本文主要是介绍【分布式Tensorflow(0.11.0)问题 未解决】Segmentation fault (core dumped)，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
有三个测试，主函数是基本一样的，就是模型不同，但是均以 Segmentation fault (core dumped) 出错。
在我上一篇问题记录里，是以dummy数据集测试的，只有前向计算，没有参数更新和优化等操作，因此重新写了一个脚本，使用真实的数据集。
train数据集：
960831张图片（224*224），已转换为97个tfrecords文件，如下所示：
[root@dl1 train]# ls
train_224_0.tfrecords train_224_32.tfrecords train_224_55.tfrecords train_224_78.tfrecords
train_224_10.tfrecords train_224_33.tfrecords train_224_56.tfrecords train_224_79.tfrecords
train_224_11.tfrecords train_224_34.tfrecords train_224_57.tfrecords train_224_7.tfrecords
train_224_12.tfrecords train_224_35.tfrecords train_224_58.tfrecords train_224_80.tfrecords
train_224_13.tfrecords train_224_36.tfrecords train_224_59.tfrecords train_224_81.tfrecords
train_224_14.tfrecords train_224_37.tfrecords train_224_5.tfrecords train_224_82.tfrecords
train_224_15.tfrecords train_224_38.tfrecords train_224_60.tfrecords train_224_83.tfrecords
train_224_16.tfrecords train_224_39.tfrecords train_224_61.tfrecords train_224_84.tfrecords
train_224_17.tfrecords train_224_3.tfrecords train_224_62.tfrecords train_224_85.tfrecords
train_224_18.tfrecords train_224_40.tfrecords train_224_63.tfrecords train_224_86.tfrecords
train_224_19.tfrecords train_224_41.tfrecords train_224_64.tfrecords train_224_87.tfrecords
train_224_1.tfrecords train_224_42.tfrecords train_224_65.tfrecords train_224_88.tfrecords
train_224_20.tfrecords train_224_43.tfrecords train_224_66.tfrecords train_224_89.tfrecords
train_224_21.tfrecords train_224_44.tfrecords train_224_67.tfrecords train_224_8.tfrecords
train_224_22.tfrecords train_224_45.tfrecords train_224_68.tfrecords train_224_90.tfrecords
train_224_23.tfrecords train_224_46.tfrecords train_224_69.tfrecords train_224_91.tfrecords
train_224_24.tfrecords train_224_47.tfrecords train_224_6.tfrecords train_224_92.tfrecords
train_224_25.tfrecords train_224_48.tfrecords train_224_70.tfrecords train_224_93.tfrecords
train_224_26.tfrecords train_224_49.tfrecords train_224_71.tfrecords train_224_94.tfrecords
train_224_27.tfrecords train_224_4.tfrecords train_224_72.tfrecords train_224_95.tfrecords
train_224_28.tfrecords train_224_50.tfrecords train_224_73.tfrecords train_224_96.tfrecords
train_224_29.tfrecords train_224_51.tfrecords train_224_74.tfrecords train_224_9.tfrecords
train_224_2.tfrecords train_224_52.tfrecords train_224_75.tfrecords train_224_image_mean.npy
train_224_30.tfrecords train_224_53.tfrecords train_224_76.tfrecords
train_224_31.tfrecords train_224_54.tfrecords train_224_77.tfrecords
Main函数：
def main(_):ps_hosts = FLAGS.ps_hosts.split(",")worker_hosts = FLAGS.worker_hosts.split(",")cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})server = tf.train.Server(cluster,job_name=FLAGS.job_name,task_index=FLAGS.task_index)issync = FLAGS.issyncif FLAGS.job_name == "ps":server.join()elif FLAGS.job_name == "worker":images, labels = ...with tf.device(tf.train.replica_device_setter(worker_device="/job:worker/task:%d" % FLAGS.task_index,cluster=cluster)):global_step = tf.Variable(0, name='global_step', trainable=False)# 修改这里，调用不同的模型logits, parameters = inference(images)logits = tf.contrib.layers.flatten(logits)cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits, name='xentropy')loss_value = tf.reduce_mean(cross_entropy, name='xentropy_mean') optimizer = tf.train.GradientDescentOptimizer(learning_rate) grads_and_vars = optimizer.compute_gradients(loss_value)if issync == 1:# Synchronous moderep_op = tf.train.SyncReplicasOptimizer(optimizer,replicas_to_aggregate=len(worker_hosts),replica_id=FLAGS.task_index,total_num_replicas=len(worker_hosts),use_locking=True)train_op = rep_op.apply_gradients(grads_and_vars, global_step=global_step)init_token_op = rep_op.get_init_tokens_op()chief_queue_runner = rep_op.get_chief_queue_runner()else:# Asynchronous modetrain_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)init_op = tf.initialize_all_variables()saver = tf.train.Saver()tf.summary.scalar('cost', loss_value)summary_op = tf.summary.merge_all()sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),logdir="./alexnet_checkpoint",init_op=init_op,summary_op=None,saver=saver,global_step=global_step,save_model_secs=60)with sv.prepare_or_wait_for_session(server.target) as sess:# Syncif FLAGS.task_index == 0 and issync == 1:sv.start_queue_runners(sess, [chief_queue_runner])sess.run(init_token_op)step = 0while not sv.should_stop():try:start_time = time.time() _, loss_v, step = sess.run([train_op, loss_value, global_step])if step > 1000:breakduration = time.time() - start_timeif step >= 10:if not step % 10: print ('%s: step %d, duration = %.3f' % (da这篇关于【分布式Tensorflow(0.11.0)问题 未解决】Segmentation fault (core dumped)的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
