AWS Machine Learning Blog

MAY 8, 2023

TensorRT is an SDK developed by NVIDIA that provides a high-performance deep learning inference library. It’s optimized for NVIDIA GPUs and provides a way to accelerate deep learning inference in production environments. Triton Inference Server supports ONNX as a model format.

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Unite.AI

SEPTEMBER 13, 2024

Kernel Auto-tuning : TensorRT automatically selects the best kernel for each operation, optimizing inference for a given GPU. These techniques allow TensorRT-LLM to optimize inference performance for deep learning tasks such as natural language processing, recommendation engines, and real-time video analytics.

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Large Language Models LLM Auto-complete Natural Language Processing 130

Towards AI

JULY 21, 2023

Deep learning and semantic parsing, do we still care about information extraction? This… github.com Kite Auto Complete For all the Jupyter notebook fans, Kite code autocomplete is now supported! GPT-3 hype is cool but needs fine-tuning to be anywhere near production-ready. Where are those graphs?

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NLP Auto-complete Natural Language Processing BERT 82

Mlearning.ai

JUNE 22, 2023

Understanding the biggest neural network in Deep Learning Join 34K+ People and get the most important ideas in AI and Machine Learning delivered to your inbox for free here Deep learning with transformers has revolutionized the field of machine learning, offering various models with distinct features and capabilities.

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Marktechpost

JULY 11, 2023

The best example is OpenAI’s ChatGPT, the well-known chatbot that does everything from content generation and code completion to question answering, just like a human. Even OpenAI’s DALL-E and Google’s BERT have contributed to making significant advances in recent times. What is AutoGPT? What is BabyAGI?

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AWS Machine Learning Blog

OCTOBER 23, 2024

It is a family of embedding models with a BERT-like architecture, designed to produce high-quality embeddings from text data. Optionally, set up auto scaling for the endpoint to automatically adjust the number of instances based on the incoming request traffic. The BGE models come in three sizes: bge-large-en-v1.5:

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DataRobot Blog

FEBRUARY 2, 2023

The creation of foundation models is one of the key developments in the field of large language models that is creating a lot of excitement and interest amongst data scientists and machine learning engineers. These models are trained on massive amounts of text data using deep learning algorithms. pip install transformers==4.25.1

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BERT Large Language Models Natural Language Processing Machine Learning 52

AWS Machine Learning Blog

APRIL 17, 2023

However, as the size and complexity of the deep learning models that power generative AI continue to grow, deployment can be a challenging task. Then, we highlight how Amazon SageMaker large model inference deep learning containers (LMI DLCs) can help with optimization and deployment.

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Prompt Engineer Prompt Engineering Deep Learning Large Language Models 98

Viso.ai

DECEMBER 22, 2023

This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. These models revolutionized how machines understand and generate human language by learning from vast data, allowing them to generalize across various tasks.

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Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

Google Research AI blog

FEBRUARY 28, 2023

We also help make global conferences accessible to more researchers around the world, for example, by funding 24 students this year to attend Deep Learning Indaba in Tunisia. Dataset Description Auto-Arborist A multiview urban tree classification dataset that consists of ~2.6M

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AWS Machine Learning Blog

MARCH 6, 2023

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. This results in faster restarts and workload completion. The following GitHub repos offer examples with PyTorch and TensorFlow.

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Large Language Models LLM Machine Learning ML 100

Jay Alammar

DECEMBER 16, 2020

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. In generating the second token to complete the date, the name still is the most important with 60% importance, followed by the first portion of the date -- a model output, but an input to the second time step.

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AWS Machine Learning Blog

JANUARY 9, 2023

The models can be completely heterogenous, with their own independent serving stack. After these business logic steps are complete, the inputs are passed through to ML models. And during idle time, it should be able to turn off compute capacity completely so that you’re not charged. ML inference options.

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ML Auto-complete Auto-classification Deep Learning 87

AWS Machine Learning Blog

FEBRUARY 24, 2023

In addition, load testing can help guide the auto scaling strategies using the right metrics rather than iterative trial and error methods. Solution overview For an introduction to MMEs and MMEs with GPU, refer to Create a Multi-Model Endpoint and Run multiple deep learning models on GPU with Amazon SageMaker multi-model endpoints.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Utilizing the latest Hugging Face LLM modules on Amazon SageMaker, AWS customers can now tap into the power of SageMaker deep learning containers (DLCs). Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring. This model achieves a 91.3%

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