도커 무작정 따라하기: 도커가 처음인 사람도 60분이면 웹 서버를 올릴 수 있습니다!pyrasis
도커 무작정 따라하기
- 도커가 처음인 사람도 60분이면 웹 서버를 올릴 수 있습니다!
도커의 기본 개념부터 설치와 사용 방법까지 설명합니다.
더 자세한 내용은 가장 빨리 만나는 도커(Docker)를 참조해주세요~
http://www.pyrasis.com/private/2014/11/30/publish-docker-for-the-really-impatient-book
2016 아이펀팩토리 Dev Day 발표 자료
강연 제목 : Docker 로 Linux 없이 Linux 환경에서 개발하기
발표자 : 김진욱 CTO
<2016>
- 일시 : 2016년 9월 28 수요일 12:00~14:20
- 장소 : 넥슨 판교 사옥 지하 1층 교육실
REEF is a meta-framework for big data analytics that eases development atop resource managers like YARN and Mesos. It provides a reusable control plane for coordinating data processing tasks and an adaptation layer for different resource managers. REEF decouples applications from cluster resources and handles common control plane functions like fault tolerance and configuration management. The framework is implemented in Java and C# and supports local, YARN, Mesos, and HDInsight execution environments. Future work includes graduating REEF from the Apache Incubator and using it to build new data processing frameworks and systems.
This document discusses providing immersive sound for virtual reality. It notes that sound is half the experience of immersion. While VR technology allows immersion in digital worlds, truly immersive sound requires binaural 3D audio rendering or recording. Binaural audio uses head-related transfer functions to simulate the sound reaching each ear, allowing localization of sounds in 3D space. However, interactive binaural recording and matching sounds to visual content in real-time pose technical challenges. The document demonstrates an implementation of immersive 3D binaural audio for VR.
This document summarizes lessons learned from developing the Realm Android library. It discusses challenges such as setting up an Android library project, API design, testing, distribution methods, and issues like annotation processing, bytecode weaving, and native code support. Key points covered are how to start a library project, the importance of testing libraries extensively, and distribution options like Bintray.
This document summarizes a presentation about Packetbeat and monitoring distributed systems. It discusses how Packetbeat passively captures network packets, decodes protocols, and matches requests and responses to create JSON objects. It then sends this data to Elasticsearch for analysis. Aggregations like histograms, percentiles, and moving averages are used to analyze latency, identify slow methods, and detect anomalies in metrics over time. Other Beats like Topbeat, Filebeat, and Metricsbeat are also briefly introduced.
The document describes how to build a data science team and systems. It discusses establishing data collection and management systems, developing metrics and dashboards to analyze business data, creating predictive models using machine learning algorithms, and providing data science services like information retrieval to internal customers. The goal is to move from static, uncollected data to a fully realized big data platform and data science team that supports business analytics and decision making.
MIT researchers have developed highly efficient quadruped robots like the Cheetah that can run at speeds up to 6m/s. The Cheetah uses a proprioceptive actuation system with high torque density motors to achieve high force control bandwidth over 120Hz. Its parallelized control system with multicore CPUs and FPGAs allows control frequencies up to 4kHz. Design principles for efficient legged locomotion include energy regeneration, low transmission impedance, and low leg inertia. The researchers are continuing their work with robots like Cheetah 2 and Hermes.
DRC-HUBO is Rainbow Robotics' humanoid robot that competed in the DRC Finals. It uses a modular, lightweight exoskeletal design with effective cooling and power systems. PODO-RT is the real-time framework that controls DRC-HUBO. It uses a distributed architecture with independent processes communicating over shared memory for high-speed control. DRC-HUBO demonstrated a variety of autonomous tasks at the DRC Finals, including driving, opening doors, using tools, and traversing rough terrain.
1. The document discusses a lean approach to quality assurance using automated testing, code reviews, and dogfooding rather than traditional QA.
2. It emphasizes the importance of automated testing, code reviews, strict branching workflows, and dogfooding code to catch bugs early.
3. Quality is baked in through practices like continuous integration, enforcing builds, and code reviews before merging code rather than relying primarily on separate QA teams.
[17.01.19] docker introduction (Korean Version)Ildoo Kim
Docker(도커) 소개를 위해 사용했던 자료입니다.
제가 속한 개발팀에서는 도커 컨테이너를 기반으로 개발부터 배포까지 가능한 환경 및 인프라를 구축하여 개발팀에서 대다수의 오퍼레이션까지 관여하면서 Devops 형태로 운영합니다.
Docker(도커)를 처음 사용하거나 개념적으로 익숙하지 않은 초보를 위해 만든 자료입니다.
슬라이드에서 사용된 스크립트/코드는 아래에 있습니다.
https://github.com/ildoonet/docker_introduction
----
김일두, Software Engineer @ Kakao
Github : https://github.com/ildoonet
Linkedin : https://www.linkedin.com/in/ildoo-kim-56962034/
The document discusses various machine learning clustering algorithms like K-means clustering, DBSCAN, and EM clustering. It also discusses neural network architectures like LSTM, bi-LSTM, and convolutional neural networks. Finally, it presents results from evaluating different chatbot models on various metrics like validation score.
The document discusses challenges with using reinforcement learning for robotics. While simulations allow fast training of agents, there is often a "reality gap" when transferring learning to real robots. Other approaches like imitation learning and self-supervised learning can be safer alternatives that don't require trial-and-error. To better apply reinforcement learning, robots may need model-based approaches that learn forward models of the world, as well as techniques like active localization that allow robots to gather targeted information through interactive perception. Closing the reality gap will require finding ways to better match simulations to reality or allow robots to learn from real-world experiences.
[243] Deep Learning to help student’s Deep LearningNAVER D2
This document describes research on using deep learning to predict student performance in massive open online courses (MOOCs). It introduces GritNet, a model that takes raw student activity data as input and predicts outcomes like course graduation without feature engineering. GritNet outperforms baselines by more than 5% in predicting graduation. The document also describes how GritNet can be adapted in an unsupervised way to new courses using pseudo-labels, improving predictions in the first few weeks. Overall, GritNet is presented as the state-of-the-art for student prediction and can be transferred across courses without labels.
[234]Fast & Accurate Data Annotation Pipeline for AI applicationsNAVER D2
This document provides a summary of new datasets and papers related to computer vision tasks including object detection, image matting, person pose estimation, pedestrian detection, and person instance segmentation. A total of 8 papers and their associated datasets are listed with brief descriptions of the core contributions or techniques developed in each.
[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지NAVER D2
This document presents a formula for calculating the loss function J(θ) in machine learning models. The formula averages the negative log likelihood of the predicted probabilities being correct over all samples S, and includes a regularization term λ that penalizes predicted embeddings being dissimilar from actual embeddings. It also defines the cosine similarity term used in the regularization.
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기NAVER D2
The document discusses running a TensorFlow Serving (TFS) container using Docker. It shows commands to:
1. Pull the TFS Docker image from a repository
2. Define a script to configure and run the TFS container, specifying the model path, name, and port mapping
3. Run the script to start the TFS container exposing port 13377
The document discusses linear algebra concepts including:
- Representing a system of linear equations as a matrix equation Ax = b where A is a coefficient matrix, x is a vector of unknowns, and b is a vector of constants.
- Solving for the vector x that satisfies the matrix equation using linear algebra techniques such as row reduction.
- Examples of matrix equations and their component vectors are shown.
This document describes the steps to convert a TensorFlow model to a TensorRT engine for inference. It includes steps to parse the model, optimize it, generate a runtime engine, serialize and deserialize the engine, as well as perform inference using the engine. It also provides code snippets for a PReLU plugin implementation in C++.
The document discusses machine reading comprehension (MRC) techniques for question answering (QA) systems, comparing search-based and natural language processing (NLP)-based approaches. It covers key milestones in the development of extractive QA models using NLP, from early sentence-level models to current state-of-the-art techniques like cross-attention, self-attention, and transfer learning. It notes the speed and scalability benefits of combining search and reading methods for QA.
10. 1.3 JSX
1. 자바스크립트 안에 HTML이에요? HTML과 유사한 문법을 가진 JSX입니다.
2. 어떻게 실행되는 거에요?text/babel 영역을 바벨이 번역해서 자바스크립트로 만들어요.
3. 실행시간에 번역하면 느리지 않나요? 프로덕션에선 번역된 코드를 씁니다.
4. 튜토리얼에서는 바벨을 안쓰던데 text/jsx쓰던데? 그 방법 0.14에서 폐기됩니다. (곧)
17. 1.6 Count, state
state의 초기값은 getInitialState에
서
props.number 의 값으로 설정한다.
click 이벤트 처리할 콜백.
상태 값 변경은 this.setState에서
onClick 처리 this.handleClick에서
this.state.number로 설정된 상태 사
용.
26. 1.8 Virtual DOM
Virtual DOM (Javascript 영역) HTML DOM
상태(state)가 바뀌면 항상 Virtual DOM은 그려진다.
Virtual DOM이 변경되어도 실질적인 변경이 있을 때만 DOM에 적용된다.
개발자도 상태를 신경쓸 필요가 거의 없고 성능의 감소도 적다.
비교조정(Reconciliation)
27. 1.8 Virtual DOM
비교조정은 아직 효율적인 알고리즘을 발견하지 못했다. O(n^3)
http://grfia.dlsi.ua.es/ml/algorithms/references/editsurvey_bille.pdf
React는 휴리스틱을 동원하여 현실적인 효율을 높인다.
29. 1.8 Virtual DOM
div들 컴포넌트
같은 콤퍼넌트에 대해서 재사용을 시도하고 다른 콤포넌트는 지우고 생성한다.
다른 콤퍼넌트는 다른 트리를 만들어 낼 것으로 예상한다.
30. 1.8 Virtual DOM
키가 없는 경우
리스트의 경우 키(key)가 없는 콤퍼넌트는 첫번째 요소부터 재사용을 한다.
이 경우 state의 값은 날라가고 소유주(상위 클래스)가 값을 되살릴 의무가 있다.
키가 있는 경우에는 재사용에 앞서 순서를 조절하여 활용한다.
1 2 3 4
1 2 3 5 4
키가 있는 경우
35. 2.1 Webpack이 뭐에요?
의존성을 타고 로더를 적용해 스태틱 에셋으로 변환.
- CommonJs 모델과 AMD모두 지원
다양한 로더를 통해 확장성을 지원.
- Babel을 통한 ES6, 7의 지원. SCSS, Less 등의 지원. 인라인 파일.
- 핫 모듈 대체를 이용하여 실시간으로 기능을 갱신.
http://webpack.github.io/
37. 2.3 웹팩, 설정 파일(webpack.config.js)
entry의 hello.jsx 파일을 타고 들어갑니다.
출력될 파일은 현재디렉토리/build/hello.js
.jsx? 로 끝나는 파일을 찾아
/node_modules/이 포함된 것은 빼고
babel 로더(jsx 처리, ES6)를
적용합니다.
38. 2.4 웹팩, hello.jsx
별도의 JSX 파일에서는 CommonJs의 방식으로
react를 참조해야 합니다.
document를 참조할 때는 HTML에서 head가
아닌 body에서 참조해야한다.
46. 2.4 Hot Module Replacement
- 모듈 단위로 라이브리로드가 가능.
- 프로덕션 레벨에서 사용 가능 (이라고 설명하고 있지만 WebpackDevServer에서만 봤음.)
- 코드 스플리팅을 통해 필요한 부분만 다운로드.
- 부분적으로 적용이 가능하고 HMR 코드를 비활성화하면 관련 코드 제거.
이미 만들어진 HMR을 씁시다. (eg. React HMR, Redux HMR)
제작에 관심이 있는 분:
- example: http://webpack.github.io/example-app/
- API: http://webpack.github.io/docs/hot-module-replacement.html
55. 4.3 참고
• React 한글 버전 문서:
http://reactkr.github.io/react/docs/getting-started-ko-KR.html
• Flux 한글 버전 문서:
http://haruair.github.io/flux/docs/overview.html#content
• Redux 한글버전 문서:
http://dobbit.github.io/redux/
• 페이스북 그룹 React Korea와 Reactist
56. 4.3 참고
• Unidirectional User Interface Architectures
http://staltz.com/unidirectional-user-interface-architectures.html
• React Router
https://rackt.github.io/react-router/