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> The workspace name for this FQDN might be truncated. Truncation is done to keep `ml-<workspace-name, truncated>-<region>-<workspace-guid>` at 63 characters or less.
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> * Compute instances can be accessed only from within the virtual network.
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> * The IP address for this FQDN is **not** the IP of the compute instance. Instead, use the private IP address of the workspace private endpoint (the IP of the `*.api.azureml.ms` entries.)
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* `<instance-name>.<region>.instances.azureml.ms` - Only used by the `az ml compute connect-ssh` command to connect to computers in a managed virtual network. Not needed if you aren't using a managed network or SSH connections.
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* `<instance-name>-22.<region>.instances.azureml.ms` - Only used by the `az ml compute connect-ssh` command to connect to computers in a managed virtual network. Not needed if you aren't using a managed network or SSH connections.
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* `<managed online endpoint name>.<region>.inference.ml.azure.com` - Used by managed online endpoints
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* `models.ai.azure.com` - Used for deploying Models as a Service
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To find the private IP addresses for your A records, see the [Azure Machine Learning custom DNS](/azure/machine-learning/how-to-custom-dns#find-the-ip-addresses) article.
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To check AI-PROJECT-GUID, go to the Azure portal, select your project, settings, properties, and the workspace ID is displayed.
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> [!NOTE]
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> Project workspaces reuse the FQDNs of the associated hub workspaces. There is no reason to configure separate entries for the project workspace GUIDs.
Copy file name to clipboardExpand all lines: articles/ai-services/translator/text-translation/reference/v4/translate-api.md
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@@ -51,23 +51,18 @@ Request parameters passed with the request are as follows:
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|**`api-version`**|string|True|Version of the API requested by the client. Accepted value is `2025-05-01-preview`.|
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|**`text`**| string | True | Source text for translation. |
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|**`targets`**| array | True | User-specified values for the translated (`target`) text. |
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|**`language`**| string | True |The language code for the translated (`target`) text *specified in the `targets` array*. Accepted values are [supported language](../../../language-support.md) codes for the translation operation.|
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|**textType**| string | False | Defines whether the text being translated is plain text or HTML text. Any HTML needs to be a well-formed, complete element. Accepted values are: plain (default) or html. |
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|**language**| string | False | Specifies the language code for the `source` text. If not specified, the system autodetects the language of the source text. Accepted values are list of language code supported by the specified model. |
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|**script**| string | False | Specifies the script of the source text. |
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#### Targets
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| Parameter | Type | Required | Description |
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| --- | --- | --- | --- |
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|**`targets`**| array | True | User-specified values for the translated (`target`) text. |
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#### Targets array
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#### Targets array (user-specificed values for the translaated text)
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| Parameter | Type | Required? | Description |
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| --- | --- | --- | --- |
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|**targets.language**| string | True |The language code for the translated (`target`) text *specified in the `targets` array*. Accepted values are [supported language](../../../language-support.md) codes for the translation operation.|
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|**targets.script**| string | False | Specify the script of the translated text. |
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|**targets.deploymentName**| string | False | Default is `general`, which uses `NMT` system. `your-gpt-4omini-deployment-name` is an example deployment name for the GPT-4o-mini model. `gpt-4o` uses GPT-4o model.<br> `<custom model id>` uses the custom `NMT` model tuned by customer.<br> |
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|**targets.deploymentName**| string | False | Default is `general`, which uses `NMT` system. `your-model-name-gpt-4o-mini` is an example deployment name for the GPT-4o-mini model. `gpt-4o` uses GPT-4o model.<br> `<custom model id>` uses the custom `NMT` model tuned by customer.<br> |
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|**targets.allowFallback**| string | False | If the desired model does not support a particular pair of source and target languages, an alternative approach may be employed. In such cases, the service may default to utilizing a general system as a substitute. This action ensures that translations can still be provided even when the preferred model is unavailable. Default is `true`. If `false` system returns an error if language pair isn't supported. |
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|**targets.tone**| string | False | Desired tone of target translation. Accepted values are `formal`, `informal`, or `neutral`. |
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|**targets.gender** (For more information, *see*[Gender-specific translations](#gender-specific-translations))| string | False | Desired gender of target translation. Accepted values are `male`, `female`, or `neutral`.|
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Access to a given Azure Machine Learning workspace via Private Link is done by communicating with the following Fully Qualified Domains (called the workspace FQDNs):
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> [!IMPORTANT]
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> If you're using a hub workspace (including Azure AI Foundry hub), then you have other entries for each project workspace created from the hub.
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**Azure Public regions**:
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-```<per-workspace globally-unique identifier>.workspace.<region the workspace was created in>.api.azureml.ms```
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-```<managed online endpoint name>.<per-workspace globally-unique identifier>.inference.<region>.privatelink.api.ml.azure.us``` - Used by managed online endpoints
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The FQDNs resolve to the IP addresses of the Azure Machine Learning workspace in that region. However, resolution of the workspace Private Link FQDNs can be overridden by using a custom DNS server hosted in the virtual network. For an example of this architecture, see the [custom DNS server hosted in a virtual network](#example-custom-dns-server-hosted-in-virtual-network) example. For hub and project workspaces, the FQDNs of all project workspaces resolve to the IP address of the hub workspace.
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The FQDNs resolve to the IP addresses of the Azure Machine Learning workspace in that region. However, resolution of the workspace Private Link FQDNs can be overridden by using a custom DNS server hosted in the virtual network. For an example of this architecture, see the [custom DNS server hosted in a virtual network](#example-custom-dns-server-hosted-in-virtual-network) example. For hub and project workspaces, project workspaces inherit the FQDNs from the hub workspace.
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*`<instance-name>-22.<region>.instances.azureml.ms` - Only used by the `az ml compute connect-ssh` command to connect to computes in a private virtual network. Not needed if you aren't using a managed network or SSH connections.
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*`<managed online endpoint name>.<region>.inference.ml.azure.com` - Used by managed online endpoints
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> [!TIP]
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> If you're using hub and project workspaces, each project workspace has its own set of FQDNs. For more information, see the [workspace DNS resolution](#workspace-dns-resolution-path) section.
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#### Microsoft Azure operated by 21Vianet region
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The following FQDNs are for Microsoft Azure operated by 21Vianet regions:
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*`<managed online endpoint name>.<region>.inference.ml.azure.cn` - Used by managed online endpoints
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*`models.ai.azure.com` - Used for deploying Models as a Service
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> [!TIP]
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> If you're using hub and project workspaces, each project workspace has its own set of FQDNs. For more information, see the [workspace DNS resolution](#workspace-dns-resolution-path) section.
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#### Azure US Government
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The following FQDNs are for Azure US Government regions:
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*`<managed online endpoint name>.<region>.inference.ml.azure.us` - Used by managed online endpoints
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> [!TIP]
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> If you're using hub and project workspaces, each project workspace has its own set of FQDNs. For more information, see the [workspace DNS resolution](#workspace-dns-resolution-path) section.
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### Find the IP addresses
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To find the internal IP addresses for the FQDNs in the virtual network, use one of the following methods:
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}
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]
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```
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1. If you're using a hub workspace, use the following steps _for each_ project workspace that was created from the hub:
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1. To get the project workspace ID, use the following command:
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```azurecli
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az ml workspace show --name <project-workspace-name> --resource-group <resource-group> --query 'discovery_url'
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```
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The value returned follows the format `https://<project-workspace-id>.workspace.<region>.api.azureml.ms/mlflow/<version>/subscriptions/<subscription-id>/resourceGroups/<resource-group>/providers/Microsoft.MachineLearningServices/workspaces/<project-workspace-name>`.
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1. Take the FQDNs returned from the hub workspace that end in `workspace.<region>.api.azureml.ms` and `workspace.<region>.cert.api.azureml.ms`. Replace the GUID value at the beginning of these FQDNs with the project workspace ID. These FQDNs are in addition to the hub workspace FQDNs.
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1. Take the FQDN returned from the hub workspace that follows the format in `<workspace-name>-<region>-<GUID>.<region>.notebooks.azure.net`. Replace the GUID value with the project workspace ID. Replace the hub workspace name with the project workspace name. You might need to truncate the workspace name to keep this entry at 63 characters or less. This FQDN is in addition to the hub workspace FQDN.
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