distributed

I am a Git newbee with UNIX SCCS and Microsoft Visual SourceSafe experience. I’m just learning Git and it seems to have a huge and painful learning curve. I’ve already seen Git blow away all the data files I hadn’t committed, which concerns me. (How a utility can delete data files without warning is beyond me). Linus Torvalds, in his video on Git, claims that Git is distributed, touts the benefits of distribution, but other than everyone having a copy (clone) of the source, doesn’t really explain how distribution works. How does Git help distribution? How does Git help recover lost files? How

We have a large high-performance software system which consists of multiple interacting Java processes (not EJBs). Each process can be on the same machine or on a different machine. Certain events are generated in one process, and are then propagated in different ways to other processes for further processing and so on. For benchmarking purposes, we need to create a log of when each event passed through a "checkpoint", eventually combine these logs to obtain a timeline of how each event propagated through the system and with what latency (of course, process switching and IPC adds latency,

I work in a small iPhone development team, in our office, we have at least 4 copies of XCode running on the network at any one time. Contemplating getting everyone to have it running. We're networked together using a standard WIFI Switch, so network speed and latency isn't as good as wired network... Just wondering, is there any real time gain to be had on using distributed builds? Once it passes the relevant data back and forth over the network. At least for relatively small projects. it depends on your project, its dependencies, and the amount of data that must be transferred. 15-20 seconds

I am trying to train model in synchronous distributed fashion for data parallelism. There are 4 gpus in my machine. Each gpu should should run a worker to train on separate non-overlapping subset of the data (between graph replication). The main data file is separated into 16 smaller TFRecord files. Each worker is supposed to process 4 different files. The problem is that training freezes independently and at different times in each worker process. They freeze at some point. One of the 'ps' reports following error related to grpc: 2017-09-21 16:45:55.606842: I tensorflow/core/distributed

I have multiple 4GB GPU nodes so I want them to run huge model in parallel. I hope just splitting layers into several pieces with appropriate device scopes just enables model parallelism but it turns out that it doesn't reduce memory footprint for master node(task 0). (10 nodes configuration - master: 20g, followers:2g, 1 node configuration - master: 6~7g) Suspicious one is that gradients are not distributed because I didn't setup right device scope for them. my model is available on github.( https://github.com/nakosung/tensorflow-wavenet/tree/model_parallel_2 ) device placement log is here:

前面的两篇介绍了安装katta及ZooKeeper,后边来介绍katta的Node. 我们回到step1 后边提到的solr-katta-plugin项目,源码导入后会出现很多的错误,在项目中继承了solr-core,和solrj中的类尝试着把访问修饰private改为protected. 如:solr-core org.apache.solr.handler.component.SearchHandler类中的shardHandlerFactory成员变量 protected ShardHandlerFactory shardHandlerFactory = new HttpShardHandlerFactory(); 同时借鉴 https://issues.apache.org/jira/browse/SOLR-1395 Tomliu的做法,把 the bugs is : 1. solr's ShardDoc.java, ShardFieldSortedHitQueue line 210 : final float f1 = e1.score == null ? 0.00f : e1.score; final float f2 = e2.score == null ? 0.00f : e2.score; 等等.直到项目基本错误解决. 下载solr并且copy其中的apache