Parallel and Distributed Processing in Machine Learning
The learning process of machine learning requires high-speed, parallel distributed processing to handle large amounts of data. Parallel distributed processing distributes processing among multiple computers and performs multiple processes at the same time, thus enabling high-speed processing.
The following technologies are used for parallel distributed processing.
- MapReduce: A distributed processing framework developed by Google that divides a large data set into multiple servers, processes the data on each server, and summarizes the results at the end.
- Spark: A distributed processing framework provided by Apache that enables faster processing than MapReduce. By keeping data in memory, disk I/O can be reduced.
- TensorFlow: An open source machine learning framework developed by Google that supports distributed processing. Data can be partitioned and processed simultaneously on multiple GPUs and multiple machines.
- Horovod: A distributed deep learning framework developed by Uber that supports frameworks such as TensorFlow and PyTorch. It can perform learning processes on multiple GPUs and multiple machines at high speed.
These parallel and distributed processing technologies are necessary to achieve fast and efficient processing in machine learning learning learning processes that handle large amounts of data.
In this blog, we will present concrete implementations of these parallel and distributed processing techniques.
Implementation
Parallel distributed processing in machine learning is a process that distributes data and calculations across multiple processing units (CPUs, GPUs, computer clusters, etc.) and simultaneously processes them to reduce processing time and improve scalability, and plays an important role when processing large data sets and complex models. It plays an important role in processing large data sets and complex models. This section describes concrete implementation examples of parallel distributed processing in machine learning in on-premise/cloud environments.
Federated Learning is a new approach to training machine learning models that addresses the challenges of privacy protection and efficient model training in distributed data environments. Unlike traditional centralized model training, Federated Learning trains models on the device or client itself and performs distributed learning without sending models to a central server. This section provides an overview of Federated Learning, its various algorithms, and examples of implementations.
- Overview of mini-batch learning and examples of algorithms and implementations
Mini-batch learning is one of the most widely used and efficient learning methods in machine learning, which is computationally more efficient and applicable to large data sets compared to the usual Gradient Descent method. This section provides an overview of mini-batch learning. Mini-batch learning is a learning method in which multiple samples (called mini-batches) are processed in batches, rather than the entire dataset at once, and the gradient of the loss function is calculated for each mini-batch and the parameters are updated using the gradient.
Topics
Online learning can be used to efficiently train discriminators even when the number of training data pieces is very large. Learning even larger data sets in shorter time can be achieved by using parallel computation and distributed environments.
In this article, we will discuss the application of regression and classification analysis in a way that is more suitable for large amounts of data. In this case, we will be dealing with a relatively modest data set of 100,000 records. This is not big data (at 100 MB, it fits comfortably in the memory of a single machine), but it is large enough to demonstrate common methods of large-scale data processing. In this chapter, we will focus on how to scale algorithms to very large data volumes through parallel processing, using Hadoop (a popular framework for distributed computation) as a case study.
Hadoop features a distributed file control module called HDFS (Hadoop Distributed File System) and a distributed data processing infrastructure called MapReduce. Here, we focus on two libraries provided by Clojure that work with Hadoop, Tesser and Parkour, and describe the MapReduce mechanism based on concrete implementations.
The length of time it takes to process each iteration of batch gradient descent depends on the size of the data and the number of processors in the computer. Even though several chunks of data are processed in parallel, the data set is large and the processors are finite. While parallel computation provides higher speed, doubling the size of the data set doubles the execution time.
Hadoop will be one of several systems that have emerged in the past decade that aim to parallelize work beyond the capabilities of a single machine; Hadoop aims to run computations on many servers, rather than running code on multiple processors. In fact, a Hadoop cluster can consist of thousands of servers.
The pair-wise differencing of all items described in “Implementing a Simple Recommendation Algorithm Using Clojure (2)” is a time-consuming task to compile. One of the advantages of item-based recommendation techniques is that the pairwise differences between items are relatively stable over time. The difference matrix need only be computed periodically. This means that, as we have seen, if a user evaluates 10 items and then evaluates one more item, the user only needs to adjust the difference between the 11 items he or she has evaluated.
However, the execution time of the item-based recommender varies with the number of items to be stored, and the execution time increases in proportion to the number of items.
If the number of users is small compared to the number of items, it may be more efficient to implement a user-based recommender. For example, content aggregation sites, where the number of items may exceed the number of users by an order of magnitude, are good candidates for user-based recommender.
The Mahout library, described in “Large-Scale Clustering with Clojure and Mahout,” includes tools for creating various types of recommenders, including user-based recommenders. In this article, we will discuss these tools.
Apache Spark is an open source parallel and distributed processing infrastructure. Based on Spark Core, the engine of parallel and distributed processing, Spark consists of a set of application-specific libraries, including Spark SQL for SQL processing, Spark Streaming for stream processing, MLlib for machine learning processing, and GraphX for graph processing.
Spark Core can accept HDFS (Hadoop Distributed File System) as well as HIve, HBase, PostgreSQL, MySQL, CSV files, and other inputs as data sources.
Spark provides fast parallel distributed processing for large amounts of data, and after reading data from the data source, Spark processes it with minimal I/O to storage and network I/O. Therefore, Spark is suitable for cases where the same data is transformed in succession, or where the result set is iterated over multiple times, such as in machine learning. Spark’s features are described below in terms of “machine learning processing,” “business processing,” and “stream processing,” respectively.
Apache Spark’s data processing uses a data structure called a “Resillient Distributed Dataset (RDD),” and its programming model is to “process the RDD, generate a new RDD, and repeat the process to obtain the desired result. This is the programming model.
In this section, we will take a closer look at how this works and describe the structure and characteristics of RDDs and how the distributed structure around RDDs is implemented on clusters. First, we describe the structure and characteristics of RDDs and the processing that can be applied to RDDs.
In this article, we will discuss how to set up an environment for running Spark, which is designed to run on a cluster. However, if you want to check the behavior of an application you are creating or perform functional tests, it is possible to run Spark on a single machine, as building a cluster environment is a large and cumbersome task. In this article, we first describe how to build an environment on a single machine, and then describe the procedure for building a cluster environment.
In this article, we will discuss how to build a development environment for Apache Spark and how to build and run an application. Spark provides APIs for a variety of programming languages. In this article, we will discuss how to build an application using Scala, the language in which Apache Spark is written, under the assumption that Spark-clint is used. First, a Spark application needs to have its source code compiled and packaged into a JAR file. For this purpose, we will use sbt, a tool to manage the build process of an application development project, including compilation of source code written in Scala and Java, management of library dependencies, and packaging.
The Spark project is a cluster computing framework that emphasizes low-latency job execution and is a relatively new project that emerged from UC Berkley’s AMP Lab in 2009. Distributed File System (HDFS)), it aims to significantly accelerate job execution time by keeping much of the computation in memory.
In this article, we will discuss the key concepts required to run Spark jobs using Sparkling, a Clojure library.
GraphX will be a distributed graph processing library designed to work in conjunction with Spark. Like the MLlib library described in “Large-scale Machine Learning with Apache Spark and MLlib,” GraphX provides a set of abstractions built on top of Spark’s RDDs. By representing graph vertices and edges as RDDs, GraphX can handle very large graphs in a scalable manner.
By restricting the types of computations that can be represented and introducing techniques for partitioning and distributing graphs, graph parallel systems can execute sophisticated graph algorithms several orders of magnitude faster and more efficiently than typical data parallel systems.
Parallel computation and stochastic optimization go relatively well together, and parallel computation is possible by modifying the methods described so far. Here, a variety of parallel computations are possible. In this section, we list methods for various situations. An overview of the various methods is as follows. Simple averaging, mini-batch method, asynchronous distributed SGD, stochastic gradient descent, stochastic optimization in a distributed environment
Coalitional learning is: – a framework in which a server with a large amount of computing and storage power – and a large number of clients with a modest amount of data collection power and computing resources – cooperate and learn efficiently by communicating over a narrow bandwidth line.
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