{"id":158,"date":"2020-12-29T18:08:43","date_gmt":"2020-12-29T12:38:43","guid":{"rendered":"https:\/\/data-flair.training\/news\/?p=158"},"modified":"2020-12-29T18:16:21","modified_gmt":"2020-12-29T12:46:21","slug":"distributed-machine-learning-tools","status":"publish","type":"post","link":"https:\/\/data-flair.training\/news\/distributed-machine-learning-tools\/","title":{"rendered":"It’s Time for a Distributed Learning for Machine Learning – Next Generation ML Tools"},"content":{"rendered":"

Machine Learning today is one of the hottest aspects of computer Science.<\/strong><\/em><\/p>\n

-Steve Ballmer<\/strong><\/em><\/p>\n<\/blockquote>\n

There are two ways for accelerating machine learning workloads- Vertical scaling or horizontal scaling. <\/span><\/p>\n

But in terms of the degree of distribution within a machine learning ecosystem, they are classified into three categories which are- centralized, decentralized, and fully distributed.\u00a0<\/span><\/p>\n

The distributed system is more appropriate and efficient than a centralized or any other system. <\/span><\/p>\n

So, here we’ve enlisted some of the popular tools that enable distributed Machine learning.<\/span><\/p>\n

Best Tools for Distributed Machine Learning for 2021<\/h2>\n

1. DistBelief<\/span><\/h3>\n

DistBelief is one of the most important tools for Distributed Machine Learning. <\/span><\/p>\n

It was developed by Google and able to support data and model parallel training with huge capability like tens or thousands of CPU cores.\u00a0<\/span><\/p>\n

DistBelief can also handle the training of a giant model with 1.7 billion parameters.<\/span><\/p>\n

2. Apache Spark<\/span><\/h3>\n

Many Machine Learning algorithms have transformations in Linear Algebra and that are highly iterative in nature.\u00a0<\/span><\/p>\n

For such tasks, the paradigm of the map and the reduced operations are not suitable. To resolve this problem, the introduction of Apache Spark took place.<\/span><\/p>\n

The core difference between MapReduce and Spark is that the MapReduce tasks require you to write all the data to the disks to execute it.\u00a0<\/span><\/p>\n

The Spark, on the other hand, can keep all data in memory and saves expensive reads from the disk.<\/span><\/p>\n

3. MapReduce and Hadoop<\/span><\/h3>\n

Developed by Google, MapReduce is an efficient framework for processing large amounts of data. <\/span><\/p>\n

Processing of data in a distributed setting follows a two-phase process.\u00a0<\/span><\/p>\n

In the first phase, which is the map phase, the data is split into tuples. <\/span><\/p>\n

It then follows the second phase, the reduce phase, where these tuples are grouped to generate a single output value per key.\u00a0<\/span><\/p>\n

In every phase of execution, Hadoop and MapReduce both depend on distributed file systems.<\/span><\/p>\n

4. Caffe2<\/span><\/h3>\n

Caffe2 is a deep learning framework that distributes machine learning by using AllReduce algorithms. <\/span><\/p>\n

It does this by using NCCL and custom code.\u00a0<\/span><\/p>\n

NCCL between GPUs on a single host and custom code between hosts based on Facebook\u2019s Gloo library.<\/span><\/p>\n

5. TensorFlow<\/span><\/h3>\n

TensorFlow emerged from DistBelief and both were developed by Google. <\/span><\/p>\n

It borrows the concepts of computation graph and parameter server from DistBelief.\u00a0<\/span><\/p>\n

TensorFlow doesn\u2019t require any custom code composed of fundamental math operations for defining a new type of neural network layer.<\/span><\/p>\n

6. Microsoft Cognitive Toolkit<\/span><\/h3>\n

There are various ways one can achieve data-parallel distribution by using Microsoft Cognitive Toolkit<\/a>. <\/span><\/p>\n

Most of them use the Ring AllReduce tactic. Over fault-tolerance, it makes the trade-off of linear scalability.\u00a0<\/span><\/p>\n

7. Petuum<\/span><\/h3>\n

To keep track of the model being trained, Petuum uses a Parameter server paradigm. <\/span><\/p>\n

Petuum provides an abstraction layer that enables it to run on systems using HDFS and Hadoop job scheduler.\u00a0<\/span><\/p>\n

This simplifies the compatibility with the pre-existing clusters. <\/span><\/p>\n

To achieve good scalability on large datasets, ML\u2019s error tolerance, dependencies, and non-uniform convergence are the aims of this approach.\u00a0<\/span><\/p>\n

8. DIANNE( Distributed Artificial Neural Networks)<\/span><\/h3>\n

DIANNE is a Java-based distributed deep learning framework. <\/span><\/p>\n

For executing the necessary computations, it uses the torch native backend.\u00a0<\/span><\/p>\n

It enables model-parallelism as each building block of the neural network is deployed on a specific node.<\/span><\/p>\n

9. MXNet<\/span><\/h3>\n

MXNet can easily achieve linear speedup on a small cluster of 10 machines equipped with a GPU as compared to a single machine when training GoogleNet. <\/span><\/p>\n

Like TensorFlow, the representation of models is just as dataflow graphs.\u00a0\u00a0<\/span><\/p>\n

10. Baidu AllReduce<\/span><\/h3>\n

To train stochastic gradient descent models on separate mini-batches of the training data, common high-performance computing technology is used by AllReduce.\u00a0<\/span><\/p>\n

Baidu claims to have linear speedup when applying this technique to train deep learning networks<\/span>.\u00a0<\/span><\/p>\n

Summary<\/span><\/h2>\n

So these are some of the important tools for Distributed Machine Learning. <\/span><\/p>\n

These tools have plenty of advantages and make the task of developers easier.\u00a0<\/span><\/p>\n

All it requires is to use the right tool in the right situation to get the most benefit out of these tools.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Machine Learning today is one of the hottest aspects of computer Science. -Steve Ballmer There are two ways for accelerating machine learning workloads- Vertical scaling or horizontal scaling. But in terms of the degree...<\/p>\n","protected":false},"author":6722,"featured_media":159,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[104],"tags":[157,141,158],"class_list":["post-158","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology","tag-distributed-machine-learning-tools","tag-technology","tag-tools-for-distributed-machine-learning"],"yoast_head":"\nIt's Time for a Distributed Learning for Machine Learning - Next Generation ML Tools - DataFlair<\/title>\n<meta name=\"description\" content=\"Check the list of Top 10 Most Popular Distributed machine learning Tools that are experts choice. 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