Distributed Tensorflow 1.0 Supervisor stuck if log

2019-09-18 12:40发布

站内问答 / 前沿技术
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I build the TF 1.0 binary on centOS 8 for CPU. My distributed training code for MNIST data works fine if the Supervisor’s logdir is in local disk. But if I change Supervisor’s logdir to HDFS, the code will stuck at Supervisor’s initialization:

sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
                     logdir='hdfs://cdh-2:8020/tmp/example',
                     global_step=global_step,
                     init_op=init_op)

I used gdb and found the C stack trace. It seems it has problems in _wrap_RecursivelyCreateDir()

#0  0x00007f180cda9f3c in tensorflow::io::internal::SplitPath(tensorflow::StringPiece) ()
   from /opt/niara/analyzer/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so
#1  0x00007f180cdaa01a in tensorflow::io::Dirname(tensorflow::StringPiece) () from /opt/niara/analyzer/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so
#2  0x00007f180cdc0f89 in tensorflow::FileSystem::RecursivelyCreateDir(std::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) ()
   from /opt/niara/analyzer/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so
#3  0x00007f180cdbe780 in tensorflow::Env::RecursivelyCreateDir(std::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) ()
   from /opt/niara/analyzer/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so
#4  0x00007f180b5be41a in RecursivelyCreateDir(std::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, TF_Status*) ()
   from /opt/niara/analyzer/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so
#5  0x00007f180b5be538 in _wrap_RecursivelyCreateDir () from /opt/niara/analyzer/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so
#6  0x00007f1822221213 in PyEval_EvalFrameEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#7  0x00007f1822222d2e in PyEval_EvalCodeEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#8  0x00007f182222136a in PyEval_EvalFrameEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#9  0x00007f1822222d2e in PyEval_EvalCodeEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#10 0x00007f18221a07f1 in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#11 0x00007f1822171293 in PyObject_Call () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#12 0x00007f18221838cf in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#13 0x00007f1822171293 in PyObject_Call () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#14 0x00007f18221dd660 in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#15 0x00007f18221d41d8 in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#16 0x00007f1822171293 in PyObject_Call () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#17 0x00007f1822220422 in PyEval_EvalFrameEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#18 0x00007f1822222d2e in PyEval_EvalCodeEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#19 0x00007f18221a07f1 in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#20 0x00007f1822171293 in PyObject_Call () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#21 0x00007f18221838cf in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#22 0x00007f1822171293 in PyObject_Call () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#23 0x00007f18221dd660 in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#24 0x00007f18221d41d8 in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#25 0x00007f1822171293 in PyObject_Call () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
---Type <return> to continue, or q <return> to quit---
#26 0x00007f1822220422 in PyEval_EvalFrameEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#27 0x00007f1822222d2e in PyEval_EvalCodeEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#28 0x00007f18221a08f8 in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#29 0x00007f1822171293 in PyObject_Call () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#30 0x00007f18221838cf in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#31 0x00007f1822171293 in PyObject_Call () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#32 0x00007f18221dd660 in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#33 0x00007f18221d41d8 in ?? () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#34 0x00007f1822171293 in PyObject_Call () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#35 0x00007f1822220422 in PyEval_EvalFrameEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#36 0x00007f1822222d2e in PyEval_EvalCodeEx () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#37 0x00007f1822222e42 in PyEval_EvalCode () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#38 0x00007f1822242c60 in PyRun_FileExFlags () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#39 0x00007f1822242e3f in PyRun_SimpleFileExFlags () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#40 0x00007f18222586f4 in Py_Main () from /opt/niara/analyzer/lib/libpython2.7.so.1.0
#41 0x000000393ae1ed1d in __libc_start_main () from /lib64/libc.so.6
#42 0x0000000000400649 in _start ()

Following is the training code: ''' pc-01$ python example.py --job_name="ps" --task_index=0 pc-02$ python example.py --job_name="worker" --task_index=0 pc-03$ python example.py --job_name="worker" --task_index=1 pc-04$ python example.py --job_name="worker" --task_index=2 '''

from __future__ import print_function

import tensorflow as tf
import time

# cluster specification
parameter_servers = ["an-node:2222"]
workers = [ "an-node:2223",
            "cdh-2:2222",
            "cdh-3:2222"]
cluster = tf.train.ClusterSpec({"ps":parameter_servers, "worker":workers})

# input flags
tf.app.flags.DEFINE_string("job_name", "", "Either 'ps' or 'worker'")
tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")
FLAGS = tf.app.flags.FLAGS

# start a server for a specific task
server = tf.train.Server(cluster, 
                         job_name=FLAGS.job_name,
                         task_index=FLAGS.task_index)

# config
batch_size = 100
learning_rate = 0.0005
training_epochs = 20
logs_path = "/tmp/mnist/1"

# load mnist data set
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

if FLAGS.job_name == "ps":
  server.join()
elif FLAGS.job_name == "worker":

    # Between-graph replication
    with tf.device(tf.train.replica_device_setter(
        worker_device="/job:worker/task:%d" % FLAGS.task_index,
        cluster=cluster)):

        # count the number of updates
        global_step = tf.get_variable('global_step', [], 
                                       initializer = tf.constant_initializer(0),
                                       trainable = False)

        # input images
        with tf.name_scope('input'):
          # None -> batch size can be any size, 784 -> flattened mnist image
          x = tf.placeholder(tf.float32, shape=[None, 784], name="x-input")
          # target 10 output classes
          y_ = tf.placeholder(tf.float32, shape=[None, 10], name="y-input")

        # model parameters will change during training so we use tf.Variable
        tf.set_random_seed(1)
        with tf.name_scope("weights"):
            W1 = tf.Variable(tf.random_normal([784, 100]))
            W2 = tf.Variable(tf.random_normal([100, 10]))

        # bias
        with tf.name_scope("biases"):
            b1 = tf.Variable(tf.zeros([100]))
            b2 = tf.Variable(tf.zeros([10]))

        # implement model
        with tf.name_scope("softmax"):
            # y is our prediction
            z2 = tf.add(tf.matmul(x,W1),b1)
            a2 = tf.nn.sigmoid(z2)
            z3 = tf.add(tf.matmul(a2,W2),b2)
            y  = tf.nn.softmax(z3)

        # specify cost function
        with tf.name_scope('cross_entropy'):
            # this is our cost
            cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

        # specify optimizer
        with tf.name_scope('train'):
            # optimizer is an "operation" which we can execute in a session
            grad_op = tf.train.GradientDescentOptimizer(learning_rate)
            train_op = grad_op.minimize(cross_entropy, global_step=global_step)

        with tf.name_scope('Accuracy'):
            # accuracy
            correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        # create a summary for our cost and accuracy
        tf.summary.scalar("cost", cross_entropy)
        tf.summary.scalar("accuracy", accuracy)

        # merge all summaries into a single "operation" which we can execute in a session 
        summary_op = tf.summary.merge_all()
        init_op = tf.initialize_all_variables()
        print("Variables initialized ...")

    sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
                             logdir='hdfs://cdh-2:8020/user/niara/example',
                             global_step=global_step,
                             init_op=init_op)

    begin_time = time.time()
    frequency = 100
    with sv.prepare_or_wait_for_session(server.target) as sess:

        # create log writer object (this will log on every machine)
        writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())

        # perform training cycles
        start_time = time.time()
        for epoch in range(training_epochs):

            # number of batches in one epoch
            batch_count = int(mnist.train.num_examples/batch_size)

            count = 0
            for i in range(batch_count):
                batch_x, batch_y = mnist.train.next_batch(batch_size)

                # perform the operations we defined earlier on batch
                _, cost, summary, step = sess.run([train_op, cross_entropy, summary_op, global_step],
                                                  feed_dict={x: batch_x, y_: batch_y})
                writer.add_summary(summary, step)

                count += 1
                if count % frequency == 0 or i+1 == batch_count:
                    elapsed_time = time.time() - start_time
                    start_time = time.time()
                    print("Step: %d," % (step+1), 
                          " Epoch: %2d," % (epoch+1),
                          " Batch: %3d of %3d," % (i+1, batch_count),
                          " Cost: %.4f," % cost,
                          " AvgTime: %3.2fms" % float(elapsed_time*1000/frequency))
                    count = 0


        print("Test-Accuracy: %2.2f" % sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
        print("Total Time: %3.2fs" % float(time.time() - begin_time))
        print("Final Cost: %.4f" % cost)

    sv.stop()
    print("done”)

1条回答
家丑人穷心不美
2楼-- · 2019-09-18 13:37

I finally resolved this issue. In the How to run TensorFlow on Hadoop page, it stated that the paths of libjvm.so and libhdfs.so both should be in LD_LIBRARY_PATH. But in the hadoop distribution I am using, the libhdfs.so is not in $HADOOP_HDFS_HOME/lib/native folder. After I located the path of libhdfs.so on my cluster and appended it to LD_LIBRARY_PATH, the problem was resolved.

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