TensorMSA is a framework for machine learning and deep learning. Main purpose of developing this framework is to provide automated pipe lines (data extraction > data preprocessing > train model > evaluate model > service model). Use of effective pipeline is really important when we proceed real project. There are so many hard tasks which has to be done to build data driven model if you don’t have a framework or pipeline. Let’s talk about problems of ML project without effective tools and our approach on defined problems.

Problems

  • Set up environment for deep learning is not a easy task
  • Build pipe line from data to train model
  • Difficulties of understading deep learning and implement those algorithms
  • Manage model and data for service on legacy systems (usually works on Java)
  • Build applications with using data driven models
  • Continuously update model by the environment and data changes
  • Hyper Parameter tunning for deep learning is also very exhausting job
  • Managing and scheduling GPU server resource

Solutions

  • Easy to set up cluster with Docker images
  • Manage GPU resources with Celery and own job manager
  • REST APIs corresponding to Tensorflow
  • JAVA API component interface with python REST APIS
  • Easy to use UI for deep learning and machine learning
  • Pipe lines for various type of data and algorithms
  • Data collectors from various kind of source and types
  • Data preprocess for text, image and frame data sets
  • Support various deep learning and machine learning reusable componets
  • AutoML for hyperparameter tunning

More information

Articles

Enjoy Deep Learning with TensorMSA
TensorMSA 소개

제11회 공개SW개발자대회 금상(TensorMSA)

제11회공개sw개발자대회 금상 TensorMSA(소개) 제11회공개sw개발자대회 금상 TensorMSA(소개) 제11회 공개sw개발자대회 일반부문 금상 수상작 TensorMSA 소개 (Tensorflow MicroServiceArchitecture) Source: www.slideshare.net/healess/11sw-tensormsa   . 제11회 공개소프트웨어 개발자대회 제11회 공개소프트웨어 개발자대회 미래창조과학부 주최,공개SW 국제 공모전,공개소프트웨어개발자대회,행사일 Source: project.oss.kr/board/noticeView.do?board_seq=829

R Programming

XgBoost Test

#install.packages("drat", repos="https://cran.rstudio.com") #drat:::addRepo("dmlc") #install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source") #install.packages("xgboost") #install.packages(c("dplyr", "hflights")) #install.packages("dummies") #install.packages("MASS") require(xgboost) library(dplyr) library(hflights) library(dummies) library(MASS) setwd("C:/Users/POSCOUSER/Desktop/") train = read.csv("train.csv") test = read.csv("test.csv") col_names = colnames(test) # 트레인 데이터 준비 train_x = subset(train, Read more…

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