vania-janet/multiturn-rag-retrieval-data huggingface dataset:
If you rely on these changed metrics, update accordingly.<br><br><br><br> * From gitVersion to git_version<br> * From gitCommit to git_commit<br> * From gitTreeState to git_tree_state<br> * From buildDate to build_date<br> * From goVersion to go_version.<br><br><br> 







 Update after master 

The following table shows the actions that you must take after you update the Kubernetes master.



Changes to make after you update the master to Kubernetes 1.19

 Type Description 

 Calico data store driver change When you update your cluster to version 1.19, Calico is updated to use Kubernetes data store driver (KDD). If you have any automation that depends on the Calico configuration file for your cluster, first download the new KDD-based Calico configuration file by running ibmcloud ks cluster config -c <cluster_name_or_ID> --network. Then, update your automation to use the new Calico configuration file. Additionally, because worker nodes can no longer access etcd with admin keys, ensure that you update your worker nodes so that the previous etcd secrets are removed. 
 Certificate signing request (CSR) The CertificateSigningRequest API is promoted to certificates.k8s.io/v1. You can use the CertificateSigningRequest API to generate server certificates that are signed by the cluster certificate authority (CA) for TLS communication within the cluster. For more details, see the [Kubernetes version 1.19 change log](https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG/CHANGELOG-1.19.mdapi-change-1) and [Kubernetes CSR documentation](https://kubernetes.io/docs/reference/access-authn-authz/certificate-signing-requests/). If you are currently using the v1beta CertificateSigningRequest API, make the following changes before you update to v1.
[train-language-model](https://cloud.ibm.com/apidocs/speech-to-texttrain-a-custom-language-model) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id, word_type_to_add, customization_weight Train a custom language model. 
 [upgrade-language-model](https://cloud.ibm.com/apidocs/speech-to-textupgrade-a-custom-language-model) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id Upgrade a custom language model. 
 [add-corpus](https://cloud.ibm.com/apidocs/speech-to-textadd-a-corpus) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id, corpus_name, corpus_file, allow_overwrite Add a corpus. 
 [delete-corpus](https://cloud.ibm.com/apidocs/speech-to-textdelete-a-corpus) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id, corpus_name Delete a corpus. 
 [get-corpus](https://cloud.ibm.com/apidocs/speech-to-textget-a-corpus) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id, corpus_name Get a corpus.
Updating toolchains using Schematics 

After the toolchains are deployed, you can update them by using Schematics.

Updates can include, for example, adding or removing integrations, or updating pipeline variables.

To update schematics, take the following steps:



1.  Open [Schematics Workspaces](https://cloud.ibm.com/schematics/workspaces) and click the relevant workspace for your DevSecOps toolchains. Use the following options:



*  Settings lists all the inputs that you can edit.
*  Jobs shows the actions that are run by Schematics.



2.  Click Settings and then search for the inputs that you want to update.

The variables prefixed with ci, cd and cc apply to the CI, CD, and CC toolchains respectively. Nonprefixed variables apply to all the toolchains. Also, modifying the default values for the prefixed variable inputs causes the prefixed variables inputs to take precedence over nonprefixed variable inputs.
3.  For each input that you want to update, take the following steps:



1.  Click the Actions icon ![Actions icon](https://cloud.ibm.com/docs-content/v1/content/880fa35b13cb9428675d718aa36ca2825b4c87c8/icons/actions-icon-vertical.svg) on an input.
2.  Update the values in the dialog that opens and click Save.



4.  When you have updated all the required input changes, click Jobs.
5.  Click Generate plan.
6.  On the Jobs page, verify that the plan succeeded. If not then review the problematic input changes.
7.  Click Apply plan to update the toolchains. Check that the apply job succeededs.
Review the [v0.15 upgrade guide](https://developer.hashicorp.com/terraform/language/v1.1.x/upgrade-guides/0-15). 
 v0.15 You can upgrade directly to the [Terraform v1.0](https://developer.hashicorp.com/terraform/language/v1.1.x/upgrade-guides/1-0) version. Review the [v1.0 upgrade guide](https://developer.hashicorp.com/terraform/language/v1.1.x/upgrade-guides/1-0). 







 Upgrading a Terraform v0.12 Workspace to v0.13 

Upgrading a v0.12 Workspace to use the v0.13 version of Terraform is a multi-step task. You must carefully review the [Terraform upgrade guide](https://developer.hashicorp.com/terraform/language/upgrade-guides) for the related version upgrade.

Use the following steps to upgrade to the current Terraform version in the Schematics Workspace.



1. Upgrade the Terraform configuration files to use the newer syntax and semantics.
2. Migrate the Terraform state file to be compatible with the newer version. Schematics does not support built in modification the Terraform state file. Therefore, you need to follow these steps.



1. Prepare the upgraded version of Terraform configuration files and [Terraform state file](https://cloud.ibm.com/docs/schematics?topic=schematics-schematics-cli-referencestate-file-cmds), in your local machine.
2. [Create the new Schematics Workspace](https://cloud.ibm.com/docs/schematics?topic=schematics-schematics-cli-referenceschematics-workspace-new) with the new Terraform configuration files and Terraform state file.
3.
* UI
* CLI
* API
* Terraform







 Updating your software 

To update the software that's in your private catalog, you can add a new version or update and republish an existing version.



 Before you begin 

To complete this task, you need to be assigned the editor role on the catalog management service. For more information, see [Assigning users access](https://cloud.ibm.com/docs/account?topic=account-catalog-access).

To update your software by using Terraform, make sure that you have completed the following:



* Install the Terraform CLI and configure the IBM Cloud Provider plug-in for Terraform. For more information, see the tutorial for [Getting started with Terraform on IBM Cloud®](https://cloud.ibm.com/docs/ibm-cloud-provider-for-terraform?topic=ibm-cloud-provider-for-terraform-getting-started). The plug-in abstracts the IBM Cloud APIs that are used to complete this task.
* Create a Terraform configuration file that is named main.tf. In this file, you define resources by using HashiCorp Configuration Language. For more information, see the [Terraform documentation](https://www.terraform.io/docs/language/index.html).







 Update an existing version by using the console 

The following steps walk through an example of making updates to a product's readme file to show the complete process for updating an existing software version.



1. In the IBM Cloud® console, go to Manage > Catalogs > Private catalogs, and select your catalog from the list.
2. Click the name of your software product.
3. Select Private products to navigate to the list of products that are in your private catalog.
4. Click the Actions icon !
Replace {url} and {apikey} with your service URL and API key, and replace {model_id} with the model ID of the classifications model you want to delete.



* The following example deletes a classification model.

curl --user "apikey":"{apikey}" "{url}/v1/models/classifications/{model_id}?version=2021-03-23" --request DELETE







 Migrating from Natural Language Classifier to Natural Language Understanding 

On 9 August 2021, IBM announced the deprecation of the IBM Watson® Natural Language Classifier service. The service will no longer be available from 8 August 2022. As of 9 September 2021, you can't create new instances, and access to free instances will be removed. Existing premium plan instances are supported until 8 August 2022. Any instance that still exists on that date will be deleted. As an alternative, we encourage Natural Language Classifier users to consider migrating to the Natural Language Understanding service.



 When training data is available 

You can directly use the available training data to train classifications in Natural Language Understanding. Natural Language Understanding accepts the same CSV file format.





 When training data is not available 

You can fetch the data you used to train Natural Language Classifier from the service. Refer to [this tutorial](https://github.com/watson-developer-cloud/doc-tutorial-downloads/blob/master/natural-language-understanding/custom_classifications_introduction.ipynb).
The Watson Assistant service continually monitors all ML models, and automatically retrains those models that have not been retrained in the previous 6 months. Watson Assistant retrains using the selected algorithm version. If the version you had selected is no longer supported, Watson Assistant retrains using the version labeled as Previous. This means that your assistant will automatically have the supported technologies applied. Assistants that have been modified during the previous 6 months will not be affected.

In most cases, this retraining will be seamless from an end-user point of view. The same inputs will result in the same actions, intents, and entities being detected. In some cases, the retraining might cause changes in accuracy.





 Instructions for Watson Assistant for IBM Cloud Pak for Data 

IBM Cloud Pak for Data

When upgrading your instance of Watson Assistant for IBM Cloud Pak for Data, as long as your existing models have been trained using an algorithm version that is still supported, your models will not be retrained during or after the upgrade.

New algorithm versions will be included in major releases (for example, 4.0.0 or 5.0.0) or minor releases (for example, 4.5.0 or 4.6.0). A monthly release might include a new algorithm version if there is more than 6 months between major or minor releases. Each algorithm version supports 2 major/minor releases or a maximum time of 12 months, whichever is first. For more information, see the [IBM Cloud Pak for Data Software Support Lifecycle Addendum](https://www.ibm.com/support/pages/ibm-cloud-pak-data-software-support-lifecycle-addendum)

Each new release includes full support for the version listed as Latest in the most recent prior release. This version is then labeled as Previous after you upgrade. In addition, each new release will support running models that were trained on the version prior to that so that upgrading won't impact your runtime. For example, if you upgrade from IBM Cloud Pak for Data 4.6 to 4.7, and were using Latest (01-Jun-2022) that version becomes listed as Previous (01 June 2022) and remains your selected version.



 Automatic retraining after upgrading
For example, you can update commands such as pip/pip3 for discovering Python packages that require a later pip version.

The following example shows how to use the cra-custom-script to update the pip version:

!/usr/bin/env bash

if [ "${PIPELINE_DEBUG:-0}" == 1 ]]; then
trap env EXIT
env | sort
set -x
fi

python3 -m pip install --upgrade pip

If your Dockerfile uses an image from a private Docker registry, you can use the cra-custom-script-path parameter to authenticate to a private Docker registry before you run Code Risk Analyzer and to allow Code Risk Analyzer to pull this image for scanning.

The following example shows how to use the cra-custom-script to authenticate to the ibmcloud container registry:

!/usr/bin/env bash

if [ "${PIPELINE_DEBUG:-0}" == 1 ]]; then
trap env EXIT
env | sort
set -x
fi

ibmcloud cr login





 Debugging the Code Risk Analyzer in DevSecOps 

To help with debugging, you can run the Code Risk Analyzer locally as a command-line interface (CLI) on your own local machine. For information about running the ibmcloud cra bom-generate command to generate a BOM file, see [Bill of Materials (BOM)](https://cloud.ibm.com/docs/ContinuousDelivery?topic=ContinuousDelivery-cd-configure-cra-reposbom-generate-command). After you generate the BOM file, use the ibmcloud cra cve command to list any vulnerabilities. For more information about running the ibmcloud cra cve command, see [Vulnerability scan](https://cloud.ibm.com/docs/ContinuousDelivery?topic=ContinuousDelivery-cd-configure-cra-reposvulnerability-command).

Make sure that the run-cra task does not contain any errors.
* If you update custom words in a custom model, adding a new translation for a word that already exists overwrites the word's existing translation.



Because custom models are based on languages, not on specific voices, you do not need to create new custom models. But you might find it easier to work with new models to avoid confusion between existing and new custom words that the models contain.

For more information about about working with custom models and entries for custom words, see the following topics:



* [Creating and managing custom models](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customModels)
* [Creating and managing custom entries](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customWords)
* [Using a custom model for speech synthesis](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-custom-using)
Example of listing version information 

The following example request shows information for an upgraded custom language model:

IBM Cloud

curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations?language=en-US"

IBM Cloud Pak for Data

curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations?language=en-US"

The versions field indicates that the custom model is available for two versions of the base model: the older version, en-US_BroadbandModel.v2018-07-31, and the newer version, en-US_BroadbandModel.v2020-01-16.

{
"customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96",
"created": "2016-06-01T14:21:26.894Z",
"updated": "2020-01-18T18:42:25.324Z",
"language": "en-US",
"dialect": "en-US",
"versions": [
"en-US_BroadbandModel.v2018-07-31",
"en-US_BroadbandModel.v2020-01-16"
],
"owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
"name": "Example model",
"description": "Example custom language model",
"base_model_name": "en-US_BroadbandModel",
"status": "available",
"progress": 100
}
Show more







 Making speech recognition requests with upgraded custom models 

By default, the service uses the latest version of a custom model that you pass with a speech recognition request. You issue requests with upgraded custom models as you do with any model. For more information, see
Enter the name and URL, and click Update > Next.







 Editing your readme file 

When users install the software, they can view product information by clicking the Readme file link. This information is generated from the readme file that you uploaded to your source repository.



1. From the Edit readme tab, preview how the information in the readme file will be displayed to users when they install the software.
2. To make updates, click the Edit icon ![Edit icon](https://cloud.ibm.com/docs-content/v1/content/4d8c6eec891cff72b666dc24bd3211810c77fb42/icons/edit-tagging.svg) next to the Readme section title.
3. Click Save.
4. Click Next.



If you are importing a virtual server image for VPC, the readme file is not automatically generated. Copy and paste the contents of the [readme file template](https://cloud.ibm.com/media/docs/downloads/software/sw-readme-tab-template.md) and make updates as needed.





 Validating the software 

When you validate your software, you're confirming that the current version can be successfully installed on the deployment target. The validation steps vary based on your deployment method.

To monitor the progress of the validation process, click View logs.



 Helm chart 



1. From the Validate product tab, select your cluster.
2. If the deployment target is a Kubernetes cluster, select a namespace or create a new one. If the deployment target is a Red Hat OpenShift cluster, select a project or create a new one.
3. Click Next.
4. Configure your IBM Cloud Schematics workspace.
5. Click Next.
6. If applicable, review the license agreements, and select I have read and agree to the following license agreements:.
7. Click Validate.







 Terraform 



1.
{
"customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96",
"created": "2016-06-01T18:42:25.324Z",
"updated": "2016-06-01T22:11:13.298Z",
"language": "en-US",
"owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
"name": "Example model",
"description": "Example custom acoustic model",
"base_model_name": "en-US_BroadbandModel",
"status": "training",
"progress": 0
}

The response includes status and progress fields that report the current state of the model. The meaning of the progress field depends on the model's status. The status field can have one of the following values:



* pending indicates that the model was created but is waiting either for valid training data to be added or for the service to finish analyzing data that was added. The progress field is 0.
* ready indicates that the model contains valid data and is ready to be trained. The progress field is 0.

If the model contains a mix of valid and invalid audio resources, training of the model fails unless you set the strict query parameter to false. For more information, see [Training failures for custom acoustic models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acousticfailedTraining-acoustic).
* training indicates that the model is being trained. The progress field changes from 0 to 100 when training is complete.
* available indicates that the model is trained and ready to use. The progress field is 100.
* upgrading indicates that the model is being upgraded. The progress field is 0.
* failed indicates that training of the model failed. The progress field is 0.
Versions 0.13 through 0.15 require a stepwise upgrade, 0.13 to 0.14, 0.14 to 0.15, 0.15 to 1.0.

The process is the same for each version step. It is mandatory that a Terraform Apply is run after each version change. This updates the Terraform state file with schema changes related to that version and that version only. After successfully upgrading a single version, the next version update can be performed.



1. Read the Terraform [upgrade guide](https://developer.hashicorp.com/terraform/language/v1.1.x/upgrade-guides) for the release and implement any required config changes.
2. Follow the process outlined in [Upgrading the Terraform template version 1.x and above](https://cloud.ibm.com/docs/schematics?topic=schematics-migrating-terraform-versionterraform-version-upgrade1x-process) to upgrade a single version to the target version.
3. Verify in the Workspace settings page the TF version is now set to the desired version.
4. Run a Generate Plan operation against the workspace. Validate that the command runs successfully without error and no unexpected messages are logged. The Plan should result in no proposed changes to the resources.
5. Run a Apply Plan operation against the workspace. This step is mandatory to perform a Terraform state file update. Validate that the command runs successfully without error and no unexpected messages are logged.
6. You have now successfully upgraded a single version step.
is available for installations of version 1.5.0.<-- </section "id="section-watson-assistant-150-patch-1" "> --><-- <section "id="section-resizing-the-redis-statefulset-memory-and-cpu-values-after-applying-patch-1-for-watson-assistant-150" "> --> Resizing the Redis statefulset memory and cpu values after applying patch 1 for Watson Assistant 1.5.0 Watson Assistant uses Redis to store web session-related data. Here are steps to resize Redis statefulset memory and cpu values after applying Watson Assistant 1.5.0 patch 1]Watson Assistant 1_.5_.0 patch 1] ! .<-- <ol> -->1. Use oc get wa to see your instance name:
oc get wa
NAME VERSION READY READYREASON UPDATING UPDATINGREASON DEPLOYED VERIFIED AGE
watson-assistant---wa-qa 1.5.0 True Stable False Stable 18/18 18/18 11h
2. Export your instance name as a variable that you can use in each step, for example:
export INSTANCENAME=watson-assistant---wa-qa
3. Change the updateStrategy in both Redis statefulsets to type RollingUpdate:
oc patch statefulset c-$INSTANCENAME-redis-m -p '{"spec":{"updateStrategy":{"type":"RollingUpdate"}}}'
oc patch statefulset c-$INSTANCENAME-redis-s -p '{"spec":{"updateStrategy":{"type":"RollingUpdate"}}}'
4. Update the Redis statefulsets with the resized cpu and memory values:
Member CPU
Repeat the prior steps for each peer you are updating.







 Upgrading nodes from Fabric v1.4 to v2.5 

Upgrading IBM Blockchain Platform nodes directly from Hyperledger Fabric 1.4.x to the latest Fabric version is possible, but deploying a new Fabric 2.2.x peer instead of upgrading is recommended. Fabric installation is done by IBM Blockchain Platform, but can take hours or days depending on the size of the database to be built. See the [Fabric documentation on upgrading](https://hyperledger-fabric.readthedocs.io/en/release-2.2/upgrade_to_newest_version.htmlupgrading-to-2-2-from-the-1-4-x-long-term-support-release) for more information.

If your Fabric v1.4 nodes use Node.js chaincode, use the following sequence to upgrade these nodes from Fabric v1.4 to v2.5:



1. Deploy new peers with Fabric v2.2
2. Update the Node.js chaincode on these peers to 2.5 shim
3. Install and instantiate the new chaincode
4. Upgrade the peers from Fabric v2.2 to v2.5







 Step one: Back up your ledger (optional) 

It is recommended to take regular backups of the persistent volumes of your nodes as part of the normal process of network administration. For example, in our topic on backing up and restoring components and networks, an [example schedule](https://cloud.ibm.com/docs/blockchain?topic=blockchain-backup-restorebackup-restore-schedule-snapshot) is provided which recommends that backups be taken of the ordering nodes and the peers (including the state database of the peer, if using CouchDB) each night.

However, if you are not taking regular backups, it is recommended that you minimally take a backup before attempting to upgrade a node, as it allows for a node to be restored to an earlier running state in cases where the upgrade fails.
Update the Helm repo to retrieve the latest version of all Helm charts in this repo.

helm repo update
2. Optional: Download the latest Helm chart to your local machine. Then, extract the package and review the release.md file to find the latest release information.

helm pull iks-charts/ibm-iks-cluster-autoscaler
3. Find the name of the cluster autoscaler release that you installed in your cluster.

helm list -n <namespace> | grep cluster-autoscaler

Example output

myhelmchart 1 Mon Jan 7 14:47:44 2022 DEPLOYED ibm-iks-cluster-autoscaler-1.0.1 kube-system
4. Upgrade the cluster autoscaler release to the latest version.

helm upgrade --force --recreate-pods <helm_chart_name> iks-charts/ibm-iks-cluster-autoscaler
5. Verify that the [cluster autoscaler configmap](https://cloud.ibm.com/docs/openshift?topic=openshift-cluster-scaling-enable)workerPoolsConfig.json section is set to "enabled": true for the worker pools that you want to scale.

oc describe cm iks-ca-configmap -n kube-system

Example output

NAME: iks-ca-configmap
Namespace: kube-system
Labels: <none>
Annotations: kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"v1","data":{"workerPoolsConfig.json":"[n {"name": "docs","minSize": 1,"maxSize": 3,"enabled":true}n]n"},"kind":"ConfigMap",...

Data
====
workerPoolsConfig.json:
----
[