Logging of request and response payloads from your Seldon Deployment can be accomplished by adding a logging section to any part of the Seldon deployment graph. An example is shown below:
apiVersion: machinelearning.seldon.io/v1 kind: SeldonDeployment metadata: name: seldon-model spec: name: test-deployment predictors: - componentSpecs: - spec: containers: - image: seldonio/mock_classifier:1.3 name: classifier graph: children:  endpoint: type: REST name: classifier type: MODEL logger: url: http://mylogging-endpoint mode: all name: example replicas: 1
The logging for the top level requets response is provided by:
logger: url: http://mylogging-endpoint mode: all
In this example both request and response payloads as specified by the
mode attribute are sent as CloudEvents to the url
The specification is:
url: Any url. Optional. If not provided then it will default to the default knative borker in the namespace of the Seldon Deployment.
Setting Global Default¶
If you don’t want to set up the custom logger every time, you are able to set it with the defaultRequestLoggerEndpointPrefix Helm Chart Variable as outlined in the helm chart advanced settings section.
You just have to provide the prefix, which would then always be suffixed by “.
namespace is the namespace where your model is running, and hence there will be a requirement to run a request logger on every namespace.
An example would be setting it to a request logger who’s service can be accessible through
custom-request-logger, and assuming we deploy our request logger in the namespace
deep-learning, then we should set the helm variable as:
#...other variables executor: defaultRequestLoggerEndpointPrefix: 'http://default-broker.' #...other variables
So when the model runs in the
deep-learning namespace, it will send all the input and output requests to the service
You will still need to make sure the model is deployed with a specification on what requests will be logged, i.e. all, request or response (as outlined above).