TVE (POC) Infrastructure Requirements

TVE (POC) Infrastructure Requirements

Hyperscience can be installed on-premise or in a private cloud, and on Linux VMs using Docker or Podman containers. The platform is accessible through a web application and API.

For short technical validations with small volumes (e.g., 1000 files), customers often run a single VM with the Application DB and File Store located locally. We also include a PostgreSQL container in our standard installation package to make this even easier. Volume aside, this set-up is not recommended for any production cases given the issues it creates for High Availability/Disaster Recovery.

Specifications

Internet Browsers

For the best possible user experience, we recommend browser dimensions of at least 1280 x 720 pixels.

Servers

Two Virtual Machines (VMs) with the following specifications:

Below, you can find a table with the supported container environments for each operating system.

| Operating system | Supported container environments | | RHEL 7.9 and earlier | - Hyperscience v37-v39.2 with trainers with GPUs: Docker 19.0.3 and later

- All other configurations of Hyperscience v37-v39.2: Docker 1.13 and later | | RHEL 8.4 and later | - Podman 3.3.1 and later | | Ubuntu 16.04 (LTS) and later 16.x versions | - Hyperscience v37-v39.2 with trainers with GPUs: Docker 19.0.3 and later

- All other configurations of Hyperscience v37-v39.2: Docker 1.13 and later | | Ubuntu 18.04 | - Hyperscience v37-v40 with trainers with GPUs: Docker 19.0.3 and later

- All other configurations of Hyperscience v40 and earlier: Docker 1.13 and later | | Ubuntu 20.04 | - Hyperscience v41: Docker 25.0.4 and later | | Ubuntu 22.04 and 24.04 | - Hyperscience v41 and later: Docker 25.0.4 and later |

Application database

If you choose to use the PostgreSQL container included in the installation package, you do not need to provision a database. Otherwise, the supported options are:

Trainer

The Hyperscience Trainer runs separately from the main application and communicates to the main application via the API. The Trainer supports select long-running tasks and very large file downloads / uploads that might otherwise negatively impact document processing time.

VM CPU cores

We require 16 cores for each CPU in a trainer VM if you are processing Semi-structured documents. If you have only 8 cores for these CPUs, you can expect 60-70% longer training times, inconsistent system behavior, and an increased risk of crashes during training, particularly on datasets with longer, denser documents.

RAM

The trainer requires 64GB of RAM for each CPU in a trainer VM, which will maximize the performance of the 16-core CPUs described above.

Installation overview

Our software bundle is provided as a single easy-to-install tarball. Sample installation instructions can be found in TVE (POC) Installation Process. We also help our customers install over screenshare. The bundle is typically delivered over SFTP or can be done via a different file transfer option of your choice.