Hyperscience can be installed on-premise or in a private cloud, and on Linux VMs using Docker or Podman containers. The application is accessible through a web app 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.
- Hyperscience v31 and earlier: Internet Explorer 11 and the latest version of Google Chrome
- Hyperscience v32 to v35: Internet Explorer 11 and the latest versions of Google Chrome and Microsoft Edge
- Hyperscience v36: the latest versions of Google Chrome and Microsoft Edge
For the best possible user experience, we recommend a minimum browser width of 1280 pixels.
Two Virtual Machines (VMs) with the following specifications:
- One of the following:
- Ubuntu 16.04 or later
- Hyperscience v28 and earlier: RHEL 7, 7.5, 7.7, or 7.8
- Hyperscience v30 to v32: RHEL 7, 7.5, 7.7, 7.8, or 7.9
- Hyperscience v33 and later: RHEL 7, 7.5, 7.7, 7.8, 7.9, 8.4 or 8.5
Below, you can find a table with the supported container environments for each operating system.
Supported container environments
RHEL 7.9 and earlier
Docker 1.13 or later
RHEL 8.4 and later
Podman 3.3.1 or later
Ubuntu 16.04 (LTS) and later
Docker 1.13 or later
- Container environments:
- No running containers
- Set to autostart
- Preferred storage driver: overlay2
- A minimum of 8 cores per CPU in each VM
- 32 GB RAM
- Sufficient disk space
- 60 GB on the volume where downloading, untarring, and deploying the application (usually the root "/" volume)
- 40 GB on whatever volume Docker or Podman is set up to use for the application image (usually "/var")
- 100 GB or more for image storage on the root volume or on an extra volume mounted elsewhere
Note that burstable-performance machines are not supported. Such machines are:
- AWS: T-series. To learn more, see Amazon’s Burstable performance instances.
- Azure: B-series. To learn more, see Microsoft’s B-series burstable virtual machine sizes.
- Google Cloud: shared core. To learn more, see Google’s General-purpose machine family.
Note that servers may consume 100% of machine resources for prolonged periods of time. For virtualized images, the expectation is that a vCPU would be equivalent to a physical CPU core in understanding capacity requirements for our platform. The nature of burstable-performance machines does not allow them to constantly utilize 100% of the CPUs’ resources, which results in system slowness.
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
In v28 and earlier, we require 8 cores for each CPU in a VM running the Hyperscience trainer.
In v30 and later, 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.
In v28 and earlier, the trainer requires 32GB of RAM. If you attempt to start the trainer on a machine with less than 32GB of RAM in v28 and later, an error message will be shown.
In v30 and later, 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.
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:
- Hyperscience v27 or earlier: PostgreSQL 9.5 and 10.x
- Hyperscience v28: PostgreSQL 9.5, 10.x, and 12.x
- Hyperscience v30-v33.1.8: PostgreSQL 10.x and 12.x
- Hyperscience v33.1.9-v34: PostgreSQL 10.x, 12.x, and 13.x
- Hyperscience v35 or later: PostgreSQL 10.x, 12.x, 13.x, and 14.x
- Amazon RDS for PostgreSQL
- Hyperscience v28 or earlier: Oracle 12 with DBMS_ALERT privileges
- Hyperscience v30-v31: Oracle 12 and 19c, both requiring DBMS_ALERT privileges
- Hyperscience v32-v33: Oracle 12.2 and 19c, both requiring DBMS_ALERT privileges
- Hyperscience v34 or later: Oracle 19c with DBMS_ALERT privileges
- Amazon RDS for Oracle
- Microsoft SQL Server (MSSQL):
- Hyperscience v30.0.5 or earlier: MSSQL 2016 and 2017
- Hyperscience v30.0.6 or later: MSSQL 2016, 2017, and 2019
- Service Broker must be enabled.
- Amazon RDS for SQL Server
- Azure SQL Managed Instance
- Supported in Hyperscience v28 and later
- User DDL privileges for table/index creation and modification
Our software bundle is provided as a single easy-to-install tarball. Sample installation instructions can be found in Installing Hyperscience. 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.