vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Getting started with Slurm 

Slurm on IBM Cloud enables customers to deploy HPC clusters on IBM Cloud infrastructure that use the [Slurm Workload Manager](https://github.com/SchedMD/slurm) as a scheduling software. The deployment is performed by using Terraform and IBM Cloud Schematics as automation frameworks. The following steps outline the high-level flow of events that are performed:



1. Create a workspace with the Terraform code from IBM Cloud Schematics. This step defines the set of configuration properties that are used to perform the automation.
2. Generate a plan to confirm whether the configuration properties are valid, so that when you run the Terraform code, all of the resources are provisioned correctly. If the validation fails, fix the configuration properties and try again.
3. Apply a plan triggers the actual deployment of the IBM Cloud resources to have an HPC cluster up and running by the time the deployment completes. If the deployment fails, identify the reason for failure, fix the problem, and try again. If a change is needed to the configuration properties, it might be better to generate a plan again.



Before you can deploy your Slurm cluster, you need to create or gather some information. To get started, complete the following steps.



 Step 1: Generate API key 

Generate an API key for your IBM Cloud account where the Slurm cluster will be deployed. For more information, see [Managing user API keys](https://cloud.ibm.com/docs/account?topic=account-userapikey).





 Step 2: Create SSH key 

Create an SHH key in your IBM Cloud account. This is your SSH key that you will use to access the Slurm cluster. For more information, see [Managing SSH keys](https://cloud.ibm.com/docs/vpc?topic=vpc-managing-ssh-keys).





 Step 3: Next steps 

After you've gathered the necessary input values to define your cluster configuration, you are ready to deploy your Slurm cluster.
You do not need to delete and re-add the MSPs in the configuration of any application channels that have already been created. For these MSPs, the endorsement policy is added as part of the process of [deploying the smart contract using the v2.x lifecycle](https://cloud.ibm.com/docs/blockchain?topic=blockchain-ibp-console-smart-contracts-v2).



The application and channel capabilities are relevant to peers, while the orderer and channel capabilities are relevant to the ordering service.

While all capabilities can be edited as part of a channel configuration update request, you have the opportunity to edit these capabilities in a few places:



* Channel and orderer: in the [system channel](https://cloud.ibm.com/docs/blockchain?topic=blockchain-ibp-console-governibp-console-govern-capabilities-system-channel) by ordering service admins.
* Application and orderer: during [channel creation](https://cloud.ibm.com/docs/blockchain?topic=blockchain-ibp-console-governibp-console-govern-capabilities-application-channels).



Note that what you see in this section will depend on the configuration of your channel and the Fabric level of your peers and ordering nodes, as only valid and possible capability upgrades will appear. For example, if your channel, orderer, and application capabilities are already at the highest level, you will only see the capability levels that have been selected. Similarly, you will not see potential capability updates if your nodes in the channel are not at a sufficient Fabric level to process the capability. Note that in orderers that use a system channel the default orderer capability shown is inherited from the system channel. Orderers that do not use a system channel will default to using v2_0 capabilities. While the default application capability is set to a reasonable level that will ensure that peers who attempt to join the channel will not crash. The application capability can always be updated later.
If you select a catalog image that belongs to a different account, review [Using cross-account image references in a private catalog in Terraform](https://cloud.ibm.com/docs/vpc?topic=vpc-planning-custom-images&interface=uiprivate-catalog-image-reference-vpc-terraform) for more considerations and limitations. To create a private catalog, see the tutorial [Onboarding a virtual server image with Terraform](https://cloud.ibm.com/docs/account?topic=account-catalog-vsi-tutorial&interface=ui).





 Gathering information to create an instance by using Terraform 

Ready to create an instance? Before you can run the ibm_is_instance command, you need to know the details about the instance, such as what profile or image that you want to use.

Gather the following information by using DataSource command.



1. Gather instance profile details. Run the following command for the profile that you select. See [x86 instance profiles](https://cloud.ibm.com/docs/vpc?topic=vpc-profiles&interface=uiprofiles) for a list of available profiles. For more information, see the Terraform documentation on [ibm_is_instance_profiles](https://registry.terraform.io/providers/IBM-Cloud/ibm/latest/docs/data-sources/is_instance_profiles). Use an instance profile by referring to the instance profile data source. For more information, see the Terraform documentation on [ibm_is_instance_profile](https://registry.terraform.io/providers/IBM-Cloud/ibm/latest/docs/data-sources/is_instance_profile).

data "ibm_is_instance_profile" "example_profile" {
name = "bx2-2x8"
}
2. List the available images for creating your instance. The command depends on what image that you want to use. You can use a stock image, a custom image from your account, or an image that was shared with your account from a private catalog.
If you are enabled as an Administrator or a Modeler in Planning Analytics with Watson, you can design dimensions, hierarchies, views, and attributes to define the business logic for your application.
* [Steps to building a model](https://www.ibm.com/docs/planning-analytics/2.0.0?topic=mipaw-steps-building-model)

You design a model in response to a business need. For example, a company wants to plan its expenses for the next 12 months by expense line. The company collects the information from a number of departments that are spread across a wide geographical area.
* [Dimensions](https://www.ibm.com/docs/planning-analytics/2.0.0?topic=mipaw-dimensions)

Dimensions are lists of related members. Two or more dimensions are used to make a cube that can be used for planning and analysis.
* [Cubes](https://www.ibm.com/docs/planning-analytics/2.0.0?topic=mipaw-cubes)

Planning Analytics with Watson stores the data that you need for planning and analysis in cubes.
* [Rules](https://www.ibm.com/docs/planning-analytics/2.0.0?topic=mipaw-rules)

You can create complex data calculations in Planning Analytics with Watson with business rules.
* [Configure drill-through to reveal detailed data](https://www.ibm.com/docs/planning-analytics/2.0.0?topic=mipaw-configure-drill-through-reveal-detailed-data)

You can associate a cube cell with extra data, which can provide underlying detail for the cell or other information relevant to the cell. When you complete the configuration that is required to establish this association, users can drill through to the additional data directly from an Exploration view.
* [Object security](https://www.ibm.com/docs/planning-analytics/2.0.0?topic=mipaw-object-security)
Why do I see the values of my feature flags or properties as default values (0 for Number type, false for Boolean type and empty string for String type) in certain environments? 

  What’s happening 

Default values are assigned to the feature flags or properties in certain environments.

  Why it’s happening 

A feature flag is always created under an environment.

Though the flag is created under an environment, it is made available to all environments with default values. Flag Values, tags, and segment values are relevant only to the environment where the update is executed.

  How to fix it 

Feature flag or Property values can be updated specifically to an environment.
Data privacy and governance 

IBM Cloud® Schematics provides powerful tools to automate your cloud infrastructure provisioning and management process, the configuration and operation of your cloud resources, and the deployment of your app workloads. IBM Cloud Schematics allows clients to locate data in multiple supported IBM Cloud® regions. By providing clients with data mobility to serve the local needs of clients, IBM, and Schematics take data privacy and governance seriously.

IBM Cloud data privacy processing processes and procedures are documented within the IBM Cloud DPA. This Data Processing Addendum (DPA) and its applicable DPA Exhibits apply to the processing of personal data by IBM Cloud on behalf of Client (Client Personal Data). The processing of personal data is subject to the General Data Protection Regulation 2016/679 (GDPR). It is also subject to any other data protection laws that are identified at [Data Protection Laws](https://www.ibm.com/support/customer/csol/terms/?id=DPA-DPL&lc=endetail-document). To provide services according to the agreement between Client and IBM Cloud. The IBM Cloud DPA can be found at [Data Processing Addendum](https://www.ibm.com/support/customer/csol/terms/?cat=dpa).

In addition to the DPA, the cloud services contain DPA exhibits that detail the types of data. It is processed by this service and the relevant processing locations (including hosting locations) where client PI is processed. The relevant DPA exhibit for Schematics can be found on the [IBM Cloud Terms site](https://www.ibm.com/support/customer/csol/terms/?cat=cloud-dpa-exhibits).

IBM Cloud relies on Standard Contractual Clauses (as the primary data transfer mechanism) in the client contracts. IBM Cloud also relies on numerous supplementary measures to help clients ensure an adequate level of protection when they transfer personal data outside of the EU/EEA. For more information, see EU Standard Contractual Clauses that are signed by all [IBM Cloud Data Importers](https://www.ibm.com/support/customer/csol/terms/?id=SW-EUSSC), if applicable.
You can increase the number IOPS available to your deployment by increasing disk space.





 Quorum Queues 

High-availability can be managed with [quorum queues](https://www.rabbitmq.com/quorum-queues.html). Using quorum queues impacts performance; it needs more memory and disk space for the WAL that it uses to maintain state for operations. It also needs more disk I/O as it persists all data on disk. If you have implemented quorum queues, or are considering them, the RabbitMQ documentation has a good write-up of their effect on both [resource use](https://www.rabbitmq.com/quorum-queues.htmlresource-use) and [performance](https://www.rabbitmq.com/quorum-queues.htmlperformance).





 RabbitMQ Alarm Monitoring 

When a disk or memory alarm is triggered, RabbitMQ emits a [connection.blocked notification](https://www.rabbitmq.com/connection-blocked.html) to publishing connections. Many drivers support the protocol necessary to catch the notification so you can design your application to respond to RabbitMQ alarms.

You can also monitor alarms from the [RabbitMQ HTTP API](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=messages-for-rabbitmq-management-pluginrabbitmq-management-http-api). Use the GET /api/nodes endpoints, and look for mem_alarm and disk_free_alarm in the response.

For more checks related to memory alarms, you can gather information that is related to a single node's memory by using the GET /api/nodes/{node}/memory endpoint.





 Standard Health Checks
Users cannot digress away from nodes that are configured to jump to another node. So, if you configure all of your final branch nodes to jump to a common closing node, digressions cannot occur. Consider tracking user satisfaction through metrics or some other means.
* Test possible digression chains: If a user digresses away from the current node to another node that allows digressions away, the user could potentially digress away from that other node, and repeat this pattern one or more times again. If the starting node in the digression chain is configured to return after the digression, then the user will eventually be brought back to the current dialog node. In fact, any subsequent nodes in the chain that are configured not to return are excluded from being considered as digression targets. Test scenarios that digress multiple times to determine whether individual nodes function as expected.
* Remember that the current node gets priority: Remember that nodes outside the current flow are only considered as digression targets if the current flow cannot address the user input. It is even more important in a node with slots that allows digressions away, in particular, to make it clear to users what information is needed from them, and to add confirmation statements that are displayed after the user provides a value.

Any slot can be filled during the slot-filling process. So, a slot might capture user input unexpectedly. For example, you might have a node with slots that collects the information necessary to make a dinner reservation. One of the slots collects date information. While providing the reservation details, the user might ask, What's the weather meant to be tomorrow? You might have a root node that conditions on #forecast which could answer the user. However, because the user's input includes the word tomorrow and the reservation node with slots is being processed, your assistant assumes the user is providing or updating the reservation date instead. The current node always gets priority. If you define a clear confirmation statement, such as, Ok, setting the reservation date to tomorrow, the user is more likely to realize there was a miscommunication and correct it.
See [Getting started with IBM Analytics Engine](https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-getting-started) to learn more about the service and to start using it. You will also find tutorials and code samples to get you off to a fast start.
* Which distribution is used in IBM Analytics Engine?

IBM Analytics Engine is based on open source Hortonworks Data Platform (HDP). To find the currently supported version see [Architecture and concepts on IBM Analytics Engine](https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-IAE-overview).
* Which HDP components are supported in IBM Analytics Engine?

To see the full list of supported components and versions, see the [Architecture and concepts of IBM Analytics Engine](https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-IAE-overview).
* What node sizes are available in IBM Analytics Engine?

To see the currently supported node sizes, see the [Architecture and concepts of IBM Analytics Engine](https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-IAE-overview).
* Why is there so little HDFS space on the clusters?

What if I want to run a cluster that has a lot of data to be processed at one time?

The clusters in IBM Analytics Engine are intended to be used as a compute clusters and not as persistent storage for data. Data should be persisted in [IBM Cloud Object Storage](https://www.ibm.com/cloud/object-storage). This provides a more flexible, reliable, and cost effective way to build analytics applications. See this whitepaer on [Splitting the load](https://www.ibm.com/downloads/cas/KDPB1REE) to learn more about this topic. The Hadoop Distributed File System (HDFS) should be used at most only for intermediate storage during processing. All final data (or even intermediate data) should be written to IBM Cloud Object Storage before the cluster is deleted. If your intermediate storage requirements exceed the HDFS space available on a node, you can add more nodes to the cluster.
The assistant recognizes the value, and uses it to fill the $time context variable for the second slot. It then shows the prompt for the next slot, How many people will be dining?
5. Type 6.

The assistant recognizes the value, and uses it to fill the $guests context variable for the third slot. Now that all of the slots are filled, it shows the node response, OK. I am making you a reservation for 6 on 2017-12-29 at 17:00:00.



![Shows a test dialog in the Try it out pane that successfully fills in the node slots.](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/slots-test-simple-node.png)

It worked! Congratulations. You have successfully created a node with slots.





 Summary 

In this tutorial you created a node with slots that can capture the information necessary to reserve a table at a restaurant.





 Next steps 

Improve the experience of users who interact with the node. Complete the follow-on tutorial, [Improving a node with slots](https://cloud.ibm.com/docs/assistant?topic=assistant-tutorial-slots-complex). It covers simple improvements, such as how to reformat the date (2017-12-28) and time (17:00:00) values that are returned by the system. It also covers more complex tasks, such as what to do if the user does not provide the type of value that your dialog expects for a slot.
The assistant recognizes the value, and uses it to fill the $time context variable for the second slot. It then shows the prompt for the next slot, How many people will be dining?
5. Type 6.

The assistant recognizes the value, and uses it to fill the $guests context variable for the third slot. Now that all of the slots are filled, it shows the node response, OK. I am making you a reservation for 6 on 2017-12-29 at 17:00:00.



![Shows a test dialog in the Try it out pane that successfully fills in the node slots.](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/slots-test-simple-node.png)

It worked! Congratulations. You have successfully created a node with slots.





 Summary 

In this tutorial you created a node with slots that can capture the information necessary to reserve a table at a restaurant.





 Next steps 

Improve the experience of users who interact with the node. Complete the follow-on tutorial, [Improving a node with slots](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-tutorial-slots-complex). It covers simple improvements, such as how to reformat the date (2017-12-28) and time (17:00:00) values that are returned by the system. It also covers more complex tasks, such as what to do if the user does not provide the type of value that your dialog expects for a slot.
Launch the web UI 

After you provision an instance of the IBM Cloud Monitoring service in the IBM Cloud, and configure a monitoring agent for your node, you can view, monitor, and manage data through the service's web UI.

You launch the web UI within the context of the IBM Cloud Monitoring instance, from the IBM Cloud UI.

Complete the following steps to launch the monitoring UI:



1. Log in to your IBM Cloud account.

Open the [IBM Cloud dashboard](https://cloud.ibm.com/login).

After you log in with your user ID and password, the IBM Cloud Dashboard opens.
2. In the navigation menu, select Observability.
3. Select Monitoring.

The list of available IBM Cloud Monitoring instances that are available on IBM Cloud is displayed.
4. Select one instance. Then, click Open dashboard.



The IBM Cloud Monitoring Web UI opens. By default, the Overview tab is displayed.

By default, users are automatically added as members of the Monitor Operations team that is predefined for each Monitoring instance. Users have full permissions to see all the data in the web UI.

An administrator can restrict access to data by managing users in teams and controlling what data is visible. For example, to restrict users viewing permissions, an administrator can create a default team with limited scope and visibility. Then, manually assign users to other teams. For more information, see [Working with teams](https://cloud.ibm.com/docs/monitoring?topic=monitoring-teamsteams).





 Step 5. Next steps 



* To monitor the usage and costs of your service, see [Viewing your usage](https://cloud.ibm.com/docs/billing-usage?topic=billing-usage-viewingusageviewingusage).
FAQs: Bandwidth graphs 



  What are public network bandwidth graphs? 

Public network bandwidth graphs show traffic to and from the internet. Inbound shows traffic from the internet that is coming into your server. Outbound shows traffic from your server that is going out to the internet.





  What are private network bandwidth graphs? 

Private network bandwidth graphs show traffic to and from the private network. Inbound shows traffic from the private network that is coming into your server. Outbound shows traffic from your server that is going out to the private network.





  What is the projected usage and why doesn't it match my graphs? 

The estimated usage data is not real-time data for your server. Usage is calculated and stored for the previous day to provide a quick way to monitor and display bandwidth usage. It can be slightly off from the monthly view graphs. The estimated usage data is rounded at daily intervals; whereas, the monthly graphs are rounded at a monthly interval. Additionally, the graph images are showing data usage up to the time when they were displayed, but the estimated totals are only up to midnight of the previous day.





  What is the difference between monthly and billing bandwidth data? 

When you purchase your first server or product, you are assigned an anniversary date. All products that are purchased after this date are pro-rated to your anniversary date. The monthly data is displayed off a calendar month; whereas, the billing data is displayed off your anniversary date. All bandwidth billing is calculated based on your anniversary date.
Local File System 

Crawl documents that are stored in a local file system.

IBM Cloud Pak for Data

IBM Cloud Pak for Data only

This information applies only to installed deployments.



 What documents are crawled 



* Only file types that are supported by Discovery in your file path are crawled; all others are ignored. For more information, see [Supported file types](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-collectionssupportedfiletypes).
* Only files in the /mnt directory or one of its subdirectories can be accessed by the crawler.
* Only files with file extensions that match the file extension filter rules that you specify are crawled. Added with the 4.7.0 release.
* When a source is recrawled, new documents are added, updated documents are modified to the current version, and deleted documents are deleted from the collection's index.
* All Discovery data source connectors are read-only. Regardless of the permissions that are granted to the crawl account, Discovery never writes, updates, or deletes any content in the original data source.







 Prerequisite steps 

Before you connect to the Local File System data source, complete the following step:



* [Create a persistent volume claim on the crawler pod](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-connector-lfs-cp4dmount-persistent-volume)



The service uses Portworx storage by default. However, if you are using Network File System (NFS) storage, see [Prerequisite steps for NFS storage](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-connector-lfs-cp4dconnector-lfs-cp4d-prereq-nfs) instead.



 Creating and mounting a persistent volume claim on the crawler pod 

Before you can crawl a local file system, you must create a persistent volume claim and mount it on the crawler pod. You also need to copy the files that you want to crawl to the Discovery cluster that you are working on.