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Automation Suite on Linux installation guide

Last updated Mar 26, 2026

How to manually clean up logs

Cleaning up Ceph logs

Moving Ceph out of read-only mode

If you installed AI Center and use Ceph storage, take the following steps to move Ceph out of read-only mode:

Check if Ceph is at full capacity:
```
kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph status
kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph status
```
If Ceph is at full capacity, you must adjust the read-only threshold to start up the rgw gateways.

Scale down the ML skills:

kubectl -n uipath scale deployment <skill> --replicas=0
kubectl -n uipath scale deployment <skill> --replicas=0

Put the cluster in write mode:

ceph osd set-full-ratio 0.95 <95 is the default value so you could increase to 96 and go up 
incrementall>
ceph osd set-full-ratio 0.95 <95 is the default value so you could increase to 96 and go up 
incrementall>

Run Garbage Collection:

kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- radosgw-admin gc process --include-all
kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- radosgw-admin gc process --include-all

When storage goes down, run the following commands:

kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph status
kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph df
kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph status
kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph df

At this point, the storage should be lower, and the cluster should be healthy.

Disabling streaming logs

To ensure everything is in a good state, disable streaming logs by taking the following steps.

Disable aut-sync on UiPath and AI Center.
Disable streaming logs for AI Center.

If you have ML skills that have already been deployed, run the following commands:

kubectl set env deployment [REPLICASET_NAME] LOGS_STREAMING_ENABLED=false
kubectl set env deployment [REPLICASET_NAME] LOGS_STREAMING_ENABLED=false

Find out which buckets use the most space:

kubectl -n rook-ceph exec deploy/rook-ceph-tools -- radosgw-admin bucket stats | jq -r '["BucketName","NoOfObjects","SizeInKB"], ["--------------------","------","------"], (.[] | [.bucket, .usage."rgw.main"."num_objects", .usage."rgw.main".size_kb_actual]) | @tsv' | column -ts $'\t'
kubectl -n rook-ceph exec deploy/rook-ceph-tools -- radosgw-admin bucket stats | jq -r '["BucketName","NoOfObjects","SizeInKB"], ["--------------------","------","------"], (.[] | [.bucket, .usage."rgw.main"."num_objects", .usage."rgw.main".size_kb_actual]) | @tsv' | column -ts $'\t'

Install s3cmd to prepare for cleaning up the sf-logs:

pip3 install awscli s3cmd
export PATH=/usr/local/bin:$PATH
pip3 install awscli s3cmd
export PATH=/usr/local/bin:$PATH

Clean up the sf-logs logs. For details, see How to clean up old logs stored in the sf-logs bundle.

Complete the cleanup operation:

kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- radosgw-admin gc process --include-all
kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- radosgw-admin gc process --include-all

If the previous steps do not solve the issue, clean up the AI Center data.

Check if the storage was reduced:

kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph df
kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph df

Once storage is no longer full, reduce the backfill setting:

kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph osd set-full-ratio 0.95
kubectl -n rook-ceph exec -it deploy/rook-ceph-tools -- ceph osd set-full-ratio 0.95

Check if the ML skills are affected by the multipart upload issue:

echo $(kubectl -n rook-ceph exec deploy/rook-ceph-tools -- radosgw-admin bucket list --max-entries 10000000 --bucket train-data | jq '[.[] | select (.name | contains("_multipart")) | .meta.size] | add') | numfmt --to=iec-i
echo $(kubectl -n rook-ceph exec deploy/rook-ceph-tools -- radosgw-admin bucket list --max-entries 10000000 --bucket train-data | jq '[.[] | select (.name | contains("_multipart")) | .meta.size] | add') | numfmt --to=iec-i

If they are affected by this issue, and the returned value is high, you may need to do a backup and restore.

Cleaning up s3 logs

If you use an s3-compatible storage provider, take the following steps to clean up your logs:

Get the key for storage access.

Find the large items:

export PATH=/usr/local/bin:$PATH
kubectl  get secret -n logging  logging-secrets -o json |jq -r .data
 
# Then base64 decode the "S3_ACCESSKEY" and "S3_SECRETKEY"
export PATH=/usr/local/bin:$PATH
kubectl  get secret -n logging  logging-secrets -o json |jq -r .data
 
# Then base64 decode the "S3_ACCESSKEY" and "S3_SECRETKEY"

Configure the AWS CLI using the credentials decoded in the previous step. To configure AWS, run the following command:

aws configure

--
Once aws cli is configured, you can run below commands to check content of sf logs
aws configure

--
Once aws cli is configured, you can run below commands to check content of sf logs

Delete the sf-logs. For more details, see the AWS documentation.

aws s3 rm --endpoint-url <AWS-ENDPOINT> --no-verify-ssl --recursive s3://sf-logs --include="2022* --exclude="2022_12_8"

# You can craft an include and exclude command to help with this. use --dryrun first
aws s3 rm --endpoint-url <AWS-ENDPOINT> --no-verify-ssl --recursive s3://sf-logs --include="2022* --exclude="2022_12_8"

# You can craft an include and exclude command to help with this. use --dryrun first

Delete the train-data.

To clean up logs automatically, you can also configure a cleanup policy on your external object store.

Note:

CORS and bucket retention policy are subject to change based on the ObjectStore provider. Refer to your ObjectStore provider documentation for the same.

We recommend retention of 15 days for the logs generated by the Automation Suite platform. These log objects are found in the automation-suite-logs folder of the platform bucket.

The following example shows the steps needed for AWS:

Create policy.json with the following content:

{
    "Rules": [
        {
            "Filter": {
                "Prefix": "automation-suite-logs/"
            },
            "Status": "Enabled",
            "Expiration": {
                "Days": 15
            },
            "ID": "DeleteOldLogs"
        }
    ]
}
{
    "Rules": [
        {
            "Filter": {
                "Prefix": "automation-suite-logs/"
            },
            "Status": "Enabled",
            "Expiration": {
                "Days": 15
            },
            "ID": "DeleteOldLogs"
        }
    ]
}

To apply policy.json to the bucket, run the following command:

aws s3api put-bucket-lifecycle-configuration --bucket BUCKET_NAME --lifecycle-configuration file://policy.json
aws s3api put-bucket-lifecycle-configuration --bucket BUCKET_NAME --lifecycle-configuration file://policy.json

Cleaning up Azure logs

To clean up logs from the Azure bucket you can use az cli. The following example shows the commands to delete logs for specific date:

az storage blob delete-batch  --source <CONTAINER> --pattern  "automation-suite-logs/2025_01_01/*" --account-name <ACCOUNT_NAME> --debug
az storage blob delete-batch  --source <CONTAINER> --pattern  "automation-suite-logs/2025_01_01/*" --account-name <ACCOUNT_NAME> --debug

<CONTAINER> represents the storage container, such as the bucket name mentioned under infra.external_object_storage.bucket_name, used for storing Automation Suite

logs.

<ACCOUNT_NAME> represents the Azure storage account name.

By default, your logs are uploaded to the automation-suite-logs directory. Inside this directory, you will see subdirectories that are named by date.

When you want to delete logs for a specific date, update the date in the path to match your target. For example, to delete the logs for April 15, 2025 (YYYY/MM/DD), use the following directory pattern: automation-suite-logs/2025_04_15.

To automate the deletion of logs older than 30 days, enable a lifecycle policy on your Azure Storage account. The following policy.json example deletes logs that are more than 30 days old:

{
  "rules": [
    {
      "enabled": true,
      "name": "DeleteOldLogs",
      "type": "Lifecycle",
      "definition": {
        "actions": {
          "baseBlob": {
            "delete": {
              "daysAfterModificationGreaterThan": 30
            }
          }
        },
        "filters": {
          "blobTypes": [
            "blockBlob",
            "appendBlob"
          ],
          "prefixMatch": [
            "uipath-as-platform/automation-suite-logs/"
          ]
        }
      }
    }
  ]
}
{
  "rules": [
    {
      "enabled": true,
      "name": "DeleteOldLogs",
      "type": "Lifecycle",
      "definition": {
        "actions": {
          "baseBlob": {
            "delete": {
              "daysAfterModificationGreaterThan": 30
            }
          }
        },
        "filters": {
          "blobTypes": [
            "blockBlob",
            "appendBlob"
          ],
          "prefixMatch": [
            "uipath-as-platform/automation-suite-logs/"
          ]
        }
      }
    }
  ]
}

Note:

The value uipath-as-platform represents the container name. Update this value to match the container configured in your environment.

To apply the lifecycle policy, run the following command:

az storage account management-policy  create  --account-name <ACCOUNT_NAME>  --resource-group  <RESOURCE_GROUP_NAME> --policy @policy.json
az storage account management-policy  create  --account-name <ACCOUNT_NAME>  --resource-group  <RESOURCE_GROUP_NAME> --policy @policy.json

Here, policy.json is the file that contains the policy definition shown above.

On this page

Cleaning up Ceph logs
Moving Ceph out of read-only mode
Disabling streaming logs
Cleaning up s3 logs
Cleaning up Azure logs

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Automation Suite on Linux installation guide

Cleaning up Ceph logs​

Moving Ceph out of read-only mode​

Disabling streaming logs​

Cleaning up s3 logs​