# Input data
<a name="sms-data-input"></a>

The input data are the data objects that you send to your workforce to be labeled. There are two ways to send data objects to Ground Truth for labeling: 
+ Send a list of data objects that require labeling using an input manifest file.
+ Send individual data objects in real time to a perpetually running, streaming labeling job. 

If you have a dataset that needs to be labeled one time, and you do not require an ongoing labeling job, create a standard labeling job using an input manifest file. 

If you want to regularly send new data objects to your labeling job after it has started, create a streaming labeling job. When you create a streaming labeling job, you can optionally use an input manifest file to specify a group of data that you want labeled immediately when the job starts. You can continuously send new data objects to a streaming labeling job as long as it is active. 

**Note**  
Streaming labeling jobs are only supported through the SageMaker API. You cannot create a streaming labeling job using the SageMaker AI console.

The following task types have special input data requirements and options:
+ For [3D point cloud](https://docs.amazonaws.cn/sagemaker/latest/dg/sms-point-cloud.html) labeling job input data requirements, see [3D Point Cloud Input Data](sms-point-cloud-input-data.md). 
+ For [video frame](https://docs.amazonaws.cn/sagemaker/latest/dg/sms-video-task-types.html) labeling job input data requirements, see [Video Frame Input Data](sms-video-frame-input-data-overview.md).

**Topics**
+ [Input manifest files](sms-input-data-input-manifest.md)
+ [Automate data setup for labeling jobs](sms-console-create-manifest-file.md)
+ [Supported data formats](sms-supported-data-formats.md)
+ [Ground Truth streaming labeling jobs](sms-streaming-labeling-job.md)
+ [Input Data Quotas](input-data-limits.md)
+ [Select Data for Labeling](sms-data-filtering.md)