Deploy on a local server with docker-compose
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:
- 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)
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.
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):
Additionally, you will need to install additional software depending on the deep learning you may have chosen.
On Ubuntu 18.04+, you can install the drivers with:
- sudo apt update
- sudo apt install --no-install-recommends nvidia-driver-418
You will need to install the HDDL driver on your host to take advantage of the Movidius chips. See the OpenVino manual.
You will need to install the NEO OpenCL driver on your host to take advantage of the GPU chips. See the OpenVino manual.
You do not need to install additional software.
To deploy on a local server, you need to get a manifest for your application using a Deepomatic CLI command.
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.