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Kak-mogno-stat-nastoyashey-professionalnoy-modelyu-2.jpg' alt='Juliet Model Sets 31 - 40' title='Juliet Model Sets 31 - 40' />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. Giant-Defy-Advanced-1.jpg' alt='Juliet Model Sets 31 - 40' title='Juliet Model Sets 31 - 40' />Forum NonNude Photo Sets Archive Sexually suggestive photo sets. Reposts and older content. Torrentz will always love you. Farewell. 20032016 Torrentz. 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. 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. Eolo Lento more. 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. 00,0. 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. zerosnlines, 7. Y np. zerosnlines, dtypefloat. Xn, pos floatval. Yn parts0. Xtrain, Ytrain parsetrain. Xtest, Ytest parsetest. SVCgamma0. 0. 01. Xtrain, Ytrain. Xtest. Ytest, predicted. Xnp. zerosnlines,7. Ynp. zerosnlines,dtypefloat    forn,line inenumeratelines        lineline. Xn,posfloatval            Ynparts0   return. X,YXtrain,YtrainparsetrainXtest,Ytestparsetestfrom sklearn import svm,metricsclassifiersvm. SVCgamma0. 0. 01classifier. 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. conf Spark. Conf. conf. set. Masteryarn client. App. Namesklearn predict. Spark. Contextconfconffrom pyspark import Spark. Conffrom pyspark import Spark. ContextconfSpark. Confconf. set. 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. 8m file format and return a numpy array. X np. zeros1, 7. X0, pos floatval. Read the mnist. 8m file format and return a numpy array        import numpy asnp    Xnp. X0,posfloatval    return. Xinputsinputdata. To import the scikit learn model and load the training data.