azure.azcollection.azure_rm_ml_batch_endpoint_invoke module – Invoke ML Batch Endpoint resources
Note
This module is part of the azure.azcollection collection (version 3.18.0).
You might already have this collection installed if you are using the ansible package. It is not included in ansible-core. To check whether it is installed, run ansible-galaxy collection list.
To install it, use: ansible-galaxy collection install azure.azcollection. You need further requirements to be able to use this module, see Requirements for details.
To use it in a playbook, specify: azure.azcollection.azure_rm_ml_batch_endpoint_invoke.
New in azure.azcollection 3.18.0
Synopsis
- Invoke ML Batch Endpoint resources.
Requirements
The below requirements are needed on the host that executes this module.
- python >= 2.7
- The host that executes this module must have the azure.azcollection collection installed via galaxy
- All python packages listed in collection’s requirements.txt must be installed via pip on the host that executes modules from azure.azcollection
- Full installation instructions may be found https://galaxy.ansible.com/azure/azcollection
Parameters
Parameter | Comments |
|---|---|
ad_user string | Active Directory username. Use when authenticating with an Active Directory user rather than service principal. |
adfs_authority_url string added in azure.azcollection 0.0.1 | Azure AD authority url. Use when authenticating with Username/password, and has your own ADFS authority. |
api_profile string added in azure.azcollection 0.0.1 | Selects an API profile to use when communicating with Azure services. Default value of Default: |
auth_source string added in azure.azcollection 0.0.1 | Controls the source of the credentials to use for authentication. Can also be set via the When set to When set to When set to When set to When set to The Choices:
|
cert_validation_mode string added in azure.azcollection 0.0.1 | Controls the certificate validation behavior for Azure endpoints. By default, all modules will validate the server certificate, but when an HTTPS proxy is in use, or against Azure Stack, it may be necessary to disable this behavior by passing Choices:
|
client_id string | Azure client ID. Use when authenticating with a Service Principal or Managed Identity (msi). Can also be set via the |
cloud_environment string added in azure.azcollection 0.0.1 | For cloud environments other than the US public cloud, the environment name (as defined by Azure Python SDK, eg, Default: |
deployment_name string | Name of the deployment to target. |
disable_instance_discovery boolean added in azure.azcollection 2.3.0 | Determines whether or not instance discovery is performed when attempting to authenticate. Setting this to true will completely disable both instance discovery and authority validation. This functionality is intended for use in scenarios where the metadata endpoint cannot be reached such as in private clouds or Azure Stack. The process of instance discovery entails retrieving authority metadata from https://login.microsoft.com/ to validate the authority. By setting this to **True**, the validation of the authority is disabled. As a result, it is crucial to ensure that the configured authority host is valid and trustworthy. Set via credential file profile or the Choices:
|
experiment_name string | Name of the experiment for pipeline component deployment. |
input string | Reference to input data to use for batch inferencing. It can be a path on the datastore, public URI, a registered data asset, or a local folder path. |
input_type string | Type of the input, specifying whether it’s a file or a folder. Use this when you are using a path on datastore or public URI. Choices:
|
inputs dictionary | Dictionary of Inputs of invoke jobs. |
instance_count integer | Number of instances the prediction will run on. |
invocation_definition string | Batch Endpoint Invocations definition in YAML. |
job_name string | Name of the job for batch invoke. |
log_mode string | Parent argument. |
log_path string | Parent argument. |
mini_batch_size integer | Size of each mini batch that the input data will be split into for prediction. |
ml_workspace string / required | Name of the Azure ML workspace. |
name string | Name of the Azure ML batch endpoint. |
oidc_token string added in azure.azcollection 3.17.0 | Direct OpenID Connect (OIDC) token (JWT) used for workload identity authentication. This token is exchanged with Azure AD using OIDC federated identity. Useful in CI systems (for example GitHub Actions, Azure DevOps) where an OIDC token is provided directly by the platform. Can also be set via the |
oidc_token_file_path path added in azure.azcollection 3.17.0 | Path to a file containing an OpenID Connect (OIDC) token (JWT) used for workload identity authentication. The file must contain only raw token. This method is commonly used in platforms that materialize OIDC tokens as files Can also be set via the |
output_path string | Path on the datastore where output files will be uploaded to. |
outputs dictionary | Dictionary to specify where to store the results. |
password string | Active Directory user password. Use when authenticating with an Active Directory user rather than service principal. |
profile string | Security profile found in ~/.azure/credentials file. |
resource_group string / required | Name of resource group. |
secret string | Azure client secret. Use when authenticating with a Service Principal. |
subscription_id string | Your Azure subscription Id. |
tenant string | Azure tenant ID. Use when authenticating with a Service Principal. |
thumbprint string added in azure.azcollection 1.14.0 | The thumbprint of the private key specified in x509_certificate_path. Use when authenticating with a Service Principal. Required if x509_certificate_path is defined. |
x509_certificate_path path added in azure.azcollection 1.14.0 | Path to the X509 certificate used to create the service principal in PEM format. The certificate must be appended to the private key. Use when authenticating with a Service Principal. |
Notes
Note
- For authentication with Azure you can pass parameters, set environment variables, use a profile stored in ~/.azure/credentials, or log in before you run your tasks or playbook with
az login. - Authentication is also possible using a service principal or Active Directory user.
- To authenticate via OIDC, pass client_id, tenant and oidc_token or oidc_token_file_path, or set environment variables AZURE_CLIENT_ID, AZURE_TENANT and AZURE_FEDERATED_TOKEN or AZURE_FEDERATED_TOKEN_FILE.
- To authenticate via service principal, pass subscription_id, client_id, secret and tenant or set environment variables AZURE_SUBSCRIPTION_ID, AZURE_CLIENT_ID, AZURE_SECRET and AZURE_TENANT.
- To authenticate via Active Directory user, pass ad_user and password, or set AZURE_AD_USER and AZURE_PASSWORD in the environment.
- Alternatively, credentials can be stored in ~/.azure/credentials. This is an ini file containing a [default] section and the following keys: subscription_id, client_id, secret and tenant or subscription_id, ad_user and password. It is also possible to add additional profiles. Specify the profile by passing profile or setting AZURE_PROFILE in the environment.
See Also
See also
- Sign in with Azure CLI
-
How to authenticate using the
az logincommand.
Examples
- name: Invoke ML Batch Endpoint
azure.azcollection.azure_rm_ml_batch_endpoint_invoke:
resource_group: myResourceGroup
ml_workspace: myMLWorkspace
Return Values
Common return values are documented here, the following are the fields unique to this module:
Key | Description |
|---|---|
changed boolean | Will return True if successfully invoked Returned: always Sample: |
ml_batch_endpoint_invoke dictionary | Batch Endpoints that match the query. Returned: always Sample: |
Collection links
© 2012–2018 Michael DeHaan
© 2018–2025 Red Hat, Inc.
Licensed under the GNU General Public License version 3.
https://docs.ansible.com/projects/ansible/latest/collections/azure/azcollection/azure_rm_ml_batch_endpoint_invoke_module.html
