System requirements

All the Cloud Pak containers are based on Red Hat Universal Base Images (UBI), and are Red Hat and IBM certified. To use the Cloud Pak images, the administrator must make sure that the target cluster on Red Hat OpenShift Container Platform has the capacity for all the capabilities that you plan to install.

For each stage in your operations (a minimum of three stages is expected "development, preproduction, and production"), you must allocate a cluster of nodes before you install the Cloud Pak. Development, preproduction, and production are stages that are best run on different compute nodes. To achieve resource isolation, each namespace is a virtual cluster within the physical cluster and a Cloud Pak deployment is scoped to a single namespace. High-level resource objects are scoped within namespaces. Low-level resources, such as nodes and persistent volumes, are not in namespaces.

Note: Use the shared_configuration.sc_deployment_license parameter to define the purpose of the "custom" deployment type (shared_configuration.sc_deployment_type). Valid values are production and non-production.

The Detailed system requirements page provides a cluster requirements guideline for IBM Cloud Pak® for Business Automation. To find information on the supported versions of OpenShift Container Platform for example, open the rendered report for 24.0.0 and go to the Containers tab.

The minimum cluster configuration and physical resources that are needed to run the Cloud Pak include the following elements:
  • Hardware architecture: Intel (amd64 or x86_64 the 64-bit edition for Linux® x86) on all platforms, Linux on IBM Z, or Linux on Power.
  • Node counts: Dual compute nodes for non-production and production clusters. A minimum of three nodes are needed for medium and large production environments and large test environments. Any cluster configuration needs to adapt to the size of the project and the workload that is expected.

Licensed pods

The following table lists all the components that are licensed in production deployments. Each Cloud Pak for Business Automation capability has at least one component that is licensed and components that are not licensed. For more information, see Licensing.

Tip: The unlicensed components are not included in Table 1, but are included in the hardware requirements tables.
Table 1. Cloud Pak for Business Automation multi-pattern production deployment licensed pods
Capability component Licensed for production/non-production Pod name
Application Engine Yes/No

No for playback functions.

No for automation services only.

No for external workplace use.

<metadata.name>-<ae instance name>-aae-ae-deployment

By default, <ae instance name> is workspace.

Automation Decision Services: Runtime Yes <metadata.name>-ads-runtime
Automation Document Processing: OCR Extraction Yes <metadata.name>-ocr-extraction
Automation Document Processing: Classify Process Yes <metadata.name>-classify-process
Automation Document Processing: Processing Extraction Yes <metadata.name>-processing-extraction
Automation Document Processing: Natural Language Extractor Yes <metadata.name>-natural-language-extractor
Business Automation Insights: BPC Yes/No <metadata.name>-bai-bpmn
Business Automation Insights: Cockpit Yes/No <metadata.name>-insights-engine-cockpit
Business Automation Insights: Engine Yes/No <metadata.name>-insights-engine
Business Automation Insights: Flink task managers Yes/No <metadata.name>-insights-engine-flink
Business Automation Workflow Server Yes <metadata.name>-<baw_instance>-baw-server-n
Operational Decision Manager: Decision Center Yes <metadata.name>-odm-decisioncenter
Operational Decision Manager: Decision Runner Yes (always licensed as non-production) <metadata.name>-odm-decisionrunner
Operational Decision Manager: Decision Server Runtime Yes <metadata.name>-odm-decisionserverruntime
FileNet Content Manager: CPE Yes

No for Automation Document Processing.

No for Business Automation Workflow.

No for Automation Workstream Services.

No for Business Automation Application.

<metadata.name>-cpe-deploy
FileNet Content Manager: CSS Yes <metadata.name>-css-deploy
FileNet Content Manager: Enterprise Records Yes <metadata.name>-ier-deploy
FileNet Content Manager: Content Collector for SAP Yes <metadata.name>-iccsap-deploy

By default, <metadata.name> is icp4adeploy.

Deployment profiles

Based on your cluster requirement, you can pick a deployment profile (sc_deployment_profile_size) and enable it during installation. Cloud Pak for Business Automation provides small, medium, and large deployment profiles. You can set the profile during installation, in an update, and during an upgrade.

The default profile is small. Before you install the Cloud Pak, you can change the profile to medium or large. You can scale up or down a profile anytime after installation. However, if you install with a medium profile and another Cloud Pak specifies a medium or large profile then if you scale down to size small, the profile for the foundational services remains as it is. You can scale down the foundational services to small only if no other Cloud Pak specifies a medium or large size.
Attention: The values in the hardware requirements tables were derived under specific operating and environment conditions. The information is accurate under specific conditions, but the results that are obtained in your operating environments might vary significantly. Therefore, IBM cannot provide any representations, assurances, guarantees, or warranties regarding the performance of the profiles in your environment.

It is recommended that you set the IBM Cloud Platform UI (Zen) service to the same size as Cloud Pak for Business Automation. For a small-sized deployment, the size of the Cloud Pak foundational services instance is set to starterset. The possible values include small, medium, and large. To determine the real size that is needed for Cloud Pak foundational services, do proper performance testing with your intended workload and modify the CRs to the correct size. For more information, see Hardware requirements and recommendations for foundational services.

The following table describes each deployment profile.

Table 2. Deployment profiles and estimated workloads
Profile Description Scaling (per 8-hour day) Minimum number of worker nodes
Small (no HA) For environments that are used by 10 developers and 25 users. For environments that are used by a single department with a few users; useful for application development.
  • Processes 10,000 documents
  • Processes 5,000 human workflows
  • Processes 500,000 Straight Thru Processes processes
  • Processes 1.25 million Straight Thru Service Flows
  • Processes 5,000 transactions
  • Processes 500,000 decisions
  • Supports failover
8
Medium For environments that are used by 20 developers and 125 users. For environments that are used by a single department and by limited users.
  • Processes 100,000 documents
  • Processes 25,000 human workflows
  • Processes 1 million Straight Thru Processes processes
  • Processes 3.5 million Straight Thru Service Flows
  • Processes 25,000 transactions
  • Processes 2,000,000 decisions
  • Supports HA and failover
  • Provides at least two replicas of most services, if configuring failover
16
Large For environments that are used by 50 developers and 625 users. For environments that are shared by multiple departments and users.
  • Processes 1,000,000 documents
  • Processes 125,000 human workflows
  • Processes 2 million Straight Thru Processes processes
  • Processes 7 million Straight Thru Service Flows
  • Processes 125,000 transactions
  • Processes 5,000,000 decisions
  • Supports HA and failover
  • Provides at least two replicas of most services, if configuring failover
32

You can use custom resource templates to update the hardware requirements of the services that you want to install.

The following sections provide the default resources for each capability. For more information about the minimum requirements of foundational services, see Hardware requirements and recommendations for foundational services.

Note: The system requirements for small, medium, and large profiles are derived under specific operating and environment conditions. Due to the differences in hardware, networking, and storage, the resources vary for each workload in different environments. It is important that you run a performance test with peaked workload in a Test or UAT environment. Monitor the resource usage (for example CPU and memory) to determine the resource usage for each component. Based on the resource usage, make changes to CPU request, CPU limit, memory request, and memory limit in the custom resource for each component.

Ephemeral storage is storage that is tied to the lifecycle of a pod, so when a pod finishes or is restarted, that storage is deleted. It is used in any situation where your workloads need or generate transient local data, like logging. Use the /bin/df tool to monitor ephemeral storage usage on the volume where ephemeral container data is located. You can manage local ephemeral storage by setting quotas that define the limit ranges and the number of requests.

Small profile hardware requirements

  • Table 3 Cloud Pak for Business Automation operator default requirements for a small profile
  • Table 4 EDB Postgres default requirements for a small profile
  • Table 5 Foundation default requirements for a small profile
  • Table 6 Automation Decision Services default requirements for a small profile
  • Table 7 Automation Document Processing default requirements for a small profile
  • Table 8 Automation Workstream Services default requirements for a small profile
  • Table 9 Business Automation Application default requirements for a small profile
  • Table 10 Business Automation Workflow default requirements with or without Automation Workstream Services for a small profile
  • Table 11 FileNet® Content Manager default requirements for a small profile
  • Table 12 Operational Decision Manager default requirements for a small profile
  • Table 13 Workflow Process Service Authoring default requirements for a small profile
For capabilities that have their own operator, including IBM Workflow Process Service Runtime, IBM FileNet Content Manager, and IBM Process Federation Server, their requirements are listed separately:
Table 3. Cloud Pak for Business Automation operator default requirements for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
ibm-cp4a-operator 500 1000 256 2048 NA 1 No
ibm-content-operator 500 1000 256 2048 NA 1 No
ibm-ads-operator 10 500 64 512 NA 1 No
ibm-odm-operator 10 500 256 768 NA 1 No
ibm-dpe-operator 10 1000 256 768 750 1 No
ibm-pfs-operator 100 500 20 1024 NA 1 No
ibm-workflow-operator 100 500 20 1024 NA 1 No
ibm-cp4a-wfps-operator 100 500 20 500 NA 1 No
ibm-insights-engine-operator 500 1000 256 2048 800 1 No

CPU Request and CPU Limit values are measured in units of millicore (m).

Memory Request and Memory Limit values are measured in units of mebibyte (Mi).

Note: If you plan to install the cp4a operator for example in all namespaces for more than one instance, add more resources. You can use the oc patch csv command to add more resources:
oc patch csv ibm-cp4a-operator.v24.0.0 --type=json -p '[
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/cpu",
"value": "4"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/memory",
"value": "8Gi"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/cpu",
"value": "1500m"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/memory",
"value": "1600Mi"
},
]'
Table 4. EDB Postgres default requirements for a small profile
CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production Ephemeral storage Limit (Mi) Ephemeral storage Request (Mi)
1000 2000 4096 8192 1 No 1024 500
Table 5. Foundation default requirements for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi)
Business Automation Insights: Business Performance Center 100 4000 512 2000 1 Yes/No 1050 1150
Business Automation Insights: Flink task managers 1000 1000 1728 1728 Default parallelism

8

Yes/No 500 2048
Business Automation Insights: Flink job manager 1000 1000 1728 1728 1 No 500 2048
Business Automation Insights: Management REST API 100 1000 50 160 1 No 371 395
Business Automation Insights: Management back end 100 500 350 512 1 No 381 410
Navigator 1000 1000 3072 3072 1 No    
Navigator Watcher 250 500 256 512 1 No    
App Engine playback 300 500 256 1024 1 No 512 2048
BAStudio 1100 2000 1752 3072 1 No 1024 2048
Resource Registry 100 500 256 512 1 No 128 2048
Note: Business Automation Insights uses Kafka and OpenSearch. Business Automation Insights also creates the bai-setup and bai-core-application-setup jobs and requests 200 m of CPU and 350 Mi of memory. The CPU and memory limits are the same as the requests. The pods for these jobs run for a short time at the beginning of the installation and then stop, and the resources are then released.
Table 6. Automation Decision Services default requirements for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
ads-runtime 500 1000 2048 3072 300 500 1 Yes
ads-credentials 250 1000 800 1536 300 600 1 No
ads-gitservice 500 1000 800 1536 400 600 1 No
ads-parsing 250 1000 800 1536 300 500 1 No
ads-restapi 500 1000 800 1536 300 1228.8 1 No
ads-run 500 1000 800 1536 300 700 1 No
Note: Automation Decision Services also creates some jobs that request 200m CPU and 256Mi Memory. The following jobs are created at the beginning of the installation and do not last long:
  • ads-ltpa-creation
  • ads-runtime-bai-registration
  • ads-ads-runtime-zen-translation-job
  • ads-designer-zen-translation-job

The ads-rr-integration and ads-ads-rr-as-runtime-synchro jobs are started every 15 minutes, and are also short-lived.

Table 7. Automation Document Processing default requirements for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
OCR Extraction 200 1000 1024 2560 3072 3 Yes
Classify Process 200 500 400 2048 3072 1 Yes
Processing Extraction 500 1000 1024 6656 5120 5 Yes
Natural Language Extractor 200 500 600 1440 3072 2 Yes
PostProcessing 200 1000 400 1229 3072 1 No
Setup 200 1000 600 2048 3072 2 No
Deep Learning 1000 2000 3072 15360 7680 2 No
Backend 200 1000 400 2048 4608 2 No
Webhook 200 300 400 500 1024 1 No
RabbitMQ 100 1000 100 1024 3072 2 No
OCR engine 2 Runtime (wdu-runtime) 200 4000 1024 7629 4096 1 No
OCR engine 2 Extraction (wdu-extraction) 300 1000 500 1024 3072 1 No
Common Git Gateway Service (git-service) 500 1000 512 1536 Not applicable 1 No
Content Designer Repo API (CDRA) 500 1000 1024 3072 Not applicable 1 No
Content Designer UI and REST (CDS) 500 1000 512 3072 2048 1 No
Content Project Deployment Service (CPDS) 500 1000 512 3072 Not applicable 1 No
Mongo database (mongodb) 500 1000 512 1024 Not applicable 1 No
Viewer service (viewone) 500 1000 1024 3072 Not applicable 1 No
Important:
  • Document Processing - The Deep Learning optional container can use NVIDIA GPU if it is available. NVIDIA is the only supported GPU for Deep Learning in the Document Processing pattern. The GPU worker nodes must have a unique label, for example ibm-cloud.kubernetes.io/gpu-enabled:true. You add this label value to the deployment script or to the YAML file of your custom resource when you configure the YAML for deployment. To install the NVIDIA GPU operator, follow these installation instructions. For high-availability, you need a minimum of 2 GPU so that 2 replicas of Deep Learning pods can be started. You can change the replica to 1 if you have 1 GPU on the node.
  • For Document Processing, the CPU of the worker nodes must meet TensorFlow AVX requirements. For more information, see Hardware requirements for TensorFlow with pip.
Note:
  • Document Processing requires databases for project configuration and processing. These databases must be Db2 or PostgreSQL. The hardware and storage requirements for the databases depend on the system load for each document processing project.
  • The previous table shows the requirements if deep learning object detection is enabled. If you process fixed-format documents, you might want to improve performance by disabling deep learning object detection. For more information about the system requirements for Document Processing engine components in this scenario, see IBM Automation Document Processing system requirements when disabling deep learning object detection for fixed-format documents.
  • If you deploy with only the document_processing pattern, you can reduce the sizing for some of the required components. For more information, see IBM Automation Document Processing system requirements for a light production deployment (document_processing pattern only).
  • Use a maximum of 70% of the available space for projects. For example, if you have 5000 Mb, use 3500 Mb for your projects. Because the model size is 153 Mb, it means you can create a maximum of 22 projects. If you want to set up more than 22 projects, increase the ephemeral storage for both the Deep Learning and Processing Extraction containers.
  • The OCR engine 2 Runtime container enables support for low-quality documents and handwriting recognition when the ca_configuration.ocrextraction.use_iocr parameter is set to auto or all.
  • OCR engine 2 Extraction is an optional container that is used to make gRPC requests to the OCR engine 2 Runtime service, and you deploy it by setting the ca_configuration.ocrextraction.use_iocr parameter to auto or all.
Table 8. Automation Workstream Services default requirements for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
Workflow Server 500 2000 2048 3060 1 Yes
Notes:
  • For components that are included in your Automation Workstream Services instance from FileNet Content Manager, see Table 11.
  • Automation Workstream Services also creates some jobs that request 200m CPU and 128Mi Memory:
    • basimport-job is created only with Business Automation Studio
    • db-init-job
    • content-init-job
    • ltpa-job
    • oidc-registry-job
    • oidc-registry-job-for-webpd is created only with Workflow Center
Table 9. Business Automation Application default requirements for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
App Engine 300 500 256 1024 512 2048 1 Yes/No
Resource Registry 100 500 256 512 128 2048 1 No
Table 10. Business Automation Workflow default requirements with or without Automation Workstream Services for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
Workflow Server 500 2000 2048 3060 1 Yes
Workflow Authoring 500 2000 2048 3072 1 No
Intelligent Task Prioritization 500 2000 1024 2560 1 No
Workforce Insights 500 2000 1024 2560 1 No
Notes:
  • For components that are included in your Business Automation Workflow instance from FileNet Content Manager, see Table 11.
  • Intelligent Task Prioritization and Workforce Insights are optional and are not supported on all platforms. For more information, see Detailed system requirements.
  • Business Automation Workflow also creates some jobs that request 200m CPU and 128Mi Memory:
    • basimport-job is created only with Business Automation Studio.
    • case-init-job
    • db-init-job
    • content-init-job
    • ltpa-job
Table 11. FileNet Content Manager default requirements for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
CPE 1000 1000 3072 3072 1 Yes
CSS 1000 1000 4096 4096 1 Yes
Enterprise Records (ER) 500 1000 1536 1536 1 Yes
Content Collector for SAP (CC4SAP) 500 1000 1536 1536 1 Yes
CMIS 500 1000 1536 1536 1 No
GraphQL 500 1000 1536 1536 1 No
Task Manager 500 1000 1536 1536 1 No
Note: Not all containers are used in every workload. If a feature the Content Services GraphQL API is not used, that container requires less resources or is optionally not deployed.

In high-volume indexing scenarios, where ingested docs are full-text indexed, the CSS utilization can exceed the CPE utilization. Sometimes, this might be 3 - 5 times larger.

For optional processing such as thumbnail generation or text filtering, at least 1 GB of native memory is required by the CPE for each. If both types of processing are expected, add at least 2 GB to the memory requests/limits for the CPE.

With the processing of content, resources required increase with the complexity and size of the content. Increase both memory and CPU for the CPE and CSS services to reflect the type and size of documents in your system. Resource requirements might also increase over time as the amount of data in the system grows.

Table 12. Operational Decision Manager default requirements for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
Decision Center 1000 1000 4096 4096 1024 2048 1 Yes
Decision Runner 500 500 2048 2048 200 1024 1 Yes
Decision Server Runtime 500 1000 2048 2048 200 1024 1 Yes
Decision Server Console 500 500 1024 1024 200 1024 1 No
Note: Operational Decision Manager also creates an odm-oidc-job-registration job that requests 200m CPU and 256Mi Memory. The pod is created at the beginning of the installation and does not last long.
Table 13. IBM Workflow Process Service Authoring default requirements for a small profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
IBM Workflow Process Service Authoring 1100 2000 1752 3072 1 No

Medium profile hardware requirements

  • Table 14 Cloud Pak for Business Automation operator default requirements for a medium profile
  • Table 15 EDB Postgres default requirements for a medium profile
  • Table 16 Foundation default requirements for a medium profile
  • Table 17 Automation Decision Services default requirements for a medium profile
  • Table 18 Automation Document Processing default requirements for a medium profile
  • Table 19 Automation Workstream Services default requirements for a medium profile
  • Table 20 Business Automation Application default requirements for a medium profile
  • Table 21 Business Automation Workflow default requirements with or without Automation Workstream Services for a medium profile
  • Table 22 FileNet Content Manager default requirements for a medium profile
  • Table 23 Operational Decision Manager default requirements for a medium profile
  • Table 24 Workflow Process Service Authoring default requirements for a medium profile
For capabilities that have their own operator, including IBM Workflow Process Service Runtime, IBM FileNet Content Manager, and IBM Process Federation Server, their requirements are listed separately:
Table 14. Cloud Pak for Business Automation operator default requirements for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
ibm-cp4a-operator 500 1000 256 2048 NA 1 No
ibm-content-operator 500 1000 256 2048 NA 1 No
ibm-ads-operator 10 500 64 512 NA 1 No
ibm-odm-operator 10 500 256 768 NA 1 No
ibm-dpe-operator 10 1000 256 768 500 1 No
ibm-pfs-operator 100 500 20 1024 NA 1 No
ibm-workflow-operator 100 500 20 1024 NA 1 No
ibm-cp4a-wfps-operator 100 500 20 500 NA 1 No
ibm-insights-engine-operator 500 1000 256 2048 800 1 No
Note: If you plan to install the cp4a operator for example in all namespaces for more than one instance, add more resources. You can use the oc patch csv command to add more resources:
oc patch csv ibm-cp4a-operator.v23.2.0 --type=json -p '[
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/cpu",
"value": "4"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/memory",
"value": "8Gi"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/cpu",
"value": "1500m"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/memory",
"value": "1600Mi"
},
]'
Table 15. EDB Postgres default requirements for a medium profile
CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production Ephemeral storage Limit (Mi) Ephemeral storage Request (Mi)
1000 4000 4096 8192 1 No 1024 500
Table 16. Foundation default requirements for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi)
Business Automation Insights: Business Performance Center 100 4000 512 2000 2 Yes/No 1050 1150
Business Automation Insights: Flink task managers 1000 1000 1728 1728 Default parallelism

8

Yes/No 500 2048
Business Automation Insights: Flink job manager 1000 1000 1728 1728 1 No 500 2048
Business Automation Insights: Management REST API 100 1000 50 160 2 No 371 395
Business Automation Insights: Management back end 100 500 350 512 2 No 381 410
Navigator 2000 3000 4096 4096 2 No    
Navigator Watcher 250 500 256 512 1 No    
App Engine playback 300 500 256 1024 2 No 512 2048
BAStudio 1000 2000 1752 3072 2 No 1024 2048
Resource Registry 100 500 256 512 3 No 128 2048
Note: Business Automation Insights uses Kafka and OpenSearch. Business Automation Insights also creates the bai-setup and bai-core-application-setup jobs and requests 200 m of CPU and 350 Mi of memory. The CPU and memory limits are the same as the requests. The pods for these jobs run for a short time at the beginning of the installation and then stop, and the resources are then released.
Table 17. Automation Decision Services default requirements for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
ads-runtime 500 1000 2048 3072 2150.4 3072 2 Yes
ads-credentials 250 1000 800 1536 300 600 2 No
ads-gitservice 500 1000 800 1536 400 700 2 No
ads-parsing 250 1000 800 1536 300 600 2 No
ads-restapi 500 1000 800 1536 300 1228.8 2 No
ads-run 500 1000 800 1536 300 700 2 No
Note: Automation Decision Services also creates some jobs that request 200m CPU and 256Mi Memory. The following jobs are created at the beginning of the installation and do not last long:
  • ads-ltpa-creation
  • ads-runtime-bai-registration
  • ads-ads-runtime-zen-translation-job
  • ads-designer-zen-translation-job

The ads-rr-integration and ads-ads-rr-as-runtime-synchro jobs are started every 15 minutes, and are also short-lived.

Table 18. Automation Document Processing default requirements for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Limit (Mi) Number of Replicas Pods are licensed for production/non-production
OCR Extraction 200 1000 1024 2560 3072 4 Yes
Classify Process 200 500 400 2048 3072 2 Yes
Processing Extraction 500 1000 1024 6656 5120 7 Yes
Natural Language Extractor 200 500 600 1440 3072 2 Yes
PostProcessing 200 1000 400 1229 3072 2 No
Setup 200 1000 600 2048 3072 4 No
Deep Learning 1000 2000 3072 15360 7680 2 No
Backend 200 1000 400 2048 4608 4 No
Webhook 200 300 400 500 1024 2 No
RabbitMQ 100 1000 100 1024 3072 3 No
OCR engine 2 Runtime (wdu-runtime) 200 4000 1024 7629 4096 1 No
OCR engine 2 Extraction (wdu-extraction) 300 1000 500 1024 3072 1 No
Common Git Gateway Service (git-service) 500 1000 512 1536 Not applicable 1 No
Content Designer Repo API (CDRA) 500 1000 1024 3072 Not applicable 2 No
Content Designer UI and REST (CDS) 500 1000 512 3072 2048 2 No
Content Project Deployment Service (CPDS) 500 1000 512 1024 Not applicable 2 No
Mongo database (mongodb) 500 1000 512 1024 Not applicable 1 No
Viewer service (viewone) 500 2000 1024 4096 Not applicable 2 No
Important:
  • Document Processing - The Deep Learning optional container has the ability to use NVIDIA GPU if it is available. NVIDIA is the only supported GPU for Deep Learning in the Document Processing pattern. The GPU worker nodes must have a unique label, for example ibm-cloud.kubernetes.io/gpu-enabled:true. You add this label value to the deployment script or to the YAML file of your custom resource when you configure the YAML for deployment. To install the NVIDIA GPU operator, follow these installation instructions. For high-availability, you need a minimum of 2 GPU so that 2 replicas of Deep Learning pods can be started. You can change the replica to 1 if you have 1 GPU on the node.
  • For Document Processing, the CPU of the worker nodes must meet TensorFlow AVX requirements. For more information, see Hardware requirements for TensorFlow with pip.
Note:
  • Document Processing requires databases for project configuration and processing. These databases must be Db2 or PostgreSQL. The hardware and storage requirements for the databases depend on the system load for each document processing project.
  • The previous table shows the requirements if deep learning object detection is enabled. If you process fixed-format documents, you want to might improve performance by disabling deep learning object detection. For more information about the system requirements for Document Processing engine components in this scenario, see IBM Automation Document Processing system requirements when disabling deep learning object detection for fixed-format documents.
  • If you deploy with only the document_processing pattern, you can reduce the sizing for some of the required components. For more information, see IBM Automation Document Processing system requirements for a light production deployment (document_processing pattern only).
  • Use a maximum of 70% of the available space for projects. For example, if you have 5000 Mb, use 3500 Mb for your projects. Because the model size is 153 Mb, it means you can create a maximum of 22 projects. If you want to set up more than 22 projects, increase the ephemeral storage for both the Deep Learning and Processing Extraction containers.
  • The OCR engine 2 Runtime container enables support for low-quality documents and handwriting recognition when the ca_configuration.ocrextraction.use_iocr parameter is set to auto or all.
  • OCR engine 2 Extraction is an optional container that is used to make gRPC requests to the OCR engine 2 Runtime service, and you deploy it by setting the ca_configuration.ocrextraction.use_iocr parameter to auto or all.
Table 19. Automation Workstream Services default requirements for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
Workflow Server 500 2000 2560 3512 2 Yes
Notes:
  • For components that are included in your Automation Workstream Services instance from FileNet Content Manager, see Table 22.
  • Automation Workstream Services also creates some jobs that request 200m CPU and 128Mi Memory:
    • basimport-job is created only with Business Automation Studio
    • db-init-job
    • content-init-job
    • ltpa-job
    • oidc-registry-job
    • oidc-registry-job-for-webpd is created only with Workflow Center
Table 20. Business Automation Application default requirements for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
App Engine 300 500 256 1024 512 2048 3 Yes/No
Resource Registry 100 500 256 512 128 2048 3 No
Table 21. Business Automation Workflow default requirements with or without Automation Workstream Services for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
Workflow Server 500 2000 2560 3512 2 Yes
Workflow Authoring 500 4000 1024 3072 1 No
Intelligent Task Prioritization 500 2000 1024 2560 2 No
Workforce Insights 500 2000 1024 2560 2 No
Notes:
  • For components that are included in your Business Automation Workflow instance from FileNet Content Manager, see Table 22.
  • Intelligent Task Prioritization and Workforce Insights are optional and are not supported on all platforms. For more information, see Detailed system requirements.
  • Business Automation Workflow also creates some jobs that request 200m CPU and 128Mi Memory:
    • basimport-job is created only with Business Automation Studio.
    • case-init-job
    • db-init-job
    • content-init-job
    • ltpa-job
Table 22. FileNet Content Manager default requirements for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
CPE 1500 2000 3072 3072 2 Yes
CSS 1000 2000 8192 8192 2 Yes
Enterprise Records (ER) 500 1000 1536 1536 2 Yes
Content Collector for SAP (CC4SAP) 500 1000 1536 1536 2 Yes
CMIS 500 1000 1536 1536 2 No
GraphQL 500 2000 3072 3072 3 No
Task Manager 500 1000 1536 1536 2 No
Note: Not all containers are used in every workload. If a feature like the Content Services GraphQL API is not used, that container requires less resources or is optionally not deployed.

In high-volume indexing scenarios, where ingested docs are full-text indexed, the CSS utilization can exceed the CPE utilization. In some cases, this might be 3 - 5 times larger.

For optional processing such as thumbnail generation or text filtering, at least 1 GB of native memory is required by the CPE for each. If both types of processing are expected, add at least 2 GB to the memory requests/limits for the Content Platform Engine (CPE).

With the processing of content, resource requirements increase with the complexity and size of the content. Increase both memory and CPU for the CPE and CSS services to reflect the type and size of documents in your system. Resource requirements might also increase over time as the amount of data in the system grows.

Table 23. Operational Decision Manager default requirements for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
Decision Center 1000 1000 4096 8192 1024 2048 2 Yes
Decision Runner 500 2000 2048 2048 200 1024 2 Yes
Decision Server Runtime 2000 2000 2048 2048 200 1024 3 Yes
Decision Server Console 500 2000 512 2048 200 1024 1 No
Note: Operational Decision Manager also creates an odm-oidc-job-registration job that requests 200m CPU and 256Mi Memory. The pod is created at the beginning of the installation and does not last long.

To achieve high availability, you must adapt the cluster configuration and physical resources. You can set up a Db2® High Availability Disaster Recovery (HADR) database. For more information, see Preparing your environment for disaster recovery. For high availability and fault tolerance to be effective, set the number of replicas that you need for the respective configuration parameters in your custom resource file. The operator then manages the scaling.

Table 24. Workflow Process Service Authoring default requirements for a medium profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
Workflow Process Service Authoring 1000 2000 1752 3072 2 No

Large profile hardware requirements

  • Table 25 Cloud Pak for Business Automation operator default requirements for a large profile
  • Table 26 EDB Postgres default requirements for a large profile
  • Table 27 Foundation default requirements for a large profile
  • Table 28 Automation Decision Services default requirements for a large profile
  • Table 29 Automation Document Processing default requirements for a large profile
  • Table 30 Automation Workstream Services default requirements for a large profile
  • Table 31 Business Automation Application default requirements for a large profile
  • Table 32 Business Automation Workflow default requirements with or without Automation Workstream Services for a large profile
  • Table 33 FileNet Content Manager default requirements for a large profile
  • Table 34 Operational Decision Manager default requirements for a large profile
  • Table 35 Workflow Process Service Authoring default requirements for a large profile
For capabilities that have their own operator, including IBM Workflow Process Service Runtime, IBM FileNet Content Manager, and IBM Process Federation Server, their requirements are listed separately:
Table 25. Cloud Pak for Business Automation operator default requirements for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production Ephemeral storage Limit
ibm-cp4a-operator 500 1000 256 2048 1 No NA
ibm-content-operator 500 1000 256 2048 1 No NA
ibm-ads-operator 10 500 64 512 1 No NA
ibm-odm-operator 10 500 256 768 1 No NA
ibm-dpe-operator 10 1000 256 768 1 No 500
ibm-pfs-operator 100 500 20 1024 1 No NA
ibm-workflow-operator 100 500 20 1024 1 No NA
ibm-cp4a-wfps-operator 100 500 20 500 1 No NA
ibm-insights-engine-operator 500 1000 256 2048 800 1 No
Note: If you plan to install the cp4a operator for example in all namespaces for more than one instance, add more resources. You can use the oc patch csv command to add more resources:
oc patch csv ibm-cp4a-operator.v23.2.0 --type=json -p '[
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/cpu",
"value": "4"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/limits/memory",
"value": "8Gi"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/cpu",
"value": "1500m"
},
{
"op":"replace",
"path": "/spec/install/spec/deployments/0/spec/template/spec/containers/0/resources/requests/memory",
"value": "1600Mi"
},
]'
Table 26. EDB Postgres default requirements for a large profile
CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production Ephemeral storage Limit (Mi) Ephemeral storage Request (Mi)
1000 8000 4096 16384 1 No 1024 500
Table 27. Foundation default requirements for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi)
Business Automation Insights: Business Performance Center 100 4000 512 2000 2 Yes/No 1050 1150
Business Automation Insights: Flink task managers 1000 1000 1728 1728 Default parallelism

8

Yes/No 500 2048
Business Automation Insights: Flink job manager 1000 1000 1728 1728 1 No 500 2048
Business Automation Insights: Management REST API 100 1000 50 160 2 No 371 395
Business Automation Insights: Management back end 100 500 350 512 2 No 381 410
Navigator 2000 4000 6144 6144 4 No    
Navigator Watcher 250 500 256 512 1 No    
App Engine playback 300 500 256 1024 4 No 512 2048
BAStudio 2000 4000 1752 3072 2 No 1024 2048
Resource Registry 100 500 256 512 3 No 128 2048
Note: Business Automation Insights uses Kafka and OpenSearch. Business Automation Insights also creates the bai-setup and bai-core-application-setup jobs and requests 200 m of CPU and 350 Mi of memory. The CPU and memory limits are the same as the requests. The pods for these jobs run for a short time at the beginning of the installation and then stop, and the resources are then released.
Table 28. Automation Decision Services default requirements for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
ads-runtime 1000 2000 2048 3072 2150.4 2662.4 2 Yes
ads-credentials 250 2000 800 1536 300 700 2 No
ads-gitservice 500 2000 800 1536 400 800 2 No
ads-parsing 250 2000 800 1536 300 700 2 No
ads-restapi 500 2000 800 1536 300 1228.8 2 No
ads-run 500 2000 800 1536 300 1024 2 No
Note: Automation Decision Services also creates some jobs that request 200m CPU and 256Mi Memory. The following jobs are created at the beginning of the installation and do not last long:
  • ads-ltpa-creation
  • ads-runtime-bai-registration
  • ads-ads-runtime-zen-translation-job
  • ads-designer-zen-translation-job

The ads-rr-integration and ads-ads-rr-as-runtime-synchro jobs are started every 15 minutes, and are also short-lived.

Table 29. Automation Document Processing default requirements for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Limit (Mi) Number of Replicas Pods are licensed for production/non-production
OCR Extraction 200 1000 1024 2560 3072 6 Yes
Classify Process 200 500 400 2048 3072 2 Yes
Processing Extraction 500 1000 1024 6656 5120 14 Yes
Natural Language Extractor 200 500 600 1440 3072 2 Yes
PostProcessing 200 1000 400 1229 3072 2 No
Setup 200 1000 600 2048 3072 6 No
Deep Learning 1000 2000 3072 15360 7680 2 No
Backend 200 1000 400 2048 4608 6 No
Webhook 200 300 400 500 1024 3 No
RabbitMQ 100 1000 100 1024 3072 3 No
OCR engine 2 Runtime (wdu-runtime) 200 4000 1024 7629 4096 1 No
OCR engine 2 Extraction (wdu-extraction) 300 1000 500 1024 3072 1 No
Common Git Gateway Service (git-service) 500 1000 512 1536 Not applicable 2 No
Content Designer Repo API (CDRA) 500 2000 1024 3072 Not applicable 2 No
Content Designer UI and REST (CDS) 500 2000 512 3072 2048 2 No
Content Project Deployment Service (CPDS) 500 2000 512 2048 Not applicable 2 No
Mongo database (mongodb) 500 1000 512 1024 Not applicable 1 No
Viewer service (viewone) 1000 3000 3072 6144 Not applicable 2 No
Important:
  • Document Processing - The Deep Learning optional container has the ability to use NVIDIA GPU if it is available. NVIDIA is the only supported GPU for Deep Learning in the Document Processing pattern. The GPU worker nodes must have a unique label, for example ibm-cloud.kubernetes.io/gpu-enabled:true. You add this label value to the deployment script or to the YAML file of your custom resource when you configure the YAML for deployment. To install the NVIDIA GPU operator, follow these installation instructions. For high-availability, you need a minimum of 2 GPU so that 2 replicas of Deep Learning pods can be started. You can change the replica to 1 if you have 1 GPU on the node.
  • For Document Processing, the CPU of the worker nodes must meet TensorFlow AVX requirements. For more information, see Hardware requirements for TensorFlow with pip.
Note:
  • Document Processing requires databases for project configuration and processing. These databases must be Db2 or PostgreSQL. The hardware and storage requirements for the databases depend on the system load for each document processing project.
  • The previous table shows the requirements if deep learning object detection is enabled. If you process only fixed-format documents, you might improve performance by disabling deep learning object detection. For more information about the system requirements for Document Processing engine components in this scenario, see IBM Automation Document Processing system requirements when disabling deep learning object detection for fixed-format documents.
  • If you deploy with only the document_processing pattern, you can reduce the sizing for some of the required components. For more information, see IBM Automation Document Processing system requirements for a light production deployment (document_processing pattern only).
  • Use a maximum of 70% of the available space for projects. For example, if you have 5000 Mb, use 3500 Mb for your projects. Because the model size is 153 Mb, it means you can create a maximum of 22 projects. If you want to set up more than 22 projects, increase the ephemeral storage for both the Deep Learning and Processing Extraction containers.
  • The OCR engine 2 Runtime container enables support for low-quality documents and handwriting recognition when the ca_configuration.ocrextraction.use_iocr parameter is set to auto or all.
  • OCR engine 2 Extraction is an optional container that is used to make gRPC requests to the OCR engine 2 Runtime service, and you deploy it by setting the ca_configuration.ocrextraction.use_iocr parameter to auto or all.
Table 30. Automation Workstream Services default requirements for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
Workflow Server 1000 2000 3060 4000 4 Yes
Notes:
  • For components that are included in your Automation Workstream Services instance from FileNet Content Manager, see Table 33.
  • Automation Workstream Services also creates some jobs that request 200m CPU and 128Mi Memory:
    • basimport-job is created only with Business Automation Studio
    • db-init-job
    • content-init-job
    • ltpa-job
    • oidc-registry-job
    • oidc-registry-job-for-webpd is created only with Workflow Center
Table 31. Business Automation Application default requirements for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
App Engine 300 500 256 1024 512 2048 6 Yes/No
Resource Registry 100 500 256 512 128 2048 1 No
Table 32. Business Automation Workflow default requirements with or without Automation Workstream Services for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
Workflow Server 1000 2000 3060 4000 4 Yes
Workflow Authoring 1000 2000 2000 3000 2 No
Intelligent Task Prioritization 500 2000 1024 2560 2 No
Workforce Insights 500 2000 1024 2560 2 No
Notes:
  • For components that are included in your Business Automation Workflow instance from FileNet Content Manager, see Table 33.
  • Intelligent Task Prioritization and Workforce Insights are optional and are not supported on all platforms. For more information, see Detailed system requirements.
  • Business Automation Workflow also creates some jobs that request 200m CPU and 128Mi Memory:
    • basimport-job is created only with Business Automation Studio.
    • case-init-job
    • db-init-job
    • content-init-job
    • ltpa-job
Table 33. FileNet Content Manager default requirements for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
CPE 3000 4000 8192 8192 2 Yes
CSS 2000 4000 8192 8192 2 Yes
Enterprise Records (ER) 500 1000 1536 1536 2 Yes
Content Collector for SAP (CC4SAP) 500 1000 1536 1536 2 Yes
CMIS 500 1000 1536 1536 2 No
GraphQL 1000 2000 3072 3072 4 No
Task Manager 500 1000 1536 1536 2 No
Note: Not all containers are used in every workload. If a feature like the Content Services GraphQL API is not used, that container requires less resources or is optionally not deployed.

In high-volume indexing scenarios, where ingested docs are full-text indexed, the CSS utilization can exceed the CPE utilization. In some cases, this might be 3 - 5 times larger.

For optional processing such as thumbnail generation or text filtering, at least 1 GB of native memory is required by the CPE for each. If both types of processing are expected, add at least 2 GB to the memory requests/limits for the CPE.

With the processing of content, resources required increase with the complexity and size of the content. Increase both memory and CPU for the CPE and CSS services to reflect the type and size of documents in your system. Resource requirements might also increase over time as the amount of data in the system grows.

Table 34. Operational Decision Manager default requirements for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Ephemeral Storage Request (Mi) Ephemeral Storage Limit (Mi) Number of replicas Pods are licensed for production/non-production
Decision Center 2000 2000 4096 16384 1024 2048 2 Yes
Decision Runner 500 4000 2048 2048 200 1024 2 Yes
Decision Server Runtime 2000 2000 4096 4096 200 1024 6 Yes
Decision Server Console 500 2000 512 4096 200 1024 1 No
Note: Operational Decision Manager also creates an odm-oidc-job-registration job that requests 200m CPU and 256Mi Memory. The pod is created at the beginning of the installation and does not last long.

To achieve high availability, you must adapt the cluster configuration and physical resources. You can set up a Db2 High Availability Disaster Recovery (HADR) database. For more information, see Preparing your environment for disaster recovery. For high availability and fault tolerance to be effective, set the number of replicas that you need for the respective configuration parameters in your custom resource file. The operator then manages the scaling.

Table 35. Workflow Process Service Authoring default requirements for a large profile
Component CPU Request (m) CPU Limit (m) Memory Request (Mi) Memory Limit (Mi) Number of replicas Pods are licensed for production/non-production
Workflow Process Service Authoring 2000 4000 1752 3072 2 No