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Matthew Deng

Amog Kamsetty

Richard Liaw

Richard Liaw headshot

Will Drevo

Distributed deep learning with Ray Train is now in Beta

Christy Bergman

Displaying [New York City Yellow Taxi](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page) ride volumes, with 1 week hourly forecast.  Blue=observed, Orange=predicted, per validation dataset.  Forecast generated using Google’s Temporal Fusion Transformer algorithm implemented by Pytorch forecasting, and parallelized by Ray for faster runtime, either on a laptop or on any cloud.  Image by Author.  

forecasting blog 2 of 2 image 1

Time Series Forecasting using an LSTM version of RNN with PyTorch Forecasting and Torch Lightning

Ray Lightning

Introducing Ray Lightning: Multi-node PyTorch Lightning training made easy

Kai Fricke

XGBoost-Ray

Introducing Distributed XGBoost Training with Ray

Retrieval Augmented Generation with Huggingface Transformers and Ray

Archit Kulkarni

Ray x mlflow

Ray & MLflow: Taking Distributed Machine Learning Applications to Production

Jack Parker-Holder

Pb2: Solo Gif

Population Based Bandits: Provably Efficient Online Hyperparameter Optimization

This is part 2 of our generative AI blog series. Here, we cover how Ray empowers large language models (LLM) frameworks such as Alpa. 

Figure 5: Technical integration layered stack for LLM


Figure-5

High performance LLM training at 1000 GPU scale with Alpa and Ray

Training 175B Parameter Language Models at 1000 GPU scale with Alpa and Ray

This is part 1 of our generative AI blog series. In this blog, we talk about how to use Ray to deploy common generative model workloads in production. An upcoming blog will deep dive into why projects like Alpa are using Ray to scale large models.

layered-stack

Ray solves Generative AI and LLM Infrastructure Challenges

How Ray solves common production challenges for generative AI infrastructure

Exoshuffle is now the most cost-efficient way to sort 100TB of data on the public cloud, using only $0.97 per terabyte. Learn how this is possible with Ray.

CloudSortBenchmark 01

Ray Is the World’s Most Cost-Efficient Sorting System at $1/TB

Ray breaks the $1/TB barrier as the world’s most cost-efficient sorting system

More companies are needing to train & deploy many small ML models, often hundreds or thousands. Learn how Ray and Anyscale make that possible.

Many_model_image

Training 1 Million ML Models in Record Time | Anyscale

Training One Million Machine Learning Models in Record Time with Ray

This section is used to order the "Types" and "Tags" that show up for filters on the Blog Index

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Products / Libraries

From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

Blog

Scalable AI and Python

Posts by Amog Kamsetty

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