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Automation Suite on Linux installation guide

Last updated Mar 26, 2026

Hardware and software requirements

Terminology​

To find out more about the core concepts used in an Automation Suite deployment, see Glossary.

Product selection​

The default installation experience includes a choice of two product selections:

  • Complete (All products) - Install the complete list of products available in Automation Suite. For details, see Automation Suite products.

  • Select products - Allows you to select and install only the products you are interested in. Note, however, that the installer takes the cross-product dependencies into consideration. That means that if a product requires the installation of another product, you must install both of them. For details, refer to Automation Suite products.

    Note:

    You can enable additional products later in the same deployment at any point in time, after the initial installation, without having to reinstall. For details, see Managing products.

    We recommend validating the hardware requirements based on expected usage and ensuring the deployment has enough capacity before adding additional products. For details, see Capacity planning.

Choose your deployment profile​

You can deploy Automation Suite in single-node, lite mode, or multi-node HA-ready production mode. While most of the prerequisites for the profiles are identical, multi-node HA-ready production mode requires additional resources.

Once the deployment is completed, changing the deployment profile is generally limited. However, single-node deployments can be scaled to multi-node high availability (HA) deployments, and lite deployments can be expanded to multi-node HA-ready deployments. Before choosing your deployment profile, refer to Deployment modes and use cases.

Important:

Linux and Kubernetes knowledge is required regardless of the deployment profile you choose. If you encounter issues installing and configuring Automation Suite, contact UiPath® Professional Services.

Prerequisites at a glance​

Prerequisite type

Prerequisite

Hardware

  • A minimum of one machine for single-node deployments. This configuration is supported only for evaluation and demo scenarios, not for production deployments.
  • A minimum of three machines for multi-node deployments. This is the only configuration supported for production deployments.

To avoid potential installation issues, make sure that all nodes used in the deployment are set to the same time zone.

General machine requirements

Requirements specific to the following products:

  • Automation Suite Robots
  • AI Center

  • AI Computer Vision

  • Document Understanding

Supported RHEL version and ipcalc tool installed on all the Linux machines.For details about RHEL compatibility with previous Automation Suite versions, see RHEL compatibility matrix .

We support new minor versions of RHEL within 90 days of their release.

We support SELinux with default policies. *

FIPS 140-2

Load balancer L4 / Network Load Balancer

NFS server requirement (on-premises or cloud-managed NFS server with NFSv3/NFSv4 version on Linux based)

  • CPU - 4 vCPU
  • RAM - 8 GB
  • Storage - 1 TB
    • For external objectstore, a few GBs are needed
    • For in-cluster objectstore, the minimum must be the same as the size of the objectstore
Details...

Node ports

Software

RPM packages on each machine

SQL Server

Objectstore (Azure Blob storage, AWS S3, S3 compatible objectstore)

OCI-compliant registry

DNS

TLS 1.2+
IPv4

(IPv6 is not supported)

Swap memory must be disabled.

  • TLS certificate

  • Identity token-signing certificate (certificate to sign the authentication/bearer token)

  • The additional CA certificates for any of the external components, such as SQL Server, objectstore, registry, SMTP, etc.

*For air-gapped installations, the rke2-selinux package is not installed automatically. If you experience SELinux-related issues, you must install the SELinux policy package manually. For details, refer to the How to... Section.

Important:
  • You need root permission to install and deploy Automation Suite. For more on the specific components that require root access, see Root privileges requirement.
  • Cilium requires CAP_SYS_ADMIN permissions to function correctly. Make sure these permissions are granted.
  • Having scan agents running on your system may cause installation or runtime failures, due to the changes they make to the IPTables. To avoid this behavior, configure your scan agent so that it does not interfere with the Automation Suite installation.
  • UiPath® does not prescribe specific firewall or developer tool configurations as long as the Automation Suite requirements are met. Based on our observations, a limited number of external tools can interfere with the smooth operation of Automation Suite. If such issues arise, contact the relevant vendor for help. For additional guidance, see the Automation Suite responsibility matrix.

Hardware requirements​

Before you begin, consider the following:

  • Automation Suite supports Federal Information Processing Standard 140-2 (FIPS 140-2). You can perform a clean installation of Automation Suite on a FIPS 140-2-enabled host. You can also enable FIPS 140-2 on a machine where you previously performed an Automation Suite installation. For details, see Security and compliance.

    Note:

    Insights is currently not supported on FIPS-enabled hosts. Make sure to disable Insights when installing Automation Suite on a FIPS-enabled host.

  • The minimum hardware requirements do not protect the deployment from node failures.

  • The multi-node HA-ready production profile is resilient to only one node failure. This means that you can lose only one server node. This restriction does not apply to agent nodes. You can lose as many agent nodes and still continue to use the cluster without downtime as long as enough overall cluster capacity is available.

  • You can increase the server node tolerance to failure by following the instructions in Advanced installation experience.

The following sections list out the hardware requirements for both the Complete product selection and individual products.

Complete product selection: hardware requirements​

The following sections describe the hard requirements for the Complete product selection.

General requirements​

Hardware for all products

Single-node minimum requirement

Multi-node minimum requirements

Processor per cluster

32 (v-)CPU/cores

96 (v-)CPU/cores

Minimum processor per node

N/A

8 (v-)CPU/cores

RAM

64 GB

192 GB

Minimum RAM per node

N/A

16 GB

Cluster disks*

256 GB SSD

Min IOPS: 1100

256 GB SSD

Min IOPS: 1100

Data disk

  • Required only on the server nodes

512 GB SSD

Min IOPS: 1100

512 GB SSD

Min IOPS: 1100

etcd disk

  • Required only on the server nodes

16 GB SSD

Min IOPS: 240

16 GB SSD

Min IOPS: 240

UiPath® bundle disk

  • Required only on the first server node

  • Required only for offline

  • Required only if an external registry is not available

512 GB SSD

Min IOPS: 1100

512 GB SSD

Min IOPS: 1100

Objectstore

  • Required only on the server nodes

  • Required only if the external objectstore is not available

512 GB SSD

Min IOPS: 1100

512 GB SSD

Min IOPS: 1100

Additional disk space for Ceph data backups

  • Required only in single-node RKE2 setup with in-cluster storage

512 GB SSD

Min IOPS: 1100

N/A

AI Center

51 GB minimum

105 GB recommended for 1 training pipeline

51 GB minimum

105 GB recommended for 1 training pipeline

*The following considerations apply to cluster disk capacity:

  • You may need to increase cluster disk capacity based on your AI Center ML skills and training storage requirements.
  • If you enable Document Understanding modern projects, the minimum cluster disk capacity is 512 GB.
Note:

If you install Automation Suite in single-node evaluation mode, and you do not have a machine with 32 (v-)CPU/cores and 64 GB of RAM, you can bring machines with a minimum of 8 (v-)CPU/cores and 16 GB of RAM. For more details, see Capacity calculator.

If you choose this option, follow the multi-node installation and configuration instructions.

It is recommended to bring external objectstore whenever possible. This helps in scaling the objectstore independently of the cluster, and brings additional stability. We support the following objectstore options:

  • Azure storage account
  • AWS S3 storage bucket
  • S3 compatible storage bucket

Individual products: hardware requirements​

For details on the hardware requirements your must meet to install individual products or various product combinations in Automation Suite, use the Automation Suite Install Sizing Calculator.

Additional Automation Suite Robots requirements​

In multi-node HA-ready production environments, Automation Suite Robots require an additional agent node. In single-node evaluation environments, an additional Automation Suite Robots node is optional.

The hardware requirements for the Automation Suite Robots node depend on the way you plan to use your resources. In addition to the additional agent node requirements, you also need a minimum of 10 GB of file storage to enable package caching.

The following sections describe the factors that impact the amount of hardware the Automation Suite Robots node requires.

Robot size​

The following table describes the required CPU, memory, and storage for all robot sizes.

SizeCPUMemoryStorage
Small0.51 GB1 GB
Standard12 GB2 GB
Medium24 GB4 GB
Large610 GB10 GB
Agent node size​

The resources of the Automation Suite Robots agent node have an impact on the number of jobs that can be run concurrently. The reason is that the number of CPU cores and the amount of RAM capacity are divided by the CPU/memory requirements of the job.

For example, a node with 16 CPUs and 32 GB of RAM would be able to run any of the following:

  • 32 Small jobs
  • 16 Standard jobs
  • 8 Medium jobs
  • 2 Large jobs

Job sizes can be mixed, so at any given moment, the same node could run a combination of jobs, such as the following:

  • 10 Small jobs (consuming 5 CPUs and 10 GB of memory)
  • 4 Standard jobs (consuming 4 CPUs and 8 GB of memory)
  • 3 Medium jobs (consuming 6 CPUs and 12 GB of memory)
Kubernetes resource consumption​

Given that the node is part of a Kubernetes cluster, the Kubernetes agent present on the server (kubelet) consumes a small amount of resources. Based on our measurements, the kubelet consumes the following resources:

  • 0.6 CPU
  • 0.4 GB RAM

A node similar to the one previously described would actually have approximately 15.4 CPUs and 31.6 GB of RAM.

Automatic machine size selection​

All your cross-platform processes have the Automation Suite Robots option set to Automatic by default. This setting selects the appropriate machine size for running the process using serverless robots.

When automatically choosing the size, the criteria listed in the below table are evaluated in order. As soon as one criterion is satisfied, the corresponding machine size is chosen and the remaining criteria are not evaluated.

OrderCriterionMachine size
1Remote debugging jobMedium
2Process depends on UI Automation OR Process depends on the UiPath Document Understanding activitiesStandard
3Other unattended processSmall
Additional AI Center and Document Understanding requirements​

On top of the core service requirements that are part of full platform requirements, AI Center requires additional resources, depending on the models that you want to run or train. For more details about the required GPU hardware generations and compatible NVIDIA drivers, refer to Compatibility Matrix.

Note:

AI Center requires disk storage at runtime for the ML Skills and for the training pipeline, as follows:

  • The ML Skills require disk space on the /var/lib/rancher partition for storing the trained model for predictions. In the worst case scenario, the model size can be as big as 20 GB.
  • The training pipeline consumes the storage from the /var/lib/rancher partition for hosting the model. In the worst case scenario, the model size can be as big as 20 GB, and additionaly, it can require storage for the dataset. The minimum size of the dataset storage can be 51 GB. The recommended size is 105 GB. This must be on the dedicated disk for AI Center. The training pipeline only schedules on the node on which the dedicated AI Center disk is attached.

The following table describes the additional resources AI Center needs. In the following table, Data Disk is needed on all server nodes. Data Disk is not needed on agent nodes.

Use

CPU

RAM (GB)

GPU

Disk (GB)

Minimum for serving (ML Skill, one replica)

0.6

2

0

  • 20 GB on the rancher partition

Minimum for Training (Pipeline)

1

4

0

  • 20 GB on the rancher partition
  • Minimum 51 GB AI Center disk partition(recommended 105 GB)

DU model Serving (ML Skill, one replica)

1

4

0

  • 20 GB on the rancher partition

DU model Training

2

24

Strongly recommended

  • 20 GB on the rancher partition
  • Minimum 51 GB Data Disk (recommended 105 GB)
Note:

The following table describes the required resources for small and average AI Center implementations. Note that these numbers are general guidance.

In the following table, Data Disk is needed on all server nodes. Data Disk is not needed on agent nodes.

Use

CPU

RAM (GB)

GPU

Disk (GB)

Small implementation :

  • 3 models served
  • 1 concurrent pipeline

4

32

0

  • 80 GB on the rancher partition 1
  • 105 Data Disk 2

Average implementation :

  • 5 models served
  • 2 concurrent pipelines
  • DU model training

8

52

Strongly recommended

  • 160 GB on the rancher partition 3
  • 315 Data Disk 4

1 (3 skills +1 pipeline) * 20 GB on the rancher partition = 80 GB on the rancher partition

2 1 pipeline * 105GB = 105 Data disk

3 (5 skills + 2 pipeline + 1 DU pipeline) * 20 GB on the rancher partition = 160 GB on the rancher partition

4 (2 pipeline + 1 DU pipeline) * 105GB = 315 Data disk

Additional AI Computer Vision requirements​

This setup works on on-premises Nvidia GPUs, but also works with cloud providers such as AWS, Azure and GCP. Suggested GPU types include those from the RTX, Tesla, and Ampere family of products which have enough GPU memory and processing capability.

The main difference between these two types of GPUs is that the ones with virtualization usually have more GPU RAM and are offered by most cloud providers. Having more GPU RAM increases the maximum size of the image you can input to the model. In conclusion, virtualization GPUs are not significantly faster that the consumer GPUs.

You need a machine with the following hardware specifications:

Hardware specification Requirements

Memory
  • minimum: 8 GB
  • recommended: 16 GB

CPU
  • minimum: 2 cores with AVX2 enabled
  • recommended: 4 cores with AVX2 enabled

GPU
  • minimum: 8 GB VRAM
  • recommended: 16 GB VRAM
Storage
  • minimum: 30 GB free
Additional Document Understanding recommendations​

For increased performance, you can install Document Understanding on an additional agent node with GPU support. Note, however, that AI Center-based projects in Document Understanding are fully functional without the GPU node. Actually, Document Understanding uses CPU VMs for all its extraction and classification tasks, while for OCR we strongly recommend the usage of a GPU VM.

For more details about the CPU/GPU usage within the Document Understanding framework, refer to CPU and GPU Usage.

If you want to use an additional node with GPU support, you must meet the following requirements:

HardwareMinimum requirement
Processor8 (v-)CPU/cores
RAM52 GB
OS disk256 GB SSD Min IOPS: 1100
Data diskN/A
GPU RAM11 GB

For more details, check the AI Center considerations section.

Additional Document Understanding modern projects requirements​

With CPU inference activated, a minimum of 2 GPUs is required. To enable CPU inference, set the enable_cpu_inference property to true, as indicated in the Enabling or disabling Document Understanding section.

CAUTION:

Note:
  • Inference may be up to 10 times slower.
  • We recommend using it for documents with a maximum of 125 pages. No active limitation is in place. However, inference might fail for documents larger than 125 pages.

Without CPU inference, a minimum of 5 GPUs is required for Document Understanding modern projects. The example scenario in the following table demonstrates how 5 GPUs is enough to process 300 pages.

Note:

For Document Understanding modern projects, the minimum recommended GPU is NVIDIA T4.

FunctionNumber
Custom model pages processed per hour300
Out of the box model pages processed per hour0
Models training in parallel1
Number of pages in all projects - Design time200
Number of document types per project version3

The 5 GPUs are distributed amongst different functions, as detailed in the following table:

ServiceNumber of GPUs
OCR replicas1
Custom model training replicas1
Custom model replicas2
Out of the box model replicas1
Total5

For more information on how to allocate GPUs to each service, check the Allocating GPU resources for Document Understanding modern projects page.

In addition to the GPU demands, Document Understanding modern projects also require specific CPU resources for optimal performance. For optimal performance, a minimum of 18 vCPUs is required.

With the modern Document Understanding project, an additional 4 TB of the objectstore is required to perform the activities from the provided examples continuously for one year. You can start with a smaller number, but the activity will fail once the storage is complete, unless you explicitly scale it.

If you are provisioning for one year of continuous processing, you will need 4 TB for Document Understanding modern projects and 512 GB for the other products. The total will be 4.5 TB of storage. Similarly, if you start with six months of processing, you will need 2 TB for Document Understanding modern projects and 512 GB for the other products. In this case the total will be 2.5 TB.

Note:

For more detailed calculations and the capacity required for your needs, check the UiPath Automation Suite Install Sizing Calculator.

Provisioning MIG-enabled GPUs​

Automation Suite Document Understanding workloads support running on Virtual GPUs (VGPUs) created with NVIDIA MIG (Multi-Instance GPU) technology.

To run Document Understanding in these conditions, keep in mind the following requirements:

  • GPU memory (VRAM): at least 16 GB per VGPU
    Note:

    UiPath only support the single strategy, meaning that all VGPUs will be exactly the same.

  • Storage: at least 80 GB per VGPU
Enabling MIG-enabled GPUs in Kubernetes​

After provisioning the MIG enabled GPUs in your cluster with profiles matching or exceeding the above minimum requirements, ensure that the GPUs are schedulable Kubernetes. The node must report a non-zero number of GPUs before workloads can be scheduled on it.

To make the GPUs schedulable, follow the official documentation for installing the device plugin.

Alternatively, you can manually apply the device plugin to your gpu nodes to get started.

Apply the following configuration, replacing migEnabledPoolName with the label that matches your GPU node:

apiVersion: v1
kind: Pod
metadata:
  name: nvidia-device-plugin-pod
  namespace: gpu-resources
spec:
 affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: agentpool
            operator: In
            values:
            # To be changed to a selector that matches the GPU nodes
            - migEnabledPoolName
 containers:
 - args:
   - --fail-on-init-error=false
   env:
   - name: MPS_ROOT
     value: /run/nvidia/mps
   - name: MIG_STRATEGY
      # We only support the single strategy for now
     value: single
   - name: NVIDIA_MIG_MONITOR_DEVICES
     value: all
   - name: NVIDIA_VISIBLE_DEVICES
     value: all
   - name: NVIDIA_DRIVER_CAPABILITIES
     value: compute,utility
   image: nvcr.io/nvidia/k8s-device-plugin:v0.17.3
   imagePullPolicy: IfNotPresent
   name: nvidia-device-plugin-ctr
   securityContext:
     allowPrivilegeEscalation: true
     capabilities:
       add:
       - SYS_ADMIN
   terminationMessagePath: /dev/termination-log
   terminationMessagePolicy: File
   volumeMounts:
   - mountPath: /var/lib/kubelet/device-plugins
     name: device-plugin
 tolerations:
 - key: CriticalAddonsOnly
   operator: Exists
 - effect: NoSchedule
   key: nvidia.com/gpu
   operator: Exists
 terminationGracePeriodSeconds: 30
 volumes:
 - hostPath:
     path: /var/lib/kubelet/device-plugins
     type: ""
   name: device-plugin
apiVersion: v1
kind: Pod
metadata:
  name: nvidia-device-plugin-pod
  namespace: gpu-resources
spec:
 affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: agentpool
            operator: In
            values:
            # To be changed to a selector that matches the GPU nodes
            - migEnabledPoolName
 containers:
 - args:
   - --fail-on-init-error=false
   env:
   - name: MPS_ROOT
     value: /run/nvidia/mps
   - name: MIG_STRATEGY
      # We only support the single strategy for now
     value: single
   - name: NVIDIA_MIG_MONITOR_DEVICES
     value: all
   - name: NVIDIA_VISIBLE_DEVICES
     value: all
   - name: NVIDIA_DRIVER_CAPABILITIES
     value: compute,utility
   image: nvcr.io/nvidia/k8s-device-plugin:v0.17.3
   imagePullPolicy: IfNotPresent
   name: nvidia-device-plugin-ctr
   securityContext:
     allowPrivilegeEscalation: true
     capabilities:
       add:
       - SYS_ADMIN
   terminationMessagePath: /dev/termination-log
   terminationMessagePolicy: File
   volumeMounts:
   - mountPath: /var/lib/kubelet/device-plugins
     name: device-plugin
 tolerations:
 - key: CriticalAddonsOnly
   operator: Exists
 - effect: NoSchedule
   key: nvidia.com/gpu
   operator: Exists
 terminationGracePeriodSeconds: 30
 volumes:
 - hostPath:
     path: /var/lib/kubelet/device-plugins
     type: ""
   name: device-plugin

After deploying the plugin, the node's Allocatable section should show the correct number of VGPUs under nvidia.com/gpu, based on the MIG profile you configured. The node should now be schedulable and ready to run Document Understanding workloads.

RPM package requirements​

Before starting the Automation Suite installation, you must ensure you meet the following requirements:

  • you have a RHEL subscription
  • you enabled the BaseOS and AppStream repositories
  • you installed the required RPM packages

The following table lists the required RPM packages:

RPM packageDescription
iscsi-initiator-utils nfs-utils rpcbind util-linux nmap-ncat openssl httpd-tools gettext zstdRequired on nodes for installation.
podman>=4.0.2 nmap-ncat bind-utils openssl wget unzip conmon=>2.0.24Required on nodes for the execution of the readiness check.
iscsi-initiator-utils gettext nfs-utils rpcbind util-linux nmap-ncat openssl httpd-tools podman=>4.0.2 zstdRequired for offline installations only.
Note:

RHEL 8.4 and later have the required RPM packages in the BaseOS and AppStream repositories by default.

Manual installations​

If you perform a manual clean installation of Automation Suite, you must ensure you meet the RPM package requirements. In this case, you are responsible for installing the required RPM packages.

If you upgrade from a previous Automation Suite version, you have already installed the RPM packages.

For details on the tools you can use to install and validate RPM packages, see Validating and installing the required RPM packages.

Cloud templates​

If you install Automation Suite using our Azure, AWS, or GCP templates, no action is needed from your side. The templates provision the full infrastructure, including the RPM packages.

Microsoft SQL Server general requirements​

Note:

Unless otherwise specified in the dedicated requirements sections, these requirements are applicable to all Automation Suite products.

Important:
  • Automation Suite versions 2.2510.0 and higher are compatible with SQL versions 2019 and higher.
  • Insights is only compatible with SQL Server 2019 and higher, regardless of the Automation Suite version.
  • When you enable Process Mining for installation on Automation Suite must bring a PostgreSQL database for AutomationSuite_Airflow. Check out SQL requirements for Process Mining for details.

Additional Microsoft SQL platforms, such as Azure SQL Database or Azure SQL Managed Instance, as well as Amazon Relational Database Service are also supported as long as the Microsoft SQL Server database engine meets the requirements.

Note:

Make sure that the SQL server can be accessed from each cluster VM.

Important:

Individual product support varies.

For each product you plan to deploy, you must:

  • check the supported version of SQL Server as required by the product
  • apply the SQL Server configuration prerequisites, including SQL Server User permission, as required by the product For more information on product-specific SQL Server requirements, see Configuring Microsoft SQL Server.

The general minimum hardware requirements for Microsoft SQL Server are as follows:

  • 8 (v-)CPU
  • 32 GB RAM
  • 256 GB SSD

These minimum requirements are general guidance and do not guarantee reliable operation in a production deployment. Capacity planning is required to determine the hardware requirements that are needed for reliable operation.

For each product you plan to deploy, you must evaluate projected usage and apply the capacity planning guidance as specified by the product. This information is available in the help section of each individual product.

NFS Server general requirements​

To enable a backup, you need an external NFS server. Automation Suite supports Linux-based on-premises or cloud-managed NFS servers, version NFSv3/NFSv4.

Note:

Make sure that the NFS server can be accessed from each cluster VM.

The general minimum hardware requirements for NFS Server are as follows:

  • CPU - 4 vCPU
  • RAM - 8 GB
  • Storage - 1 TB
    Note:

    If you use an external objectstore, the storage requirement is a few GBs. If you use an in-cluster objectstore, the minimum storage size is the same as the size of the objectstore.

Disaster recovery - Active/Passive requirements​

To configure an Active/Passive deployment, make sure you meet the following requirements:

  • Hardware
  • Load balancers
  • DNS
  • Certificates
  • Objectstore
  • Traffic Manager

Hardware​

Both Automation Suite clusters must meet a set of software and hardware requirements. For details, see the hardware requirements for the multi-node mode.

Load balancer​

Both Automation Suite clusters must have a load balancer. For details, see Configuring the Load balancer.

DNS​

For details of the DNS requirements, see Configuring the DNS.

Certificates​

For details of the certificate requirements, see Certificate requirements.

You must also add the SANs to the certificate if you opened the DNS.

Objectstore​

In-cluster objectstore is not supported while deploying Automation Suite in multi-site. Instead, you must bring an external objectstore.

RHEL compatibily matrix​

The following table lists the RHEL versions supported by each version of Automation Suite:

Automation Suite versionSupported RHEL versions
2.2510.18.8, 8.9, 8.10, 9.2, 9.4, 9.5, 9.6
2.2510.08.8, 8.9, 8.10, 9.2, 9.4, 9.5, 9.6
Important:

RHEL kernel versionkernel-4.18.0-477.10.1.el8_8 is affected by an issue that interrupts the installation or management of the Automation Suite cluster. Make sure that none of the Automation Suite nodes uses this kernel version either pre- or post-installation.

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