automation-suite

2024.10

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Automation Suite on OpenShift installation guide

Last updated Mar 26, 2026

Kubernetes cluster and nodes

Cluster and permissions

You can bring your own Kubernetes cluster and follow your standard practices to provision and manage it.

An admin user must install specific required components separately, before the Automation Suite platform installation. After installing the required components, you can execute the installer. For the list of required permissions, see Granting installation permissions.

Node capacity

To estimate node capacity based on your product and scale requirements, use the UiPath Automation Suite Install Sizing Calculator.

The root volume requirement for agent (worker) nodes is 256 GB.

At a minimum, to start with the mandatory platform services (Identity, licensing, and routing) and Orchestrator, you must provision 8 vCPU and 16 GB RAM per node.

Note:

We do not recommend using spot instances in Automation Suite in production scenarios, due to stability and performance issues.

Autoscaling

We recommend enabling autoscaling on your cluster to ensure high reliability and to avoid business interruptions.

Additional Automation Suite Robots requirements

Automation Suite Robots require additional worker node(s).

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.

For details, see Storage documentation.

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.

Size	CPU	Memory	Storage
Small	0.5	1 GB	1 GB
Standard	1	2 GB	2 GB
Medium	2	4 GB	4 GB
Large	6	10 GB	10 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.

Order	Criterion	Machine size
1	Remote debugging job	Medium
2	Process depends on UI Automation OR Process depends on the UiPath Document Understanding activities	Standard
3	Other unattended process	Small

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:

Hardware	Minimum requirement
Processor	8 (v-)CPU/cores
RAM	52 GB
OS disk	256 GB SSD Min IOPS: 1100
Data disk	N/A
GPU RAM	11 GB

When adding the GPU node pool, it is important that you use --node-taints nvidia.com/gpu=present:NoSchedule instead of --node-taints sku=gpu:NoSchedule.

Important:

To ensure proper scheduling of GPU workloads, make sure your DaemonSet (NFD or Nvidia GPU Operator) YAML configuration includes a matching tolerations block. You can use the following example:

tolerations:
- key: "nvidia.com/gpu"
operator: "Equal"
value: "present"
effect: "NoSchedule"
tolerations:
- key: "nvidia.com/gpu"
operator: "Equal"
value: "present"
effect: "NoSchedule"

Automation Suite supports NVIDIA GPUs. To learn about how to configure NVDIA GPUs (such as drivers), see the OpenShift documentation.

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.

Function	Number
Custom model pages processed per hour	300
Out of the box model pages processed per hour	0
Models training in parallel	1
Number of pages in all projects - Design time	200
Number of document types per project version	3

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

Service	Number of GPUs
OCR replicas	1
Custom model training replicas	1
Custom model replicas	2
Out of the box model replicas	1
Total	5

For more information on how to allocate GPUs to each service, check the 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, you have two options:

Option A: Follow the official GPU setup documentation of your cloud provider, or the one on the NVIDIA website:
- NVIDIA Documentation: MIG Support in OpenShift Container Platform
- IBM Developer Documentation: Implementing NVIDIA MIG in Red Hat OpenShift

Option B (Alternative): Deploy the NVIDIA device plugin directly:

Create a new namespace:

kubectl create namespace gpu-resources
kubectl create namespace gpu-resources

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.

Node scheduling

We recommend enabling node taints on dedicated worker nodes for Automation Suite Robots and Document Understanding.

AI Center and DU example:

For CPU:

oc taint node <node_name> aic.ml/cpu=present:NoSchedule
oc taint node <node_name> aic.ml/cpu=present:NoSchedule

For GPU:

oc taint node <node_name> nvidia.com/gpu=present:NoSchedule
oc taint node <node_name> nvidia.com/gpu=present:NoSchedule

Automation Suite Robots example:

add a taint for serverless robots using the following command:

oc taint node <node_name> serverless.robot=present:NoSchedule
oc taint node <node_name> serverless.robot=present:NoSchedule

add the labels for serverless robots using the following command:

oc label node <node_name> serverless.robot=true serverless.daemon=true
oc label node <node_name> serverless.robot=true serverless.daemon=true

Important:

If you have custom node taints that are enforced by Gatekeeper Policy, such as specific roles for worker nodes or labels, they will not be passed to Automation Suite and may interrupt the installation process.

To learn about taints and tolerations, see Kubernetes documentation.

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Automation Suite on OpenShift installation guide

Cluster and permissions​

Node capacity​

Autoscaling​

Additional Automation Suite Robots requirements​

Robot size​

Agent node size​

Kubernetes resource consumption​

Automatic machine size selection​

Additional Document Understanding recommendations​

Additional Document Understanding modern projects requirements​

Provisioning MIG-enabled GPUs​

Enabling MIG-enabled GPUs in Kubernetes​

Node scheduling​

Was this page helpful?

Cluster and permissions

Node capacity

Autoscaling

Additional Automation Suite Robots requirements

Robot size

Agent node size

Kubernetes resource consumption

Automatic machine size selection

Additional Document Understanding recommendations

Additional Document Understanding modern projects requirements

Provisioning MIG-enabled GPUs

Enabling MIG-enabled GPUs in Kubernetes

Node scheduling