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Outlier Detection with CIFAR10 Image Classifier
This demo is based on VAE outlier detection in the alibi detect project. Here we will :
- Launch an image classifier model trianed on the CIFAR10 dataset
- Setup an outlier detector for this particular model
- Send a request to get a image classification
- Send a perturbed request to get a outlier detection
Note
This demo requires Knative installation on the cluster as the outlier detector will be installed as a kservice.Create Model
Create the model in its own dedicated namespace (needed for outlier detection). It should be a knative eventing namespace (labelled knative-eventing-injection=enabled
).
Create the model in its own dedicated namespace (needed for outlier detection). It should be a knative eventing namespace (labelled knative-eventing-injection=enabled
). For example as cluster admin for the namespace outlier
:
kubectl create namespace outlier
kubectl label namespace outlier knative-eventing-injection=enabled
kubectl label namespace outlier seldon.restricted=false
kubectl label namespace outlier serving.kubeflow.org/inferenceservice=enabled
Use the following model url with tensorflow
runtime. Set the protocol to ‘tensorflow’ and the predictor log url as “http://default-broker":
gs://seldon-models/tfserving/cifar10/resnet32
Setup Request Logger
Go to ‘Setup Request Logger’ and enter:
Logger Name: seldon-request-logger
Logger Image URI: docker.io/seldonio/seldon-request-logger:0.3.1
Environment Variables
ELASTICSEARCH_HOST: elasticsearch-master.seldon-logs.svc.cluster.local
ELASTICSEARCH_PORT: 9200
ELASTICSEARCH_PROTOCOL: http
Setup Outlier detector
Setup an outlier detector with model name cifar10
using the default settings (which sets Reply URL as seldon-request-logger in current namespace) and storage URI as follows:
gs://seldon-models/alibi-detect/od/OutlierVAE/cifar10
Make Predictions
Run a single prediction using the tensorflow payload format of an image truck. Also a perturbed image of the truck in the same format at outlier truck image. Make a couple of these requests at random using the predict tool in the UI.
Monitor outliers on the Requests Screen
Go to the requests screen to view all the historical requests. You can see the outlier value on each instance. Also you can highlight outliers based on this score and also use the filter to see only the outliers as needed.