Merge pull request #279983 from MicrosoftDocs/main

7/3 11:00 AM IST Publish

Author: PhilKang0704

articles/ai-studio/how-to/deploy-models-timegen-1.md

@@ -47,6 +47,42 @@ You can deploy TimeGEN-1 as a serverless API with pay-as-you-go billing. Nixtla
 - An [Azure AI Studio project](../how-to/create-projects.md).
 - Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, visit [Role-based access control in Azure AI Studio](../concepts/rbac-ai-studio.md).
 
+### Pricing information
+
+#### Estimate the number of tokens needed
+
+Before you create a deployment, it's useful to estimate the number of tokens that you plan to use and be billed for.
+One token corresponds to one data point in your input dataset or output dataset.
+
+Suppose you have the following input time series dataset:
+
+| Unique_id | Timestamp           | Target Variable | Exogenous Variable 1 | Exogenous Variable 2 |
+|:---------:|:-------------------:|:---------------:|:--------------------:|:--------------------:|
+| BE        | 2016-10-22 00:00:00 | 70.00           | 49593.0              | 57253.0              |
+| BE        | 2016-10-22 01:00:00 | 37.10           | 46073.0              | 51887.0              |
+
+To determine the number of tokens, multiply the number of rows (in this example, two) and the number of columns used for forecasting—not counting the unique_id and timestamp columns (in this example, three) to get a total of six tokens.
+
+Given the following output dataset:
+
+| Unique_id | Timestamp           | Forecasted Target Variable |
+|:---------:|:-------------------:|:--------------------------:|
+| BE        | 2016-10-22 02:00:00 | 46.57                      |
+| BE        | 2016-10-22 03:00:00 | 48.57                      |
+
+You can also determine the number of tokens by counting the number of data points returned after data forecasting. In this example, the number of tokens is two.
+
+#### Estimate the pricing
+
+There are four pricing meters, as described in the following table:
+
+| Pricing Meter                                 | Description                                                                             |
+|-----------------------------------------------|-----------------------------------------------------------------------------------------|
+| paygo-inference-input-tokens                  | Costs associated with the tokens used as input for inference when *finetune_steps* = 0  |
+| paygo-inference-output-tokens                 | Costs associated with the tokens used as output for inference when *finetune_steps* = 0 |
+| paygo-finetuned-model-inference-input-tokens  | Costs associated with the tokens used as input for inference when *finetune_steps* > 0  |
+| paygo-finetuned-model-inference-output-tokens | Costs associated with the tokens used as output for inference when *finetune_steps* > 0 |
+
 ### Create a new deployment
 
 These steps demonstrate the deployment of TimeGEN-1. To create a deployment:

articles/aks/create-node-pools.md

@@ -31,7 +31,7 @@ This article shows you how to create one or more node pools in an AKS cluster.
 The following limitations apply when you create AKS clusters that support multiple node pools:
 
 * See [Quotas, virtual machine size restrictions, and region availability in Azure Kubernetes Service (AKS)](quotas-skus-regions.md).
-* You can delete system node pools if you have another system node pool to take its place in the AKS cluster. Otherwise, you cannot delete the system node pool.
+* You can delete the system node pool if you have another system node pool to take its place in the AKS cluster. Otherwise, you cannot delete the system node pool.
 * System pools must contain at least one node, and user node pools may contain zero or more nodes.
 * The AKS cluster must use the Standard SKU load balancer to use multiple node pools. This feature isn't supported with Basic SKU load balancers.
 * The AKS cluster must use Virtual Machine Scale Sets for the nodes.
@@ -60,7 +60,7 @@ The following limitations apply when you create AKS clusters that support multip
         --name $CLUSTER_NAME \
         --vm-set-type VirtualMachineScaleSets \
         --node-count 2 \
-        --generate-ssh-keys \
+        --location $LOCATION \
         --load-balancer-sku standard \
         --generate-ssh-keys
     ```

articles/app-service/manage-custom-dns-buy-domain.md

@@ -71,7 +71,7 @@ For pricing information on App Service domains, visit the [App Service Pricing p
    | Setting  | Description |
    | -------- | ----------- |
    | **Auto renewal** | Your App Service domain is registered to you at one-year increments. Enable auto renewal so that your domain registration doesn't expire and that you retain ownership of the domain. Your Azure subscription is automatically charged the yearly domain registration fee at the time of renewal. If you leave it disabled, you must [renew it manually](#renew-the-domain). |
-   | **Privacy protection** | Enabled by default. Privacy protection hides your domain registration contact information from the WHOIS database. Privacy protection is already included in the yearly domain registration fee. To opt out, select **Disable**. |
+   | **Privacy protection** | Enabled by default. Privacy protection hides your domain registration contact information from the WHOIS database and is already included in the yearly domain registration fee. To opt out, select **Disable**. Privacy protection is not supported in following top-level domains (TLDs): co.uk, in, org.uk, co.in, and nl. |
 
 1. Select **Next: Tags** and set the tags you want for your App Service domain. Tagging isn't required for using App Service domains, but is a [feature in Azure that helps you manage your resources](../azure-resource-manager/management/tag-resources.md).
 

articles/azure-cache-for-redis/cache-best-practices-performance.md

@@ -5,7 +5,7 @@ description: Learn how to test the performance of Azure Cache for Redis.
 author: flang-msft
 ms.service: cache
 ms.topic: conceptual
-ms.date: 06/19/2023
+ms.date: 07/01/2024
 ms.author: franlanglois
 ---
 
@@ -17,7 +17,7 @@ Fortunately, several tools exist to make benchmarking Redis easier. Two of the m
 
 ## How to use the redis-benchmark utility
 
-1. Install open source Redis server to a client VM you can use for testing. The redis-benchmark utility is built into the open source Redis distribution. Follow the [Redis documentation](https://redis.io/docs/latest/operate/oss_and_stack/install/install-redis/) for instructions on how to install the open source image.
+1. Install open source Redis server to a client virtual machines (VMs) you can use for testing. The redis-benchmark utility is built into the open source Redis distribution. Follow the [Redis documentation](https://redis.io/docs/latest/operate/oss_and_stack/install/install-redis/) for instructions on how to install the open source image.
 
 1. The client VM used for testing should be _in the same region_ as your Azure Cache for Redis instance.
 
@@ -49,6 +49,10 @@ Fortunately, several tools exist to make benchmarking Redis easier. Two of the m
 
 - On the Premium tier, scaling out, clustering, is typically recommended before scaling up. Clustering allows Redis server to use more vCPUs by sharding data. Throughput should increase roughly linearly when adding shards in this case.
 
+- On _C0_ and _C1_ Standard caches, while internal Defender scanning is running on the VMs, you might see short spikes in server load not caused by an increase in cache requests. You see higher latency for requests while internal Defender scans are run on these tiers a couple of times a day. Caches on the _C0_ and _C1_ tiers only have a single core to multitask, dividing the work of serving internal Defender scanning and Redis requests. You can reduce the effect by scaling to a higher tier offering with multiple CPU cores, such as _C2_.
+
+  The increased cache size on the higher tiers helps address any latency concerns. Also, at the _C2_ level, you have support for as many as 2,000 client connections.
+
 ## Redis-benchmark examples
 
 **Pre-test setup**:
@@ -88,7 +92,7 @@ redis-benchmark -h yourcache.region.redisenterprise.cache.azure.net -p 10000 -a
 
 ## Example performance benchmark data
 
-The following tables show the maximum throughput values that were observed while testing various sizes of Standard, Premium, Enterprise, and Enterprise Flash caches. We used `redis-benchmark` from an IaaS Azure VM against the Azure Cache for Redis endpoint. The throughput numbers are only for GET commands. Typically, SET commands have a lower throughput. These numbers are optimized for throughput. Real-world throughput under acceptable latency conditions may be lower.
+The following tables show the maximum throughput values that were observed while testing various sizes of Standard, Premium, Enterprise, and Enterprise Flash caches. We used `redis-benchmark` from an IaaS Azure VM against the Azure Cache for Redis endpoint. The throughput numbers are only for GET commands. Typically, SET commands have a lower throughput. These numbers are optimized for throughput. Real-world throughput under acceptable latency conditions might be lower.
 
 The following configuration was used to benchmark throughput for the Basic, Standard, and Premium tiers:
 
@@ -144,7 +148,7 @@ redis-benchmark -h yourcache.region.redisenterprise.cache.azure.net -p 10000 -a
 
 #### Enterprise Cluster Policy
 
-| Instance | Size | vCPUs | Expected network bandwidth (Mbps)| GET requests per second without SSL (1-kB value size) | GET requests per second with SSL (1-kB value size) |
+| Instance | Size | vCPUs | Expected network bandwidth (Mbps)| `GET` requests per second without SSL (1-kB value size) | `GET` requests per second with SSL (1-kB value size) |
 |:---:| --- | ---:|---:| ---:| ---:|
 | E10 |  12 GB |  4 | 4,000 | 300,000 | 207,000 |
 | E20 |  25 GB |  4 | 4,000 | 680,000 | 480,000 |
@@ -156,7 +160,7 @@ redis-benchmark -h yourcache.region.redisenterprise.cache.azure.net -p 10000 -a
 
 #### OSS Cluster Policy
 
-| Instance | Size | vCPUs | Expected network bandwidth (Mbps)| GET requests per second without SSL (1-kB value size) | GET requests per second with SSL (1-kB value size) |
+| Instance | Size | vCPUs | Expected network bandwidth (Mbps)| `GET` requests per second without SSL (1-kB value size) | `GET` requests per second with SSL (1-kB value size) |
 |:---:| --- | ---:|---:| ---:| ---:|
 | E10 |  12 GB |  4 | 4,000 | 1,400,000 | 1,000,000 |
 | E20 |  25 GB |  4 | 4,000 | 1,200,000 | 900,000 |
@@ -170,7 +174,7 @@ redis-benchmark -h yourcache.region.redisenterprise.cache.azure.net -p 10000 -a
 
 In addition to scaling up by moving to larger cache size, you can boost performance by [scaling out](cache-how-to-scale.md#how-to-scale-up-and-out---enterprise-and-enterprise-flash-tiers). In the Enterprise tiers, scaling out is called increasing the _capacity_ of the cache instance. A cache instance by default has capacity of two--meaning a primary and replica node. An Enterprise cache instance with a capacity of four indicates that the instance was scaled out by a factor of two. Scaling out provides access to more memory and vCPUs. Details on how many vCPUs are used by the core Redis process at each cache size and capacity can be found at the [Enterprise tiers best practices page](cache-best-practices-enterprise-tiers.md#sharding-and-cpu-utilization). Scaling out is most effective when using the OSS cluster policy.
 
-The following tables show the GET requests per second at different capacities, using SSL and a 1-kB value size.
+The following tables show the `GET` requests per second at different capacities, using SSL and a 1-kB value size.
 
 #### Scaling out - Enterprise cluster policy