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example-gordo.yaml
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example-gordo.yaml
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apiVersion: equinor.com/v1
kind: Gordo
metadata:
name: test-project-name
spec:
deploy-version: latest
deploy-environment:
WORKFLOW_GENERATOR_DOCKER_REGISTRY: docker.io
WORKFLOW_GENERATOR_DOCKER_REPOSITORY: equinor
WORKFLOW_GENERATOR_COMPONENTS_VERSION: latest
config:
machines:
- dataset: |
tags:
- TAG-A
- TAG-B
- TAG-C
- TAG-D
train_end_date: '2019-03-01T00:00:00+00:20'
train_start_date: '2019-01-01T00:00:00+00:00'
type: TimeSeriesDataset
metadata: |
information: 'Example with a minmax scaler and feed forward autoencoder'
model: |
gordo_components.model.anomaly.diff.DiffBasedAnomalyDetector:
base_estimator:
sklearn.pipeline.Pipeline:
steps:
- sklearn.preprocessing.data.MinMaxScaler
- gordo_components.model.models.KerasAutoEncoder:
kind: feedforward_hourglass
name: trolla-minmax-ff-model
- dataset: |
tags:
- TAG-A
- TAG-B
- TAG-C
- TAG-D
train_end_date: '2019-03-01T00:00:00+00:00'
train_start_date: '2019-01-01T00:00:00+00:00'
metadata: |
information: 'Example with an LSTM autoencoder'
model: |
gordo_components.model.anomaly.diff.DiffBasedAnomalyDetector:
base_estimator:
sklearn.pipeline.Pipeline:
steps:
- sklearn.preprocessing.data.MinMaxScaler
- gordo_components.model.models.KerasLSTMAutoEncoder:
kind: lstm_hourglass
lookback_window: 10
name: trolla-minmax-lstm-ae
- dataset: |
tags:
- TAG-A
- TAG-B
- TAG-C
train_end_date: '2019-03-01T00:00:00+00:00'
train_start_date: '2019-01-01T00:00:00+00:00'
metadata: |
information: 'Example with an LSTM forecast one step autoencoder'
model: |
gordo_components.model.anomaly.diff.DiffBasedAnomalyDetector:
base_estimator:
sklearn.pipeline.Pipeline:
steps:
- sklearn.preprocessing.data.MinMaxScaler
- gordo_components.model.models.KerasLSTMForecast:
kind: lstm_hourglass
lookback_window: 10
name: trolla-minmax-lstm-forecast
- dataset: |
tags:
- TAG-A
- TAG-B
- TAG-C
- TAG-D
train_end_date: '2019-03-01T00:00:00+00:00'
train_start_date: '2019-01-01T00:00:00+00:00'
metadata: |
information: 'This shows a basic pipeline with a feed forward autoencoder'
model: |
gordo_components.model.anomaly.diff.DiffBasedAnomalyDetector:
base_estimator:
sklearn.pipeline.Pipeline:
steps:
- gordo_components.model.models.KerasAutoEncoder:
kind: feedforward_hourglass
name: trolla-ff-model
- dataset: |
tags:
- TAG-A
- TAG-B
- TAG-C
- TAG-D
train_end_date: '2019-03-01T00:00:00+00:00'
train_start_date: '2019-01-01T00:00:00+00:00'
metadata: |
information: 'This shows an example where we dont have a pipeline'
model: |
gordo_components.model.anomaly.diff.DiffBasedAnomalyDetector:
base_estimator:
gordo_components.model.models.KerasAutoEncoder:
kind: feedforward_hourglass
name: trolla-no-pipeline-ff-model
- dataset: |
tags:
- TAG-A
- TAG-B
- TAG-C
- TAG-D
train_end_date: '2019-03-01T00:00:00+00:00'
train_start_date: '2019-01-01T00:00:00+00:00'
row_filter: "(`TRA-35TT8566.PV` > 30) & (`TRA-35TT8566.PV` < 40)"
metadata: |
information: 'This shows an example with a row filter'
model: |
gordo_components.model.anomaly.diff.DiffBasedAnomalyDetector:
base_estimator:
sklearn.pipeline.Pipeline:
steps:
- sklearn.preprocessing.data.MinMaxScaler
- gordo_components.model.models.KerasAutoEncoder:
kind: feedforward_model
name: trolla-rowfilter-model
- dataset: |
asset: 1101-sfb
tags:
- TAG-A
- TAG-B
train_start_date: 2019-01-01T00:00:00+01:00
train_end_date: 2019-02-01T00:00:00+01:00
metadata: |
information: 'This shows an example where the asset key is needed to find the tags'
name: sfb-asset-key-needed
- dataset: |
tags:
- TAG-A
- TAG-B
target_tag_list:
- TAG-C
train_end_date: '2019-03-01T00:00:00+00:00'
train_start_date: '2019-01-01T00:00:00+00:00'
metadata: |
information: 'Use basic LinearRegression model'
model: |
sklearn.pipeline.Pipeline:
steps:
- sklearn.decomposition.pca.PCA
- sklearn.linear_model.base.LinearRegression
runtime:
influx:
enable: False
name: trolla-supervised-lin-reg
- dataset: |
tags:
- TAG-A
- TAG-B
target_tag_list:
- TAG-C
- TAG-D
train_end_date: '2019-03-01T00:00:00+00:00'
train_start_date: '2019-01-01T00:00:00+00:00'
metadata: |
information: 'Use basic LinearRegression model with multiple target features'
model: |
sklearn.pipeline.Pipeline:
steps:
- sklearn.decomposition.pca.PCA
- sklearn.multioutput.MultiOutputRegressor:
estimator: sklearn.linear_model.base.LinearRegression
runtime: |
influx:
enable: False
name: trolla-supervised-lin-reg-multi
globals:
model: |
gordo_components.model.anomaly.diff.DiffBasedAnomalyDetector:
base_estimator:
sklearn.pipeline.Pipeline:
steps:
- sklearn.preprocessing.data.MinMaxScaler
- gordo_components.model.models.KerasAutoEncoder:
kind: feedforward_hourglass