 = activation_fn(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 = activation_fn(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 = activation_fn(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(
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x, ksize=[1, 2, 2, 1], 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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class TrainMNIST(Trainable):
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"""Example MNIST trainable."""
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def _setup(self):
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global activation_fn
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self.timestep = 0
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# Import data
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for _ in range(10):
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try:
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self.mnist = input_data.read_data_sets(
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"/tmp/mnist_ray_demo", one_hot=True)
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break
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except Exception as e:
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print("Error loading data, retrying", e)
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time.sleep(5)
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assert self.mnist
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self.x = tf.placeholder(tf.float32, [None, 784])
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self.y_ = tf.placeholder(tf.float32, [None, 10])
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activation_fn = getattr(tf.nn, self.config['activation'])
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# Build the graph for the deep net
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y_conv, self.keep_prob = setupCNN(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=y_conv)
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cross_entropy = tf.reduce_mean(cross_entropy)
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with tf.name_scope('adam_optimizer'):
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train_step = tf.train.AdamOptimizer(
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self.config['learning_rate']).minimize(cross_entropy)
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self.train_step = train_step
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with tf.name_scope('accuracy'):
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correct_prediction = tf.equal(
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tf.argmax(y_conv, 1), 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()
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self.sess.run(tf.global_variables_initializer())
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self.iterations = 0
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self.saver = tf.train.Saver()
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def _train(self):
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for i in range(10):
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batch = self.mnist.train.next_batch(50)
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self.sess.run(
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self.train_step,
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feed_dict={
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self.x: batch[0], self.y_: batch[1], self.keep_prob: 0.5
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})
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batch = self.mnist.train.next_batch(50)
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train_accuracy = self.sess.run(
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self.accuracy,
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feed_dict={
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self.x: batch[0], self.y_: batch[1], self.keep_prob: 1.0
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})
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self.iterations += 1
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return TrainingResult(
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timesteps_this_iter=10, mean_accuracy=train_accuracy)
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def _save(self, checkpoint_dir):
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return self.saver.save(
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self.sess, checkpoint_dir + "/save", global_step=self.iterations)
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def _restore(self, path):
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return self.saver.restore(self.sess, path)
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# !!! Example of using the ray.tune Python API !!!
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--smoke-test', action='store_true', help='Finish quickly for testing')
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args, _ = parser.parse_known_args()
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register_trainable("my_class", TrainMNIST)
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mnist_spec = {
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'run': 'my_class',
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'stop': {
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'mean_accuracy': 0.99,
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'time_total_s': 600,
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},
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'config': {
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'learning_rate': lambda spec: 10 ** np.random.uniform(-5, -3),
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'activation': grid_search(['relu', 'elu', 'tanh']),
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},
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"repeat": 10,
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}
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if args.smoke_test:
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mnist_spec['stop']['training_iteration'] = 2
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mnist_spec['repeat'] = 2
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ray.init()
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hyperband = HyperBandScheduler(
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time_attr="timesteps_total", reward_attr="mean_accuracy",
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max_t=100)
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run_experiments(
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{'mnist_hyperband_test': mnist_spec}, scheduler=hyperband)
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