4. Clara Administration

            

            
platformapiserver:
  pipelineTimeoutDefault: 600 # Default timeout in seconds for pipeline-jobs (when not supplied via the pipeline definition).
  pipelineTimeoutMaximum: 1800 # Maximum timeout in seconds for any pipeline-job to have.
  pipelineTimeoutGrace: 15 # Number of seconds given to a timed out pod once termination has started.
  maxStorageUsagePercentage: 80 # maximum storage to use on the Payload Volume, Setting near or above 85 may cause system instability.

  # Configuration for the Nvidia TensorRT Inference Server
  inferenceServerConfig:
    maxInstances: 1
    modelLimits: 8

  # Configuration for the Job Logs Archive
  jobLogsArchive:
    hostVolumePath: "/clara/job-logs-archive/"
    capacity: 10Gi

  # Configuration for Model Analyzer's Metrics storage
  metricsService:
    hostVolumePath: "/clara/repository/metrics"
    capacity: 250Mi

  # Configuration for the Model Storage Service
  modelService:
    hostVolumePath: "/clara/repository/models"
    capacity: 10Gi

  # Configuration for the payload service in the Clara Deploy SDK server.
  payloadService:

    # The path on the node running Clara Deploy SDK where payloads will be stored.
    # Every payload that is created by Clara Deploy SDK will exist as a directory inside
    # this path (e.g. "/clara/payloads/c780b2cbd26b41518e7ab4fbfb876f69").
    hostVolumePath: "/clara/payloads"

    # The disk capacity to reserve for the payload volume.
    capacity: 10Gi

  # Configuration for the pod cleaner service.
  podCleaner:

    # Time between cleaner runs
    podCleanerFrequencyInMinutes: 30

    # Time since creation before pods can be deleted, if succeeded or faulted
    # Above 60 minutes, because pipeline jobs are allowed to run for one hour
    podCleanerBufferInMinutes: 1

    # Pod Cleaner can be disabled by setting this flag to false.
    podCleanerEnable: true

  # Configuration for the payload cleaner daemon.
  # This will clean up Persistent Volumes, Claims as well as Residual Data.
  payloadCleaner:
    # Time between cleaner runs
    payloadCleanerFrequencyInMinutes: 10

    # Time since creation before payloads can be deleted, if succeeded or faulted
    # Above 60 minutes, because pipeline jobs are allowed to run for one hour
    payloadCleanerBufferInMinutes: 60

    # payload Cleaner can be disabled by setting this flag to false.
    payloadCleanerEnable: true

  # Configuration for the common volume that is mounted (read-only) to all pipeline
  # service containers.
  #
  # The path that is used to mount this volume in each of the deployed pipeline
  # services is provided to those service containers via the
  # NVIDIA_CLARA_SERVICE_DATA_PATH environment variable. For example, a TensorRT
  # Inference Service may be defined in the pipeline using the following command:
  #
  # --models=$NVIDIA_CLARA_SERVICE_DATA_PATH/models
  #
  # to specify that the models repository used by the service will be stored
  # inside the 'models' subdirectory of this common volume. In the case of the
  # default hostVolumePath of "/clara/common", the path provided for the models
  # repository of the TRTIS container would then correspond to the host path of
  # "/clara/common/models".
  #
  # Since this volume is mounted read-only by services, it is expected that the
  # path to this volume already exists on the host and is populated with any
  # required data for the services that use it (such as models used by a TRTIS
  # service). If it does not exist at deployment time, it will be created and
  # will be empty.
  commonServiceVolume:

    # The path on the node running Clara Deploy SDK where the common volume will
    # be stored.
    hostVolumePath: "/clara/common"

    # The disk capacity to reserve for the common volume.
    capacity: 10Gi

  # Configuration for the service volume used by the Clara Deploy SDK server.
  #
  # Each service that is deployed by Clara Deploy SDK using a 'volume' connection
  # is provided with a persistent volume to which each of the volume connections
  # are mounted. For example, this volume connection:
  #
  # volume:
  # - name: VOlUME_PATH
  # path: /var/www
  #
  # Will provide a volume mount inside the service container that corresponds to
  # the host path of (using the default hostVolumePath)
  #
  # /clara/service-volumes/{serviceId}/var/www
  #
  # where {serviceId} is the unique ID generated by Clara Deploy SDK that was used
  # to deploy the service.
  serviceVolume:

    # The path on the node running Clara Deploy SDK where service volumes will be stored.
    # Every service that is deployed by Clara Deploy SDK will have a volume mounted in this
    # path (e.g. "/clara/service-volumes/c780b2cbd26b41518e7ab4fbfb876f69").
    hostVolumePath: "/clara/service-volumes"

    # The disk capacity to reserve for the service volume.
    capacity: 10Gi

####################################################
# Configuration Values for Log Collection #
####################################################
clara-log-collector:
  enabled: true
  # Use this path to set where aggregated logs will be stored
  # default: "/clara/log-archive"
  # claraLogPath:
  elasticSearch:
    enabled: false
    # When Elastic Search is enabled for log publishing these configurations must be set.
    # If Elastic Search is secured, provide Username and create secret using
    # kubectl create secret to store password and provide the secret name to this configuration.
    #host: localhost
    #port: 9200
    #scheme: http
    #user: es-user
    #passwordSecret: es-password-secret
        

            

            
# Configuration for the render service in the Clara Deploy SDK server.
rsds:
  # The disk capacity reserved for the datasets.
  storage: 10Gi
  # The path on the node running Clara Deploy SDK where the datasets will be stored.
  # The subfolder path will have the name jobname-stagename of the triggered job.
  hostDatasetPath: "/clara-io/datasets"
        

            

            
# Configuration for storage (hostpath) for mongodb
mongodb:
  persistence:
    hostPath: /clara/ux
        

            

            
clara dicom stop
clara render stop
clara platform stop
clara console stop
        

            

            
clara platform start
clara dicom start
clara render start
clara console start
        

            

            
clara platform restart
clara dicom restart
clara render restart
clara console restart
        

            

            
sudo -i
swapoff -a
exit
        

            

            
helm ls
        

            

            
kubectl get pods
        

            

            
kubectl get po [POD_NAME]
kubectl describe po [POD_NAME]
        

            

            
kubectl logs [POD_NAME] [CONTAINER_NAME]
        

            

            
kubectl exec -it [POD_NAME] --container [CONTAINER_NAME] -- /bin/sh