checkpoint

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: When I try to run an application (just a simple hello_world.c doesn't work) I receive this error every time: mpiexec -ckpointlib blcr -ckpoint-prefix /tmp/ -ckpoint-interval 10 -machinefile /tmp/machinefile -n 1 ./app_name [proxy:0:0@masterpi] requesting checkpoint [proxy:0:0@masterpi] checkpoint completed [proxy:0:0@masterpi] requesting checkpoint [proxy:0:0@masterpi] HYDT_ckpoint_checkpoint (./tools/ckpoint/ckpoint.c:111): Previous checkpoint has not completed.[proxy:0:0@masterpi] HYD_pmcd_pmip_control_cmd_cb (./pm/pmiserv/pmip_cb.c:905):

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: Can Checkpoint/restart be implemented using the core dump of a process? The core file contains a complete memory dump of the process, thus in theory it should be possible to restore the process to the same state it was in when the core was dumped. 回答1: No, this is not possible in general without special support from the kernel. The kernel maintains a LOT of per-process state, such as the file descriptor table, IPC objects, etc. If you were willing to make lots of simplifying assumptions, such as no open files, no open sockets, no

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm saving my session state like so: self._saver = tf.saver() self._saver.save(self._session, '/network', global_step=self._time) When I later restore I want to get the value of the global_step for the checkpoint I restore from. This is in order to set some hyper parameters from it. The hacky way to do this would be to run through and parse the file names in the checkpoint directory. But surly there has to be a better, built in way to do this? 回答1: General pattern is to have a global_step variable to keep track of steps global_step = tf

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I want to see the variables that are saved in a tensorflow checkpoint along with their values. How can I find the variable names that are saved in a tensorflow checkpoint? EDIT : I used tf.train.NewCheckpointReader which is explained here . But, it is not given in the documentation of tensorflow. Is there any other way? ` import tensorflow as tf v0 = tf.Variable([[1, 2, 3], [4, 5, 6]], dtype=tf.float32, name="v0") v1 = tf.Variable([[[1], [2]], [[3], [4]], [[5], [6]]], dtype=tf.float32, name="v1") init_all_op = tf.initialize_all_variables()

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: When saving a checkpoint, TensorFlow often saves a meta file: my_model.ckpt.meta . What is in that file, can we still restore a model even if we delete it and what kind of info did we lose if we restore a model without the meta file? 回答1: This file contains a serialized MetaGraphDef protocol buffer . The MetaGraphDef is designed as a serialization format that includes all of the information required to restore a training or inference process (including the GraphDef that describes the dataflow, and additional annotations that

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I can train and evalaute a Tensorflow Estimator model without any problems. When I do prediction, this error arises: InvalidArgumentError (see above for traceback): output_shape has incorrect number of elements: 68 should be: 2 [[Node: output = SparseToDense[T=DT_INT32, Tindices=DT_INT32, validate_indices=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ToInt32, ToInt32_1, ToInt32_2, bidirectional_rnn/bidirectional_rnn/fw/fw/time)]] All of the model functions use the same architecture: def _train_model_fn(features, labels, mode,

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have a model which runs in a distributed mode for 4000 steps. After every 120s the accuracies are calculated (as is done in the provided examples). However, at times the last checkpoint file is not found. Error: Couldn't match files for checkpoint gs://path-on-gcs/train/model.ckpt-1485 The checkpoint file is present at the location. A local run for 2000 steps runs perfectly. last_checkpoint = tf.train.latest_checkpoint(train_dir(FLAGS.output_path)) I assume that the checkpoint is still in saving process, and the files are not actually

Tensorflow c++ 实践及各种坑 在这篇文章中： Tensorflow当前官网仅包含python、C、Java、Go的发布包，并无C++ release包，并且tensorflow官网也注明了并不保证除python以外库的稳定性，在功能方面python也是最完善的。众所周知，python在开发效率、易用性上有着巨大的优势，但作为一个解释性语言，在性能方面还是存在比较大的缺陷，在各类AI服务化过程中，采用python作为模型快速构建工具，使用高级语言(如C++，java)作为服务化程序实现是大势所趋。本文重点介绍tensorflow C++服务化过程中实现方式及遇到的各种问题。 实现方案 对于tensorflow c++库的使用，有两种方法： (1) 最佳方式当然是直接用C++构建graph，但是当前c++tensorflow库并不像python api那样full-featured。可参照builds a small graph in c++ here, C++ tensorflow api中还包含cpu和gpu的数字内核实现的类，可用以添加新的op。可参照 https://www.tensorflow.org/extend/adding_an_op (2) 常用的方式，c++调用python生成好的graph。本文主要介绍该方案。 实现步骤 (1)

Innodb存储引擎 一次写入操作是一次事务，innodb 首先把事务数据写入到缓存池 Buffer Pool 和重做日志redo log中，然后就可以提交事务，响应客户端了。之后 innodb 再将新事务的数据异步地写入磁盘，真正存储起来。 Innodb主要是通过事务日志实现ACID特性 事务日志包括：重做日志redo和回滚日志undo Redo记录的是已经全部完成的事务，就是执行了提交的事务，记录文件是ib_logfile0 ib_logfile1 Undo记录的是已部分完成并且写入硬盘的未完成的事务，默认情况下回滚日志是记录下表空间中的（共享表空间或者独享表空间） 一般情况下，mysql在崩溃之后，重启服务，innodb通过回滚日志undo将所有已完成并写入磁盘的未完成事务进行回滚，然后redo中的事务全部重新执行一遍即可恢复数据，但是随着redo的量增加，每次从redo的第一条开始恢复就会浪费长的时间，所以引入了checkpoint机制 1.缓存池 buffer pool 数据的读写需要经过缓存（缓存在buffer pool 即在内存中） 数据以整页（16K）位单位读取到缓存中 缓存中的数据以LRU策略换出（最少使用策略） IO效率高，性能好（不用IO） 读操作： 数据是以页为存储单位，在缓冲池中缓存了很多数据页，当第一次读取时首先将页从磁盘读取到缓存池中

使用代码地址：github-DeepLabv3+ 官方教程 调试指令参考local_test.sh 这里主要分成两步： 1.先配置基本的开发环境 2.下载数据集并配置 Numpy Pillow 1.0 tf Slim (which is included in the “tensorflow/models/research/” checkout) Jupyter notebook Matplotlib Tensorflow 关于TensorFlow的安装，典型的指令如下，具体参考官方： # For CPU pip install tensorflow # For GPU pip install tensorflow-gpu 其他的工具包： sudo apt-get install python-pil python-numpy sudo pip install jupyter sudo pip install matplotlib 在 tensorflow/models/research/ 目录下： # From tensorflow/models/research/ export PYTHONPATH = $PYTHONPATH :`pwd` :`pwd`/slim 注意，运行后要将所以终端关闭，重启～ 快速测试，调用model_test.py: # From