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Secured dockers (4+) to share 1 GPU.

700 USD

  1. 17244
     36  0

    Winning proposal8 days700 USD

    Hi JC,

    This is a classic MLOps challenge, and your goal to securely share a high-end GPU among your team is the right approach. As a Full-Stack Developer with deep experience in Docker and cloud architecture, I can design a robust and secure system for you.

    Simply connecting containers to a GPU is not enough; you need a managed, secure, and isolated environment. Here is the professional architecture I propose:

    1. True GPU Isolation with NVIDIA MIG: For the A6000/L40 cards, we will use NVIDIA Multi-Instance GPU (MIG). This partitions the GPU at the hardware level, giving each user a dedicated slice of compute and memory. This is superior to basic time-slicing as it prevents any single user's workload from impacting the performance of others.

    2. Centralized Authentication via Traefik Reverse Proxy: I recommend Traefik, a modern reverse proxy that integrates seamlessly with Docker. It will act as the single, secure entry point.

    Function: All user requests will first hit Traefik.
    Security: Traefik will enforce user/password authentication before forwarding any request to the user's private container, ensuring only authorized team members can access their environment.

    3. Secure AI Gateway Service: To protect your AI API keys, we will implement a dedicated "AI Gateway" microservice.

    Workflow: Your team's microservices will make their AI calls to this internal gateway—they will never hold the AI keys themselves.
    Benefit: This gateway is the only component that communicates with external AI services (like OpenAI). This centralizes key management, enhances security, and simplifies monitoring.

    Deliverable:
    For this project, I will provide you with a comprehensive architectural document. This will include:

    A detailed diagram of the entire system.
    Justification for each technology choice.
    A sample docker-compose.yml file demonstrating how all services (Traefik, AI Gateway, and user containers) are defined and linked together.

    My price for delivering this complete architectural plan is $450, and I can have it ready for you in 3 days.

  2. 1405    14  3   1
    2 days180 USD

    Hi JC, this is an interesting challenge, and I'd be happy to help.
    I can propose a clean and scalable architecture where each of your 4–6 users runs their own isolated Docker microservice container, all sharing access to a single powerful GPU (like the RTX A6000 or L40).

    The architecture will include:

    Secure GPU sharing (NVIDIA Container Toolkit + MIG if needed)

    Authenticated access per user (via proxy + username/password)

    Central proxy to control access to external AI services (ensuring privacy)

    Optional VPN layer for secure internal access

    I’d be happy to provide you with a full diagram and Docker setup within a day, with deployment instructions and extensibility in mind. Let’s discuss details like the AI APIs you want to reach and your preferred infrastructure (bare metal or cloud).

  3. 9351    20  0   1
    10 days1000 USD

    Вітаю
    Маю досвід з:
    - NVIDIA MIG конфігурацією
    - Docker GPU sharing
    - NGINX proxy setup
    - Authentication системами

    Можу запропонувати архітектуру на базі:
    - MIG партиціонування GPU
    - Docker Compose з GPU device mapping
    - NGINX reverse proxy з auth
    - JupyterHub для user management

  4. 99  
    8 days1300 USD

    Hello!
    This setup needs careful handling, but I can take care of it. Each user will have a private container. All containers will share one GPU without conflict. I can set up secure login so only your team can access it. AI traffic will be sent using a single protected connection to keep your keys safe.
    It is a complex task, but I have some experience with similar systems before. Once it's ready, it will run smoothly without much effort from your side.

  5. 1 proposal concealed

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Client
Project published
11 months 21 days back
110 views
Tags
  • proxy
  • gpu
  • Microservices
  • NVIDIA
  • docker