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.


  • 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


  • 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


Enjoy Deep Learning with TensorMSA
설치 및 실행

How to Install

How To Install Tensormsa Docker-compose version Tensormsa를 구성하기 위해선 Django, Postgres, Nginx, RabbitMQ등 여러 OpenSource 프로그램이 필요하다. 각각의 Official Docker를 Compose 하여 Tensormsa를 실행하는 방법을 안내한다. Prerequisite Tensormsa를 실행하기 위해서는 Docker-compose환경이 필요하다. 설치방법은 Docker Official Read more…

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TensorMSA User Guide

TensorMSA Easy to Use UI/UX 우리의 프레임웍은 크게 두가지 형태로 서비스를 제공하는데 기본적으로 대부분의 기능을 Rest API 형태로 구축하여 서버간의 연동을 통한 사용방법과 지금 설명하고자 하는 UI/UX 기반의 서비스 제공이다. 서버간의 데이터 수집 및 모델 Read more…

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Open Source Deep Learning F/W developement team
SeungWooKim, SuSangKim, JeeHyunPaik, YoungJaeKim, SungChanPark

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