Lina  Biyinzika

Lina Biyinzika

1658514060

TensorFlowonSpark Brings TensorFlow Programs to Apache Spark Clusters.

TensorFlowOnSpark

TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters.   

By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers.

It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. Its Spark-compatible API helps manage the TensorFlow cluster with the following steps:

  1. Startup - launches the Tensorflow main function on the executors, along with listeners for data/control messages.
  2. Data ingestion
    • InputMode.TENSORFLOW - leverages TensorFlow's built-in APIs to read data files directly from HDFS.
    • InputMode.SPARK - sends Spark RDD data to the TensorFlow nodes via a TFNode.DataFeed class. Note that we leverage the Hadoop Input/Output Format to access TFRecords on HDFS.
  3. Shutdown - shuts down the Tensorflow workers and PS nodes on the executors.

Table of Contents

Background

TensorFlowOnSpark was developed by Yahoo for large-scale distributed deep learning on our Hadoop clusters in Yahoo's private cloud.

TensorFlowOnSpark provides some important benefits (see our blog) over alternative deep learning solutions.

  • Easily migrate existing TensorFlow programs with <10 lines of code change.
  • Support all TensorFlow functionalities: synchronous/asynchronous training, model/data parallelism, inferencing and TensorBoard.
  • Server-to-server direct communication achieves faster learning when available.
  • Allow datasets on HDFS and other sources pushed by Spark or pulled by TensorFlow.
  • Easily integrate with your existing Spark data processing pipelines.
  • Easily deployed on cloud or on-premise and on CPUs or GPUs.

Install

TensorFlowOnSpark is provided as a pip package, which can be installed on single machines via:

# for tensorflow>=2.0.0
pip install tensorflowonspark

# for tensorflow<2.0.0
pip install tensorflowonspark==1.4.4

For distributed clusters, please see our wiki site for detailed documentation for specific environments, such as our getting started guides for single-node Spark Standalone, YARN clusters and AWS EC2. Note: the Windows operating system is not currently supported due to this issue.

Usage

To use TensorFlowOnSpark with an existing TensorFlow application, you can follow our Conversion Guide to describe the required changes. Additionally, our wiki site has pointers to some presentations which provide an overview of the platform.

Note: since TensorFlow 2.x breaks API compatibility with TensorFlow 1.x, the examples have been updated accordingly. If you are using TensorFlow 1.x, you will need to checkout the v1.4.4 tag for compatible examples and instructions.

API

API Documentation is automatically generated from the code.

Contribute

Please join the TensorFlowOnSpark user group for discussions and questions. If you have a question, please review our FAQ before posting.

Contributions are always welcome. For more information, please see our guide for getting involved.

License

The use and distribution terms for this software are covered by the Apache 2.0 license. See LICENSE file for terms.


Author: yahoo
Source code: https://github.com/yahoo/TensorFlowOnSpark
License: Apache-2.0 license

#apache-spark  #tensorflow #python 

What is GEEK

Buddha Community

TensorFlowonSpark Brings TensorFlow Programs to Apache Spark Clusters.
Roberta  Ward

Roberta Ward

1595344320

Wondering how to upgrade your skills in the pandemic? Here's a simple way you can do it.

Corona Virus Pandemic has brought the world to a standstill.

Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.

Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!

And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!

If you are still not aware of it, don’t worry as Georgia Byng has well said,

“No time is better than the present”

– Georgia Byng, a British children’s writer, illustrator, actress and film producer.

No matter if you are a developer (be it front-end or back-end) or a data scientisttester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.

From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.

How to upgrade your skills?

Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.

These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.

Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).

xlr8rs make your development real fast by just adding your core business logic to the template.

If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂

Confused with which technology to start with?

To keep you updated, the Knoldus tech hub provides you with the information on the most trending technology and the most downloaded templates at present. This you’ll be informed and learn the one that’s most trending.

Since we believe:

“There’s always a scope of improvement“

If you still feel like it isn’t helping you in learning and development, you can provide your feedback in the feedback section in the bottom right corner of the website.

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Lina  Biyinzika

Lina Biyinzika

1658514060

TensorFlowonSpark Brings TensorFlow Programs to Apache Spark Clusters.

TensorFlowOnSpark

TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters.   

By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers.

It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. Its Spark-compatible API helps manage the TensorFlow cluster with the following steps:

  1. Startup - launches the Tensorflow main function on the executors, along with listeners for data/control messages.
  2. Data ingestion
    • InputMode.TENSORFLOW - leverages TensorFlow's built-in APIs to read data files directly from HDFS.
    • InputMode.SPARK - sends Spark RDD data to the TensorFlow nodes via a TFNode.DataFeed class. Note that we leverage the Hadoop Input/Output Format to access TFRecords on HDFS.
  3. Shutdown - shuts down the Tensorflow workers and PS nodes on the executors.

Table of Contents

Background

TensorFlowOnSpark was developed by Yahoo for large-scale distributed deep learning on our Hadoop clusters in Yahoo's private cloud.

TensorFlowOnSpark provides some important benefits (see our blog) over alternative deep learning solutions.

  • Easily migrate existing TensorFlow programs with <10 lines of code change.
  • Support all TensorFlow functionalities: synchronous/asynchronous training, model/data parallelism, inferencing and TensorBoard.
  • Server-to-server direct communication achieves faster learning when available.
  • Allow datasets on HDFS and other sources pushed by Spark or pulled by TensorFlow.
  • Easily integrate with your existing Spark data processing pipelines.
  • Easily deployed on cloud or on-premise and on CPUs or GPUs.

Install

TensorFlowOnSpark is provided as a pip package, which can be installed on single machines via:

# for tensorflow>=2.0.0
pip install tensorflowonspark

# for tensorflow<2.0.0
pip install tensorflowonspark==1.4.4

For distributed clusters, please see our wiki site for detailed documentation for specific environments, such as our getting started guides for single-node Spark Standalone, YARN clusters and AWS EC2. Note: the Windows operating system is not currently supported due to this issue.

Usage

To use TensorFlowOnSpark with an existing TensorFlow application, you can follow our Conversion Guide to describe the required changes. Additionally, our wiki site has pointers to some presentations which provide an overview of the platform.

Note: since TensorFlow 2.x breaks API compatibility with TensorFlow 1.x, the examples have been updated accordingly. If you are using TensorFlow 1.x, you will need to checkout the v1.4.4 tag for compatible examples and instructions.

API

API Documentation is automatically generated from the code.

Contribute

Please join the TensorFlowOnSpark user group for discussions and questions. If you have a question, please review our FAQ before posting.

Contributions are always welcome. For more information, please see our guide for getting involved.

License

The use and distribution terms for this software are covered by the Apache 2.0 license. See LICENSE file for terms.


Author: yahoo
Source code: https://github.com/yahoo/TensorFlowOnSpark
License: Apache-2.0 license

#apache-spark  #tensorflow #python 

Jack  Shaw

Jack Shaw

1641528000

Tensorflowonspark Brings Tensorflow Programs To Apache Spark Clusters

TensorFlowOnSpark

TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters.

By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers.

It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. Its Spark-compatible API helps manage the TensorFlow cluster with the following steps:

  1. Startup - launches the Tensorflow main function on the executors, along with listeners for data/control messages.
  2. Data ingestion
    • InputMode.TENSORFLOW - leverages TensorFlow's built-in APIs to read data files directly from HDFS.
    • InputMode.SPARK - sends Spark RDD data to the TensorFlow nodes via a TFNode.DataFeed class. Note that we leverage the Hadoop Input/Output Format to access TFRecords on HDFS.
  3. Shutdown - shuts down the Tensorflow workers and PS nodes on the executors.

Table of Contents

Background

TensorFlowOnSpark was developed by Yahoo for large-scale distributed deep learning on our Hadoop clusters in Yahoo's private cloud.

TensorFlowOnSpark provides some important benefits (see our blog) over alternative deep learning solutions.

  • Easily migrate existing TensorFlow programs with <10 lines of code change.
  • Support all TensorFlow functionalities: synchronous/asynchronous training, model/data parallelism, inferencing and TensorBoard.
  • Server-to-server direct communication achieves faster learning when available.
  • Allow datasets on HDFS and other sources pushed by Spark or pulled by TensorFlow.
  • Easily integrate with your existing Spark data processing pipelines.
  • Easily deployed on cloud or on-premise and on CPUs or GPUs.

Install

TensorFlowOnSpark is provided as a pip package, which can be installed on single machines via:

# for tensorflow>=2.0.0
pip install tensorflowonspark

# for tensorflow<2.0.0
pip install tensorflowonspark==1.4.4

For distributed clusters, please see our wiki site for detailed documentation for specific environments, such as our getting started guides for single-node Spark Standalone, YARN clusters and AWS EC2. Note: the Windows operating system is not currently supported due to this issue.

Usage

To use TensorFlowOnSpark with an existing TensorFlow application, you can follow our Conversion Guide to describe the required changes. Additionally, our wiki site has pointers to some presentations which provide an overview of the platform.

Note: since TensorFlow 2.x breaks API compatibility with TensorFlow 1.x, the examples have been updated accordingly. If you are using TensorFlow 1.x, you will need to checkout the v1.4.4 tag for compatible examples and instructions.

API

API Documentation is automatically generated from the code.

Contribute

Please join the TensorFlowOnSpark user group for discussions and questions. If you have a question, please review our FAQ before posting.

Contributions are always welcome. For more information, please see our guide for getting involved.

License

The use and distribution terms for this software are covered by the Apache 2.0 license. See LICENSE file for terms.

Author: yahoo
Source Code: https://github.com/yahoo/TensorFlowOnSpark
License: Apache-2.0 License

#python #tensorflow #apache-spark 

Edureka Fan

Edureka Fan

1606982795

What is Apache Spark? | Apache Spark Python | Spark Training

This Edureka “What is Apache Spark?” video will help you to understand the Architecture of Spark in depth. It includes an example where we Understand what is Python and Apache Spark.

#big-data #apache-spark #developer #apache #spark

Dominic  Feeney

Dominic Feeney

1639996200

TensorFlowOnSpark - Brings TensorFlow to Apache Spark Clusters

TensorFlowOnSpark

TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. 

By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers.

It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. Its Spark-compatible API helps manage the TensorFlow cluster with the following steps:

  1. Startup - launches the Tensorflow main function on the executors, along with listeners for data/control messages.
  2. Data ingestion
    • InputMode.TENSORFLOW - leverages TensorFlow's built-in APIs to read data files directly from HDFS.
    • InputMode.SPARK - sends Spark RDD data to the TensorFlow nodes via a TFNode.DataFeed class. Note that we leverage the Hadoop Input/Output Format to access TFRecords on HDFS.
  3. Shutdown - shuts down the Tensorflow workers and PS nodes on the executors.

Background

TensorFlowOnSpark was developed by Yahoo for large-scale distributed deep learning on our Hadoop clusters in Yahoo's private cloud.

TensorFlowOnSpark provides some important benefits (see our blog) over alternative deep learning solutions.

  • Easily migrate existing TensorFlow programs with <10 lines of code change.
  • Support all TensorFlow functionalities: synchronous/asynchronous training, model/data parallelism, inferencing and TensorBoard.
  • Server-to-server direct communication achieves faster learning when available.
  • Allow datasets on HDFS and other sources pushed by Spark or pulled by TensorFlow.
  • Easily integrate with your existing Spark data processing pipelines.
  • Easily deployed on cloud or on-premise and on CPUs or GPUs.

Install

TensorFlowOnSpark is provided as a pip package, which can be installed on single machines via:

# for tensorflow>=2.0.0
pip install tensorflowonspark

# for tensorflow<2.0.0
pip install tensorflowonspark==1.4.4

For distributed clusters, please see our wiki site for detailed documentation for specific environments, such as our getting started guides for single-node Spark Standalone, YARN clusters and AWS EC2. Note: the Windows operating system is not currently supported due to this issue.

Usage

To use TensorFlowOnSpark with an existing TensorFlow application, you can follow our Conversion Guide to describe the required changes. Additionally, our wiki site has pointers to some presentations which provide an overview of the platform.

Note: since TensorFlow 2.x breaks API compatibility with TensorFlow 1.x, the examples have been updated accordingly. If you are using TensorFlow 1.x, you will need to checkout the v1.4.4 tag for compatible examples and instructions.

API

API Documentation is automatically generated from the code.

Contribute

Please join the TensorFlowOnSpark user group for discussions and questions. If you have a question, please review our FAQ before posting.

Contributions are always welcome. For more information, please see our guide for getting involved.

Download Details:
Author: yahoo
Source Code: https://github.com/yahoo/TensorFlowOnSpark
License: Apache-2.0 License

#tensorflow #python #deep-learning #machine-learning #apache-spark