- Overview
- Requirements
- Pre-installation
- Preparing the installation
- Downloading the installation packages
- Configuring the OCI-compliant registry
- Granting installation permissions
- Installing and configuring the service mesh
- Installing and configuring the GitOps tool
- Installing the External Secrets Operator in Kubernetes
- Applying miscellaneous configurations
- Running uipathctl
- Installation
- Post-installation
- Migration and upgrade
- Monitoring and alerting
- Cluster administration
- Product-specific configuration
- Orchestrator advanced configuration
- Configuring Orchestrator parameters
- Configuring appSettings
- Configuring the maximum request size
- Overriding cluster-level storage configuration
- Configuring NLog
- Saving robot logs to Elasticsearch
- Configuring credential stores
- Configuring encryption key per tenant
- Cleaning up the Orchestrator database
- Skipping host library installation
- AI Trust Layer
- Troubleshooting
- The backup setup does not work due to a failure to connect to Azure Government
- Pods in the uipath namespace stuck when enabling custom node taints
- Unable to launch Automation Hub and Apps with proxy setup
- Velero backup fails with FailedValidation error
- External Secrets troubleshooting
- Temporal as a Service troubleshooting
- AI Center and Document Understanding pods fail to start with TLS certificate verification enabled
- TLS certificate validation errors
- Fluentd does not export logs in IPv6 environments
- Studio Desktop cannot load Integration Service connectors and activities
- Manual ArgoCD NetworkPolicy mitigation (GHSA-47m3-95c7-g2g8)
- Configuring resource requests and limits for uipathctl-created workloads
Configuring resource requests and limits for uipathctl-created workloads
How to adjust CPU and memory requests and limits when uipathctl-created workloads fail to schedule or complete.
Description
When you run a uipathctl command, a Kubernetes workload created by the command may fail because of its CPU or memory requirements.
You may encounter one of the following symptoms:
- The pod remains in the
Pendingstate, and its events containInsufficient cpuorInsufficient memory. - The pod is terminated with the
OOMKilledstatus. - The workload is CPU-throttled or does not complete before the command times out.
- An admission policy rejects the pod because its resource requests or limits do not comply with a
ResourceQuotaorLimitRangeconfigured in the namespace.
The issue can affect uipathctl-created workloads used for prerequisite checks, health checks, support bundle generation, migrations, Helm operations, and upgrades.
Solution
First, identify the affected pod and inspect its events:
kubectl get pods -A
kubectl describe pod <pod-name> -n <namespace>
kubectl get pods -A
kubectl describe pod <pod-name> -n <namespace>
Depending on the reported condition:
- For
OOMKilled, increase the memory limit. Also increase the memory request if the pod must be scheduled on a node with more available memory. - For a
Pendingpod withInsufficient cpuorInsufficient memory, reduce the request only if the workload can safely run with less guaranteed capacity. Otherwise, add cluster capacity. - For quota or limit-range errors, specify requests and limits that comply with the policy configured in the target namespace.
- For slow or CPU-throttled workloads, increase the CPU limit.
You can change the resource requirements in input.json or override them for a single command.
Configuring resource requirements in input.json
uipathctl groups its workloads into the following resource classes:
| Class | Operations | Default requests (CPU / memory) | Default limits (CPU / memory) |
|---|---|---|---|
diagnostic | Health checks and read-only validation probes | 50m / 64Mi | 250m / 256Mi |
operational | Prerequisite checks, support bundles, Helm operations, and identity data migration | 1m / 1k | 2 / 2Gi |
workload | Object store, PVC, and MongoDB migrations, and the upgrade file server | 250m / 512Mi | 2 / 2Gi |
To change the defaults for commands that use the Automation Suite configuration, add workload_resources to input.json. For example, to increase the resources available to data-heavy migration workloads, use the following configuration:
{
"workload_resources": {
"workload": {
"requests": {
"cpu": "500m",
"memory": "1Gi"
},
"limits": {
"cpu": "2",
"memory": "4Gi"
}
}
}
}
{
"workload_resources": {
"workload": {
"requests": {
"cpu": "500m",
"memory": "1Gi"
},
"limits": {
"cpu": "2",
"memory": "4Gi"
}
}
}
}
You can configure the diagnostic, operational, and workload classes independently. You can also specify only the values you want to change. Any omitted CPU or memory value retains its default.
Requests cannot exceed their corresponding limits. Use standard Kubernetes resource quantities, such as 500m or 1 for CPU and 256Mi or 1Gi for memory.
Overriding resource requirements for a single command
For commands that expose the --probe-cpu and --probe-memory options, you can override the resource requirements of the helper pod created by that command. For example:
uipathctl health check --probe-cpu 500m --probe-memory 512Mi
uipathctl health check --probe-cpu 500m --probe-memory 512Mi
Each option sets both the request and the limit for the specified resource. If you specify only one option, the other resource retains its effective value.
The options are available for the following commands:
uipathctl health checkuipathctl health testuipathctl health diagnoseuipathctl config add-host-adminuipathctl config enable-basic-authuipathctl service aicenter sync-skillsuipathctl service aicenter sync-skill-statusuipathctl service orchestrator notifications-migration start
When both configuration methods apply, uipathctl resolves the values in the following order, where the later value takes precedence:
- The built-in default.
- The
workload_resourcesvalue ininput.jsonor the applied cluster configuration. - The
--probe-cpuor--probe-memorycommand-line option.
After updating the resource requirements, run the affected command again and confirm that the uipathctl-created workload starts successfully.
Set the smallest requests and limits that allow the workload to complete. Requests that are too high can prevent scheduling, whereas limits that are too low can cause throttling or termination.