tensorflow on spark

The TensorFlow training model is usually written using the Python API and simply records how the models are invoked in Java after they are saved. In Python, the model is saved using the following API: # Save binary model Output_graph_def = tf.graph_util.convert_variables_to_constants (Sess, Sess.graph_def, Output_node_ names=[' Y_conv_add '] with Tf.gfile.FastGFile ('/LOGS/MNIST.PB ', mode= ' WB ') as F: F.write (output_graph_def. Serializetostri

I. Installation of CUDASpecific installation process See my other blog, ubuntu16.04 installation configuration deep learning environmentSecond, installation TensorFlow1. Specific installation process In fact, the official website is written in more detail, summed up the words can be divided into two types: Install release version and source code compiled installation. Because the source code compiled installation is cumbersome, and need to install Google's own compiler Bazel, so I choose to inst

C # writing TensorFlow AI applicationsTensorflowsharp get started using C # to write TensorFlow AI application learning.TensorFlow Brief Introduction TensorFlow is Google's second-generation machine learning system, according to Google, in some benchmarks, tensorflow performance is twice times faster than the first

Installation Environment: Windows 64bit Gpu:geforce GT 720 python:3.5.3 Cuda:8 First download the Anaconda3 version of Win10 64bit and install the Python3.5 release. Because currently TensorFlow only supports Python3.5 for Windows. You can download the Anaconda installation package directly, there is no problem. (Tsinghua Mirror https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/) There are two versions of TensorFlo

TensorFlow version 1.4 is now publicly available-this is a big update. We are very pleased to announce some exciting new features here and hope you enjoy it. Keras In version 1.4, Keras has migrated from Tf.contrib.keras to the core package Tf.keras. Keras is a very popular machine learning framework that contains a number of advanced APIs that can minimize the time between your creativity and your achievable implementation. Keras can be integrated

TensorFlow: A graph is used to indicate that a calculation task is performed in the context of a conversation called a session using tensor to represent data through variables (Variable) to maintain state using FE Ed and fetch can assign or fetch data from any operation (arbitrary operation) TensorFlow is a programming system that uses diagrams to represent computational tasks. The node in the diagram is ca

The task scheduling system for Spark is as follows:From the Chinese Academy of Sciences to see the cause rddobject generated DAG, and then entered the Dagscheduler stage, Dagscheduler is the state-oriented high-level scheduler, Dagscheduler the DAG split into a lot of tasks, Each group of tasks is a state, whenever encountering shuffle will produce a new state, you can see a total of three state;dagscheduler need to record those rdd is deposited into

You are welcome to reprint it. Please indicate the source, huichiro.Summary This article will give a brief review of the origins of the quasi-Newton method L-BFGS, and then its implementation in Spark mllib for source code reading.Mathematical Principles of the quasi-Newton Method Code Implementation The regularization method used in the L-BFGS algorithm is squaredl2updater. The breezelbfgs function in the breeze library of the scalanlp member

Overview A spark job is divided into multiple stages. The last stage contains one or more resulttask. The previous stages contains one or more shufflemaptasks. Run resulttask and return the result to the driver application. Shufflemaptask separates the output of a task from Multiple Buckets Based on the partition of the task. A shufflemaptask corresponds to a shuffledependency partition, and the total number of partition is the same as that of parall

Spark is especially suitable for multiple operations on specific data, such as mem-only and MEM disk. Mem-only: high efficiency, but high memory usage, high cost; mem Disk: After the memory is used up, it will automatically migrate to the disk, solving the problem of insufficient memory, it brings about the consumption of Data replacement. Common spark tuning workers include nman, jmeter, and jprofile. Th

Listen to Liaoliang's spark the IMF saga 19th lesson: Spark Sort, job is: 1, Scala two order, use object apply 2; read it yourself RangepartitionerThe code is as follows:/*** Created by Liaoliang on 2016/1/10.*/Object Secondarysortapp {def main (args:array[string]) {val conf=NewSparkconf ()//Create a Sparkconf objectConf.setappname ("Secondarysortapp")//set the application name, the program run monitoring i

The code is as follows:Packagecom.dt.spark.streamingimportorg.apache.spark.sql.sqlcontextimportorg.apache.spark. {sparkcontext,sparkconf}importorg.apache.spark.streaming. {streamingcontext,duration}/*** logs are analyzed using sparkstreaming combined with sparksql. * assuming e-commerce website click Log Format (Simplified) The following:*userid,itemid,clicktime* requirements: processing the item click order within 10 minutes Top10, and display the name of the product. The correspondence between

Reference: Https://spark.apache.org/docs/latest/sql-programming-guide.html#overviewhttp://www.csdn.net/article/2015-04-03/2824407Spark SQL is a spark module for structured data processing. IT provides a programming abstraction called Dataframes and can also act as distributed SQL query engine.1) in Spark, Dataframe is a distributed data set based on an RDD, similar to a two-dimensional table in a traditiona

In the conf file of your spark path, the CP copy Spark-defaults.conf.template is spark-defaults.conf and add the following file spark.eventLog.enabled trueSpark.eventLog.dir hdfs://master:9000/historySpark.eventLog.compress true Distribute configuration to other child nodes I'm using rsync. rsync sparkconf Path/spark