AI News, Use BigDL on HDInsight Spark for Distributed Deep Learning

Use BigDL on HDInsight Spark for Distributed Deep Learning

Companies are turning to deep learning to solve hard problems like image classification, speech recognition, object recognition, and machine translation.

It natively integrates into Spark, supports popular neural net topologies, and achieves feature parity with other open-source deep learning frameworks.

BigDL also provides 100+ basic neural networks building blocks allowing users to create novel topologies to suit their unique applications.

Thus, with Intel’s BigDL, the users are able to leverage their existing Spark infrastructure to enable Deep Learning applications without having to invest into bringing up separate frameworks to take advantage of neural networks capabilities.

such as number of compute nodes, cores, and batch size, a BigDL application leverages stable Spark infrastructure for node communications and resource management during its execution.

For this particular case, the Jupyter Notebook will automatically set up a default spark context so you don’t need to do the above configuration, but you do need to configure a few other Spark related configuration which will be explained in the sample Jupyter Notebook.

In this blog post, we have demonstrated the basic steps to set up a BigDL environment on Apache Spark for Azure HDInsight, and you can find a more detailed step to use BigDL to analyze MNIST dataset in the engineering blog post “How to use BigDL on Apache Spark for Azure HDInsight.”

Leveraging BigDL Spark library, a user can easily write scalable distributed Deep Learning applications within familiar Spark infrastructure without an intimate knowledge of the configuration of the underlying compute cluster.

Use BigDL on HDInsight Spark for Distributed Deep Learning

Companies are turning to deep learning to solve hard problems like image classification, speech recognition, object recognition, and machine translation.

It natively integrates into Spark, supports popular neural net topologies, and achieves feature parity with other open-source deep learning frameworks.

BigDL also provides 100+ basic neural networks building blocks allowing users to create novel topologies to suit their unique applications.

Thus, with Intel’s BigDL, the users are able to leverage their existing Spark infrastructure to enable Deep Learning applications without having to invest into bringing up separate frameworks to take advantage of neural networks capabilities.

such as number of compute nodes, cores, and batch size, a BigDL application leverages stable Spark infrastructure for node communications and resource management during its execution.

For this particular case, the Jupyter Notebook will automatically set up a default spark context so you don’t need to do the above configuration, but you do need to configure a few other Spark related configuration which will be explained in the sample Jupyter Notebook.

In this blog post, we have demonstrated the basic steps to set up a BigDL environment on Apache Spark for Azure HDInsight, and you can find a more detailed step to use BigDL to analyze MNIST dataset in the engineering blog post “How to use BigDL on Apache Spark for Azure HDInsight.”

Leveraging BigDL Spark library, a user can easily write scalable distributed Deep Learning applications within familiar Spark infrastructure without an intimate knowledge of the configuration of the underlying compute cluster.

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Companies are turning to deep learning to solve hard problems, like image classification, speech recognition, object recognition, and machine translation.

It natively integrates into Spark, supports popular neural net topologies, and achieves feature parity with other open-source deep learning frameworks.

BigDL also provides 100+ basic neural network building blocks allowing users to create novel topologies to suit their unique applications.

Thus, with Intel’s BigDL, the users are able to leverage their existing Spark infrastructure to enable Deep Learning applications without having to invest into bringing up separate frameworks to take advantage of neural networks capabilities.

While providing a high-level control “knobs”, such as number of compute nodes, cores, and batch size, a BigDL application leverages stable Spark infrastructure for node communications and resource management during its execution.

The section below is largely based on the BigDL Documentation and there are two major steps: There are a few additional steps in the blog post in order to illustrate how it can work with the MNIST dataset.

git clone https://github.com/intel-analytics/BigDL.git #install Maven as it is not installed by default sudo apt-get install -y maven #change maven setting based on BigDL documentation export BIGDL_ROOT=$(pwd)/BigDL export MAVEN_OPTS='-Xmx2g -XX:ReservedCodeCacheSize=512m' pushd ${BIGDL_ROOT} bash make-dist.sh -P spark_2.0 #

pwd -P )' hadoop fs -mkdir /mnistdataset cd '$DIR' for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte do if [ !

It is a small computer vision dataset used for writing simple machine learning applications, such as LeNet described later, akin to “hello world” exercise in programming courses.

It's a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

There are four files in the dataset: train-images-idx3-ubyte contains train images, train-labels-idx1-ubyte is a train label file, t10k-images-idx3-ubyte has validation images, and t10k-labels-idx1-ubyte contains validation labels.

BigDL program starts with import com.intel.analytics.bigdl._ and then initializes the Engine (including the number of executor nodes, the number of physical cores on each executor):

For this particular case, the Jupyter Notebook will automatically set up a default spark context so you don’t need to do the above configuration, but you do need to configure a few other Spark related configuration which will be explained in the Jupyter Notebook.

Finally (after optionally specifying the validation data and methods for the Optimizer), we train the model by calling Optimizer.optimize(): The complete and fully functional Python Jupyter Notebook is available to you.

Leveraging the BigDL Spark library, a user can easily write scalable distributed Deep Learning applications within familiar Spark infrastructure without an intimate knowledge of the configuration of the underlying compute cluster.

Azure HDInsight training resources – Learn about big data using open source technologies

We have received requests from customers to have detailed documentation on how to architect, deploy, manage, monitor and secure big data solutions for use-cases and scenarios such as advanced analytics, streaming, business Iintelligence, ETL, and many more.

We are pleased to announce the release of the HDInsight Developer Guide, a guide that covers both basic as well as advanced scenarios useful for any developer, data scientists, or data engineer getting started or learning more with Azure HDInsight.

The guide starts with a basic overview and use-cases, followed by best practices on how to configure cluster, plan capacity, and develop applications for different workloads such as Hive, Spark, and optimize workloads based.

The instructor-led and self-paced video courses span from short webinars, to multi-day workshops, to longer-term deep dives on demand.

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