tensorflow on spark

This weekend, I decided it is time:i is going to update my Python environment and get Keras and TensorFlow installed So I could the start doing tutorials (particularly for deep learning) using R. Although I used to is a systems administrator (about years ago), I don ' t do much installing or configuring so I guess T Hat ' s why I ' ve put the this task off for so long. And it wasn ' t unwarranted:it took me the whole weekend to get the install working

Deep Learning Library packages Theano, Lasagne, and TensorFlow support GPU installation in Ubuntu With the popularity of deep learning, more and more people begin to use deep learning to train their own models. GPU training is much faster than the CPU, allowing models that require one week of training to be completed within one day. This post explains how to install Theano, Lasagne, TensorFlow trained with

Preface Recently learning TensorFlow, you need to install its environment. Originally intended to install an Ubuntu system for the computer, it was too troublesome to choose to install it in Windows. Because TensorFlow needs more dependent environment, it is time-consuming and laborious to install, and error prone. Search on the internet found the installation of Anaconda, after the practice found that the

IntroducedIn general, there are two ways to fault-tolerant distributed datasets: data checkpoints and the updating of record data .For large-scale data analysis, data checkpoint operations are costly and require a large data set to be replicated between machines through a network connection in the data center, while network bandwidth tends to be much lower than memory bandwidth and consumes more storage resources.Therefore, Spark chooses how to record

Problem 1:reduce task number not appropriateSolution: Need to adjust the default configuration according to the actual situation, the adjustment method is to modify the parameter spark.default.parallelism. Typically, the reduce number is set to 2-3 times the number of cores. The number is too large, causing a lot of small tasks, increasing the overhead of starting tasks, the number is too small, the task runs slowly. Therefore, the number of tasks to reasonably modify reduce is spark.default.pa

First Test the spark API in Spark's native mode and run Spark-shell as Local:Let's start with the parallelize:Results after map operation:Below is a look at the filter operation:Filter execution Results:We use the most authentic Scala functional style of programming:Execution Result:As you can see from the results, the results are the same as that of the previous step.But in this way, the style of the compo

To operate HDFs: first make sure that HDFs is up:To start the Spark cluster:Run on the Spark cluster with Spark-shell:View the "LICENSE.txt" file that was uploaded to HDFs before:Read this file with Spark:Count the number of rows in the file using the Counts:We can see that count time is 0.239708sCaches the RDD and executes count to make the cache effective:The e

Application:Application is the spark user who created the Sparkcontext instance object and contains the driver program:Spark-shell is an application because Spark-shell created a Sparkcontext object when it was started, with the name SC:Job:As opposed to Spark's action, each action, such as Count, Saveastextfile, and so on, corresponds to a job instance that contains multi-tasking parallel computations.Driv

"Original Hadoopspark hands-on Practice 10" Spark SQL Programming Basics and hands-on practice (bottom)Goal:1. Deep understanding of the principles of spark SQL programming2. Use simple commands to verify how spark SQL works3. Use a complete case to verify how spark SQL works, and actually do it yourself4. Successful c

First, prepareUpload apache-hive-1.2.1.tar.gz and Mysql--connector-java-5.1.6-bin.jar to NODE01Cd/toolsTAR-ZXVF apache-hive-1.2.1.tar.gz-c/ren/Cd/renMV apache-hive-1.2.1 hive-1.2.1This cluster uses MySQL as the hive metadata storeVI Etc/profileExport hive_home=/ren/hive-1.2.1Export path= $PATH: $HIVE _home/binSource/etc/profileSecond, install MySQLYum-y install MySQL mysql-server mysql-develCreating a hive Database Create databases HiveCreate a hive user grant all privileges the hive.* to [e-mai

Provides various official and user release code examples. For code reference, you are welcome to exchange and learn about spark grassland system development, spark grassland system source code, distribution system micro-distribution, it is a three-level distribution mall based on the public platform. The three-level distribution should achieve an infinite loop model, and an innovation of the enterprise mark

3, hands-on generics in Scalageneric generic classes and generic methods, that is, when we instantiate a class or invoke a method, you can specify its type, because Scala generics and Java generics are consistent and are not mentioned here. 4, hands on. Implicit conversions, implicit parameters, implicit classes in Scalaimplicit conversion is one of the key points that many people learn about Scala, which is the essence of Scala:Let's take a look at the example of hidden parameters: The

3, hands-on generics in Scala generic generic classes and generic methods, that is, when we instantiate a class or invoke a method, you can specify its type, because Scala generics and Java generics are consistent and are not mentioned here. 4, hands on. Implicit conversions, implicit parameters, implicit classes in Scala Implicit conversion is one of the key points that many people learn about Scala, which is the essence of Scala: Let's take a look at the example of hidden parameters:

Http://spark.apache.org/docs/1.2.1/streaming-programming-guide.htmlHow to shard data in sparkstreamingLevel of Parallelism in Data processingCluster resources can be under-utilized if the number of parallel tasks used on any stage of the computation are not high E Nough. For example, for distributed reduce operations like reduceByKey reduceByKeyAndWindow and, the default number of parallel tasks are controlled by The spark.default.parallelism configuration property. You can pass the level of par

configuration file are: Run the ": WQ" command to save and exit. Through the above configuration, we have completed the simplest pseudo-distributed configuration. Next, format the hadoop namenode: Enter "Y" to complete the formatting process: Start hadoop! Start hadoop as follows: Use the JPS command that comes with Java to query all daemon processes: Start hadoop !!! Next, you can view the hadoop running status on the Web page used to monitor the cluster status in hadoop. The specific pa

There is a simple demo of spark-streaming, and there are examples of Kafka successful running, where the combination of both, is also commonly used one. 1. Related component versionFirst confirm the version, because it is different from the previous version, so it is necessary to record, and still do not use Scala, using Java8,spark 2.0.0,kafka 0.10. 2. Introduction of MAVEN PackageFind some examples of a c

The Spark standalone uses the Master/slave architecture, which includes the following classes: Class: Org.apache.spark.deploy.master.Master Description: Responsible for the entire cluster of resource scheduling and application management. Message type: Receives messages sent by worker 1. Registerworker 2. Executorstatechanged 3. Workerschedulerstateresponse 4. Heartbeat messages sent to the worker 1. Registeredworker 2. Registerworkerfailed 3. Reco

Yesterday want to run a machine learning code, in the WIN10 system to configure the day of the Python environment, is really a headache, ready to write a blog to help the next need to configure the Environment brothers.1. Download AnacondaAccording to yesterday's experience, found that Anaconda is really useful. : https://www.anaconda.com/download/I'm under the 64-bit.After the good is installed, the installation process is very simple, here will not write, but the suggestion is to add to the en

Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow MNIST convolutional neural network. Https://github.com/nlintz/TensorFlow-Tutorials/blob/master/05_convolutional_net.py.TensorFlow builds a CNN model to train the MNIST dataset. Build a model. Define input data and pre-process data. Read the data MNIST to obtain the training

Mnist Data Set IntroductionMnist is an entry-level computer vision dataset that contains a variety of handwritten digital pictures:The Mnist dataset contains callout information, which represents 5, 0, 4, and 1, respectively.The official website of the Mnist dataset is Yann LeCun ' s websiteAutomatic downloadFirst posted on GitHub address: https://github.com/tensorflow/tensorflow/tree/master/