Deepomatic Platform
  • Overview
  • Release notes
    • January 2025
    • November 21, 2024
    • October 17, 2024
    • September 19, 2024
    • July 18, 2024
    • June 27, 2024
    • May 23, 2024
    • April 18, 2024
    • March 21, 2024
    • February 22, 2024
    • January 18, 2024
    • December 13, 2023
    • October 26, 2023
    • July 20, 2023
    • June 29, 2023
    • May 29, 2023
    • April 27, 2023
    • March 30, 2023
    • February 17, 2023
    • January 19, 2023
    • December 22, 2022
    • November 18, 2022
    • October 19, 2022
    • September 19, 2022
    • July 27, 2022
    • June 26, 2022
    • May 17, 2022
    • April 13, 2022
    • March 17, 2022
    • February 10, 2022
    • December 21, 2021
    • October 26, 2021
  • Getting started
  • ADMIN & USER MANAGEMENT
    • Invite and manage users
      • Invite group of users at once
      • SSO
        • Azure Active Directory
  • Deepomatic Engage
    • Integrate applications
      • Deepomatic vocabulary
      • Deepomatic connectors
        • Set-up
        • Camera Connector
        • Work Order Connector
      • API integration
        • Authentication
        • Errors
        • API reference
          • Work order management
          • Analysis
            • Guide field workers
            • Perform an analysis
            • Correct an analysis
          • Data retrieval
          • Endpoints' list
      • Batch processing
        • Format
        • Naming conventions
        • Processing
        • Batch status & errors
      • Data export
    • Use the mobile application
      • Configure a mobile application
      • Create & visualize work orders
      • Complete work orders
      • Offline experience
    • Manage your business operations with customisable solutions
      • Roles
      • Alerting
      • Field services
        • Reviewing work orders
        • Exploring work orders
        • Grouping work orders
        • Monitoring assets performance
      • Insights
  • Security
    • Security
    • Data Protection
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  • Hardware requirements
  • Compatible deep learning accelerators (recommended):
  • Software requirement
  • Operating system
  • Core requirements
  • Accelerator related requirements
  • Set up

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  1. Deepomatic Engage
  2. Deploy applications

Deploy on a local server with docker-compose

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Hardware requirements

Deepomatic can run using the following minimum requirements:

  • A processor supporting the x86-64 instruction set.

  • 4GB of free RAM.

  • 10GB of storage.

The recommended requirements are however:

  • A quad core processor supporting the x86-64 instruction set.

  • 8GB of free RAM.

  • 40GB of storage.

Deep learning models require lot of computation and it is recommended to use a specific hardware accelerator to make computation tractable. See below for a list of supported accelerators:

Compatible deep learning accelerators (recommended):

  • NVIDIA GPU accelerators, of the family , , or , with CUDA compute capability 3.0 or more.

  • Intel Movidius accelerators, see for example .

  • Intel GPU: Iris Pro Graphics and Intel HD Graphics, on Intel Core from 6th to 10th generation or Intel Xeon (excluding the e5 family)

  • Intel CPU: Intel Core from 6th to 10th generation or Intel Xeon (excluding the e5 family)

Software requirement

Operating system

Deepomatic relies on Docker to distribute its software and the operating system does not matter much as long as you manager to install the drivers for your deep learning accelerator. That said, we recommend a Linux distribution, and more specifically Ubuntu 18.04.

Core requirements

Deepomatic relies on Docker to distribute its software. You will thus need to install the following softwares on a compatible OS (we recommend Ubuntu 18.04):

Accelerator related requirements

Additionally, you will need to install additional software depending on the deep learning you may have chosen.

Nvidia GPU

On Ubuntu 18.04+, you can install the drivers with:

  • sudo apt update

  • sudo apt install --no-install-recommends nvidia-driver-418

Intel Movidius

Intel GPU

Intel CPU

You do not need to install additional software.

Set up

The manifest you get is specific to a hardware equipped with a Nvidia GPU. It can easily be adapted to deploy your application on hardware equipped with Intel Movidius, Intel GPU or Intel CPU. Contact your Customer Success Manager if you need to deploy your application on such hardware.

You need to install and .

You will need to install the HDDL driver on your host to take advantage of the Movidius chips.

You will need to install the NEO OpenCL driver on your host to take advantage of the GPU chips.

To deploy on a local server, you need to get a manifest for your application using .

Tesla
Quadro
RTX
GTX
Aaeon's manufactured modules
Docker 19.03+
Docker-Compose
Nvidia drivers 410.48+
Nvidia Docker
See the OpenVino manual.
See the OpenVino manual.
a Deepomatic CLI command