Mlearning.ai

JULY 9, 2023

The following libraries are included in the file: requirements.txt datasets transformers accelerate einops safetensors The complete example can be viewed at: Falcon 7B HuggingFace Spaces. LLM from a CPU-Optimized (GGML) format: LLaMA.cpp is a C++ library that provides a high-performance inference engine for large language models (LLMs).

Large Language Models 52

Large Language Models LLM Python Auto-complete 52

AWS Machine Learning Blog

APRIL 20, 2023

With advancements in hardware design, a wide range of CPU- and GPU-based infrastructures are available to help you speed up inference performance. Analyze the default and advanced Inference Recommender job results, which include ML instance type recommendation latency, performance, and cost metrics. sm_client = boto3.client("sagemaker",

ML 83

ML Auto-classification Python Auto-complete 83

AWS Machine Learning Blog

MAY 8, 2023

With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Overall, TensorRT’s combination of techniques results in faster inference and lower latency compared to other inference engines. Note that the cell takes around 30 minutes to complete. !docker

ML 87

ML BERT Deep Learning Auto-complete 87

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"

Generative AI 71

Generative AI Computer Vision Auto-complete Inference Engine 71