ai-center
2022.4
true
- Getting Started
- Network requirements
- Single-node requirements and installation
- Multi-node requirements and installation
- Post-installation
- Accessing AI Center
- Provision an AI Center tenant
- Updating Orchestrator and Identity Server certificates
- Resizing PVC
- Adding a new node to the cluster
- ML packages offline installation
- Configuring the cluster
- Configuring the FQDN post-installation
- Backing up and restoring the cluster
- Using the monitoring stack
- Setting up a Kerberos authentication
- Provisioning a GPU
- Using the configuration file
- Node scheduling
- Migration and upgrade
- Basic Troubleshooting Guide
Requirements
AI Center Installation Guide
Last updated Jun 6, 2024
Requirements
Note: Before choosing your deployment profile, see Supported use cases for single-node and multi-node installations.
What you need | Requirements | Configuration | Installation |
---|---|---|---|
One Linux machine (RHEL 8.2, 8.3, 8.4, 8.5, 8.6) | Single-node machine requirements | Configuring the machines | |
SQL Server (MS SQL 2016, 2017, 2019 - Enterprise and Standard) | - | Configuring MS SQL Server | - |
DNS | - | - |
This configuration is supported only for evaluation and demo scenarios.
Bundle |
Minimum |
Recommended |
---|---|---|
(v-)CPU total | 16 (v-)CPU | 32 (v-)CPU |
RAM | 52 GiB | 96 GiB |
Cluster disk for each node |
256 GiB SSD Min IOPS: 1100 |
256 GiB SSD Min IOPS: 1100 |
Data disk |
512 GiB SSD for online 1 TiB SSD for offline Min IOPS 1100 |
2 TiB SSD Min IOPS 1100 |
etcd disk |
16 GiB SSD Min IOPS: 240 |
16 GiB SSD Min IOPS: 240 |
UiPath Bundle Disk (For offline installation only, on the first server node) |
512 GiB SSD Min IOPS: 1100 |
512 GiB SSD Min IOPS: 1100 |
Note: Following the requirements above is enough for three skills and one pipeline running in parallel with the default configuration.
Bundle | Requirement |
---|---|
(v-)CPU | 8 (v-)CPU |
RAM | 52 GiB |
Cluster disk |
256 GiB SSD Min IOPS: 1100 |
Data disk | N/A |
GPU RAM | 11 GiB |
Note: Trainable Document Understanding ML Packages provided by UiPath will work on both CPU or GPU for datasets up to 500 images
in size. GPU is strongly recommended to achieve faster training times and better model performance. Validation Station retraining
loop is not supported on deployments without a GPU, due to the fact that dataset sizes increase too fast and may hit a compute
wall with CPU very quickly. If you run UiPath Document OCR Local Server on AI Center to process more than 2 million pages of documents a year, GPU is strongly recommended for a better product experience.