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
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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Open Source Deep Learning F/W developement team
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