Troubleshooting Watson OpenScale

My model evaluations are failing

When you provide the minimum number of payload records for model evaluations, the evaluations do not run successfully and fail to produce the expected metrics. The Evaluation window on your dashboard either displays metrics from the last successful evaluation or displays an error message.

The performance of the evaluations might be affected by the latency that occurs during payload logging data processing.

To fix this issue, you can slow down the rate of scoring requests by completing one of the following steps to send bulk scoring requests:

If you use the IBM Watson Machine Learning Python SDK, you can append multiple request values to the values array, as shown in the following example:

scoring_payload = {wml_client.deployments.ScoringMetaNames.INPUT_DATA:
       [{'fields':
           ['GENDER','AGE','MARITAL_STATUS','PROFESSION'],
           'values': [
               ['M',24,'Single','Student'],
               ['F',21,'Single','Retail'],
               ...
               ['M',40,'Married','Retail']
               ]
        }
      ]}
predictions = client.deployments.score(deployment_id, scoring_payload)

If you use the Watson OpenScale Python SDK, you can append multiple request values to the values array in the request argument. Then, append the corresponding multiple response values to the values array in the response argument, as shown in the following example:

store_record_info = client.data_sets.store_records(
      request_body=[PayloadRecord(
           scoring_id='42e62c3ae2244f0d851009dec4754d74',
           request={
              'fields': ['GENDER','AGE','MARITAL_STATUS','PROFESSION'],
              'values': [
                  ['M',24,'Single','Student'],
                  ['F',21,'Single','Retail'],
                  ...
                  ['M',40,'Married','Retail']
                  ]
              },
           response={
              'fields': ['probability', 'prediction'],
              'values': [
                  [[0.9800331274776278, 0.01996687252237212], True],
                  [[0.7256561304103799, 0.2743438695896201], False],
                  ...
                  [[0.9998955605441558, 0.00010443945584419821], False]
                  ]
              },
           response_time=4600
       )],
      data_set_id='997b1474-00d2-4g05-ac02-287ebfc603b5',
  )

By sending bulk scoring requests, you can slow down the scoring requests by sending, for example, 10 seconds of scoring requests at once instead of sending a small request every second.

I can't see the monitoring metrics when my evaluation finishes

After your evaluation runs successfully, the fairness, quality, and drift monitoring metrics do not appear on your dashboard.

The metrics might not appear because the events that are related to the evaluations might be stuck in the internal queuing service.

To fix this issue, you can restart the data mart service pods by completing the following steps:

1. Check the data mart service pods by running the following command:

   oc get pod -l release=<your openscale release name>,component=aios-datamart
   

   The pods are specified with the component=aios-datamart and release=<your openscale release name> labels. The release name is usually specified as aiopenscale or wos-cr if you don't specify a custom release name during installation.

2. Restart the pods by running the following command:

   oc delete pod -l release=<your openscale release name>,component=aios-datamart
   

3. View the current status of the pods by running the oc get pod -l release=,component=aios-datamart command, as shown in the following example:

   NAME                                           READY   STATUS    RESTARTS   AGE
   aiopenscale-ibm-aios-datamart-858c4ccd-4dcvb   1/1     Running   0          14h
   

Why is my Spark job failing?

You're running model evaluation, and a Spark job fails.

A Spark job might fail because the resource quota for the Spark instance is insufficient to run the Spark job.

If this issue arises, you can increase the resource quota for the Spark instance. To increase the resource quota, complete the following steps:

1. First, get the Spark instance URL by going to the Spark instance details page on the Cloud Pak for Data cluster.

   • If you're using a Spark 4.0 instance, then https://<CLUSTER_HOST>/v2/spark/v3/instances/<INSTANCE_ID> is the Spark instance URL, substituting in the CLUSTER_HOST and INSTANCE_ID for your cluster and instance.
   • If you aren't using a Spark 4.0 instance, then https://<CLUSTER_HOST>/ae/spark/v2/<INSTANCE_ID> is the Spark instance URL, substituting in the CLUSTER_HOST and INSTANCE_ID for your cluster and instance.

2. Generate an API key to authenticate to Cloud Pak for Data APIs. For more information, see Generating an API authorization token.

3. Get the existing resource quota by running the following command:

   curl -iX GET -ivk -H "Content-Type: application/json" -H "Authorization: Bearer ${TOKEN}" <INSTANCE_URL>
   

4. Update the resource quota.

   If you're using a Spark 4.0 instance, then update the resource quota by running the following command:

  curl -i -X PUT \
        -H "Accept:application/json" \
        -H "Authorization: Bearer ${TOKEN}" \
        -H "Content-Type:application/json" \
        -d \
   '{
          "max_cores": 40,
          "max_memory": 80
   } \
       <INSTANCE_URL>/resource_consumption_limits

If you aren't using a Spark 4.0 instance, then update the resource quota by running the following command:

curl -i -X PUT \
         -H "Accept:application/json" \
         -H "Authorization: ZenApiKey ${TOKEN}" \
         -H "Content-Type:application/json" \
         -d \
   '{
            "cpu_quota": 40,
            "memory_quota": "80g"
   } \
         <INSTANCE_URL>/resource_quota

I'm unable to run a Db2 common configuration notebook

When you run the Db2 configuration notebook, one or more of the cells fails with the following exception error:

Caused by: javax.net.ssl.SSLHandshakeException: The server selected protocol version TLS11 is not accepted by client preferences [TLS12]

This error occurs because the client, the Spark job, is trying to communicate with a server, Db2, by using a Transport Layer Security (TLS) protocol that isn't supported by the server.

To fix this issue, update the SSL_VERSIONS setting on your database. When this error occurs, the client typically uses TLS version 1.2 and the server supports only TLS version 1.1. In this case, you can update your Db2 configuration by using the following command. In this example, TLSV12 references TLS version 1.2:

db2 update dbm cfg using SSL_VERSIONS TLSV12

The user who runs this command must have admin access, which is often db2inst1. Run the command after all applications that are using the Db2 server are stopped. To resolve this issue, you might call the following commands in order:

1. db2stop force
2. db2 update dbm cfg using SSL_VERSIONS TLSV12
3. db2start

When I use AutoAI, why am I getting an error about mismatched data?

You receive an error message about mismatched data when using AutoAI for binary classification. Note that AutoAI is only supported in IBM Watson OpenScale for IBM Cloud Pak for Data.

For binary classification type, AutoAI automatically sets the data type of the prediction column to boolean.

To fix this, implement one of the following solutions:

• Change the label column values in the training data to integer values, such as 0 or 1 depending on the outcome.
• Change the label column values in the training data to string value, such as A and B.

Why am I getting errors during model configuration?

The following error messages appear when you are configuring model details: Field feature_fields references column <name>, which is missing in input_schema of the model. Feature not found in input schema.

The preceding messages while completing the Model details section during configuration indicate a mismatch between the model input schema and the model training data schema:

To fix the issue, you must determine which of the following conditions is causing the error and take corrective action: If you use IBM Watson Machine Learning as your machine learning provider and the model type is XGBoost/scikit-learn refer to the Machine Learning Python SDK documentation for important information about how to store the model. To generate the drift detection model, you must use scikit-learn version 0.20.2 in notebooks. For all other cases, you must ensure that the training data column names match with the input schema column names.

Why are my class labels missing when I use XGBoost?

Native XGBoost multiclass classification does not return class labels.

By default, for binary and multiple class models, the XGBoost framework does not return class labels.

For XGBoost binary and multiple class models, you must update the model to return class labels.

Why are the payload analytics not displaying properly?

Payload analytics does not display properly and the following error message displays: AIQDT0044E Forbidden character " in column name <column name>

For proper processing of payload analytics, Watson OpenScale does not support column names with double quotation marks (") in the payload. This affects both scoring payload and feedback data in CSV and JSON formats.

Remove double quotation marks (") from the column names of the payload file.

Error: An error occurred while computing feature importance

You receive the following error message during processing: Error: An error occurred while computing feature importance.

Having an equals sign (=) in the column name of a dataset causes an issue with explainability.

Remove the equals sign (=) from the column name and send the dataset through processing again.

Why are some of my active debias records missing?

Active debias records do not reach the payload logging table.

When you use the active debias API, there is a limit of 1000 records that can be sent at one time for payload logging.

To avoid loss of data, you must use the active debias API to score in chunks of 1000 records or fewer.

For more information, see Reviewing debiased transactions.

Watson OpenScale does not show any available schemas

When a user attempts to retrieve schema information for Watson OpenScale, none are available. After attempting directly in DB2, without reference to Watson OpenScale, checking what schemas are available for the database userid also returns none.

Insufficient permissions for the database userid is causing database connection issues for Watson OpenScale.

Make sure the database user has the correct permissions needed for Watson OpenScale.

A monitor run fails with an OutOfResources exception error message

You receive an OutOfResources exception error message.

Although there's no longer a limit on the number of rows you can have in the feedback payload, scoring payload, or business payload tables. The 50,000 limit now applies to the number of records you can run through the quality and bias monitors each billing period.

After you reach your limit, you must either upgrade to a Standard plan or wait for the next billing period.

Missing deployments

A deployed model does not show up as a deployment that can be selected to create a subscription.

There are different reasons that a deployment does not show up in the list of available deployed models. If the model is not a supported type of model because it uses an unsupported algorithm or framework, it won't appear. Your machine learning provider might not be configured properly. It could also be that there are issues with permissions.

Use the following steps to resolve this issue:

1. Check that the model is a supported type. Not sure? For more information, see Supported machine learning engines, frameworks, and models.
2. Check that a machine learning provider exists in the Watson OpenScale configuration for the specific deployment space. For more information, see Deployment spaces.
3. Check that the CP4D admin user has permission to access the deployment space.

"Failure converting response to expected model EntityStreamSizeException: actual entity size (Some(8644836)) exceeded content length limit (8388608 bytes)! You can configure this by setting akka.http.[server|client].parsing.max-content-length or calling HttpEntity.withSizeLimit before materializing the dataBytes stream".

oc patch woservice <release name> -p '{"spec": {"datamart": {"additional_jvm_options":"-Dakka.http.client.parsing.max-content-length=100m"} }}' --type=merge

Attribute name "table name" contains invalid character(s) among " ,;{}()\\n\\t=". Please use alias to rename it.;

db2 update db cfg using DFT_EXTENT_SZ 32