![]() ![]() A better approach would be to group some of the requests and call the real-time endpoint in batch inference mode, which processes your requests in one forward pass of the model and returns the bulk response for the request in real time or near-real time. For example, if you need to process a continuous stream of data with low latency and high throughput, invoking a real-time endpoint for each request separately would require more resources and can take longer to process all the requests because the processing is being done serially. In some use cases, real-time inference requests can be grouped in small batches for batch processing to create real-time or near-real-time responses. Batch transform is cost-effective because unlike real-time hosted endpoints that have persistent hardware, batch transform clusters are torn down when the job is complete and therefore the hardware is only used for the duration of the batch job. For batch transform, a batch job is run that takes batch input as a dataset and a pre-trained model, and outputs predictions for each data point in the dataset. ![]() Batch transforms are useful in situations where the responses don’t need to be real time and therefore you can do inference in batch for large datasets in bulk. ![]() Today we are excited to announce that you can now perform batch transforms with Amazon SageMaker JumpStart large language models (LLMs) for Text2Text Generation. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |