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Commit d0ccf01

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arjunshenoymecarjunshenoymec
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Merge branch 'AICoE:master' into master
2 parents f66dfde + d46d0a1 commit d0ccf01

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‎Dockerfile‎

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FROM continuumio/miniconda3
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SHELL ["/bin/bash", "-c"]
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ADD . /
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RUN chmod +x set_uid.sh
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ADD environment.yml /tmp/environment.yml
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RUN chmod g+w /etc/passwd
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RUN conda env create -f /tmp/environment.yml
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FROM registry.access.redhat.com/ubi8/python-38:latest
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# Add application sources to a directory that the assemble script expects them
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# and set permissions so that the container runs without root access
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USER 0
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ADD . /tmp/src
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RUN /usr/bin/fix-permissions /tmp/src
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USER 1001
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# Ensure that assigned uid has entry in /etc/passwd.
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CMD ./set_uid.sh && /opt/conda/envs/prophet-env/bin/python ${APP_FILE}
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# Install the dependencies
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RUN /usr/libexec/s2i/assemble
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# Set the default command for the resulting image
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CMD /usr/libexec/s2i/run

‎Pipfile‎

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prometheus_client = "*"
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prometheus_api_client = "*"
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tornado = "*"
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fbprophet = "*"
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pystan = "==2.19.1.1"
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prophet = "*"
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dateparser = "*"
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mlflow = "*"
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keras = "*"
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numpy = "*"
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scikit-learn = "*"
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tensorflow = "*"

‎Pipfile.lock‎

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‎environment.yml‎

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---
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name: prophet-env
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name: PAD
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channels:
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- conda-forge
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dependencies:
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- python=3.8
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- pip
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- fbprophet
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- prophet
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- pip:
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- pandas
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- schedule
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- dateparser
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- mlflow
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- numpy
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- keras
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- tensorflow
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- scikit-learn

‎model.py‎

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import datetime
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import logging
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import pandas
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from fbprophet import Prophet
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from prophet import Prophet
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from prometheus_api_client import Metric
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# Set up logging

‎model_lstm.py‎

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"""doctsring for packages."""
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import logging
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from prometheus_api_client import Metric
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from keras.models import Sequential
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from keras.layers import Dense
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from keras.layers import LSTM
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.layers import LSTM
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler

‎openshift/k8s-deployment.yaml‎

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---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: prometheus-anomaly-detector
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spec:
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selector:
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matchLabels:
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app: prometheus-anomaly-detector
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replicas: 1
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template:
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metadata:
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labels:
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app: prometheus-anomaly-detector
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spec:
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containers:
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- env:
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- name: FLT_PROM_URL
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value: "http://demo.robustperception.io:9090/"
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- name: FLT_PROM_ACCESS_TOKEN
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value: "my-oauth-token"
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- name: FLT_METRICS_LIST
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value: "up"
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- name: FLT_RETRAINING_INTERVAL_MINUTES
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value: "15m"
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- name: FLT_ROLLING_TRAINING_WINDOW_SIZE
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value: "24h"
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- name: FLT_DEBUG_MODE
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value: "True"
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- name: APP_FILE
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value: "app.py"
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name: prometheus-anomaly-detector
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image: quay.io/aicoe/prometheus-anomaly-detector:latest
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ports:
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- containerPort: 8080

‎openshift/k8s-service.yaml‎

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---
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apiVersion: v1
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kind: Service
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metadata:
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name: prometheus-anomaly-detector
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labels:
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app: prometheus-anomaly-detector
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operated-prometheus: 'true'
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spec:
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selector:
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app: prometheus-anomaly-detector
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ports:
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- protocol: TCP
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port: 8080
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targetPort: 8080
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name: metrics

‎openshift/k8s-servicemonitor.yaml‎

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---
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apiVersion: monitoring.coreos.com/v1
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kind: ServiceMonitor
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metadata:
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name: prometheus-anomaly-detector-monitor
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spec:
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endpoints:
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- port: metrics
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interval: 60s
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selector:
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matchLabels:
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operated-prometheus: 'true'
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app: prometheus-anomaly-detector
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namespaceSelector:
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matchNames:
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- prometheus-anomaly-detector # update namespace to where the service is deployed

‎requirements.txt‎

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prometheus_client
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prometheus_api_client
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tornado
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fbprophet
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pystan==2.19.1.1
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prophet
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mlflow
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numpy
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Keras
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scikit-learn
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tensorflow

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