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Commit 2b4a7d1f authored by Lee's avatar Lee Committed by chicm-ms
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Fix tensorflow MNIST dataset download bug in multiple trials (#815) 

* Fix tensorflow MNIST dataset download bug in multiple trials

* Modify the maxTrialNum and trialConcurrency in config
parent 2fa77bcc
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Showing with 369 additions and 274 deletions
examples/trials/mnist-annotation/mnist.py
View file @ 2b4a7d1f
......@@ -4,8 +4,9 @@ import argparse
import logging
import math
import tempfile
import tensorflow as tf
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
FLAGS = None
......@@ -152,12 +153,21 @@ def bias_variable(shape):
return tf.Variable(initial)
def download_mnist_retry(data_dir, max_num_retries=20):
"""Try to download mnist dataset and avoid errors"""
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(data_dir, one_hot=True)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
def main(params):
'''
Main function, build mnist network, run and send result to NNI.
'''
# Import data
mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
mnist = download_mnist_retry(params['data_dir'])
print('Mnist download data done.')
logger.debug('Mnist download data done.')
......
examples/trials/mnist-cascading-search-space/mnist.py
View file @ 2b4a7d1f
'''
mnist.py is an example to show: how to use iterative search space to tune architecture network for mnist.
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import, division, print_function
import argparse
import codecs
import json
import logging
import math
import sys
import tempfile
import tensorflow as tf
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import nni
logger = logging.getLogger('mnist_cascading_search_space')
FLAGS = None
......@@ -95,10 +89,19 @@ class MnistNetwork(object):
child_accuracy = tf.equal(tf.argmax(output_layer, 1), tf.argmax(self.y, 1))
self.accuracy = tf.reduce_mean(tf.cast(child_accuracy, "float")) # add a reduce_mean
def download_mnist_retry(data_dir, max_num_retries=20):
"""Try to download mnist dataset and avoid errors"""
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(data_dir, one_hot=True)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
def main(params):
# Import data
mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
mnist = download_mnist_retry(params['data_dir'])
# Create the model
# Build the graph for the deep net
mnist_network = MnistNetwork(params)
......@@ -117,15 +120,15 @@ def main(params):
for i in range(params['batch_num']):
batch = mnist.train.next_batch(params['batch_size'])
mnist_network.train_step.run(feed_dict={mnist_network.x: batch[0], mnist_network.y: batch[1]})
if i % 100 == 0:
train_accuracy = mnist_network.accuracy.eval(feed_dict={
mnist_network.x: batch[0], mnist_network.y: batch[1]})
print('step %d, training accuracy %g' % (i, train_accuracy))
test_acc = mnist_network.accuracy.eval(feed_dict={
mnist_network.x: mnist.test.images, mnist_network.y: mnist.test.labels})
nni.report_final_result(test_acc)
def generate_defualt_params():
......
examples/trials/mnist-distributed/dist_mnist.py
View file @ 2b4a7d1f
......@@ -13,10 +13,10 @@
# limitations under the License.
# ==============================================================================
#
# NNI (https://github.com/Microsoft/nni) modified this code to show how to
#
# NNI (https://github.com/Microsoft/nni) modified this code to show how to
# integrate distributed tensorflow training with NNI SDK
#
#
"""Distributed MNIST training and validation, with model replicas.
A simple softmax model with one hidden layer is defined. The parameters
......@@ -54,19 +54,22 @@ import nni
flags = tf.app.flags
flags.DEFINE_string("data_dir", "/tmp/mnist-data",
"Directory for storing mnist data")
flags.DEFINE_boolean("download_only", False,
"Only perform downloading of data; Do not proceed to "
"session preparation, model definition or training")
flags.DEFINE_integer("task_index", None,
"Worker task index, should be >= 0. task_index=0 is "
"the master worker task the performs the variable "
"initialization ")
flags.DEFINE_integer("num_gpus", 1, "Total number of gpus for each machine."
"If you don't use GPU, please set it to '0'")
flags.DEFINE_integer("replicas_to_aggregate", None,
"Number of replicas to aggregate before parameter update"
"is applied (For sync_replicas mode only; default: "
"num_workers)")
flags.DEFINE_boolean(
"download_only", False,
"Only perform downloading of data; Do not proceed to "
"session preparation, model definition or training")
flags.DEFINE_integer(
"task_index", None, "Worker task index, should be >= 0. task_index=0 is "
"the master worker task the performs the variable "
"initialization ")
flags.DEFINE_integer(
"num_gpus", 1, "Total number of gpus for each machine."
"If you don't use GPU, please set it to '0'")
flags.DEFINE_integer(
"replicas_to_aggregate", None,
"Number of replicas to aggregate before parameter update"
"is applied (For sync_replicas mode only; default: "
"num_workers)")
flags.DEFINE_integer("train_steps", 20000,
"Number of (global) training steps to perform")
flags.DEFINE_boolean(
......@@ -96,237 +99,256 @@ IMAGE_PIXELS = 28
# {'cluster': cluster,
# 'task': {'type': 'worker', 'index': 1}})
def generate_default_params():
'''
Generate default hyper parameters
'''
return {
'learning_rate': 0.01,
'batch_size': 100,
'hidden_units': 100,
}
'''
Generate default hyper parameters
'''
return {
'learning_rate': 0.01,
'batch_size': 100,
'hidden_units': 100,
}
def download_mnist_retry(data_dir, max_num_retries=20):
"""Try to download mnist dataset and avoid errors"""
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(data_dir, one_hot=True)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
def main(unused_argv):
# Receive NNI hyper parameter and update it onto default params
RECEIVED_PARAMS = nni.get_next_parameter()
PARAMS = generate_default_params()
PARAMS.update(RECEIVED_PARAMS)
# Parse environment variable TF_CONFIG to get job_name and task_index
# If not explicitly specified in the constructor and the TF_CONFIG
# environment variable is present, load cluster_spec from TF_CONFIG.
tf_config = json.loads(os.environ.get('TF_CONFIG') or '{}')
task_config = tf_config.get('task', {})
task_type = task_config.get('type')
task_index = task_config.get('index')
FLAGS.job_name = task_type
FLAGS.task_index = task_index
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
if FLAGS.download_only:
sys.exit(0)
if FLAGS.job_name is None or FLAGS.job_name == "":
raise ValueError("Must specify an explicit `job_name`")
if FLAGS.task_index is None or FLAGS.task_index == "":
raise ValueError("Must specify an explicit `task_index`")
print("job name = %s" % FLAGS.job_name)
print("task index = %d" % FLAGS.task_index)
cluster_config = tf_config.get('cluster', {})
ps_hosts = cluster_config.get('ps')
worker_hosts = cluster_config.get('worker')
ps_hosts_str = ','.join(ps_hosts)
worker_hosts_str = ','.join(worker_hosts)
FLAGS.ps_hosts = ps_hosts_str
FLAGS.worker_hosts = worker_hosts_str
# Construct the cluster and start the server
ps_spec = FLAGS.ps_hosts.split(",")
worker_spec = FLAGS.worker_hosts.split(",")
# Get the number of workers.
num_workers = len(worker_spec)
cluster = tf.train.ClusterSpec({"ps": ps_spec, "worker": worker_spec})
if not FLAGS.existing_servers:
# Not using existing servers. Create an in-process server.
server = tf.train.Server(
cluster, job_name=FLAGS.job_name, task_index=FLAGS.task_index)
if FLAGS.job_name == "ps":
server.join()
is_chief = (FLAGS.task_index == 0)
if FLAGS.num_gpus > 0:
# Avoid gpu allocation conflict: now allocate task_num -> #gpu
# for each worker in the corresponding machine
gpu = (FLAGS.task_index % FLAGS.num_gpus)
worker_device = "/job:worker/task:%d/gpu:%d" % (FLAGS.task_index, gpu)
elif FLAGS.num_gpus == 0:
# Just allocate the CPU to worker server
cpu = 0
worker_device = "/job:worker/task:%d/cpu:%d" % (FLAGS.task_index, cpu)
# The device setter will automatically place Variables ops on separate
# parameter servers (ps). The non-Variable ops will be placed on the workers.
# The ps use CPU and workers use corresponding GPU
with tf.device(
tf.train.replica_device_setter(
worker_device=worker_device,
ps_device="/job:ps/cpu:0",
cluster=cluster)):
global_step = tf.Variable(0, name="global_step", trainable=False)
# Variables of the hidden layer
hid_w = tf.Variable(
tf.truncated_normal(
[IMAGE_PIXELS * IMAGE_PIXELS, PARAMS['hidden_units']],
stddev=1.0 / IMAGE_PIXELS),
name="hid_w")
hid_b = tf.Variable(tf.zeros([PARAMS['hidden_units']]), name="hid_b")
# Variables of the softmax layer
sm_w = tf.Variable(
tf.truncated_normal(
[PARAMS['hidden_units'], 10],
stddev=1.0 / math.sqrt(PARAMS['hidden_units'])),
name="sm_w")
sm_b = tf.Variable(tf.zeros([10]), name="sm_b")
# Ops: located on the worker specified with FLAGS.task_index
x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS])
y_ = tf.placeholder(tf.float32, [None, 10])
hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b)
hid = tf.nn.relu(hid_lin)
y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b))
cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
opt = tf.train.AdamOptimizer(PARAMS['learning_rate'])
if FLAGS.sync_replicas:
if FLAGS.replicas_to_aggregate is None:
replicas_to_aggregate = num_workers
else:
replicas_to_aggregate = FLAGS.replicas_to_aggregate
opt = tf.train.SyncReplicasOptimizer(
opt,
replicas_to_aggregate=replicas_to_aggregate,
total_num_replicas=num_workers,
name="mnist_sync_replicas")
train_step = opt.minimize(cross_entropy, global_step=global_step)
if FLAGS.sync_replicas:
local_init_op = opt.local_step_init_op
if is_chief:
local_init_op = opt.chief_init_op
ready_for_local_init_op = opt.ready_for_local_init_op
# Initial token and chief queue runners required by the sync_replicas mode
chief_queue_runner = opt.get_chief_queue_runner()
sync_init_op = opt.get_init_tokens_op()
init_op = tf.global_variables_initializer()
train_dir = tempfile.mkdtemp()
if FLAGS.sync_replicas:
sv = tf.train.Supervisor(
is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
local_init_op=local_init_op,
ready_for_local_init_op=ready_for_local_init_op,
recovery_wait_secs=1,
global_step=global_step)
else:
sv = tf.train.Supervisor(
is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
recovery_wait_secs=1,
global_step=global_step)
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=["/job:ps",
"/job:worker/task:%d" % FLAGS.task_index])
# The chief worker (task_index==0) session will prepare the session,
# while the remaining workers will wait for the preparation to complete.
if is_chief:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." %
FLAGS.task_index)
if FLAGS.existing_servers:
server_grpc_url = "grpc://" + worker_spec[FLAGS.task_index]
print("Using existing server at: %s" % server_grpc_url)
sess = sv.prepare_or_wait_for_session(server_grpc_url, config=sess_config)
else:
sess = sv.prepare_or_wait_for_session(server.target, config=sess_config)
print("Worker %d: Session initialization complete." % FLAGS.task_index)
if FLAGS.sync_replicas and is_chief:
# Chief worker will start the chief queue runner and call the init op.
sess.run(sync_init_op)
sv.start_queue_runners(sess, [chief_queue_runner])
# Perform training
time_begin = time.time()
print("Training begins @ %f" % time_begin)
local_step = 0
while True:
# Training feed
batch_xs, batch_ys = mnist.train.next_batch(PARAMS['batch_size'])
train_feed = {x: batch_xs, y_: batch_ys}
_, step = sess.run([train_step, global_step], feed_dict=train_feed)
local_step += 1
now = time.time()
print("%f: Worker %d: training step %d done (global step: %d)" %
(now, FLAGS.task_index, local_step, step))
if step > 0 and step % 5000 == 0 and is_chief:
# Receive NNI hyper parameter and update it onto default params
RECEIVED_PARAMS = nni.get_next_parameter()
PARAMS = generate_default_params()
PARAMS.update(RECEIVED_PARAMS)
# Parse environment variable TF_CONFIG to get job_name and task_index
# If not explicitly specified in the constructor and the TF_CONFIG
# environment variable is present, load cluster_spec from TF_CONFIG.
tf_config = json.loads(os.environ.get('TF_CONFIG') or '{}')
task_config = tf_config.get('task', {})
task_type = task_config.get('type')
task_index = task_config.get('index')
FLAGS.job_name = task_type
FLAGS.task_index = task_index
mnist = download_mnist_retry(FLAGS.data_dir)
if FLAGS.download_only:
sys.exit(0)
if FLAGS.job_name is None or FLAGS.job_name == "":
raise ValueError("Must specify an explicit `job_name`")
if FLAGS.task_index is None or FLAGS.task_index == "":
raise ValueError("Must specify an explicit `task_index`")
print("job name = %s" % FLAGS.job_name)
print("task index = %d" % FLAGS.task_index)
cluster_config = tf_config.get('cluster', {})
ps_hosts = cluster_config.get('ps')
worker_hosts = cluster_config.get('worker')
ps_hosts_str = ','.join(ps_hosts)
worker_hosts_str = ','.join(worker_hosts)
FLAGS.ps_hosts = ps_hosts_str
FLAGS.worker_hosts = worker_hosts_str
# Construct the cluster and start the server
ps_spec = FLAGS.ps_hosts.split(",")
worker_spec = FLAGS.worker_hosts.split(",")
# Get the number of workers.
num_workers = len(worker_spec)
cluster = tf.train.ClusterSpec({"ps": ps_spec, "worker": worker_spec})
if not FLAGS.existing_servers:
# Not using existing servers. Create an in-process server.
server = tf.train.Server(
cluster, job_name=FLAGS.job_name, task_index=FLAGS.task_index)
if FLAGS.job_name == "ps":
server.join()
is_chief = (FLAGS.task_index == 0)
if FLAGS.num_gpus > 0:
# Avoid gpu allocation conflict: now allocate task_num -> #gpu
# for each worker in the corresponding machine
gpu = (FLAGS.task_index % FLAGS.num_gpus)
worker_device = "/job:worker/task:%d/gpu:%d" % (FLAGS.task_index, gpu)
elif FLAGS.num_gpus == 0:
# Just allocate the CPU to worker server
cpu = 0
worker_device = "/job:worker/task:%d/cpu:%d" % (FLAGS.task_index, cpu)
# The device setter will automatically place Variables ops on separate
# parameter servers (ps). The non-Variable ops will be placed on the workers.
# The ps use CPU and workers use corresponding GPU
with tf.device(
tf.train.replica_device_setter(
worker_device=worker_device,
ps_device="/job:ps/cpu:0",
cluster=cluster)):
global_step = tf.Variable(0, name="global_step", trainable=False)
# Variables of the hidden layer
hid_w = tf.Variable(
tf.truncated_normal(
[IMAGE_PIXELS * IMAGE_PIXELS, PARAMS['hidden_units']],
stddev=1.0 / IMAGE_PIXELS),
name="hid_w")
hid_b = tf.Variable(tf.zeros([PARAMS['hidden_units']]), name="hid_b")
# Variables of the softmax layer
sm_w = tf.Variable(
tf.truncated_normal(
[PARAMS['hidden_units'], 10],
stddev=1.0 / math.sqrt(PARAMS['hidden_units'])),
name="sm_w")
sm_b = tf.Variable(tf.zeros([10]), name="sm_b")
# Ops: located on the worker specified with FLAGS.task_index
x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS])
y_ = tf.placeholder(tf.float32, [None, 10])
hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b)
hid = tf.nn.relu(hid_lin)
y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b))
cross_entropy = -tf.reduce_sum(
y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
opt = tf.train.AdamOptimizer(PARAMS['learning_rate'])
if FLAGS.sync_replicas:
if FLAGS.replicas_to_aggregate is None:
replicas_to_aggregate = num_workers
else:
replicas_to_aggregate = FLAGS.replicas_to_aggregate
opt = tf.train.SyncReplicasOptimizer(
opt,
replicas_to_aggregate=replicas_to_aggregate,
total_num_replicas=num_workers,
name="mnist_sync_replicas")
train_step = opt.minimize(cross_entropy, global_step=global_step)
if FLAGS.sync_replicas:
local_init_op = opt.local_step_init_op
if is_chief:
local_init_op = opt.chief_init_op
ready_for_local_init_op = opt.ready_for_local_init_op
# Initial token and chief queue runners required by the sync_replicas mode
chief_queue_runner = opt.get_chief_queue_runner()
sync_init_op = opt.get_init_tokens_op()
init_op = tf.global_variables_initializer()
train_dir = tempfile.mkdtemp()
if FLAGS.sync_replicas:
sv = tf.train.Supervisor(
is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
local_init_op=local_init_op,
ready_for_local_init_op=ready_for_local_init_op,
recovery_wait_secs=1,
global_step=global_step)
else:
sv = tf.train.Supervisor(
is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
recovery_wait_secs=1,
global_step=global_step)
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=[
"/job:ps", "/job:worker/task:%d" % FLAGS.task_index
])
# The chief worker (task_index==0) session will prepare the session,
# while the remaining workers will wait for the preparation to complete.
if is_chief:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." %
FLAGS.task_index)
if FLAGS.existing_servers:
server_grpc_url = "grpc://" + worker_spec[FLAGS.task_index]
print("Using existing server at: %s" % server_grpc_url)
sess = sv.prepare_or_wait_for_session(
server_grpc_url, config=sess_config)
else:
sess = sv.prepare_or_wait_for_session(
server.target, config=sess_config)
print("Worker %d: Session initialization complete." % FLAGS.task_index)
if FLAGS.sync_replicas and is_chief:
# Chief worker will start the chief queue runner and call the init op.
sess.run(sync_init_op)
sv.start_queue_runners(sess, [chief_queue_runner])
# Perform training
time_begin = time.time()
print("Training begins @ %f" % time_begin)
local_step = 0
while True:
# Training feed
batch_xs, batch_ys = mnist.train.next_batch(PARAMS['batch_size'])
train_feed = {x: batch_xs, y_: batch_ys}
_, step = sess.run([train_step, global_step], feed_dict=train_feed)
local_step += 1
now = time.time()
print("%f: Worker %d: training step %d done (global step: %d)" %
(now, FLAGS.task_index, local_step, step))
if step > 0 and step % 5000 == 0 and is_chief:
val_feed = {
x: mnist.validation.images,
y_: mnist.validation.labels
}
interim_val_xent = sess.run(cross_entropy, feed_dict=val_feed)
print(
"After %d training step(s), validation cross entropy = %g"
% (step, interim_val_xent))
# Only chief worker can report intermediate metrics
nni.report_intermediate_result(interim_val_xent)
if step >= FLAGS.train_steps:
break
time_end = time.time()
print("Training ends @ %f" % time_end)
training_time = time_end - time_begin
print("Training elapsed time: %f s" % training_time)
# Validation feed
val_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
interim_val_xent = sess.run(cross_entropy, feed_dict=val_feed)
print("After %d training step(s), validation cross entropy = %g" % (step, interim_val_xent))
# Only chief worker can report intermediate metrics
nni.report_intermediate_result(interim_val_xent)
if step >= FLAGS.train_steps:
break
time_end = time.time()
print("Training ends @ %f" % time_end)
training_time = time_end - time_begin
print("Training elapsed time: %f s" % training_time)
# Validation feed
val_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
val_xent = sess.run(cross_entropy, feed_dict=val_feed)
print("After %d training step(s), validation cross entropy = %g" %
(FLAGS.train_steps, val_xent))
val_xent = sess.run(cross_entropy, feed_dict=val_feed)
print("After %d training step(s), validation cross entropy = %g" %
(FLAGS.train_steps, val_xent))
# Only chief worker can report final metrics
if is_chief:
nni.report_final_result(val_xent)
# Only chief worker can report final metrics
if is_chief:
nni.report_final_result(val_xent)
if __name__ == "__main__":
tf.app.run()
tf.app.run()
examples/trials/mnist-hyperband/mnist.py
View file @ 2b4a7d1f
......@@ -3,8 +3,9 @@
import logging
import math
import tempfile
import tensorflow as tf
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import nni
......@@ -142,13 +143,21 @@ def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def download_mnist_retry(data_dir, max_num_retries=20):
"""Try to download mnist dataset and avoid errors"""
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(data_dir, one_hot=True)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
def main(params):
'''
Main function, build mnist network, run and send result to NNI.
'''
# Import data
mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
mnist = download_mnist_retry(params['data_dir'])
print('Mnist download data done.')
logger.debug('Mnist download data done.')
......
examples/trials/mnist/mnist.py
View file @ 2b4a7d1f
......@@ -4,8 +4,9 @@ import argparse
import logging
import math
import tempfile
import tensorflow as tf
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import nni
......@@ -143,13 +144,21 @@ def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def download_mnist_retry(data_dir, max_num_retries=20):
"""Try to download mnist dataset and avoid errors"""
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(data_dir, one_hot=True)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
def main(params):
'''
Main function, build mnist network, run and send result to NNI.
'''
# Import data
mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
mnist = download_mnist_retry(params['data_dir'])
print('Mnist download data done.')
logger.debug('Mnist download data done.')
......
examples/trials/mnist/mnist_before.py
View file @ 2b4a7d1f
......@@ -3,8 +3,9 @@ import argparse
import logging
import math
import tempfile
import tensorflow as tf
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
FLAGS = None
......@@ -143,13 +144,21 @@ def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def download_mnist_retry(data_dir, max_num_retries=20):
"""Try to download mnist dataset and avoid errors"""
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(data_dir, one_hot=True)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
def main(params):
'''
Main function, build mnist network, run and send result to NNI.
'''
# Import data
mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
mnist = download_mnist_retry(params['data_dir'])
print('Mnist download data done.')
logger.debug('Mnist download data done.')
......
test/config_test/examples/mnist-annotation.test.yml
View file @ 2b4a7d1f
authorName: nni
experimentName: default_test
maxExecDuration: 5m
maxTrialNum: 2
trialConcurrency: 1
maxTrialNum: 4
trialConcurrency: 2
tuner:
builtinTunerName: Random
......
test/config_test/examples/mnist.test.yml
View file @ 2b4a7d1f
authorName: nni
experimentName: default_test
maxExecDuration: 5m
maxTrialNum: 2
trialConcurrency: 1
maxTrialNum: 4
trialConcurrency: 2
searchSpacePath: ./mnist_search_space.json
tuner:
......
tools/nni_annotation/examples/mnist_generated.py
View file @ 2b4a7d1f
import nni
"""A deep MNIST classifier using convolutional layers."""
import logging
import math
import tempfile
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import nni
FLAGS = None
logger = logging.getLogger('mnist_AutoML')
......@@ -123,12 +127,23 @@ def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def download_mnist_retry(data_dir, max_num_retries=20):
"""Try to download mnist dataset and avoid errors"""
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(data_dir, one_hot=True)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
def main(params):
"""
Main function, build mnist network, run and send result to NNI.
"""
mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
def main(params):
# Import data
mnist = download_mnist_retry(params['data_dir'])
print('Mnist download data done.')
logger.debug('Mnist download data done.')
mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'],
......
tools/nni_annotation/examples/mnist_with_annotation.py
View file @ 2b4a7d1f
......@@ -21,8 +21,9 @@
import logging
import math
import tempfile
import tensorflow as tf
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
FLAGS = None
......@@ -168,13 +169,21 @@ def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def download_mnist_retry(data_dir, max_num_retries=20):
"""Try to download mnist dataset and avoid errors"""
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(data_dir, one_hot=True)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
def main(params):
'''
Main function, build mnist network, run and send result to NNI.
'''
# Import data
mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
mnist = download_mnist_retry(params['data_dir'])
print('Mnist download data done.')
logger.debug('Mnist download data done.')
......
tools/nni_annotation/examples/mnist_without_annotation.py
View file @ 2b4a7d1f
......@@ -21,8 +21,9 @@
import logging
import math
import tempfile
import tensorflow as tf
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import nni
......@@ -172,13 +173,21 @@ def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def download_mnist_retry(data_dir, max_num_retries=20):
"""Try to download mnist dataset and avoid errors"""
for _ in range(max_num_retries):
try:
return input_data.read_data_sets(data_dir, one_hot=True)
except tf.errors.AlreadyExistsError:
time.sleep(1)
raise Exception("Failed to download MNIST.")
def main(params):
'''
Main function, build mnist network, run and send result to NNI.
'''
# Import data
mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
mnist = download_mnist_retry(params['data_dir'])
print('Mnist download data done.')
logger.debug('Mnist download data done.')
......
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