From df9336d09a3087eaf2906db5151debc3ebbe1a42 Mon Sep 17 00:00:00 2001 From: josephthaliath Date: Wed, 14 Jun 2023 10:58:41 +0530 Subject: [PATCH] Update to installation documentation Issue-Id: AIMLFW-48 Change-Id: I58a2f78bb6ebc81776c94ccf60c048cf72b9d66b Signed-off-by: josephthaliath --- docs/installation-guide.rst | 9 +++++---- docs/release-notes.rst | 6 +++--- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/docs/installation-guide.rst b/docs/installation-guide.rst index aadae13..2a00c60 100755 --- a/docs/installation-guide.rst +++ b/docs/installation-guide.rst @@ -161,7 +161,7 @@ Use the :file:`insert.py` in ``ric-app/qp repository`` to upload the qoe data in .. code:: bash - git clone https://gerrit.o-ran-sc.org/r/ric-app/qp + git clone -b f-release https://gerrit.o-ran-sc.org/r/ric-app/qp cd qp/qp Update :file:`insert.py` file with the following content: @@ -262,6 +262,8 @@ To uninstall Kserve run the below commands ./bin/uninstall_kserve.sh +.. _reference4: + Deploy trained qoe prediction model on Kserve --------------------------------------------- @@ -305,7 +307,6 @@ Check running state of pod using below command kubectl get pods -n kserve-test -.. _reference4: Test predictions using model deployed on Kserve ----------------------------------------------- @@ -429,7 +430,7 @@ Training job creation with DME as data source #. AIMLFW should be installed by following steps in section :ref:`Software Installation and Deployment ` #. RANPM setup should be installed and configured as per steps mentioned in section :ref:`Prepare Non-RT RIC DME as data source for AIMLFW ` -#. To create training job, follow the steps in the demo video: `Training Job creation `__ +#. To create training job, follow the steps in the demo video: `Training Job creation `__ #. 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 `. 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 @@ -483,7 +484,7 @@ Training job creation with standalone Influx DB as data source #. AIMLFW should be installed by following steps in section :ref:`Software Installation and Deployment ` #. Standalone Influx DB should be setup and configured as mentioned in section :ref:`Install Influx DB as datalake ` -#. To create training job, follow the steps in the demo video: `Training Job creation `__ +#. To create training job, follow the steps in the demo video: `Training Job creation `__ #. 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 `. 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 diff --git a/docs/release-notes.rst b/docs/release-notes.rst index ffe31e0..073be1e 100644 --- a/docs/release-notes.rst +++ b/docs/release-notes.rst @@ -7,7 +7,7 @@ Release-Notes ------------- -This document provides the release notes for the G release of AIMLFW Installation and Deployment +This document provides the release notes for the H release of AIMLFW Installation and Deployment .. contents:: :depth: 3 @@ -23,7 +23,7 @@ Version history | 2022-12-08 | 1.0.0 | Joseph Thaliath | G release | | | | | | +--------------------+--------------------+--------------------+--------------------+ -| 2023-06-07 | 2.0.0 | Joseph Thaliath | H release | +| 2023-06-07 | 1.1.0 | Joseph Thaliath | H release | | | | | | +--------------------+--------------------+--------------------+--------------------+ @@ -47,7 +47,7 @@ Release Data | **Release designation** | H release | | | | +--------------------------------------+--------------------------------------+ -| **Release date** | 2023-06-07 | +| **Release date** | 2023-06-29 | | | | +--------------------------------------+--------------------------------------+ | **Purpose of the delivery** | AIMLFW Installation and Deployment | -- 2.16.6