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Blog postServerless is a kind of buzzword in recent years. It brings me a new concept of providing a web service without depending on the fixed amount of server machines (virtually), enabling us to build a more agile and flexible platform responding to changes faster.
Serverless Framework is one of the most notable framework implementing the concept, “serverless”. It supports a lot of major cloud service providers such as AWS, Azure. We can launch a new web-based service with minimal code writ6 days ago Read more -
Blog postContinuing from the latest article, I’m going to cover another topic of MLIR as well.
mlir-opt is a tool working as a utility to manipulate the MLIR code by applying various kinds of passes and optimizations legally. It enables us to convert a dialect of MLIR to another dialect easily. There is a tremendous amount of functionality and options in mlir-opt. Hence I’m afraid I cannot cover the whole topic of mlir-opt on this small page. (mlir-opt --help emits 372 lines for options!)
1 week ago Read more -
Blog postContinuing from the last article to create minimal Dialect to print tensor element with MLIR, I am going to illustrate the structure of the codebase of Dialect.
As noted previously, I put the whole repository on Lewuathe/mlir-hello. Please take a look into that if you need to know more.
Code Structure The official site contains the general guide to create Dialect. Here is the illustration of the structure of the repository.
├── CMakeLists.txt ├── README.md ├── hello-opt │3 weeks ago Read more -
Blog postMachine learning is one of the hottest fields these days. No one can stop this emerging field’s progress, and no one can know the true potential of machine learning applications. The new concept or new design of the model is appearing day by day. That’s true.
But it’s also true that every machine learning (ML) application, artificial intelligence (AI), is still running the ordinary von-Neumann computer we are all familiar with. Hence, all ML applications and AI need to be compiled int3 weeks ago Read more -
Blog postI am usually conservative for upgrading the libraries my machine depends on. I do not click the update button even the OS installer urges me to do so because it is likely to temporarily slow down the productivity to deal with the problem that occurred just after the upgrade. That’s not fun.
But this time, I was careless. I accidentally approved upgrading the Xcode in my mac. The target version is 12.3. I believed there should have been no problem in just upgrading one build toolchain.4 weeks ago Read more -
Blog postWe tend to use fluentd (td-agent) to keep the access log of the service persistently. Fluentd is one of the most reliable and flexible middleware to collect various kinds of application logging. We can quickly broaden the target of logging by using many types of plugins maintained by the community.
As is often the case with the middleware libraries, we have found unexpected behavior of fluentd due to our lack of full knowledge of the library. Our Rails application often loses the appl2 months ago Read more -
Blog postIf you are an expert in modern C++, you must be familiar with the move semantics of C++. Move semantics provides us a chance to improve the performance by eliminating the unnecessary copy of the object, introduced in C++11. If your code contains a large object and sees the time when it’s copied often, it’s worth considering it.
Additionally, you may also be familiar with the smart pointer of C++. std::unique_ptr is a type of smart pointer. It prohibits the programmer from copying the3 months ago Read more -
Blog postYou might have encountered a situation where you cannot build the latest Docker image when using the Buildx. If so, you may find this article helpful to give you a little insight into your question, “Why I keep seeing the stale image in the list!”.
What I tried was building the Docker image supporting ARM64 architecture. To achieve my goal, it requires me to enable Buildx experimental feature of Docker. It allows us to build a Docker image supporting multiple architectures. When I hav5 months ago Read more -
Blog postI recently tried to run Presto on Arm architecture system to evaluate how it can cost-effectively achieve faster performance as part of my work. Thanks to AWS, we can make use of server machines having Arm processors such as Graviton 1/2. We have succeeded in experimenting without having much difficulty. The result of the research was described in the following articles.
Presto Experiment with Graviton Processor High Performance SQL: AWS Graviton2 Benchmarks with Presto and Arm Treasure D5 months ago Read more -
Blog postHey, this is a part of the series describing the situation where I encountered weird behavior in the programming :) Today is about Java. When I wrote a code to do bit manipulation in Java, the unexpected outcome shows up. Unfortunately, I could not find the official specification behind this behavior. Thus this aims to get a chance to find the answer from someone who read this article.
Masking the most significant bit What we wanted to do was getting the most significant bit in the 2’5 months ago Read more
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Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively
Key Features
- Build, train and run machine learning models in the browser using TensorFlow.js
- Create smart web applications from scratch with the help of useful examples
- Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function
Book Description
TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach.
Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge.
By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.
What you will learn
- Use the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset
- Deploy tfjs-converter to convert Keras models and load them into TensorFlow.js
- Apply the Bellman equation to solve MDP problems
- Use the k-means algorithm in TensorFlow.js to visualize prediction results
- Create tf.js packages with Parcel, Webpack, and Rollup to deploy web apps
- Implement tf.js backend frameworks to tune and accelerate app performance
Who this book is for
This book is for web developers who want to learn how to integrate machine learning techniques with web-based applications from scratch. This book will also appeal to data scientists, machine learning practitioners, and deep learning enthusiasts who are looking to perform accelerated, browser-based machine learning on Web using TensorFlow.js. Working knowledge of JavaScript programming language is all you need to get started.
Table of Contents
- Machine Learning for the Web
- Importing Pre-trained Models into TensorFlow.js
- TensorFlow.js Ecosystem
- Polynomial Regression
- Classification with Logistic Regression
- Unsupervised Learning
- Sequential Data Analysis
- Dimensionality Reduction
- Solving Markov decision problems
- Deploying Machine Learning Applications
- Tuning applications to achieve high performance
- Future Works around TensorFlow.js
In this book you will see how to use the main JavaScript deep learning frameworks and web programming in the browser with the capture of inputs and the WebGL implementation. Deep learning in the browser is currently at an embryonic stage, but this is the best time to bet on it before it becomes a giant, and this book will get you in on the action.
Are you ready to embark on the adventure?
Spark: Big Data Cluster Computing in Production goes beyond general Spark overviews to provide targeted guidance toward using lightning-fast big-data clustering in production. Written by an expert team well-known in the big data community, this book walks you through the challenges in moving from proof-of-concept or demo Spark applications to live Spark in production. Real use cases provide deep insight into common problems, limitations, challenges, and opportunities, while expert tips and tricks help you get the most out of Spark performance. Coverage includes Spark SQL, Tachyon, Kerberos, ML Lib, YARN, and Mesos, with clear, actionable guidance on resource scheduling, db connectors, streaming, security, and much more.
Spark has become the tool of choice for many Big Data problems, with more active contributors than any other Apache Software project. General introductory books abound, but this book is the first to provide deep insight and real-world advice on using Spark in production. Specific guidance, expert tips, and invaluable foresight make this guide an incredibly useful resource for real production settings.
- Review Spark hardware requirements and estimate cluster size
- Gain insight from real-world production use cases
- Tighten security, schedule resources, and fine-tune performance
- Overcome common problems encountered using Spark in production
Spark works with other big data tools including MapReduce and Hadoop, and uses languages you already know like Java, Scala, Python, and R. Lightning speed makes Spark too good to pass up, but understanding limitations and challenges in advance goes a long way toward easing actual production implementation. Spark: Big Data Cluster Computing in Production tells you everything you need to know, with real-world production insight and expert guidance, tips, and tricks.
Professional Hadoop is the complete reference and resource for experienced developers looking to employ Apache Hadoop in real-world settings. Written by an expert team of certified Hadoop developers, committers, and Summit speakers, this book details every key aspect of Hadoop technology to enable optimal processing of large data sets. Designed expressly for the professional developer, this book skips over the basics of database development to get you acquainted with the framework's processes and capabilities right away. The discussion covers each key Hadoop component individually, culminating in a sample application that brings all of the pieces together to illustrate the cooperation and interplay that make Hadoop a major big data solution. Coverage includes everything from storage and security to computing and user experience, with expert guidance on integrating other software and more.
Hadoop is quickly reaching significant market usage, and more and more developers are being called upon to develop big data solutions using the Hadoop framework. This book covers the process from beginning to end, providing a crash course for professionals needing to learn and apply Hadoop quickly.
- Configure storage, UE, and in-memory computing
- Integrate Hadoop with other programs including Kafka and Storm
- Master the fundamentals of Apache Big Top and Ignite
- Build robust data security with expert tips and advice
Hadoop's popularity is largely due to its accessibility. Open-source and written in Java, the framework offers almost no barrier to entry for experienced database developers already familiar with the skills and requirements real-world programming entails. Professional Hadoop gives you the practical information and framework-specific skills you need quickly.