Auto-complete, Inference Engine and Natural Language Processing

Host ML models on Amazon SageMaker using Triton: TensorRT models

AWS Machine Learning Blog

MAY 8, 2023

With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Additionally, TensorRT employs CUDA streams to enable parallel processing of models, further improving GPU utilization and performance. Note that the cell takes around 30 minutes to complete. !docker

ML BERT Deep Learning Auto-complete

Build a personalized avatar with generative AI using Amazon SageMaker

AWS Machine Learning Blog

AUGUST 2, 2023

It also provides a built-in queuing mechanism for queuing up requests, and a task completion notification mechanism via Amazon SNS, in addition to other native features of SageMaker hosting such as auto scaling. To host the asynchronous endpoint, we must complete several steps. amazonaws.com/djl-inference:0.21.0-deepspeed0.8.3-cu117"

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Host ML models on Amazon SageMaker using Triton: TensorRT models

Build a personalized avatar with generative AI using Amazon SageMaker

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