My mapreduce1 presentation

DEVIEW 2014 [2B3]ARCUS차별기능,사용이슈,그리고카카오적용사례

Lab seminar introduces Ting Chen's recent 3 works: - Pix2seq: A Language Modeling Framework for Object Detection (ICLR’22) - A Unified Sequence Interface for Vision Tasks (NeurIPS’22) - A Generalist Framework for Panoptic Segmentation of Images and Videos (submitted to ICLR’23)

La gestione dei log è da sempre un argomento complesso e nel tempo si sono cercate varie soluzioni più o meno complesse, spesso difficili da integrare nel proprio stack applicativo. Daremo un’ overview generale dei principali sistemi di aggregazione evoluta dei log in realtime (Fluentd, Greylog, eccetera) e illustreremo del motivo ci ha spinto a scegliere ELK per risolvere un’esigenza del nostro cliente; ovvero di consultare i log in modo piu comprensibile da persone non tecniche. Lo stack ELK (Elasticsearch Logstash Kibana) permette agli sviluppatori di consultare i log in fase di debug / produzione senza avvalersi dello staff sistemistico. Dimostreremo come abbiamo eseguito il deployment dello stack ELK e lo abbiamo implementato per interpretare e strutturare i log applicativi di Magento.

In this presentation presented in AI & ML meetup on 2nd Feb, Sangram Mishra develops the same NLP solution using NLTK and OpenNLP, Sangram compares and contrasts the two open source technologies for deeper understanding and insights on choosing and using them for real-world projects.

An introductory but very precise slide on mathematics of RNN/LSTM algorithms. You would get a clearer understanding on RNN back/forward propagation with this. *This slide is not finished yet. If you like it, please give me some feedback to motivate me. I made this slide as an intern in DATANOMIQ Gmbh URL: https://www.datanomiq.de/

This document provides an overview of caching strategies when using an API gateway. It first discusses different types of caches like HTTP caches, DNS caches, and database caches. It then focuses on caching strategies for web services, specifically caching within an API gateway. It discusses patterns like cache-aside, read-through, and write-through. It also covers using replication caches for API gateway infrastructure data and distributing caches between nodes. Finally, it provides examples of caching technologies that could be used in an API gateway like Ehcache, Infinispan, Hazelcast, and Redis.

JJUG CCC 2016 Spring I-5 Session

The document discusses the Vision Transformer (ViT) model for computer vision tasks. It covers: 1. How ViT tokenizes images into patches and uses position embeddings to encode spatial relationships. 2. ViT uses a class embedding to trigger class predictions, unlike CNNs which have decoders. 3. The receptive field of ViT grows as the attention mechanism allows elements to attend to other distant elements in later layers. 4. Initial results showed ViT performance was comparable to CNNs when trained on large datasets but lagged CNNs trained on smaller datasets like ImageNet.

2017/9/5公開

Tensorflow KR 2차 모임 라이트닝톡

MLOps KR 커뮤니티 그룹에서 진행한 MLOps 춘추 전국 시대 정리 발표자료입니다! 다운로드 받으시면 고화질로 볼 수 있습니다!! :)

데이터야놀자 2021 https://datayanolja.kr/ https://www.youtube.com/watch?v=j-xiEKFF2-U

Donald Knuth is an American computer scientist, mathematician, and professor emeritus at Stanford University. He began writing "The Art of Computer Programming" in 1962, which is a comprehensive monograph that covers various programming algorithms and their analysis. The work is divided into multiple volumes that cover different aspects of computer programming such as fundamental algorithms, sorting and searching, and syntactic algorithms. In developing the book, Knuth also popularized the use of asymptotic notation or "Big O" notation to characterize the growth rate of functions. Frustrated with publishing tools at the time, he developed the TeX computer typesetting system, which later became known as LaTeX. Knuth is strongly opposed to software patents, arguing that ideas that should be easily

1. O manual apresenta as diferentes assinaturas do selo I'm green da Braskem, incluindo assinaturas completas, simples e com descritivos. 2. São descritas as proporções corretas para aplicação de cada assinatura e são fornecidas versões em cores e monocromáticas. 3. O documento também especifica a área de reserva do selo e reduções máximas de acordo com diferentes suportes e técnicas de impressão.

What is a superpixel? This presentation describes Superpixel algorithms such as watershed, mean-shift, SLIC, BSLIC (SLIC superpixels based on boundary term) references: [1] Luc Vincent and Pierre Soille. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6):583–598, 1991. [2] D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603–619, May 2002. [3] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274 - 2282, May 2012. [4] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels, EPFL Technical Report no. 149300, June 2010. [5] Hai Wang, Xiongyou Peng, Xue Xiao, and Yan Liu, BSLIC: SLIC Superpixels Based on Boundary Term, Symmetry 2017, 9(3), Feb 2017.

Data Pipeline Data Lake

An increasing number of popular applications become data-intensive in nature. In the past decade, the World Wide Web has been adopted as an ideal platform for developing data-intensive applications, since the communication paradigm of the Web is sufficiently open and powerful. Data-intensive applications like data mining and web indexing need to access ever-expanding data sets ranging from a few gigabytes to several terabytes or even petabytes. Google leverages the MapReduce model to process approximately twenty petabytes of data per day in a parallel fashion. In this talk, we introduce the Google’s MapReduce framework for processing huge datasets on large clusters. We first outline the motivations of the MapReduce framework. Then, we describe the dataflow of MapReduce. Next, we show a couple of example applications of MapReduce. Finally, we present our research project on the Hadoop Distributed File System. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into account for launching speculative map tasks, because it is assumed that most maps are data-local. Unfortunately, both the homogeneity and data locality assumptions are not satisfied in virtualized data centers. We show that ignoring the datalocality issue in heterogeneous environments can noticeably reduce the MapReduce performance. In this paper, we address the problem of how to place data across nodes in a way that each node has a balanced data processing load. Given a dataintensive application running on a Hadoop MapReduce cluster, our data placement scheme adaptively balances the amount of data stored in each node to achieve improved data-processing performance. Experimental results on two real data-intensive applications show that our data placement strategy can always improve the MapReduce performance by rebalancing data across nodes before performing a data-intensive application in a heterogeneous Hadoop cluster.

This document provides an overview of Google's Bigtable distributed storage system. It describes Bigtable's data model as a sparse, multidimensional sorted map indexed by row, column, and timestamp. Bigtable stores data across many tablet servers, with a single master server coordinating metadata operations like tablet assignment and load balancing. The master uses Chubby, a distributed lock service, to track which tablet servers are available and reassign tablets if servers become unreachable.

This document describes MapReduce, a programming model and software framework for processing large datasets in a distributed manner. It introduces the key concepts of MapReduce including the map and reduce functions, distributed execution across clusters of machines, and fault tolerance. The document outlines how MapReduce abstracts away complexities like parallelization, data distribution, and failure handling. It has been used successfully at Google for large-scale tasks like search indexing and machine learning.

This document provides an overview of MapReduce, a programming model developed by Google for processing and generating large datasets in a distributed computing environment. It describes how MapReduce abstracts away the complexities of parallelization, fault tolerance, and load balancing to allow developers to focus on the problem logic. Examples are given showing how MapReduce can be used for tasks like word counting in documents and joining datasets. Implementation details and usage statistics from Google demonstrate how MapReduce has scaled to process exabytes of data across thousands of machines.

The document discusses network performance profiling of Hadoop jobs. It presents results from running two common Hadoop benchmarks - Terasort and Ranked Inverted Index - on different Amazon EC2 instance configurations. The results show that the shuffle phase accounts for a significant portion (25-29%) of total job runtime. They aim to reproduce existing findings that network performance is a key bottleneck for shuffle-intensive Hadoop jobs. Some questions are also raised about inconsistencies in reported network bandwidth capabilities for EC2.

MapReduce is a programming model and an associated implementation for processing and generating large data sets on a distributed computing environment. It allows users to write map and reduce functions to process input key/value pairs in parallel across large clusters of commodity machines. The MapReduce framework handles parallelization, scheduling, input/output distribution, and fault tolerance automatically, allowing developers to focus just on the logic of their map and reduce functions. The paper presents the MapReduce model and describes its implementation at Google for processing terabytes of data across thousands of machines efficiently and with fault tolerance.

The document describes MapReduce, a programming model and software framework for processing large datasets in a distributed computing environment. It discusses how MapReduce allows users to specify map and reduce functions to parallelize tasks across large clusters of machines. It also covers how MapReduce handles parallelization, fault tolerance, and load balancing transparently through an easy-to-use programming interface.

The document discusses using Map-Reduce for machine learning algorithms on multi-core processors. It describes rewriting machine learning algorithms in "summation form" to express the independent computations as Map tasks and aggregating results as Reduce tasks. This formulation allows the algorithms to be parallelized efficiently across multiple cores. Specific machine learning algorithms that have been implemented or analyzed in this Map-Reduce framework are listed.

This document discusses using R for large-scale data analysis on distributed data clouds. It recommends splitting large datasets into segments using MapReduce or UDFs, then building separate models for each segment in R. PMML can be used to combine the separate models into an ensemble model. The Sawmill framework is proposed to preprocess data in parallel, build models for each segment using R, and combine the models into a PMML file for deployment. Running R on each segment sequentially allows scaling to large datasets, with examples showing processing times for different numbers of segments.

MapReduce is a programming model for processing large datasets in parallel across clusters of machines. It involves splitting the input data into independent chunks which are processed by the "map" step, and then grouping the outputs of the maps together and inputting them to the "reduce" step to produce the final results. The MapReduce paper presented Google's implementation which ran on a large cluster of commodity machines and used the Google File System for fault tolerance. It demonstrated that MapReduce can efficiently process very large amounts of data for applications like search, sorting and counting word frequencies.