AI Computer Vision
2021.10
false
  • Release notes
    • 2021.10.6
  • Overview
  • Setup and configuration
    • Software requirements
    • Hardware requirements
    • Deploying the server
    • Connecting to the server
  • Data storage
AI Computer Vision User Guide
Automation CloudAutomation Cloud Public SectorAutomation SuiteStandalone
Last updated Jun 27, 2024

Software requirements

The supported operating systems for the Computer Vision server are:

  • Microsoft Windows 10 21H2, Windows 11
  • Ubuntu v16.04, v18.04, v20.04, v22.04
  • Red Hat Enterprise Linux 8

Microsoft Windows

The Windows Computer Vision server uses a container-based deployment with Docker in Windows Subsystem for Linux (WSL) 2. The following need to be installed:

  • WSL 2
  • Docker Desktop for Windows or Docker Engine (if installed directly in WSL)
  • Nvidia Windows 11 Display Driver
  • Nvidia Container Toolkit

Ubuntu

The following resources must be installed on the machine you want to deploy to:

  • CUDA v11.1
  • cuDNN8 v8.2.1
  • Docker
  • Nvidia Container Toolkit

For convenience, UiPath provides a script to install these prerequisites. This script is provided "as is", without any implied or explicit guarantee. To install the prerequisites using this script, run the following line in the terminal of the GPU machine:

curl -fsSL https://raw.githubusercontent.com/UiPath/Infrastructure/main/ML/ml_prereq_all.sh | sudo bash -s -- --env gpucurl -fsSL https://raw.githubusercontent.com/UiPath/Infrastructure/main/ML/ml_prereq_all.sh | sudo bash -s -- --env gpu
Note: A reboot is required after running the installation script.

This line runs a script hosted by UiPath, which automatically downloads and installs the above resources. Once the script is finished and the resources are installed, to start a server instance of any Machine Learning model, a zip file containing the model is needed. This zip file contains an entry point script and a local speed test script.

If you would like to know more about the technical details of this script, you can visit the UiPath Infrastructure Github Repository.

Linux RHEL

The following resources must be installed on the machine you want to deploy to:

  • CUDA v11.1
  • cuDNN8 v8.2.1
  • Podman

For convenience, UiPath provides a script to install these prerequisites. This script is provided "as is", without any implied or explicit guarantee. To install the prerequisites using this script, run the following line in the terminal of the GPU machine:

curl -fsSL https://raw.githubusercontent.com/UiPath/Infrastructure/main/ML/ml_prereq_podman_rhel8.sh | sudo bash -s -- --env gpucurl -fsSL https://raw.githubusercontent.com/UiPath/Infrastructure/main/ML/ml_prereq_podman_rhel8.sh | sudo bash -s -- --env gpu
Note: A reboot is required after running the installation script.

This line runs a script hosted by UiPath, which automatically downloads and installs the above resources. Once the script is finished and the resources are installed, to start a server instance of any Machine Learning model, a zip file containing the model is needed. This zip file contains an entry point script and a local speed test script.

If you would like to know more about the technical details of this script, you can visit the UiPath Infrastructure Github Repository.

  • Microsoft Windows
  • Ubuntu
  • Linux RHEL

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