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[tune] Remove TF MNIST example + add TrialRunner hook to execut… (#5868)

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* remove test

* add trial runner

* remvoerestore

* Remove other mnist examples

* tunetest

* revert

* v1

* Revert "v1"

This reverts commit c8bddaf2db7a8270c43c02021cac0e75df15ed20.

* Revert "revert"

This reverts commit b58f56884a0c288d3a6f997d149ab4d496ddd7a3.

* errors

* format
This commit is contained in:
Richard Liaw 2019-10-13 20:33:56 -07:00 • committed by GitHub
parent 52e5c9b22d
commit 1650f7b174
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13 changed files with 26 additions and 788 deletions
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14
ci/jenkins_tests/run_tune_tests.sh
View file

@ -46,10 +46,6 @@ $SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE}
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/tune/tests/tutorial.py
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/tune/examples/tune_mnist_ray.py \
--smoke-test
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/tune/examples/pbt_example.py \
--smoke-test
@ -68,14 +64,6 @@ $SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE}
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
bash -c 'pip install tensorflow==1.15.0rc1 && python /ray/python/ray/tune/examples/async_hyperband_example.py --smoke-test'
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/tune/examples/tune_mnist_ray_hyperband.py \
--smoke-test
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/tune/examples/tune_mnist_async_hyperband.py \
--smoke-test
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/tune/examples/lightgbm_example.py
@ -126,7 +114,7 @@ $SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE}
--smoke-test
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/tune/examples/memnn_example.py \
python /ray/python/ray/tune/examples/pbt_memnn_example.py \
--smoke-test
# uncomment once statsmodels is updated.

14
doc/source/tune-examples.rst
View file

@ -16,7 +16,6 @@ General Examples
- `pbt_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_example.py>`__: Example of using a Trainable class with PopulationBasedTraining scheduler.
- `pbt_ppo_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_ppo_example.py>`__: Example of optimizing a distributed RLlib algorithm (PPO) with the PopulationBasedTraining scheduler.
- `logging_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__: Example of custom loggers and custom trial directory naming.
- `pbt_memnn_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_memnn_example.py>`__: Example of training a Memory NN on bAbI with Keras using PBT.
Search Algorithm Examples
-------------------------
@ -26,10 +25,11 @@ Search Algorithm Examples
- `Nevergrad example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/nevergrad_example.py>`__: Optimize a simple toy function with the gradient-free optimization package `Nevergrad <https://github.com/facebookresearch/nevergrad>`_ with 4 parallel workers.
- `Bayesian Optimization example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bayesopt_example.py>`__: Optimize a simple toy function using `Bayesian Optimization <https://github.com/fmfn/BayesianOptimization>`_ with 4 parallel workers.
Keras Examples
--------------
Tensorflow/Keras Examples
-------------------------
- `tune_mnist_keras <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_keras.py>`__: Converts the Keras MNIST example to use Tune with the function-based API and a Keras callback. Also shows how to easily convert something relying on argparse to use Tune.
- `pbt_memnn_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_memnn_example.py>`__: Example of training a Memory NN on bAbI with Keras using PBT.
PyTorch Examples
@ -39,14 +39,6 @@ PyTorch Examples
- `mnist_pytorch_trainable <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mnist_pytorch_trainable.py>`__: Converts the PyTorch MNIST example to use Tune with Trainable API. Also uses the HyperBandScheduler and checkpoints the model at the end.
TensorFlow Examples
-------------------
- `tune_mnist_ray <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_ray.py>`__: A basic example of tuning a TensorFlow model on MNIST using the Trainable class.
- `tune_mnist_ray_hyperband <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_ray_hyperband.py>`__: A basic example of tuning a TensorFlow model on MNIST using the Trainable class and the HyperBand scheduler.
- `tune_mnist_async_hyperband <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_async_hyperband.py>`__: Example of tuning a TensorFlow model on MNIST using AsyncHyperBand.
XGBoost Example
---------------

14
python/ray/tune/examples/README.rst
View file

@ -16,7 +16,6 @@ General Examples
- `pbt_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_example.py>`__: Example of using a Trainable class with PopulationBasedTraining scheduler.
- `pbt_ppo_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_ppo_example.py>`__: Example of optimizing a distributed RLlib algorithm (PPO) with the PopulationBasedTraining scheduler.
- `logging_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__: Example of custom loggers and custom trial directory naming.
- `pbt_memnn_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_memnn_example.py>`__: Example of training a Memory NN on bAbI with Keras using PBT.
Search Algorithm Examples
-------------------------
@ -26,10 +25,11 @@ Search Algorithm Examples
- `Nevergrad example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/nevergrad_example.py>`__: Optimize a simple toy function with the gradient-free optimization package `Nevergrad <https://github.com/facebookresearch/nevergrad>`_ with 4 parallel workers.
- `Bayesian Optimization example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bayesopt_example.py>`__: Optimize a simple toy function using `Bayesian Optimization <https://github.com/fmfn/BayesianOptimization>`_ with 4 parallel workers.
Keras Examples
--------------
Tensorflow/Keras Examples
-------------------------
- `tune_mnist_keras <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_keras.py>`__: Converts the Keras MNIST example to use Tune with the function-based API and a Keras callback. Also shows how to easily convert something relying on argparse to use Tune.
- `pbt_memnn_example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_memnn_example.py>`__: Example of training a Memory NN on bAbI with Keras using PBT.
PyTorch Examples
@ -39,14 +39,6 @@ PyTorch Examples
- `mnist_pytorch_trainable <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mnist_pytorch_trainable.py>`__: Converts the PyTorch MNIST example to use Tune with Trainable API. Also uses the HyperBandScheduler and checkpoints the model at the end.
TensorFlow Examples
-------------------
- `tune_mnist_ray <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_ray.py>`__: A basic example of tuning a TensorFlow model on MNIST using the Trainable class.
- `tune_mnist_ray_hyperband <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_ray_hyperband.py>`__: A basic example of tuning a TensorFlow model on MNIST using the Trainable class and the HyperBand scheduler.
- `tune_mnist_async_hyperband <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_async_hyperband.py>`__: Example of tuning a TensorFlow model on MNIST using AsyncHyperBand.
XGBoost Example
---------------

18
python/ray/tune/examples/pbt_memnn_example.py
View file

@ -5,15 +5,15 @@ References Keras and is based off of https://keras.io/examples/babi_memnn/.
from __future__ import print_function
from tensorflow.python.keras.models import Sequential, Model, load_model
from tensorflow.python.keras.layers.embeddings import Embedding
from tensorflow.python.keras.layers import (Input, Activation, Dense, Permute,
Dropout)
from tensorflow.python.keras.layers import add, dot, concatenate
from tensorflow.python.keras.layers import LSTM
from tensorflow.python.keras.optimizers import RMSprop
from tensorflow.python.keras.utils.data_utils import get_file
from tensorflow.python.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import (Input, Activation, Dense, Permute,
Dropout)
from tensorflow.keras.layers import add, dot, concatenate
from tensorflow.keras.layers import LSTM
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.utils import get_file
from tensorflow.keras.preprocessing.sequence import pad_sequences
from ray.tune import Trainable
import argparse
import tarfile

247
python/ray/tune/examples/tune_mnist_async_hyperband.py
View file

@ -1,247 +0,0 @@
#!/usr/bin/env python
#
# 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.
See extensive documentation at
https://www.tensorflow.org/get_started/mnist/pros
"""
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import tempfile
import time
import ray
from ray.tune import grid_search, run
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
FLAGS = None
status_reporter = None # used to report training status back to Ray
activation_fn = None # e.g. tf.nn.relu
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 = activation_fn(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 = activation_fn(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 = activation_fn(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 main(_):
# Import data
for _ in range(10):
try:
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
break
except Exception:
time.sleep(5)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
# Build the graph for the deep net
y_conv, keep_prob = deepnn(x)
with tf.name_scope("loss"):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope("adam_optimizer"):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
graph_location = tempfile.mkdtemp()
print("Saving graph to: %s" % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 10 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 1.0
})
# !!! Report status to ray.tune !!!
if status_reporter:
status_reporter(
timesteps_total=i, mean_accuracy=train_accuracy)
print("step %d, training accuracy %g" % (i, train_accuracy))
train_step.run(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
})
print("test accuracy %g" % accuracy.eval(feed_dict={
x: mnist.test.images,
y_: mnist.test.labels,
keep_prob: 1.0
}))
# !!! Entrypoint for ray.tune !!!
def train(config={"activation": "relu"}, reporter=None):
global FLAGS, status_reporter, activation_fn
status_reporter = reporter
activation_fn = getattr(tf.nn, config["activation"])
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_dir",
type=str,
default="/tmp/tensorflow/mnist/input_data",
help="Directory for storing input data")
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
# !!! Example of using the ray.tune Python API !!!
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
mnist_spec = {
"num_samples": 10,
"stop": {
"mean_accuracy": 0.99,
"timesteps_total": 600,
},
"config": {
"activation": grid_search(["relu", "elu", "tanh"]),
},
}
if args.smoke_test:
mnist_spec["stop"]["training_iteration"] = 2
mnist_spec["num_samples"] = 1
ray.init()
from ray.tune.schedulers import AsyncHyperBandScheduler
run(train,
name="tune_mnist_test",
scheduler=AsyncHyperBandScheduler(
time_attr="timesteps_total",
metric="mean_accuracy",
mode="max",
max_t=600,
),
**mnist_spec)

241
python/ray/tune/examples/tune_mnist_ray.py
View file

@ -1,241 +0,0 @@
#!/usr/bin/env python
#
# 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.
See extensive documentation at
https://www.tensorflow.org/get_started/mnist/pros
"""
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import tempfile
import time
import ray
from ray import tune
from ray.tune import grid_search, register_trainable
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import tensorflow as tf
FLAGS = None
status_reporter = None # used to report training status back to Ray
activation_fn = tf.nn.relu # e.g. tf.nn.relu
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 = activation_fn(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 = activation_fn(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 = activation_fn(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 main(_):
# Import data
for _ in range(10):
try:
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
break
except Exception:
time.sleep(5)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
# Build the graph for the deep net
y_conv, keep_prob = deepnn(x)
with tf.name_scope("loss"):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope("adam_optimizer"):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
graph_location = tempfile.mkdtemp()
print("Saving graph to: %s" % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 10 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 1.0
})
# !!! Report status to ray.tune !!!
if status_reporter:
status_reporter(
timesteps_total=i, mean_accuracy=train_accuracy)
print("step %d, training accuracy %g" % (i, train_accuracy))
train_step.run(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
})
print("test accuracy %g" % accuracy.eval(feed_dict={
x: mnist.test.images,
y_: mnist.test.labels,
keep_prob: 1.0
}))
# !!! Entrypoint for ray.tune !!!
def train(config={"activation": "relu"}, reporter=None):
global FLAGS, status_reporter, activation_fn
status_reporter = reporter
activation_fn = getattr(tf.nn, config["activation"])
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_dir",
type=str,
default="/tmp/tensorflow/mnist/input_data",
help="Directory for storing input data")
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
# !!! Example of using the ray.tune Python API !!!
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
register_trainable("train_mnist", train)
mnist_spec = {
"stop": {
"mean_accuracy": 0.99,
"time_total_s": 600,
},
"config": {
"activation": grid_search(["relu", "elu", "tanh"]),
# You can pass any serializable object as well
"foo": grid_search([np.array([1, 2]),
np.array([2, 3])]),
},
}
if args.smoke_test:
mnist_spec["stop"]["training_iteration"] = 2
ray.init()
tune.run("train_mnist", name="tune_mnist_test", **mnist_spec)

241
python/ray/tune/examples/tune_mnist_ray_hyperband.py
View file

@ -1,241 +0,0 @@
#!/usr/bin/env python
#
# 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.
See extensive documentation at
https://www.tensorflow.org/get_started/mnist/pros
"""
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import time
import ray
from ray import tune
from ray.tune import Trainable, sample_from
from ray.tune.schedulers import HyperBandScheduler
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
activation_fn = None # e.g. tf.nn.relu
def setupCNN(x):
"""setupCNN 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 = activation_fn(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 = activation_fn(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 = activation_fn(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)
class TrainMNIST(Trainable):
"""Example MNIST trainable."""
def _setup(self, config):
global activation_fn
self.timestep = 0
# Import data
for _ in range(10):
try:
self.mnist = input_data.read_data_sets(
"/tmp/mnist_ray_demo", one_hot=True)
break
except Exception as e:
print("Error loading data, retrying", e)
time.sleep(5)
assert self.mnist
self.x = tf.placeholder(tf.float32, [None, 784])
self.y_ = tf.placeholder(tf.float32, [None, 10])
activation_fn = getattr(tf.nn, config.get("activation", "relu"))
# Build the graph for the deep net
y_conv, self.keep_prob = setupCNN(self.x)
with tf.name_scope("loss"):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=self.y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope("adam_optimizer"):
train_step = tf.train.AdamOptimizer(
config.get("learning_rate", 1e-4)).minimize(cross_entropy)
self.train_step = train_step
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(
tf.argmax(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()
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(save_relative_paths=True)
def _train(self):
for i in range(10):
batch = self.mnist.train.next_batch(50)
self.sess.run(
self.train_step,
feed_dict={
self.x: batch[0],
self.y_: batch[1],
self.keep_prob: 0.5
})
batch = self.mnist.train.next_batch(50)
train_accuracy = self.sess.run(
self.accuracy,
feed_dict={
self.x: batch[0],
self.y_: batch[1],
self.keep_prob: 1.0
})
return {"mean_accuracy": train_accuracy}
def _save(self, checkpoint_dir):
path = self.saver.save(self.sess, os.path.join(checkpoint_dir, "save"))
return path
def _restore(self, checkpoint_path):
self.saver.restore(self.sess, checkpoint_path)
# !!! Example of using the ray.tune Python API !!!
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
mnist_spec = {
"stop": {
"mean_accuracy": 0.99,
"time_total_s": 600,
},
"config": {
"learning_rate": sample_from(
lambda spec: 10**np.random.uniform(-5, -3)),
"activation": "relu",
},
"num_samples": 10,
}
if args.smoke_test:
mnist_spec["stop"]["training_iteration"] = 20
mnist_spec["num_samples"] = 1
ray.init()
hyperband = HyperBandScheduler(
time_attr="training_iteration",
metric="mean_accuracy",
mode="max",
max_t=10)
tune.run(
TrainMNIST,
name="mnist_hyperband_test",
scheduler=hyperband,
**mnist_spec)

2
python/ray/tune/ray_trial_executor.py
View file

@ -523,7 +523,7 @@ class RayTrialExecutor(TrialExecutor):
else:
return "? CPUs, ? GPUs"
def on_step_begin(self):
def on_step_begin(self, trial_runner):
"""Before step() called, update the available resources."""
self._update_avail_resources()

6
python/ray/tune/result.py
View file

@ -39,20 +39,20 @@ MEAN_ACCURACY = "mean_accuracy"
# Number of episodes in this iteration.
EPISODES_THIS_ITER = "episodes_this_iter"
# (Optional/Auto-filled) Accumulated number of episodes for this experiment.
# (Optional/Auto-filled) Accumulated number of episodes for this trial.
EPISODES_TOTAL = "episodes_total"
# Number of timesteps in this iteration.
TIMESTEPS_THIS_ITER = "timesteps_this_iter"
# (Auto-filled) Accumulated number of timesteps for this entire experiment.
# (Auto-filled) Accumulated number of timesteps for this entire trial.
TIMESTEPS_TOTAL = "timesteps_total"
# (Auto-filled) Time in seconds this iteration took to run.
# This may be overriden to override the system-computed time difference.
TIME_THIS_ITER_S = "time_this_iter_s"
# (Auto-filled) Accumulated time in seconds for this entire experiment.
# (Auto-filled) Accumulated time in seconds for this entire trial.
TIME_TOTAL_S = "time_total_s"
# (Auto-filled) The index of this training iteration.

4
python/ray/tune/tests/test_trial_runner.py
View file

@ -2081,12 +2081,12 @@ class TrialRunnerTest(unittest.TestCase):
ray.init(num_cpus=4, num_gpus=2)
runner = TrialRunner()
def on_step_begin(self):
def on_step_begin(self, trialrunner):
self._update_avail_resources()
cnt = self.pre_step if hasattr(self, "pre_step") else 0
setattr(self, "pre_step", cnt + 1)
def on_step_end(self):
def on_step_end(self, trialrunner):
cnt = self.pre_step if hasattr(self, "post_step") else 0
setattr(self, "post_step", 1 + cnt)

5
python/ray/tune/tests/test_tune_restore.py
View file

@ -71,11 +71,6 @@ class TuneExampleTest(unittest.TestCase):
ray.shutdown()
_register_all()
def testTensorFlowMNIST(self):
from ray.tune.examples.tune_mnist_ray_hyperband import TrainMNIST
validate_save_restore(TrainMNIST)
validate_save_restore(TrainMNIST, use_object_store=True)
def testPBTKeras(self):
from ray.tune.examples.pbt_tune_cifar10_with_keras import Cifar10Model
from tensorflow.python.keras.datasets import cifar10

4
python/ray/tune/trial_executor.py
View file

@ -142,11 +142,11 @@ class TrialExecutor(object):
raise NotImplementedError("Subclasses of TrialExecutor must provide "
"get_running_trials() method")
def on_step_begin(self):
def on_step_begin(self, trial_runner):
"""A hook called before running one step of the trial event loop."""
pass
def on_step_end(self):
def on_step_end(self, trial_runner):
"""A hook called after running one step of the trial event loop."""
pass

4
python/ray/tune/trial_runner.py
View file

@ -326,7 +326,7 @@ class TrialRunner(object):
if self.is_finished():
raise TuneError("Called step when all trials finished?")
with warn_if_slow("on_step_begin"):
self.trial_executor.on_step_begin()
self.trial_executor.on_step_begin(self)
next_trial = self._get_next_trial() # blocking
if next_trial is not None:
with warn_if_slow("start_trial"):
@ -367,7 +367,7 @@ class TrialRunner(object):
if self.is_finished():
self._server.shutdown()
with warn_if_slow("on_step_end"):
self.trial_executor.on_step_end()
self.trial_executor.on_step_end(self)
def get_trial(self, tid):
trial = [t for t in self._trials if t.trial_id == tid]