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

Values

Key

Type

Default

Description

model.image.name

string

"seldonio/mock_classifier:1.0"

model.name

string

"classifier"

name

string

"seldon-od-transformer"

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"