TokyoCabinet and TokyoTyrant are open source databases written by Akira Koyasu. TokyoCabinet provides embedded databases using hash, B+ tree, fixed-length, and table formats. TokyoTyrant builds on TokyoCabinet and provides a client-server database with a server and client command line interface. Both have Java APIs and support features like putting, getting, listing, and removing key-value pairs.
Kyoto Products includes Kyoto Cabinet and Kyoto Tycoon. Kyoto Cabinet is a lightweight database library that provides a straightforward implementation of DBM with high performance and scalability. Kyoto Tycoon is a lightweight database server that provides a persistent cache based on Kyoto Cabinet with features like expiration, high concurrency, and replication. Both support various database types and languages.
Tokyo Cabinet is a library of routines for managing a database. The database is a simple data file containing records, each is a pair of a key and a value. Every key and value is serial bytes with variable length. Both binary data and character string can be used as a key and a value. There is neither concept of data tables nor data types. Records are organized in hash table, B+ tree, or fixed-length array.
The document describes how to use Gawk to perform data aggregation from log files on Hadoop by having Gawk act as both the mapper and reducer to incrementally count user actions and output the results. Specific user actions are matched and counted using operations like incrby and hincrby and the results are grouped by user ID and output to be consumed by another system. Gawk is able to perform the entire MapReduce job internally without requiring Hadoop.
This document discusses M3, Uber's time series database. It provides an overview of M3 and compares it to Graphite, which Uber previously used. M3 was built to have better resiliency, efficiency, and scalability than Graphite. It provides both a Graphite-compatible query interface and its own query language called M3QL. The document describes M3's architecture, storage, indexing, and how it handles high write and read throughput. It also covers instrumentation, profiling, load testing, and optimizations used in M3's Go code.
This document discusses using Ruby for big data applications. It provides an overview of Cassandra for NoSQL storage, Hadoop for batch processing, Solr for indexing, and Storm for real-time processing. It then demonstrates using Ruby to interact with these systems through REST APIs and Java interoperability. Advanced topics discussed include integrating these technologies with Rails applications and combining real-time and batch processing.
The document discusses using Ruby for big data applications, including using Ruby with NoSQL databases like Cassandra and Hadoop for distributed storage and processing, and integrating Ruby with real-time streaming frameworks like Storm. It also covers using REST APIs to allow Ruby applications to interact with these big data systems and perform batch and real-time processing of data.
The document compares on-heap and off-heap caching options. It discusses heap memory usage in the JVM and alternatives like off-heap memory using memory mapped files, ByteBuffers, and Unsafe. Popular off-heap caches like Chronicle, Hazelcast, and Redis are presented along with comparisons of their features, performance, and garbage collection impact. The document aims to help developers choose the most suitable cache for their application needs.
Kyoto Products includes Kyoto Cabinet and Kyoto Tycoon. Kyoto Cabinet is a lightweight database library that provides a straightforward implementation of DBM with high performance and scalability. Kyoto Tycoon is a lightweight database server that provides a persistent cache based on Kyoto Cabinet with features like expiration, high concurrency, and replication. Both support various database types and languages.
Tokyo Cabinet is a library of routines for managing a database. The database is a simple data file containing records, each is a pair of a key and a value. Every key and value is serial bytes with variable length. Both binary data and character string can be used as a key and a value. There is neither concept of data tables nor data types. Records are organized in hash table, B+ tree, or fixed-length array.
The document describes how to use Gawk to perform data aggregation from log files on Hadoop by having Gawk act as both the mapper and reducer to incrementally count user actions and output the results. Specific user actions are matched and counted using operations like incrby and hincrby and the results are grouped by user ID and output to be consumed by another system. Gawk is able to perform the entire MapReduce job internally without requiring Hadoop.
This document discusses M3, Uber's time series database. It provides an overview of M3 and compares it to Graphite, which Uber previously used. M3 was built to have better resiliency, efficiency, and scalability than Graphite. It provides both a Graphite-compatible query interface and its own query language called M3QL. The document describes M3's architecture, storage, indexing, and how it handles high write and read throughput. It also covers instrumentation, profiling, load testing, and optimizations used in M3's Go code.
This document discusses using Ruby for big data applications. It provides an overview of Cassandra for NoSQL storage, Hadoop for batch processing, Solr for indexing, and Storm for real-time processing. It then demonstrates using Ruby to interact with these systems through REST APIs and Java interoperability. Advanced topics discussed include integrating these technologies with Rails applications and combining real-time and batch processing.
The document discusses using Ruby for big data applications, including using Ruby with NoSQL databases like Cassandra and Hadoop for distributed storage and processing, and integrating Ruby with real-time streaming frameworks like Storm. It also covers using REST APIs to allow Ruby applications to interact with these big data systems and perform batch and real-time processing of data.
The document compares on-heap and off-heap caching options. It discusses heap memory usage in the JVM and alternatives like off-heap memory using memory mapped files, ByteBuffers, and Unsafe. Popular off-heap caches like Chronicle, Hazelcast, and Redis are presented along with comparisons of their features, performance, and garbage collection impact. The document aims to help developers choose the most suitable cache for their application needs.
Despite being a slow interpreter, Python is a key component in high-performance computing (HPC). Python is easy to use. C++ is fast. Together they are a beautiful blend. A new tool, pybind11, makes this approach even more attractive to HPC code. It focuses on the niceties C++11 brings in. Beyond the syntactic sugar around the Python C API, it is interesting to see how pybind11 handles the vast difference between the two languages, and what matters to HPC.
This document provides an overview of Java Virtual Machine (JVM) concepts including the Java process life cycle, class loading, JVM memory layout, garbage collection, and tools for monitoring JVM performance and debugging issues. Key topics covered include the Java main method, class loader hierarchy, object size calculation, generational garbage collection, and commands like jinfo, jstack, jstat, jmap for viewing thread dumps, GC statistics, and heap information.
The document provides information on application performance tuning education. It discusses key performance metrics like TPS and considerations for CPU usage, memory usage, garbage collection. It then summarizes Java/Tomcat performance tuning factors and garbage collection options. The last part discusses Java profiling and troubleshooting tools like JDK tools, HPROF, jhat, jmap, jstack, jstat and jvisualvm. It also provides an example Tomcat shell script configuration for setting JVM options and using profiling agents.
Choosing the right high availability strategyMariaDB plc
The document discusses high availability techniques for MariaDB including master-slave replication, multi-master clustering, and topologies. It covers fundamental concepts like failover, switchover, and rejoin. Synchronous and asynchronous replication are compared. Optimizations for replication performance are also outlined.
This document discusses supporting HDF5 in GrADS, an interactive desktop tool for analyzing earth science data. It outlines how GrADS currently handles different data formats, including HDF4 and netCDF. The document proposes two options for supporting HDF5 in GrADS - linking with the NetCDF-4 library, which would be easy but limited, or linking directly with the HDF5 library, which would require a new interface but provide more general HDF5 support.
Go provided a 25% performance improvement over Python for a data integration task. Further optimizations in Go, like using goroutines and minimizing memory allocations, resulted in a 3.5x faster runtime than the original Python code. While Python has many useful libraries, Go is better suited for CPU-intensive and high-throughput workloads due to its low overhead concurrency model and compiled speed. The team concluded Go would be preferable for their data ingestion needs due to its performance advantages.
This document discusses garbage collection and automatic memory management in Java. It covers the basic strategies used in garbage collection like mark and sweep. It also discusses the different garbage collectors used in HotSpot like SerialGC, ParallelGC, ConcMarkSweepGC and G1. It provides an overview of garbage collection in J9 and JRockit as well. The document also touches on alternatives to garbage collection like weak and soft references.
A lot of data is best represented as time series: Operational data, financial data, and even in data warehouses the dominant dimension is often time. We present Chronix, a time series database based on Apache Solr and Spark which is able to handle trillions of time series data points and perform interactive queries. Chronix Spark is open source software and battle-proven at a German car manufacturer and an international telco.
We demonstrate several use cases of Chronix from real-life. Afterwards we lift the curtain and deep-dive into the Chronix architecture esp. how we're using Solr to store time series data and how we've hooked up Solr with Spark. We provide some benchmarks showing how Chronix has outperformed other time series databases in both performance and storage-efficiency.
Chronix is open source under the Apache License (http://chronix.io).
This document discusses cache and concurrency considerations for Apache Cassandra. It covers metrics and monitors for cache performance, how the JVM performs in big data systems, examples of Cassandra in real-world systems like Facebook and Twitter, techniques for achieving fast writes and reads, and tools for optimizing performance. It emphasizes locality, non-blocking collections, and techniques for handling garbage collection and compactions efficiently.
This document discusses Uber's transition from a monolithic architecture to a microservices architecture and the adoption of Go as a primary programming language. It provides examples of some key Go services at Uber including Geofences, an early service, and Geobase, a more recent service. It also discusses Uber's development of open source Go libraries and tools like Ringpop, TChannel, go-torch, and others to help establish Go as a first-class language at Uber.
MapReduce - Basics | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2skCodH
This CloudxLab Understanding MapReduce tutorial helps you to understand MapReduce in detail. Below are the topics covered in this tutorial:
1) Thinking in Map / Reduce
2) Understanding Unix Pipeline
3) Examples to understand MapReduce
4) Merging
5) Mappers & Reducers
6) Mapper Example
7) Input Split
8) mapper() & reducer() Code
9) Example - Count number of words in a file using MapReduce
10) Example - Compute Max Temperature using MapReduce
11) Hands-on - Count number of words in a file using MapReduce on CloudxLab
FOSDEM 2020: Querying over millions and billions of metrics with M3DB's indexRob Skillington
The cardinality of monitoring data we are collecting today continues to rise, in no small part due to the ephemeral nature of containers and compute platforms like Kubernetes. Querying a flat dataset comprised of an increasing number of metrics requires searching through millions and in some cases billions of metrics to select a subset to display or alert on. The ability to use wildcards or regex within the tag name and values of these metrics and traces are becoming less of a nice-to-have feature and more useful for the growing popularity of ad-hoc exploratory queries.
In this talk we will look at how Prometheus introduced the concept of a reverse index existing side-by-side with a traditional column based TSDB in a single process. We will then walk through the evolution of M3’s metric index, starting with ElasticSearch and evolving over the years to the current M3DB reverse index. We will give an in depth overview of the alternate designs and dive deep into the architecture of the current distributed index and the optimizations we’ve made in order to fulfill wildcards and regex queries across billions of metrics.
Functional, Type-safe, Testable Microservices with ZIO and gRPCNadav Samet
Many use cases for Scala involve developing and deploying microservices. Although once favored, HTTP microservices don’t have type-safe, documented definitions that can be safely evolved over time. gRPC was designed by Google to solve this problem, however current Scala gRPC libraries aren’t designed to work with modern effect systems like ZIO.
Enter ZIO gRPC, a new library created by Nadav Samet, the author of the popular ScalaPB library, which is the underlying technology behind all Scala gRPC libraries. ZIO gRPC allows companies to write purely functional, type-safe, and testable gRPC services and clients.
ZIO gRPC supports all types of RPCs (unary, client streaming, server streaming, and bidirectional), and fully uses ZIO typed errors for RPC error codes, ZIO interruption for canceling RPC calls, and ZIO environment for propagating RPC context; and supports ZLayer construction out of the box.
Learn how to create and ship type-safe, testable microservices as you watch Nadav live code a simple and boilerplate-free service in just a few minutes!
This document discusses garbage collection in Java. It begins by explaining the motivation for garbage collection in Java, such as avoiding memory leaks and heap corruption. It then covers the goals of garbage collectors, including minimizing memory overhead, maximizing application throughput while keeping pause times low. Different types of garbage collectors are described, such as serial, parallel, CMS, and G1 collectors. Key concepts like generations, GC roots, and safe points are also summarized.
Mesos provides a distributed systems kernel that allows organizations to dynamically share resources between distributed applications like Hadoop, Spark, and Storm. It addresses issues with static resource partitioning, like increased complexity and poor resource utilization. Mesos introduces an abstraction layer that bundles all machines in a cluster into a single shared pool. It provides APIs for building frameworks to run applications that leverage the shared resources.
Groovy speech I held last year for introducing a new JVM language as substitute of Java. Easy and intuitive, it offers new features unknow to its parent yet.
Новый InterSystems: open-source, митапы, хакатоныTimur Safin
Presentation for the 1st InterSystems Meetup in the Minsk:
- New and better InterSystems changes their practice.
- open-source repositories, meetups, and hackathon;
- CPM (package manager) as a good example of open-source project
Guide for visualizing JMA's GSM outputs using GrADSJMA_447
The document discusses visualizing JMA high-resolution GSM data with GrADS. It provides an overview of the JMA data service that provides GRIB2 forecast data. It then discusses preparing the data for visualization with GrADS, including using tools to create control and index files from GRIB2 data. Finally, it covers basic interactive operation and customization of images using GrADS.
Presentació a càrrec de Víctor Pérez, tècnic de Càlcul i Aplicacions del CSUC, duta a terme a la "4a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 17 de març de 2021 en format virtual.
go-git is a 100% Go libray used to interact with git repositories. Even if it already supports most of the functionality it still lags a bit in performance when compared with the git CLI or some other libraries. I'll explain some of the problems that we face when dealing with git repos and some examples of performance improvements done to the library.
Despite being a slow interpreter, Python is a key component in high-performance computing (HPC). Python is easy to use. C++ is fast. Together they are a beautiful blend. A new tool, pybind11, makes this approach even more attractive to HPC code. It focuses on the niceties C++11 brings in. Beyond the syntactic sugar around the Python C API, it is interesting to see how pybind11 handles the vast difference between the two languages, and what matters to HPC.
This document provides an overview of Java Virtual Machine (JVM) concepts including the Java process life cycle, class loading, JVM memory layout, garbage collection, and tools for monitoring JVM performance and debugging issues. Key topics covered include the Java main method, class loader hierarchy, object size calculation, generational garbage collection, and commands like jinfo, jstack, jstat, jmap for viewing thread dumps, GC statistics, and heap information.
The document provides information on application performance tuning education. It discusses key performance metrics like TPS and considerations for CPU usage, memory usage, garbage collection. It then summarizes Java/Tomcat performance tuning factors and garbage collection options. The last part discusses Java profiling and troubleshooting tools like JDK tools, HPROF, jhat, jmap, jstack, jstat and jvisualvm. It also provides an example Tomcat shell script configuration for setting JVM options and using profiling agents.
Choosing the right high availability strategyMariaDB plc
The document discusses high availability techniques for MariaDB including master-slave replication, multi-master clustering, and topologies. It covers fundamental concepts like failover, switchover, and rejoin. Synchronous and asynchronous replication are compared. Optimizations for replication performance are also outlined.
This document discusses supporting HDF5 in GrADS, an interactive desktop tool for analyzing earth science data. It outlines how GrADS currently handles different data formats, including HDF4 and netCDF. The document proposes two options for supporting HDF5 in GrADS - linking with the NetCDF-4 library, which would be easy but limited, or linking directly with the HDF5 library, which would require a new interface but provide more general HDF5 support.
Go provided a 25% performance improvement over Python for a data integration task. Further optimizations in Go, like using goroutines and minimizing memory allocations, resulted in a 3.5x faster runtime than the original Python code. While Python has many useful libraries, Go is better suited for CPU-intensive and high-throughput workloads due to its low overhead concurrency model and compiled speed. The team concluded Go would be preferable for their data ingestion needs due to its performance advantages.
This document discusses garbage collection and automatic memory management in Java. It covers the basic strategies used in garbage collection like mark and sweep. It also discusses the different garbage collectors used in HotSpot like SerialGC, ParallelGC, ConcMarkSweepGC and G1. It provides an overview of garbage collection in J9 and JRockit as well. The document also touches on alternatives to garbage collection like weak and soft references.
A lot of data is best represented as time series: Operational data, financial data, and even in data warehouses the dominant dimension is often time. We present Chronix, a time series database based on Apache Solr and Spark which is able to handle trillions of time series data points and perform interactive queries. Chronix Spark is open source software and battle-proven at a German car manufacturer and an international telco.
We demonstrate several use cases of Chronix from real-life. Afterwards we lift the curtain and deep-dive into the Chronix architecture esp. how we're using Solr to store time series data and how we've hooked up Solr with Spark. We provide some benchmarks showing how Chronix has outperformed other time series databases in both performance and storage-efficiency.
Chronix is open source under the Apache License (http://chronix.io).
This document discusses cache and concurrency considerations for Apache Cassandra. It covers metrics and monitors for cache performance, how the JVM performs in big data systems, examples of Cassandra in real-world systems like Facebook and Twitter, techniques for achieving fast writes and reads, and tools for optimizing performance. It emphasizes locality, non-blocking collections, and techniques for handling garbage collection and compactions efficiently.
This document discusses Uber's transition from a monolithic architecture to a microservices architecture and the adoption of Go as a primary programming language. It provides examples of some key Go services at Uber including Geofences, an early service, and Geobase, a more recent service. It also discusses Uber's development of open source Go libraries and tools like Ringpop, TChannel, go-torch, and others to help establish Go as a first-class language at Uber.
MapReduce - Basics | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2skCodH
This CloudxLab Understanding MapReduce tutorial helps you to understand MapReduce in detail. Below are the topics covered in this tutorial:
1) Thinking in Map / Reduce
2) Understanding Unix Pipeline
3) Examples to understand MapReduce
4) Merging
5) Mappers & Reducers
6) Mapper Example
7) Input Split
8) mapper() & reducer() Code
9) Example - Count number of words in a file using MapReduce
10) Example - Compute Max Temperature using MapReduce
11) Hands-on - Count number of words in a file using MapReduce on CloudxLab
FOSDEM 2020: Querying over millions and billions of metrics with M3DB's indexRob Skillington
The cardinality of monitoring data we are collecting today continues to rise, in no small part due to the ephemeral nature of containers and compute platforms like Kubernetes. Querying a flat dataset comprised of an increasing number of metrics requires searching through millions and in some cases billions of metrics to select a subset to display or alert on. The ability to use wildcards or regex within the tag name and values of these metrics and traces are becoming less of a nice-to-have feature and more useful for the growing popularity of ad-hoc exploratory queries.
In this talk we will look at how Prometheus introduced the concept of a reverse index existing side-by-side with a traditional column based TSDB in a single process. We will then walk through the evolution of M3’s metric index, starting with ElasticSearch and evolving over the years to the current M3DB reverse index. We will give an in depth overview of the alternate designs and dive deep into the architecture of the current distributed index and the optimizations we’ve made in order to fulfill wildcards and regex queries across billions of metrics.
Functional, Type-safe, Testable Microservices with ZIO and gRPCNadav Samet
Many use cases for Scala involve developing and deploying microservices. Although once favored, HTTP microservices don’t have type-safe, documented definitions that can be safely evolved over time. gRPC was designed by Google to solve this problem, however current Scala gRPC libraries aren’t designed to work with modern effect systems like ZIO.
Enter ZIO gRPC, a new library created by Nadav Samet, the author of the popular ScalaPB library, which is the underlying technology behind all Scala gRPC libraries. ZIO gRPC allows companies to write purely functional, type-safe, and testable gRPC services and clients.
ZIO gRPC supports all types of RPCs (unary, client streaming, server streaming, and bidirectional), and fully uses ZIO typed errors for RPC error codes, ZIO interruption for canceling RPC calls, and ZIO environment for propagating RPC context; and supports ZLayer construction out of the box.
Learn how to create and ship type-safe, testable microservices as you watch Nadav live code a simple and boilerplate-free service in just a few minutes!
This document discusses garbage collection in Java. It begins by explaining the motivation for garbage collection in Java, such as avoiding memory leaks and heap corruption. It then covers the goals of garbage collectors, including minimizing memory overhead, maximizing application throughput while keeping pause times low. Different types of garbage collectors are described, such as serial, parallel, CMS, and G1 collectors. Key concepts like generations, GC roots, and safe points are also summarized.
Mesos provides a distributed systems kernel that allows organizations to dynamically share resources between distributed applications like Hadoop, Spark, and Storm. It addresses issues with static resource partitioning, like increased complexity and poor resource utilization. Mesos introduces an abstraction layer that bundles all machines in a cluster into a single shared pool. It provides APIs for building frameworks to run applications that leverage the shared resources.
Groovy speech I held last year for introducing a new JVM language as substitute of Java. Easy and intuitive, it offers new features unknow to its parent yet.
Новый InterSystems: open-source, митапы, хакатоныTimur Safin
Presentation for the 1st InterSystems Meetup in the Minsk:
- New and better InterSystems changes their practice.
- open-source repositories, meetups, and hackathon;
- CPM (package manager) as a good example of open-source project
Guide for visualizing JMA's GSM outputs using GrADSJMA_447
The document discusses visualizing JMA high-resolution GSM data with GrADS. It provides an overview of the JMA data service that provides GRIB2 forecast data. It then discusses preparing the data for visualization with GrADS, including using tools to create control and index files from GRIB2 data. Finally, it covers basic interactive operation and customization of images using GrADS.
Presentació a càrrec de Víctor Pérez, tècnic de Càlcul i Aplicacions del CSUC, duta a terme a la "4a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 17 de març de 2021 en format virtual.
go-git is a 100% Go libray used to interact with git repositories. Even if it already supports most of the functionality it still lags a bit in performance when compared with the git CLI or some other libraries. I'll explain some of the problems that we face when dealing with git repos and some examples of performance improvements done to the library.
This document discusses Tokyo Cabinet and Tokyo Tyrant, which are key-value databases and a remote service wrapper. Tokyo Cabinet supports hash tables, B-trees, fixed-length arrays and tables. Tokyo Tyrant adds network support and features like high concurrency, multiple protocols, hot backup and replication. The document demonstrates installing and using both with Python via the pytc and pytyrant bindings. Performance tests show Tokyo Cabinet hash tables and B-trees have similar speed while Tokyo Tyrant has slower network overhead compared to direct Tokyo Cabinet access.
Tokyo Cabinet is a high-performance embedded database that provides key-value, B-tree, and table data storage. It has APIs for Perl, Java, Lua, and Ruby. Tokyo Cabinet Hash and Btree databases allow fast storage and retrieval of data. Tokyo Tyrant adds network functionality, allowing remote access via its own protocol or HTTP. It also enables features like replication and Lua scripting. Tokyo Dystopia provides full-text search capabilities. Ruby libraries exist to interface with Tokyo Cabinet and Tokyo Tyrant from Ruby applications.
Tokyo Cabinet is a simple and fast key-value database library that provides an implementation of DBM with fast storage and retrieval of records. It stores records in a file-based hash table that allows retrieval of 10 million records in under 24 seconds. The library is thread-safe, supports record compression, and provides OO-style APIs. It uses a file format with four sections: metadata, bucket array, free block pool, and record entities organized into a hash table and binary search tree for collision handling.
Tokyo Cabinet is a key-value database manager that provides several database structures like hash, B-tree, fixed-length, and table. It runs quickly on Linux, Solaris, and Mac OS X and offers advantages like small database file sizes, fast processing speeds, high performance in multi-threaded environments, and simple APIs. Benchmark tests showed Tokyo Cabinet was 31 times faster than MySQL for writing and retrieving 10,000 records.
The document discusses various storage options available in AWS, including S3, EBS, and local instance storage. S3 provides unlimited, highly durable object storage, while EBS offers virtual block-level storage for applications and databases. Local instance storage is best for low latency use cases but data is ephemeral. The options each have different performance, durability, cost and management characteristics. The document provides best practices and use cases for each storage type, and discusses how they can be used together for various applications.
This document discusses benchmarking deep learning frameworks like Chainer. It begins by defining benchmarks and their importance for framework developers and users. It then examines examples like convnet-benchmarks, which objectively compares frameworks on metrics like elapsed time. It discusses challenges in accurately measuring elapsed time for neural network functions, particularly those with both Python and GPU components. Finally, it introduces potential solutions like Chainer's Timer class and mentions the DeepMark benchmarks for broader comparisons.
This document summarizes an internship project using deep reinforcement learning to develop an agent that can automatically park a car simulator. The agent takes input from virtual cameras mounted on the car and uses a DQN network to learn which actions to take to reach a parking goal. Several agent configurations were tested, with the three-camera subjective view agent showing the most success after modifications to the reward function and task difficulty via curriculum learning. While the agent could sometimes learn to park, the learning was not always stable, indicating further refinement is needed to the deep RL approach for this automatic parking task.
The document summarizes a meetup discussing deep learning and Docker. It covered Yuta Kashino introducing BakFoo and his background in astrophysics and Python. The meetup discussed recent advances in AI like AlphaGo, generative adversarial networks, and neural style transfer. It provided an overview of Chainer and arXiv papers. The meetup demonstrated Chainer 1.3, NVIDIA drivers, and Docker for deep learning. It showed running a TensorFlow tutorial using nvidia-docker and provided Dockerfile examples and links to resources.
ng-japan 2018 https://ngjapan.org/
にてお話しした「Protractor under the hood」の資料です。
angular-cli の e2eテストコマンド ng e2e の裏側で、Protractorがどんな仕組みを作ってテストを実行しているかについてお話ししました。
動画はこちらです。
https://youtu.be/_eMrDsLjIOM?t=13345
Japan Container Days v18.04 Meetup でLTしました。
最近はいろいろな場面でファンクションを使いたいと思うことが増えました。ファンクションを本格的に使っていくに当たっては、今や当然となったCI/CDも考える必要があります。AWS LamdbaでCI/CDするためのフローを紹介します。
This document is a presentation in Japanese on PHP performance optimizations. It discusses looping structures like foreach and for loops, and compares the opcode execution and performance of each. It shows that foreach loops are faster than for loops in PHP due to fewer opcodes being executed. It also discusses counting array elements and comparing empty vs count, showing count has better performance. Overall it presents techniques to understand PHP internals and optimize code performance.
This document provides an introduction to the Scala programming language for Java programmers. It discusses Scala's motivation as a scalable and more productive alternative to Java. Key features covered include static typing, object-oriented and functional programming, traits, pattern matching, and actors. Examples are provided to illustrate concepts like functions as first-class values, partially applied functions, and collection operations. The document concludes by mentioning additional Scala concepts and providing references for further reading.
The document provides information about JavaOne 2012 held in San Francisco from the perspective of a "Petite Bourgeoisie" or person of modest means. It summarizes the key details of JavaOne including location, dates, costs, benefits and concerns for attending. Preparation is estimated to take a full day and costs around 450,000 yen including flights, lodging, meals and conference pass. Benefits include experiencing Java culture firsthand and networking with leading engineers. Basic English phrases are also provided to help navigate the conference.
The document discusses upcoming technologies in Java SE8 and Java EE7. It describes projects like Project Jigsaw, Project Lambda, HotRockit, and Nashorn that are part of Java SE8. Project Jigsaw introduces a modularization framework that does away with the classpath. Project Lambda adds support for lambda expressions and default methods to support functional-style programming. HotRockit combines the HotSpot and JRockit JVMs. Nashorn implements a JavaScript engine for the JVM.
the Topics in JavaSE7 and some of Key features in JavaEE6.
the codes in this presentation are here. http://www.slideshare.net/akirakoyasu/java-up-to-date-sources
Guava is Java libraries by Google.
This is Introduction of Guava with some API and sample codes.
Its samples are here.
http://www.slideshare.net/akirakoyasu/hello-guava-samples
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
cd ~/path/to/tokyoproducts\ntchmgr create dbfile.tch\ntchmgr put dbfile.tch "hoge" "fuga"tchmgr list dbfile.tch\ntchmgr get dbfile.tch "hoge"\ntchmgr out dbfile.tch "hoge"\nrm dbfile.tch\n
cd ~/path/to/tokyoproducts\ntchmgr create dbfile.tch\ntchmgr put dbfile.tch "hoge" "fuga"tchmgr list dbfile.tch\ntchmgr get dbfile.tch "hoge"\ntchmgr out dbfile.tch "hoge"\nrm dbfile.tch\n
cd ~/path/to/tokyoproducts\ntchmgr create dbfile.tch\ntchmgr put dbfile.tch "hoge" "fuga"tchmgr list dbfile.tch\ntchmgr get dbfile.tch "hoge"\ntchmgr out dbfile.tch "hoge"\nrm dbfile.tch\n
cd ~/path/to/tokyoproducts\ntchmgr create dbfile.tch\ntchmgr put dbfile.tch "hoge" "fuga"tchmgr list dbfile.tch\ntchmgr get dbfile.tch "hoge"\ntchmgr out dbfile.tch "hoge"\nrm dbfile.tch\n
cd ~/path/to/tokyoproducts\ntchmgr create dbfile.tch\ntchmgr put dbfile.tch "hoge" "fuga"tchmgr list dbfile.tch\ntchmgr get dbfile.tch "hoge"\ntchmgr out dbfile.tch "hoge"\nrm dbfile.tch\n
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cd ~/path/to/tokyoproducts\nttserver dbfile.tch\n^C\n
cd ~/path/to/tokyoproducts\nttserver dbfile.tch\n^C\n
console1:$ cd ~/path/to/tokyoproducts\nconsole1:$ ttserver dbfile.tch\n\nconsole2:$ tcrmgr put localhost "hoge" "fuga"\nconsole2:$ tcrmgr put localhost "foo" "bar"\nconsole2:$ tcrmgr list localhost\nconsole2:$ tcrmgr get localhost "hoge"\nconsole2:$ tcrmgr out localhost "hoge"\n\nconsole1:$ ^C\nconsole1:$ tchmgr list dbfile.tch\n
console1:$ cd ~/path/to/tokyoproducts\nconsole1:$ ttserver dbfile.tch\n\nconsole2:$ tcrmgr put localhost "hoge" "fuga"\nconsole2:$ tcrmgr put localhost "foo" "bar"\nconsole2:$ tcrmgr list localhost\nconsole2:$ tcrmgr get localhost "hoge"\nconsole2:$ tcrmgr out localhost "hoge"\n\nconsole1:$ ^C\nconsole1:$ tchmgr list dbfile.tch\n
console1:$ cd ~/path/to/tokyoproducts\nconsole1:$ ttserver dbfile.tch\n\nconsole2:$ tcrmgr put localhost "hoge" "fuga"\nconsole2:$ tcrmgr put localhost "foo" "bar"\nconsole2:$ tcrmgr list localhost\nconsole2:$ tcrmgr get localhost "hoge"\nconsole2:$ tcrmgr out localhost "hoge"\n\nconsole1:$ ^C\nconsole1:$ tchmgr list dbfile.tch\n
console1:$ cd ~/path/to/tokyoproducts\nconsole1:$ ttserver dbfile.tch\n\nconsole2:$ tcrmgr put localhost "hoge" "fuga"\nconsole2:$ tcrmgr put localhost "foo" "bar"\nconsole2:$ tcrmgr list localhost\nconsole2:$ tcrmgr get localhost "hoge"\nconsole2:$ tcrmgr out localhost "hoge"\n\nconsole1:$ ^C\nconsole1:$ tchmgr list dbfile.tch\n
console1:$ cd ~/path/to/tokyoproducts\nconsole1:$ ttserver dbfile.tch\n\nconsole2:$ tcrmgr put localhost "hoge" "fuga"\nconsole2:$ tcrmgr put localhost "foo" "bar"\nconsole2:$ tcrmgr list localhost\nconsole2:$ tcrmgr get localhost "hoge"\nconsole2:$ tcrmgr out localhost "hoge"\n\nconsole1:$ ^C\nconsole1:$ tchmgr list dbfile.tch\n
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console1:$ cd ~/path/to/tokyoproducts\nconsole1:$ mkdir ulog\nconsole1:$ ttserver -sid 1 -ulog ulog/ dbfile.tch\n\nconsole2:$ cd ~/path/to/tokyoproducts2\nconsole2:$ cat ttcopy.sh\nconsole2:$ tcrmgr copy localhost '@/path/to/tokyoproducts2/ttcopy.sh'\nconsole2:$ ls -l ~/path/to/tokyoproducts\nconsole2:$ mv ~/path/to/tokyoproducts/dbfile.tch.xxxx ./\nconsole2:$ echo xxxxx > slave.rts\nconsole2:$ mv dbfile.tch.xxxx dbfile.tch\nconsole2:$ ttserver -sid 2 -port 1979 -mhost localhost -mport 1978 -rts slave.rts dbfile.tch\n\nconsole3:$ tcrmgr list localhost\nconsole3:$ tcrmgr list -port 1979 localhost\nconsole3:$ tcrmgr put localhost "cto" "mukaihira"\nconsole3:$ tcrmgr get -port 1979 localhost "cto"\n
console1:$ cd ~/path/to/tokyoproducts\nconsole1:$ mkdir ulog\nconsole1:$ ttserver -sid 1 -ulog ulog/ dbfile.tch\n\nconsole2:$ cd ~/path/to/tokyoproducts2\nconsole2:$ cat ttcopy.sh\nconsole2:$ tcrmgr copy localhost '@/path/to/tokyoproducts2/ttcopy.sh'\nconsole2:$ ls -l ~/path/to/tokyoproducts\nconsole2:$ mv ~/path/to/tokyoproducts/dbfile.tch.xxxx ./\nconsole2:$ echo xxxxx > slave.rts\nconsole2:$ mv dbfile.tch.xxxx dbfile.tch\nconsole2:$ ttserver -sid 2 -port 1979 -mhost localhost -mport 1978 -rts slave.rts dbfile.tch\n\nconsole3:$ tcrmgr list localhost\nconsole3:$ tcrmgr list -port 1979 localhost\nconsole3:$ tcrmgr put localhost "cto" "mukaihira"\nconsole3:$ tcrmgr get -port 1979 localhost "cto"\n
console1:$ cd ~/path/to/tokyoproducts\nconsole1:$ mkdir ulog\nconsole1:$ ttserver -sid 1 -ulog ulog/ dbfile.tch\n\nconsole2:$ cd ~/path/to/tokyoproducts2\nconsole2:$ cat ttcopy.sh\nconsole2:$ tcrmgr copy localhost '@/path/to/tokyoproducts2/ttcopy.sh'\nconsole2:$ ls -l ~/path/to/tokyoproducts\nconsole2:$ mv ~/path/to/tokyoproducts/dbfile.tch.xxxx ./\nconsole2:$ echo xxxxx > slave.rts\nconsole2:$ mv dbfile.tch.xxxx dbfile.tch\nconsole2:$ ttserver -sid 2 -port 1979 -mhost localhost -mport 1978 -rts slave.rts dbfile.tch\n\nconsole3:$ tcrmgr list localhost\nconsole3:$ tcrmgr list -port 1979 localhost\nconsole3:$ tcrmgr put localhost "cto" "mukaihira"\nconsole3:$ tcrmgr get -port 1979 localhost "cto"\n