--- /dev/null
+.. This work is licensed under a Creative Commons Attribution 4.0 International License.
+.. http://creativecommons.org/licenses/by/4.0
+
+.. Copyright (c) 2022 Samsung Electronics Co., Ltd. All Rights Reserved.
+
+
+Installation Guide
+==================
+
+.. contents::
+ :depth: 3
+ :local:
+
+Abstract
+--------
+
+This document describes how to install AIMLFW, it's dependencies and required system resources.
+
+
+Version history
+
++--------------------+--------------------+--------------------+--------------------+
+| **Date** | **Ver.** | **Author** | **Comment** |
+| | | | |
++--------------------+--------------------+--------------------+--------------------+
+| 2022-11-30 | 0.1.0 | | First draft |
+| | | | |
++--------------------+--------------------+--------------------+--------------------+
+| | | | |
+| | | | |
++--------------------+--------------------+--------------------+--------------------+
+| | | | |
+| | | | |
+| | | | |
++--------------------+--------------------+--------------------+--------------------+
+
+
+Introduction
+------------
+
+.. <INTRODUCTION TO THE SCOPE AND INTENTION OF THIS DOCUMENT AS WELL AS TO THE SYSTEM TO BE INSTALLED>
+
+
+This document describes the supported software and hardware configurations for the reference component as well as providing guidelines on how to install and configure such reference system.
+
+The audience of this document is assumed to have good knowledge in RAN network nd Linux system.
+
+
+Hardware Requirements
+---------------------
+.. <PROVIDE A LIST OF MINIMUM HARDWARE REQUIREMENTS NEEDED FOR THE INSTALL>
+
+Below are the minimum requirements for installing the AIMLFW
+
+1. OS: Ubuntu 18.04 server
+2. 8 cpu cores
+3. 16 GB RAM
+4. 60 GB harddisk
+
+Software Installation and Deployment
+------------------------------------
+.. <DESCRIBE THE FULL PROCEDURES FOR THE INSTALLATION OF THE O-RAN COMPONENT INSTALLATION AND DEPLOYMENT>
+
+.. code:: bash
+
+ git clone "https://gerrit.o-ran-sc.org/r/aiml-fw/aimlfw-dep"
+ cd aimlfw-dep
+
+Update recipe file “RECIPE_EXAMPLE/example_recipe_latest_stable.yaml” which includes update of VM IP and datalake details
+Note: In case the Influx DB datalake is not available, this can be skipped at this stage and can be updated after installing datalake.
+
+.. code:: bash
+
+ bin/install_traininghost.sh
+
+
+
+Check running state of all pods and services using below command
+
+.. code:: bash
+
+ kubectl get pods --all-namespaces
+ kubectl get svc --all-namespaces
+
+
+Check the AIMLFW dashboard by using the following url
+
+.. code:: bash
+
+ http://localhost:32005/
+
+In case Influx DB datalake not available, it can be installed using the steps mentioned in section “Install influx db as datalake”. Once installed the access details of the datalake can be updated in RECIPE_EXAMPLE/example_recipe_latest_stable.yaml . Once updated, follow the below steps for reinstall of some components:
+
+.. code:: bash
+
+ bin/uninstall.sh
+ bin/install.sh -f RECIPE_EXAMPLE/example_recipe_latest_stable.yaml
+
+Following are the steps to build sample training pipeline image for QoE prediction example.
+This step is required before triggering training for the QoE prediction example.
+
+.. code:: bash
+
+ cd /tmp/
+ git clone "https://gerrit.o-ran-sc.org/r/portal/aiml-dashboard"
+ docker build -f aiml-dashboard/kf-pipelines/Dockerfile.pipeline -t traininghost/pipelineimage:latest aiml-dashboard/kf-pipelines/.
+
+Software Uninstallation
+-----------------------
+
+.. code:: bash
+
+ bin/uninstall_traininghost.sh
+
+Install Influx DB as datalake
+-----------------------------
+
+.. code:: bash
+
+ helm install my-release bitnami/influxdb
+ kubectl exec -it <pod name> bash
+
+From below command we can get username, org name, org id and access token
+
+.. code:: bash
+
+ cat bitnami/influxdb/influxd.bolt | tr -cd "[:print:]"
+
+eg: {"id":"0a576f4ba82db000","token":"xJVlOom1GRUxDNkldo1v","status":"active","description":"admin's Token","orgID":"783d5882c44b34f0","userID":"0a576f4b91edb000","permissions" ...
+
+Use the tokens further in the below configurations and in the recipe file.
+
+Following are the steps to add qoe data to influx DB
+
+
+Execute below from inside influx Db container to create a bucket:
+
+.. code:: bash
+
+ influx bucket create -n UEData -o primary -t <token>
+
+
+Install the following dependencies
+
+.. code:: bash
+
+ sudo pip3 install pandas
+ sudo pip3 install influxdb_client
+
+
+Use the insert.py in ric-app/qp repository to upload the qoe data in influx DB
+
+
+.. code:: bash
+
+ git clone https://gerrit.o-ran-sc.org/r/ric-app/qp
+ cd qp/qp
+
+update insert.py file with the following content:
+
+.. code:: bash
+
+ import pandas as pd
+ from influxdb_client import InfluxDBClient
+ from influxdb_client.client.write_api import SYNCHRONOUS
+ import datetime
+
+
+ class INSERTDATA:
+
+ def __init__(self):
+ self.client = InfluxDBClient(url = "http://localhost:8086", token="<token>")
+
+
+ def explode(df):
+ for col in df.columns:
+ if isinstance(df.iloc[0][col], list):
+ df = df.explode(col)
+ d = df[col].apply(pd.Series)
+ df[d.columns] = d
+ df = df.drop(col, axis=1)
+ return df
+
+
+ def jsonToTable(df):
+ df.index = range(len(df))
+ cols = [col for col in df.columns if isinstance(df.iloc[0][col], dict) or isinstance(df.iloc[0][col], list)]
+ if len(cols) == 0:
+ return df
+ for col in cols:
+ d = explode(pd.DataFrame(df[col], columns=[col]))
+ d = d.dropna(axis=1, how='all')
+ df = pd.concat([df, d], axis=1)
+ df = df.drop(col, axis=1).dropna()
+ return jsonToTable(df)
+
+
+ def time(df):
+ df.index = pd.date_range(start=datetime.datetime.now(), freq='10ms', periods=len(df))
+ df['measTimeStampRf'] = df['measTimeStampRf'].apply(lambda x: str(x))
+ return df
+
+
+ def populatedb():
+ df = pd.read_json('cell.json.gz', lines=True)
+ df = df[['cellMeasReport']].dropna()
+ df = jsonToTable(df)
+ df = time(df)
+ db = INSERTDATA()
+ write_api = db.client.write_api(write_options=SYNCHRONOUS)
+ write_api.write(bucket="UEData",record=df, data_frame_measurement_name="liveCell",org="primary")
+
+ populatedb()
+
+
+Update <token> in insert.py file
+
+Follow below command to port forward to access Influx DB
+
+.. code:: bash
+
+ kubectl port-forward svc/my-release-influxdb 8086:8086
+
+To insert data:
+
+.. code:: bash
+
+ python3 insert.py
+
+To check inserted data in Influx DB , execute below command inside the influx DB container:
+
+.. code:: bash
+
+ influx query 'from(bucket: "UEData") |> range(start: -1000d)' -o primary -t <token>
+
+
+Install Kserve for deploying models
+-----------------------------------
+
+To install Kserve run the below commands
+
+.. code:: bash
+
+ curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.7/hack/quick_install.sh" | bash
+
+Deploy trained qoe prediction model on Kserve
+---------------------------------------------
+
+Create namespace using command below
+
+.. code:: bash
+
+ kubectl create namespace kserve-test
+
+Create qoe.yaml file with below contents
+
+.. code:: bash
+
+ apiVersion: "serving.kserve.io/v1beta1"
+ kind: "InferenceService"
+ metadata:
+ name: qoe-model
+ spec:
+ predictor:
+ tensorflow:
+ storageUri: "<update Model URL here>"
+ runtimeVersion: "2.5.1"
+ resources:
+ requests:
+ cpu: 0.1
+ memory: 0.5Gi
+ limits:
+ cpu: 0.1
+ memory: 0.5Gi
+
+
+To deploy model update the Model URL in the qoe.yaml file and execute below command to deploy model
+
+.. code:: bash
+
+ kubectl apply -f qoe.yaml -n kserve-test
+
+Check running state of pod using below command
+
+.. code:: bash
+
+ kubectl get pods -n kserve-test
+
+
+Test predictions using model deployed on Kserve
+-----------------------------------------------
+
+Use below command to obtain Ingress port for Kserve.
+
+.. code:: bash
+
+ kubectl get svc istio-ingressgateway -n istio-system
+
+Obtain nodeport corresponding to port 80.
+In the below example, the port is 31206
+NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
+istio-ingressgateway LoadBalancer 10.105.222.242 <pending> 15021:31423/TCP,80:31206/TCP,443:32145/TCP,31400:32338/TCP,15443:31846/TCP 4h15m
+
+
+Create predict.sh file with following contents
+
+.. code:: bash
+
+ model_name=qoe-model
+ curl -v -H "Host: $model_name.kserve-test.example.com" http://<IP of where Kserve is deployed>:<ingress port for Kserve>/v1/models/$model_name:predict -d @./input_qoe.json
+
+Update the IP of host where Kserve is deployed and ingress port of Kserve obtained using above method.
+
+Create sample data for predictions in file input_qoe.json. Add the following content in input_qoe.json file.
+
+.. code:: bash
+
+ {"signature_name": "serving_default", "instances": [[[2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56],
+ [2.56, 2.56]]]}
+
+
+Use command below to trigger predictions
+
+.. code:: bash
+
+ source predict.sh
Code Review / aiml-fw / aimlfw-dep.git / commitdiff
