Remove 2022 Remove Clustering Remove Deep Learning
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Google Research, 2022 & beyond: Algorithmic advances

Google Research AI blog

In 2022, we continued this journey, and advanced the state-of-the-art in several related areas. We continued our efforts in developing new algorithms for handling large datasets in various areas, including unsupervised and semi-supervised learning , graph-based learning , clustering , and large-scale optimization.

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Biggest Trends in Data Visualization Taking Shape in 2022

Smart Data Collective

Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields). The post Biggest Trends in Data Visualization Taking Shape in 2022 appeared first on SmartData Collective. In forecasting future events.

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Meta’s open AI hardware vision

Hacker News

Over the course of 2023, we rapidly scaled up our training clusters from 1K, 2K, 4K, to eventually 16K GPUs to support our AI workloads. Today, we’re training our models on two 24K-GPU clusters. We don’t expect this upward trajectory for AI clusters to slow down any time soon. Building AI clusters requires more than just GPUs.

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Scaling Large Language Model (LLM) training with Amazon EC2 Trn1 UltraClusters

Flipboard

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. In October 2022, we launched Amazon EC2 Trn1 Instances , powered by AWS Trainium , which is the second generation machine learning accelerator designed by AWS. We use Slurm as the cluster management and job scheduling system.

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Reduce energy consumption of your machine learning workloads by up to 90% with AWS purpose-built accelerators

Flipboard

For reference, GPT-3, an earlier generation LLM has 175 billion parameters and requires months of non-stop training on a cluster of thousands of accelerated processors. The Carbontracker study estimates that training GPT-3 from scratch may emit up to 85 metric tons of CO2 equivalent, using clusters of specialized hardware accelerators.

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“Looking beyond GPUs for DNN Scheduling on Multi-Tenant Clusters” paper summary

Mlearning.ai

Introduction Training deep learning models is a heavy task from computation and memory requirement perspective. Enterprises, research and development teams shared GPU clusters for this purpose. on the clusters to get the jobs and allocate GPUs, CPUs, and system memory to the submitted tasks by different users.

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Top NLP Skills, Frameworks, Platforms, and Languages for 2023

ODSC - Open Data Science

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others.