Put the fun back into computing.Use Linux, BSD.Search Distributions.Important Note.Please note that we have put together a series of common search results for people looking for distributions that are beginner friendly, offer UEFI support, Secure Boot support, do not use systemd or that have a Raspberry Pi edition.Clicking any of the above links will take you immediately to the appropriate search results.If you are looking for an article, tutorial or feature, please use our.Article Search page.Package search.Search the Distro.In this howto i will describe how to install mytop package on CentOS 5.MyTOP is top command clone to monitor mysql performance including queries, threads etc.A framework and distribution system for reusable PHP components.Open source.SiteContents/2-7C22D5236A4543EB827F3BD8936E153E/media/lamp_15.png' alt='How To Install Php With Mysql Support Centos Repositories' title='How To Install Php With Mysql Support Centos Repositories' />Watch database for distributions using a particular package.If you are looking for a distribution with the latest kernel, select linux from the drop down box below and type the version number into the text box next to it.Please note that the best way to obtain the GNOME version is by searching for nautilus, while KDE Plasma is represented by the plasma desktop package.Apache 2.As for versioning, if no version number is provided, this page will return any recent versions of the selected package.It is also possible to perform searches for distributions which do not contain a specific package.This returns a list of distributions where the given package is not present on the installation media.How To Install Php With Mysql Support Centos Repositories' title='How To Install Php With Mysql Support Centos Repositories' />The package version search offers the ability to search for packages which are close.The second field in the search form allows visitors to switch between.Most people will probably want to use the like option as it will.When no version is specified, like.Have fun and let us know how we can improve the search engine Search by Distribution Criteria Simple Search FormThis section allows you to search for a particular distribution based on certain criteria.Select the criteria from the drop down and check boxes below and hit the Submit Query button to get a list of known distributions that match your choice.Country of origin All.Algeria.Argentina.Australia.Austria.Belgium. Bhutan.Bosnia and Herzegovina.Brazil.Bulgaria.Cambodia.Canada.Chile. China.Cuba.Czech Republic.Denmark.Ecuador. Egypt.Finland.France. Germany.Greece.Guatemala.Hong Kong.Hungary.India. Indonesia.Iran.Ireland. Isle of Man.Israel.Italy. Japan.Jordan.Latvia. Lithuania.Malaysia.Malta. Mexico.Mongolia.Nepal. Netherlands.New Zealand. Curso Java Desktop Dvd 15 . Nigeria.Norway. Oman.Peru.Philippines.Poland.Portugal. Puerto Rico.Runion.Romania. Russia.Serbia.Singapore.Slovakia.Slovenia.South Africa.South Korea.Spain.Sri Lanka.Sweden.Switzerland.Taiwan.Thailand.Turkey. Ukraine.United Arab Emirates.United Kingdom.USAVenezuela.Vietnam.Based on All.Android.Arch. Cent.OSCRUXDebian.Debian StableDebian TestingDebian UnstableFedora.Free.BSDGentoo. Independent.KDE neon.KNOPPIXLFSMageia.Mandriva.Manjaro.Open. BSDopen.SUSEPCLinux.OSPuppy.Red Hatr.Pathsidux.Slackware.Sli. Taz.Solaris.Ubuntu. Ubuntu LTSTiny Core.Zenwalk.Not based on None.Arch.Cent. OSDebian.Fedora.Free. BSDGentoo.Independent.KDE neon.KNOPPIXLFSMageia.Mandriva.Manjaro.Mint. Open.BSDopen.SUSEPCLinux.OSPuppy.Red Hatr.Pathsidux.Slackware.Sli. Taz.Solaris.Ubuntu. Tiny Core.Zenwalk.Desktop interface All.No desktop.After.Step. Android.Awesome.Blackboxbspwm.Budgie.Cinnamon. Consort.Deepindwm.Enlightenment.Equinox.Firefox.Fluxboxflwm.FVWMGNOMEHackedboxi.Ice.WMion. JWMKDEKDE Plasma.Kodi XBMCLesstif.Lumina.LXDELXQt.MATEMaynard.Metacity.Mezzo. Moblin.Openbox.Pantheon.Pearlpekwm.Ratpoison.Razor qt.SLWMSugar.Trinity.TWMUnity.Web. UIWMaker.WMFSWMIXfce.Architecture Allacorn.Package management All.OtherUnknown.DEBFlatpak.RPMPacman.PETPortage.Snap. 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Discontinued.The following distributions match your criteria sorted by popularity 1.Manjaro Linux 3Manjaro Linux is a fast, user friendly, desktop oriented operating system based on Arch Linux.Key features include intuitive installation process, automatic hardware detection, stable rolling release model, ability to install multiple kernels, special Bash scripts for managing graphics drivers and extensive desktop configurability.Manjaro Linux offers Xfce as the core desktop options, as well as a minimalist Net edition for more advanced users.Community supported GNOME 3Cinnamon and KDE flavours are available.Users also benefit from the supportive and vibrant Manjaro community forum.Fedora 7Fedora formerly Fedora Core is a Linux distribution developed by the community supported Fedora Project and owned by Red Hat.Fedora contains software distributed under a free and open source license and aims to be on the leading edge of such technologies.Fedora has a reputation for focusing on innovation, integrating new technologies early on and working closely with upstream Linux communities.The default desktop in Fedora is the GNOME desktop environment and the default interface is the GNOME Shell.Other desktop environments, including KDE, Xfce, LXDE, MATE and Cinnamon, are available.Fedora Project also distributes custom variations of Fedora called Fedora spins.These are built with specific sets of software packages, offering alternative desktop environments or targeting specific interests such as gaming, security, design, scientific computing and robotics.PCLinux.OS 1. 5PCLinux.OS is a user friendly Linux distribution with out of the box support for many popular graphics and sound cards, as well as other peripheral devices.The bootable live DVD provides an easy to use graphical installer and the distribution sports a wide range of popular applications for the typical desktop user, including browser plugins and full multimedia playback.The intuitive system configuration tools include Synaptic for package management, Addlocale to add support to many languages and Mylivecd to create a customised live CD.KDE neon 2.KDE neon is a Ubuntu based Linux distribution and live DVD featuring the latest KDE Plasma desktop and other KDE community software.Besides the installable DVD image, the project provides a rapidly evolving software repository with all the latest KDE software.Two editions of the product are available a User edition, designed for those interested in checking out the latest KDE software as it gets released, and a Developers edition, created as a platform for testing cutting edge KDE applications.Sparky.Linux 2. Sparky.Linux is a lightweight, fast and simple Linux distribution designed for both old and new computers featuring customised Enlightenment and LXDE desktops.It has been built on the testing branch of Debian GNULinux.Black Lab Linux 4.Black Lab Linux formerly OS4 Open.Linux is a user friendly, commercial desktop and server Linux distribution based on Ubuntu.Some of its most interesting features include support for popular browser plugins, addition of packages for multimedia production, content creation and software development, and an innovative desktop layout based on GNOME Shell.Separate editions with KDE and Xfce desktops are also available.The company behind the distribution also sells a desktop mini system with Black Lab Linux pre installed.Ultimate Edition 4.Ultimate Edition, first released in December 2.Ubuntu and Linux Mint.The goal of the project is to create a complete, seamlessly integrated, visually stimulating, and easy to install operating system.Single button upgrade is one of several special characteristics of this distribution.Other main features include custom desktop and theme with 3.D effects, support for a wide range of networking options, including Wi.Fi and Bluetooth, and integration of many extra applications and package repositories.ROSA 4.ROSA is a Russian company developing a variety of Linux based solutions.Its flagship product, ROSA Desktop, is a Linux distribution featuring a highly customised KDE desktop and a number of modifications designed to enhance the user friendliness of the working environment.Making Python on Apache Hadoop Easier with Anaconda and CDH Cloudera Engineering Blog Enabling Python development on CDH clusters for Py.Spark, for example is now much easier thanks to new integration with Continuum Analytics Python platform Anaconda.Python has become an increasingly popular tool for data analysis, including data processing, feature engineering, machine learning, and visualization.Data scientists and data engineers enjoy Pythons rich numerical and analytical librariessuch as Num.Py, pandas, and scikit learnand have long wanted to apply them to large datasets stored in Apache Hadoop clusters.While Apache Spark, through Py.Spark, has made data in Hadoop clusters more accessible to Python users, actually using these libraries on a Hadoop cluster remains challenging.In particular, setting up a full featured and modern Python environment on a cluster can be challenging, error prone, and time consuming.For these reasons, Continuum Analytics and Cloudera have partnered to create an Anaconda parcel for CDH to enable simple distribution and installation of popular Python packages and their dependencies.Anaconda dramatically simplifies installation and management of popular Python packages and their dependencies, and this new parcel makes it easy for CDH users to deploy Anaconda across a Hadoop cluster for use in Py.Spark, Hadoop Streaming, and other contexts where Python is available and useful.The newly available Anaconda parcel Includes 3.Python packages.Simplifies the installation of Anaconda across a CDH cluster.Will be updated with each new Anaconda release.In the remainder of this blog post, youll learn how to install and configure the Anaconda parcel, as well as explore an example of training a scikit learn model on a single node and then using the model to make predictions on data in a cluster.Installing the Anaconda Parcel.From the Cloudera Manager Admin Console, click the Parcels indicator in the top navigation bar.Click the Edit Settings button on the top right of the Parcels page.Click the plus symbol in the Remote Parcel Repository URLs section, and add the following repository URL for the Anaconda parcel https repo.Click the Save Changes button at the top of the page.Click the Parcels indicator in the top navigation bar to return to the list of available parcels, where you should see the latest version of the Anaconda parcel that is available.Download Auto Tune 6 Full Crack Idm '>Download Auto Tune 6 Full Crack Idm .Click the Download button to the right of the Anaconda parcel listing.After the parcel is downloaded, click the Distribute button to distribute the parcel to all of the cluster nodes.After the parcel is distributed, click the Activate button to activate the parcel on all of the cluster nodes, which will prompt with a confirmation dialog.After the parcel is activated, Anaconda is now available on all of the cluster nodes.These instructions are current as of the day of publication.Up to date instructions will be maintained in Anacondas documentation.To make Spark aware that you want to use the installed parcels as the Python runtime environment on the cluster, you need to set the PYSPARKPYTHON environment variable.Spark determines which Python interpreter to use by checking the value of the PYSPARKPYTHON environment variable on the driver node.With the default configuration for Cloudera Manager and parcels, Anaconda will be installed to optclouderaparcelsAnaconda, but if the parcel directory for Cloudera Manager has been changed, you will need to change the below instructions to YOURPARCELDIRAnacondabinpython.To specify which Python to use on a per application basis, you can specify it on the same line as your spark submit command.This would look like.PYSPARKPYTHONoptclouderaparcelsAnacondabinpython spark submit pysparkscript.PYSPARKPYTHONoptclouderaparcelsAnacondabinpython spark submit pysparkscript.You can also use Anaconda by default in Spark applications while still allowing users to override the value if they wish.To do this, you will need follow the instructions for Advanced Configuration Snippets and add the following lines to Sparks configuration.PYSPARKPYTHON then.PYSPARKPYTHONoptclouderaparcelsAnacondabinpython.PYSPARKPYTHON then export PYSPARKPYTHONoptclouderaparcelsAnacondabinpythonfi.Now with Anaconda on your CDH cluster, theres no need to manually install, manage, and provision Python packages on your Hadoop cluster.Anaconda in Action.A commonly needed workflow for a Python using data scientist is to Train a scikit learn model on a single node.Save the results to disk.Apply the trained model using Py.Spark to generate predictions on a larger dataset.Lets take a classic machine learning classification problem as an example of what having complex Python dependencies from Anaconda installed on CDH cluster allows you to do.The MNIST dataset is a canonical machine learning classification problem that involves recognizing handwritten digits, where each row of the dataset consists of a representation of one handwritten digit from 0 to 9.The training data you will use is the original MNIST dataset 6.The prediction will be done with the MNIST8.M dataset 8,0.Both of these datasets are available from the libsvm datasets website.This dataset is used as a standard test for various machine learning algorithms.More information, including benchmarks, can be found on the MNIST Dataset website.To train the model on a single node, you will use scikit learn and then save the model to a file with pickle.X np.Y np. zerosnlines, dtypefloat.Xn, pos floatval.Yn parts0.Xtrain, Ytrain parsetrain.Xtest, Ytest parsetest.SVCgamma0.Xtrain, Ytrain. Xtest.Ytest, predicted.Xnp.Ynp. zerosnlines,dtypefloat forn,line inenumeratelines lineline.Xn,posfloatval Ynparts0 return.X,YXtrain,YtrainparsetrainXtest,Ytestparsetestfrom sklearn import svm,metricsclassifiersvm.SVCgamma0.Xtrain,Ytrainpredictedclassifier.Xtestprint metrics.Ytest,predictedimport picklewith openclassifier.With the classifier now trained, you can save it to disk and then copy it to HDFS.Next, configure and create a Spark.Context to run in yarn client mode.Spark.Conf. from pyspark import Spark.Context.Spark.Conf. conf.Masteryarn client.App.Namesklearn predict.Spark.Contextconfconffrom pyspark import Spark.Conffrom pyspark import Spark.ContextconfSpark.Confconf.Masteryarn clientconf.App.Namesklearn predictscSpark.ContextconfconfTo load the MNIST8.M data from HDFS into an RDD.Filehdfs tmpmnist.Filehdfs tmpmnist.Now lets do some preprocessing on this dataset to convert the text to a Num.Py array, which will serve as input for the scikit learn classifier.Youve installed Anaconda on every cluster node, so both Num.Py and scikit learn are available to the Spark worker processes.Read the mnist.X np. zeros1, 7. X0, pos floatval.Read the mnist.Xnp.X0,posfloatval return.Xinputsinputdata.To import the scikit learn model and load the training data.To apply the trained model to a data in a large file in HDFS, you need the trained model available in memory on the executors.To move the classifier from one node to all of the Spark workers, you can then use the Spark.Context.Var sc. broadcastclassifier1broadcast.Varsc.This broadcast variable is then available in executors thus you can use the variable in logic that needs to be executed on the cluster inside of map or flat.Map functions, for example.It is simple to apply the trained model and save the output to a file.Var.As. Text. Filehdfs tmppredictionsdef applyclassifierinputarray labelbroadcast.Var.
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