| 2022-11-30 | 0.1.0 | | First draft |
| | | | |
+--------------------+--------------------+--------------------+-----------------------+
-| 2023-06-06 | 1.0.0 | Joseph Thaliath | H Release |
+| 2023-06-06 | 1.0.0 | Joseph Thaliath | H Release |
| | | | |
+--------------------+--------------------+--------------------+-----------------------+
| 2023-08-10 | 1.0.1 | Joseph Thaliath | H Maintenance release |
| | | | |
+--------------------+--------------------+--------------------+-----------------------+
+| 2023-12-14 | 1.1.0 | Joseph Thaliath | I release |
+| | | | |
++--------------------+--------------------+--------------------+-----------------------+
Introduction
Below are the minimum requirements for installing the AIMLFW
#. OS: Ubuntu 22.04 server
-#. 8 cpu cores
-#. 16 GB RAM
+#. 16 cpu cores
+#. 32 GB RAM
#. 60 GB harddisk
.. _reference1:
http://localhost:32005/
-In case Influx DB datalake not available, it can be installed using the steps mentioned in section :ref:`install-influx-db-as-datalake`. Once installed the access details of the datalake can be updated in :file:`RECIPE_EXAMPLE/example_recipe_latest_stable.yaml`. Once updated, follow the below steps for reinstall of some components:
+In case of any change required in the RECIPE_EXAMPLE/example_recipe_latest_stable.yaml file after installation,
+the following steps can be followed to reinstall with new changes.
.. 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
-----------------------
.. _reference2:
-Install Influx DB as datalake
------------------------------
+
+Install Influx DB as datalake (Optional)
+----------------------------------------
+
+Standalone Influx DB installation can be used if DME is not used as a data source.
.. code:: bash
influx query 'from(bucket: "UEData") |> range(start: -1000d)' -o primary -t <token>
-Install Kserve for deploying models
------------------------------------
+
+.. _reference3:
+
+Prepare Non-RT RIC DME as data source for AIMLFW (optional)
+-----------------------------------------------------------
+
+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
+
+Output of ./get_access_tokens.sh can be used during feature group creation step.
+
+
+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-logg-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-logg-bucket --start 1801-01-27T05:00:22.305309038Z --stop 2023-11-14T00:00:00Z
+
+
+Feature group creation
+----------------------
+
+From AIMLFW dashboard create feature group (Training Jobs-> Create Feature Group )
+
+NOTE: Below are some example values to be used for the DME based feature group creation for qoe usecase
+
++--------------------+-------------------------------------------------------------------+
+| **Parameter** | **Value** |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Feature Group Name | featuregroup1 |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Features | pdcpBytesDl,pdcpBytesUl |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Host | <IP of VM where DME is installed> |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Port | 31812 |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Db Org | est |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Bucket Name | pm-logg-bucket |
+| | |
++--------------------+-------------------------------------------------------------------+
+| DB Token | <token obtained using get_access_tokens.sh during DME setup> |
+| | |
++--------------------+-------------------------------------------------------------------+
+| DME flag | enable it |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Source Name | <any source name. but same needs to be given when running |
+| | push_qoe_data.sh> |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Measured Obj Class | NRCellDU |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Dme port | 31823 |
+| | |
++--------------------+-------------------------------------------------------------------+
+
+
+NOTE: Below are some example values to be used for the standalone influx DB creation for qoe usecase
+
++--------------------+-------------------------------------------------------------------+
+| **Parameter** | **Value** |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Feature Group Name | featuregroup1 |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Features | pdcpBytesDl,pdcpBytesUl |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Host | <IP of VM where Influx DB is installed> |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Port | <port of Influx DB> |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Db Org | primary |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Bucket Name | UEData |
+| | |
++--------------------+-------------------------------------------------------------------+
+| DB Token | <token obtained during INflux DB installation> |
+| | |
++--------------------+-------------------------------------------------------------------+
+
+Register Model (optional)
+-------------------------
+
+Register the model using the below steps if using Model management service for training.
+
+.. code:: bash
+
+ curl --location 'http://<VM IP where AIMLFW is installed>:32006/registerModel' \
+ --header 'Content-Type: application/json' \
+ --data '{
+ "model-name":"qoe1",
+ "rapp-id": "rapp_1",
+ "meta-info" :
+ {
+ "accuracy":"90",
+ "model-type":"timeseries",
+ "feature-list":["pdcpBytesDl","pdcpBytesUl"]
+ }
+ }'
+
+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 videos stored here: `Training Job creation <https://wiki.o-ran-sc.org/display/AIMLFEW/Files+for+I+release>`__
+#. 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>` or
+ :ref:`Steps to deploy model using Kserve adapter <reference6>`
+
+NOTE: Below are some example values to be used for the QoE usecase training job creation when model management service is not used.
+
++--------------------+-------------------------------------------------------------------+
+| **Parameter** | **Value** |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Training Job Name | qoetest |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Model Management | disable |
+| Service | |
++--------------------+-------------------------------------------------------------------+
+| Training Function | qoe_pipeline_h_release |
+| | |
++--------------------+-------------------------------------------------------------------+
+| FeatureGroup Name | featuregroup1 |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Datalake Source | Influx DB |
+| | |
++--------------------+-------------------------------------------------------------------+
+| _measurement | test,ManagedElement=nodedntest,GNBDUFunction=1004,NRCellDU=c4_B2 |
+| | |
++--------------------+-------------------------------------------------------------------+
+| bucket | pm-logg-bucket |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Feature Filter | |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Hyper Parameters | epochs:1 |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Description | test |
+| | |
++--------------------+-------------------------------------------------------------------+
+
+NOTE: Below are some example values to be used for the QoE usecase training job creation when model management service is used.
+
++--------------------+-------------------------------------------------------------------+
+| **Parameter** | **Value** |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Training Job Name | qoetest |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Model Management | enable |
+| Service | |
++--------------------+-------------------------------------------------------------------+
+| Model name | qoe1 |
+| | |
++--------------------+-------------------------------------------------------------------+
+| FeatureGroup Name | featuregroup1 |
+| | |
++--------------------+-------------------------------------------------------------------+
+| Datalake Source | Influx DB |
+| | |
++--------------------+-------------------------------------------------------------------+
+| _measurement | test,ManagedElement=nodedntest,GNBDUFunction=1004,NRCellDU=c4_B2 |
+| | |
++--------------------+-------------------------------------------------------------------+
+| bucket | pm-logg-bucket |
+| | |
++--------------------+-------------------------------------------------------------------+
+| 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 videos stored here: `Training Job creation <https://wiki.o-ran-sc.org/display/AIMLFEW/Files+for+I+release>`__
+#. 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>` or
+ :ref:`Steps to deploy model using Kserve adapter <reference6>`
+
+NOTE: Below are some example values to be used for the QoE usecase training job creation when model management service is not used
+
++--------------------+-------------------------+
+| **Parameter** | **Value** |
+| | |
++--------------------+-------------------------+
+| Training Job Name | qoetest |
+| | |
++--------------------+-------------------------+
+| Model Management | disable |
+| Service | |
++--------------------+-------------------------+
+| Training Function | qoe_pipeline_g_release |
+| | |
++--------------------+-------------------------+
+| FeatureGroup Name | featuregroup1 |
+| | |
++--------------------+-------------------------+
+| Datalake Source | Influx DB |
+| | |
++--------------------+-------------------------+
+| _measurement | liveCell |
+| | |
++--------------------+-------------------------+
+| bucket | UEData |
+| | |
++--------------------+-------------------------+
+| Feature Filter | |
+| | |
++--------------------+-------------------------+
+| Hyper Parameters | epochs:1 |
+| | |
++--------------------+-------------------------+
+| Description | test |
+| | |
++--------------------+-------------------------+
+
+NOTE: Below are some example values to be used for the QoE usecase training job creation when model management service is used
+
++--------------------+-------------------------+
+| **Parameter** | **Value** |
+| | |
++--------------------+-------------------------+
+| Training Job Name | qoetest |
+| | |
++--------------------+-------------------------+
+| Model Management | enable |
+| Service | |
++--------------------+-------------------------+
+| Model Name | qoe_pipeline_g_release |
+| | |
++--------------------+-------------------------+
+| Datalake Source | Influx DB |
+| | |
++--------------------+-------------------------+
+| _measurement | liveCell |
+| | |
++--------------------+-------------------------+
+| bucket | UEData |
+| | |
++--------------------+-------------------------+
+| Feature Filter | |
+| | |
++--------------------+-------------------------+
+| Hyper Parameters | epochs:1 |
+| | |
++--------------------+-------------------------+
+| Description | test |
+| | |
++--------------------+-------------------------+
+
+.. _reference5:
+
+Obtain Model URL for deploying trained models
+---------------------------------------------
+
+URL for deployment can be obainted from AIMFW dashboard (Training Jobs-> Training Job status -> Select Info for a training job -> Model URL)
+In case of using AIMLFW Model management service, URL for downloading and deploying model using Model Management Service will be the following:
+
+.. code:: bash
+
+ http://<VM IP where AIMLFW is deployed>:32006/downloadModel/<model name>/model.zip
+
+Install only Kserve for deploying models
+----------------------------------------
To install Kserve run the below commands
./bin/install_kserve.sh
-Uninstall Kserve
-----------------
+Uninstall only Kserve
+---------------------
To uninstall Kserve run the below commands
./bin/uninstall_kserve.sh
+
.. _reference4:
Deploy trained qoe prediction model on Kserve
To deploy model update the Model URL in the :file:`qoe.yaml` file and execute below command to deploy model
+Refer :ref:`Obtain Model URL for deploying trained models <reference5>`
.. 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>`__
+Install both Kserve and Kserve adapter for deploying models
+-----------------------------------------------------------
-Once all the pods are in running state, follow the below steps to prepare ranpm setup for AIMLFW qoe usecase data access
+To install Kserve run the below commands
+Please note to update the DMS IP in example_recipe_latest_stable.yaml before installation
-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>`__
+.. code:: bash
-Note: The following steps need to be performed in the VM where the ranpm setup is installed.
+ ./bin/install_kserve_inference.sh
-.. 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
+Uninstall both Kserve and Kserve adapter for deploying models
+-------------------------------------------------------------
-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
+To uninstall Kserve run the below commands
.. code:: bash
- datalake:
- influxdb:
- host: <IP of RANPM setup>
- port: 31812
- orgname: est
- bucket: pm-logg-bucket
- token: <Token shown when ./get_access_tokens.sh is executed>
+ ./bin/uninstall_kserve_inference.sh
-Execute the below script
-.. code:: bash
- ./prepare_env_aimlfw_access.sh
+.. _reference6:
-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>`__
+Steps to deploy model using Kserve adapter
+------------------------------------------
-Execute below script to push qoe data into ranpm setup
+Prerequisites
-.. code:: bash
+#. Install chart museum
+#. Build ricdms binary
- ./push_qoe_data.sh <source name mentioned when creating feature group> <Number of rows> <Cell Identity>
-Example for executing above script
+#. Run ric dms
-.. code:: bash
-
- ./push_qoe_data.sh gnb300505 30 c4/B2
+ .. code:: bash
-Steps to check if data is upload correctly
+ export RIC_DMS_CONFIG_FILE=$(pwd)/config/config-test.yaml
+ ./ricdms
-.. code:: bash
+#. Create sample_config.json
- kubectl exec -it influxdb2-0 -n nonrtric -- bash
- influx query 'from(bucket: "pm-logg-bucket") |> range(start: -1000000000000000000d)' |grep pdcpBytesDl
+ Create sample_config.json file with the following contents
-Steps to clear the data in InfluxDB
+ .. code:: bash
-.. code:: bash
+ {
+ "xapp_name": "sample-xapp",
+ "xapp_type": "inferenceservice",
+ "version": "2.2.0",
+ "sa_name": "default",
+ "inferenceservice": {
+ "engine": "tensorflow",
+ "storage_uri": "<Model URL>",
+ "runtime_version": "2.5.1",
+ "api_version": "serving.kubeflow.org/v1beta1",
+ "min_replicas": 1,
+ "max_replicas": 1
+ }
+ }
- kubectl exec -it influxdb2-0 -n nonrtric -- bash
- influx delete --bucket pm-logg-bucket --start 1801-01-27T05:00:22.305309038Z --stop 2023-11-14T00:00:00Z
+ Refer :ref:`Obtain Model URL for deploying trained models <reference5>`
+#. Copy sample_config.json
+
+ Update the below command with kserve adapter pod name
-Training job creation with DME as data source
----------------------------------------------
+ .. code:: bash
-#. 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/81297504/h_release_training_source_dme.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)
+ kubectl cp sample_config.json ricips/<kserve adapter pod name>:pkg/helm/data/sample_config.json
-NOTE: Below are some example values to be used for the QoE usecase training job creation
+#. Generating and upload helm package
-+--------------------+-------------------------------------------------------------------+
-| **Parameter** | **Value** |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| Training Job Name | qoetest |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| Training Function | qoe_pipeline_h_release |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| Experiment Name | Default |
-| | |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| Datalake Source | Influx DB |
-| | |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| _measurement | test,ManagedElement=nodedntest,GNBDUFunction=1004,NRCellDU=c4_B2 |
-| | |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| bucket | pm-logg-bucket |
-| | |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| Feature Name | \* |
-| | |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| Feature Filter | |
-| | |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| Hyper Parameters | epochs:1 |
-| | |
-| | |
-+--------------------+-------------------------------------------------------------------+
-| Description | test |
-| | |
-| | |
-+--------------------+-------------------------------------------------------------------+
+ .. code:: bash
+ curl --request POST --url 'http://127.0.0.1:31000/v1/ips/preparation?configfile=pkg/helm/data/sample_config.json&schemafile=pkg/helm/data/sample_schema.json'
-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/81297504/h_release_training_source_influxdb.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)
+#. Check uploaded charts
-NOTE: Below are some example values to be used for the QoE usecase training job creation
+ .. code:: bash
-+--------------------+-------------------------+
-| **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 |
-| | |
-| | |
-+--------------------+-------------------------+
+ curl http://127.0.0.1:8080/api/charts
+#. Deploying the model
+ .. code:: bash
+ curl --request POST --url 'http://127.0.0.1:31000/v1/ips?name=inference-service&version=1.0.0'
+#. Check deployed Inference service
+ .. code:: bash
+ kubectl get InferenceService -n ricips
Code Review / aiml-fw / aimlfw-dep.git / commitdiff