Abstract
--------
-This document describes how to install AIMLFW, it's dependencies and required system resources.
+This document describes how to install AIMLFW, demo scenarios, it's dependencies and required system resources.
Version history
| 2022-11-30 | 0.1.0 | | First draft |
| | | | |
+--------------------+--------------------+--------------------+--------------------+
-| | | | |
+| 2023-06-06 | 1.0.0 | Joseph Thaliath | H Release |
| | | | |
+--------------------+--------------------+--------------------+--------------------+
| | | | |
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 and Linux system.
+The audience of this document is assumed to have good knowledge in AI/ML tools, Kubernetes and Linux system.
Hardware Requirements
Below are the minimum requirements for installing the AIMLFW
-#. OS: Ubuntu 18.04 server
+#. OS: Ubuntu 22.04 server
#. 8 cpu cores
#. 16 GB RAM
#. 60 GB harddisk
+.. _reference1:
+
Software Installation and Deployment
------------------------------------
.. <DESCRIBE THE FULL PROCEDURES FOR THE INSTALLATION OF THE O-RAN COMPONENT INSTALLATION AND DEPLOYMENT>
.. _install-influx-db-as-datalake:
+.. _reference2:
+
Install Influx DB as datalake
-----------------------------
kubectl get pods -n kserve-test
+.. _reference4:
Test predictions using model deployed on Kserve
-----------------------------------------------
.. code:: bash
source predict.sh
+
+.. _reference3:
+
+Prepare Non-RT RIC DME as data source for AIMLFW
+------------------------------------------------
+
+Bring up the RANPM setup by following the steps mentioned in the file install/README.md present in the repository `RANPM repository <https://gerrit.o-ran-sc.org/r/admin/repos/nonrtric/plt/ranpm>`__
+
+Once all the pods are in running state, follow the below steps to prepare ranpm setup for AIMLFW qoe usecase data access
+
+The scripts files are present in the folder demos/hrelease/scripts of repository `AIMLFW repository <https://gerrit.o-ran-sc.org/r/admin/repos/aiml-fw/aimlfw-dep>`__
+
+Note: The following steps need to be performed in the VM where the ranpm setup is installed.
+
+.. code:: bash
+
+ git clone "https://gerrit.o-ran-sc.org/r/aiml-fw/aimlfw-dep"
+ cd aimlfw-dep/demos/hrelease/scripts
+ ./get_access_tokens.sh
+
+Update the RECIPE file (:file:`RECIPE_EXAMPLE/example_recipe_latest_stable.yaml`) of AIMLFW installation with Influx DB details of RANPM setup. The token is displayed when the script :file:`./get_access_tokens.sh` is executed.
+Example of updating the RECIPE file is shown below
+
+.. code:: bash
+
+ datalake:
+ influxdb:
+ host: <IP of RANPM setup>
+ port: 31812
+ orgname: est
+ bucket: pm-bucket
+ token: <Token shown when ./get_access_tokens.sh is executed>
+
+Execute the below script
+
+.. code:: bash
+
+ ./prepare_env_aimlfw_access.sh
+
+Add feature group from AIMLFW dashboard, example on how to create a feature group is shown in this demo video: `Feature group creation demo <https://wiki.o-ran-sc.org/download/attachments/71762231/feature_group_create_final_lowres.mp4?api=v2>`__
+
+Execute below script to push qoe data into ranpm setup
+
+.. code:: bash
+
+ ./push_qoe_data.sh <source name mentioned when creating feature group> <Number of rows> <Cell Identity>
+
+Example for executing above script
+
+.. code:: bash
+
+ ./push_qoe_data.sh gnb300505 30 c4/B2
+
+Steps to check if data is upload correctly
+
+
+.. code:: bash
+
+ kubectl exec -it influxdb2-0 -n nonrtric -- bash
+ influx query 'from(bucket: "pm-bucket") |> range(start: -1000000000000000000d)' |grep pdcpBytesDl
+
+Steps to clear the data in InfluxDB
+
+.. code:: bash
+
+ kubectl exec -it influxdb2-0 -n nonrtric -- bash
+ influx delete --bucket pm-bucket --start 1801-01-27T05:00:22.305309038Z --stop 2023-11-14T00:00:00Z
+
+
+Training job creation with DME as data source
+---------------------------------------------
+
+#. AIMLFW should be installed by following steps in section :ref:`Software Installation and Deployment <reference1>`
+#. RANPM setup should be installed and configured as per steps mentioned in section :ref:`Prepare Non-RT RIC DME as data source for AIMLFW <reference3>`
+#. To create training job, follow the steps in the demo video: `Training Job creation <https://wiki.o-ran-sc.org/download/attachments/71762231/feature_group_create_training_final_lowres.mp4?api=v2>`__
+#. After training job is created and executed successfully, model can be deployed using steps mentioned in section :ref:`Deploy trained qoe prediction model on Kserve <reference4>`. Model URL for deployment can be obainted from AIMFW dashboard (Training Jobs-> Training Job status -> Select Info for a training job -> Model URL)
+
+NOTE: Below are some example values to be used for the QoE usecase training job creation
+
++--------------------+--------------------------------------------------------------+
+| **Parameter** | **Value** |
+| | |
++--------------------+--------------------------------------------------------------+
+| Training Job Name | qoetest |
+| | |
++--------------------+--------------------------------------------------------------+
+| Training Function | qoe_pipeline_h_release |
+| | |
++--------------------+--------------------------------------------------------------+
+| Experiment Name | Default |
+| | |
+| | |
++--------------------+--------------------------------------------------------------+
+| Datalake Source | Influx DB |
+| | |
+| | |
++--------------------+--------------------------------------------------------------+
+| _measurement | ManagedElement=nodedntest,GNBDUFunction=1004,NRCellDU=c4_B2 |
+| | |
+| | |
++--------------------+--------------------------------------------------------------+
+| bucket | pm-bucket |
+| | |
+| | |
++--------------------+--------------------------------------------------------------+
+| Feature Name | \* |
+| | |
+| | |
++--------------------+--------------------------------------------------------------+
+| Feature Filter | |
+| | |
+| | |
++--------------------+--------------------------------------------------------------+
+| Hyper Parameters | epochs:1 |
+| | |
+| | |
++--------------------+--------------------------------------------------------------+
+| Description | test |
+| | |
+| | |
++--------------------+--------------------------------------------------------------+
+
+
+Training job creation with standalone Influx DB as data source
+--------------------------------------------------------------
+
+#. AIMLFW should be installed by following steps in section :ref:`Software Installation and Deployment <reference1>`
+#. Standalone Influx DB should be setup and configured as mentioned in section :ref:`Install Influx DB as datalake <reference2>`
+#. To create training job, follow the steps in the demo video: `Training Job creation <https://wiki.o-ran-sc.org/download/attachments/71762231/feature_group_create_training_final_lowres.mp4?api=v2>`__
+#. After training job is created and executed successfully, model can be deployed using steps mentioned in section :ref:`Deploy trained qoe prediction model on Kserve <reference4>`. Model URL for deployment can be obainted from AIMFW dashboard (Training Jobs-> Training Job status -> Select Info for a training job -> Model URL)
+
+NOTE: Below are some example values to be used for the QoE usecase training job creation
+
++--------------------+-------------------------+
+| **Parameter** | **Value** |
+| | |
++--------------------+-------------------------+
+| Training Job Name | qoetest |
+| | |
++--------------------+-------------------------+
+| Training Function | qoe_pipeline_g_release |
+| | |
++--------------------+-------------------------+
+| Experiment Name | Default |
+| | |
+| | |
++--------------------+-------------------------+
+| Datalake Source | Influx DB |
+| | |
+| | |
++--------------------+-------------------------+
+| _measurement | liveCell |
+| | |
+| | |
++--------------------+-------------------------+
+| bucket | UEData |
+| | |
+| | |
++--------------------+-------------------------+
+| Feature Name | \* |
+| | |
+| | |
++--------------------+-------------------------+
+| Feature Filter | |
+| | |
+| | |
++--------------------+-------------------------+
+| Hyper Parameters | epochs:1 |
+| | |
+| | |
++--------------------+-------------------------+
+| Description | test |
+| | |
+| | |
++--------------------+-------------------------+
+
+
+
+
+
+
+
Code Review / aiml-fw / aimlfw-dep.git / commitdiff