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GPU Docker Management System (GDMS)

Leadtek GPU Docker Management System (GDMS) is a Docker-based GPU resource allocation and management software. GDMS uses an intuitive and graphical user interface to centrally manage AI and big data projects and development resources for universities/schools, research institutions, and corporations.

 
GDMS_LOGO

Leadtek GPU Docker Management System (GDMS) is a Docker-based GPU resource allocation and management software. GDMS uses an intuitive and graphical user interface to centrally manage AI and big data projects and development resources for universities/schools, research institutions, and corporations.

Combined with Leadtek WinFast GPU workstations and servers, GDMS allows you to maximize resource usage, manage your tasks more efficiently, and reduce the total cost of ownership. All kinds of schools, institutions, and corporations will benefit from GDMS in their development environment deployment, no matter it’s an AI development project, an AI training course, or a GPU accelerated application project.

 
 
 
 
  Manage Project Resources  

With GDMS’s simple and visualized user interface, even though knowing none of Docker commands, you can still easily start your management tasks, including project creation, deletion, startup, or suspension.

  Real-time GPU Monitoring  

You can instantly view the resource usage status of all managed GPU workstations and servers, including their CPU, system memory, GPU, and GPU memory. GDMS helps you understand the latest status of occupied and idle resources in your GPU system.

  Intuitively Graphical User Interface  
 
 
  Maximum GPU Investment  

GDMS offers two modes of allocating GPU resources: Share Mode shares one GPU among multiple users, while Exclusive Mode can occupy multiple GPUs by a single user. The two modes allow you to freely configure your hardware resources required for each AI project. For AI courses that many students attend, you no longer need to prepare GPU workstations for each student. As for company’s AI development projects, you can now provide each project team with independent GPU hardware resources without any conflicts.

 
 
  • All projects are managed by GDMS, which saves a lot of hardware maintenance cost and power consumption
  • Meet the needs of various circumstances: A high-end GPU can be shared with many users in a course or reserved to a single research project
 

GDMS greatly reduces the cost spending on GPU cards as well as:

Apply to Research and Education Scenarios

Support SSH connection and Jupyter Notebook tools for research and courses

Storage Location Setting Support

Use your existing storage path to reduce file transfer time and security concerns

Task Scheduling

Efficiently complete GPU system and project management tasks

Start AI development in a snap.

WinFast GPU Workstation
and Server

AI Deep Learning Solution

Learn More

Recommended System Requirements GDMS server requires:
  • CPU: 8-core CPU and above
  • RAM: 16GB and above
  • Storage: 2TB SSD (or HDD) and above
Recommended GDMS server model: WinFast GS1030ST
Supported GPU Servers GDMS is compatible with WinFast Workstation and Server series embedded with WinFast RTX AI Software Pack
Network Requirements
Port Port Number Description
TCP、UDP 20 FTP（Default connection port）
TCP、UDP 21 FTP（Control port）
TCP、UDP 22 SFTP、SSH
TCP 80 HTTP（Apache）
TCP 445 SAMBA
TCP、UDP 3306 MySQL database system
TCP、UDP 5900 VNC
TCP 8080 HTTP（Apache Tomcat）
GPU Server Resource Management Mode GDMS allows software administrators to manage all the created projects and containers located in GPU servers.
Distribute GPU resources through two project/container modes:
  • Exclusive Mode: GPU configuration is managed by “GPU unit”
  • Share Mode: GPU configuration is managed by "GPU RAM"
Supported GPU Server Commands Available commands and scheduled tasks:
  • Power On
  • Power Off
  • Reboot
  • Docker Image Update
GPU Server Monitoring
  • Monitor GPU usage - analyze the usage rate of each CPU, memory, GPU, and GPU memory
  • List GPU server information - IP Address, Status, and the Last Online Time
Container Monitoring
  • Monitor container usage - analyze container usage supported by GPUs
  • List container information created on GPU servers - Name, IP Address and Port, Status, and Created Time
GPU Server Communication Encrypted HTTPS communication between GDMS and GPU servers
Supported Browsers GDMS provides software administrators with a browser-based console for managing GPU resources and containers.
It supports browsers Google Chrome, Internet Explorer 10 or above, and Firefox
Supported Language(Management Console) English

GPU Docker Management System (GDMS)
Language Version Description
User's Manual
(繁體中文)2.1.6
( 2023/7/12 )		GDMS 完整版使用手冊
Total size: [ 6439 KB ] 		download
Quick Guide
(繁體中文)2.1.6
( 2023/7/12 )		GDMS 快速使用手冊
Total size: [ 2772 KB ] 		download
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