seldon/seldon-od-transformer

Chart version: 0.2.0
Api version: v2
App version: n/a
Chart to deploy an outlier detector as a transformer in an infe...
application
Chart Type
Active
Status
Unknown
License
50
Downloads
https://storage.googleapis.com/seldon-charts
Set me up:
helm repo add center https://repo.chartcenter.io
Install Chart:
helm install seldon-od-transformer center/seldon/seldon-od-transformer
Versions (0)

seldon-od-transformer

Version: 0.2.0

Chart to deploy an outlier detector as a transformer in an inference graph.

Usage

To use this chart, you will first need to add the seldonio Helm repo:

helm repo add seldonio https://storage.googleapis.com/seldon-charts
helm repo update

Once that’s done, you should then be able to use the inference graph template as:

helm template $MY_MODEL_NAME seldonio/seldon-od-transformer --namespace $MODELS_NAMESPACE

Note that you can also deploy the inference graph directly to your cluster using:

helm install $MY_MODEL_NAME seldonio/seldon-od-transformer --namespace $MODELS_NAMESPACE

Homepage: https://github.com/SeldonIO/seldon-core

Source Code

Values

Key Type Default Description
model.image.name string "seldonio/mock_classifier:1.0"
model.name string "classifier"
name string "seldon-od-transformer"
oauth.key string nil
oauth.secret string nil
outlierDetection.enabled bool true
outlierDetection.isolationforest.image.name string "seldonio/outlier-if-tranformer:0.1"
outlierDetection.isolationforest.load_path string "./models/"
outlierDetection.isolationforest.model_name string "if"
outlierDetection.isolationforest.threshold int 0
outlierDetection.mahalanobis.image.name string "seldonio/outlier-mahalanobis-tranformer:0.1"
outlierDetection.mahalanobis.max_n int -1
outlierDetection.mahalanobis.n_components int 3
outlierDetection.mahalanobis.n_stdev int 3
outlierDetection.mahalanobis.start_clip int 50
outlierDetection.mahalanobis.threshold int 25
outlierDetection.name string "outlier-detector"
outlierDetection.parameterTypes.load_path string "STRING"
outlierDetection.parameterTypes.max_n string "INT"
outlierDetection.parameterTypes.model_name string "STRING"
outlierDetection.parameterTypes.n_components string "INT"
outlierDetection.parameterTypes.n_stdev string "FLOAT"
outlierDetection.parameterTypes.reservoir_size string "INT"
outlierDetection.parameterTypes.start_clip string "INT"
outlierDetection.parameterTypes.threshold string "FLOAT"
outlierDetection.seq2seq.image.name string "seldonio/outlier-s2s-lstm-tranformer:0.1"
outlierDetection.seq2seq.load_path string "./models/"
outlierDetection.seq2seq.model_name string "seq2seq"
outlierDetection.seq2seq.reservoir_size int 50000
outlierDetection.seq2seq.threshold float 0.003
outlierDetection.type string "vae" Type of outlier detector. Valid values are: vae, mahalanobis, seq2seq and isolationforest.
outlierDetection.vae.image.name string "seldonio/outlier-vae-tranformer:0.1"
outlierDetection.vae.load_path string "./models/"
outlierDetection.vae.model_name string "vae"
outlierDetection.vae.reservoir_size int 50000
outlierDetection.vae.threshold int 10
predictorLabels.fluentd string "true"
predictorLabels.version string "v1"
replicas int 1
sdepLabels.app string "seldon"