DS2.ai Docs
  • Welcome to DS2.ai!
  • getting started
    • 1️⃣Install DS2.ai
    • 2️⃣Upload Data
    • 3️⃣Start Labeling Project
    • 5️⃣Start Training Project
    • #️⃣Collaborate with DS2.ai
  • user guide
    • Dataset
      • Data Storage
        • Data Details
      • Upload Data
        • CSV File Example
        • ZIP File Example
      • Using Public/Open Data
    • Labeling
      • Start Labeling Project
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        • Dashboard
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        • Regression
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    • Training
      • Start Training Project
      • Training Project
        • Data
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      • Training the Model
        • Training Supported Models
          • Classification
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          • Recommender System
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          • Object Detection
        • Model Training Options
          • Algorithm Option
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        • Training with AutoML
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        • Training with Generate Code
      • Share a Project
      • Using the Model
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        • Deploy
    • Deploying
      • Deploying the Model
        • External Trained Model
        • Trained Model within DS2.ai
      • Dashboard
    • Verifying
      • Start Verification Project
      • Verification Project
      • Training the Model for Verification
        • Training Supported Models for Verification
          • Classification
          • Regression
          • Natural Language
          • Recommender System
          • Image Classification
          • Object Detection
        • Model Training Options for Verification
          • Algorithm Option
          • Dataset Option
        • Verifying with AutoML
        • Verifying with Manual Setting
        • Verifying with Generate Code
      • Download Verification Report of Training Data set
      • Share a Project
    • My Jupyters
      • Jupyter Project
      • Start Jupyter Project
    • Account
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  • SDK | Python
    • Overview
    • DS2
    • Dataconnector
    • Labelproject
    • Labelfile
    • Label
    • Labelclass
    • Project
    • Model
    • Jupyterproject
    • Jupyterserver
    • Opsproject
    • Opsmodel
    • Opsservergroup
    • Util
    • Asynctask
    • MarketModel
  • API
    • Overview
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On this page
  • 1-1. Windows
  • 1-2. Mac
  • 1-2. Linux (Ubuntu, CentOS or Windows WSL2 - Ubuntu 18.04)
  • Next Steps
  1. getting started

Install DS2.ai

PreviousWelcome to DS2.ai!NextUpload Data

Last updated 2 years ago

1-1. Windows

  1. Go to the link () and install Docker Desktop.

  2. Execute the following command after running Powershell in administrator mode.

mkdir ds2ai
cd ds2ai
wget "https://assetdslab.s3.ap-southeast-1.amazonaws.com/cpu/docker-compose.yml" -outfile "docker-compose.yml"
docker-compose up -d

After successful installation, you can connect to and run ds2.

After the initial installation is completed, you can connect after the update takes about 3 to 5 minutes.

This installation method allows using CPU resources (not support GPU). It does not support Object recognition model and Auto-Labeling. (Object recognition and Auto-Labeling function only works on GPU. It requires Linux OS and built-in Nvidia graphics card)

* If you want to delete it, execute the command below to complete the deletion.

sudo docker stop ds2ai-container
sudo docker rm ds2ai-container
sudo docker rmi dslabglobal/ds2ai:stable

1-2. Mac

  1. Go to the link () and install Docker Desktop.

  2. After launching the terminal, run the following command:

mkdir ds2ai
cd ds2ai
wget https://assetdslab.s3.ap-southeast-1.amazonaws.com/cpu/docker-compose.yml
docker-compose up -d

After the initial installation is completed, you can connect after the update takes about 3 to 5 minutes.

This installation method allows using CPU resources (not support GPU). It does not support Object recognition model and Auto-Labeling. (Object recognition and Auto-Labeling function only works on GPU. It requires Linux OS and built-in Nvidia graphics card)

* If you want to delete it, execute the command below to complete the deletion.

sudo docker stop ds2ai-container
sudo docker rm ds2ai-container
sudo docker rmi dslabglobal/ds2ai:stable

1-2. Linux (Ubuntu, CentOS or Windows WSL2 - Ubuntu 18.04)

Execute the following script to proceed with the installation.

wget https://assetdslab.s3.ap-southeast-1.amazonaws.com/ds2_install.sh
sudo bash ds2_install.sh

* If you want to delete it, execute the command below to complete the deletion.

sudo docker stop ds2ai-container
sudo docker rm ds2ai-container
sudo docker rmi dslabglobal/ds2ai:stable

If nvidia-driver is not installed, object recognition artificial intelligence generation and auto-labeling are not possible. (Available only if Nvidia graphics card is built-in among Linux OS)

Next Steps

After successful installation, you can connect to and run ds2.

After successful installation, you can connect to and run ds2.After the initial installation is completed, you can connect after the update takes about 3 to 5 minutes.

Do you have any other questions?

1️⃣
https://www.docker.com/products/docker-desktop/
http://localhost:13000
https://www.docker.com/products/docker-desktop/
http://localhost:13000
http://localhost:13000
2️⃣Upload Data
[email protected]