Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Models to Unique Applications |
Kourosh Hakhamaneshi, Rehaan Ahmad |
Aug. 11, 2023 |
5637 |
308 |
How Ray and Anyscale Make it Easy to Do Massive-Scale Machine Learning on Aerial Imagery |
Richard Decal |
Nov. 08, 2022 |
1563 |
- |
Wildlife Studios Serves In-game Offers 3X Faster at 1/10th the Cost with Ray Serve |
Tricia Fu |
Nov. 08, 2022 |
1064 |
- |
Announcing Ray 2.0 |
Anyscale Ray Team |
Aug. 23, 2022 |
705 |
- |
Building Highly Available and Scalable Online Applications on Ray at Ant Group |
Tengwei Cai, Yang Liu, Chengxi Luo |
Sep. 08, 2021 |
2322 |
1 |
Four Reasons Why Leading Companies Are Betting On Ray |
Zhe Zhang, Ion Stoica, Ben Lorica |
Oct. 19, 2022 |
1580 |
- |
How Anastasia accelerated their ML processes 9x with Ray and Anyscale |
Juan Roberto Honorato |
Aug. 31, 2021 |
1290 |
- |
Online Resource Allocation with Ray at Ant Group |
Xingyu Lu, Yang Liu, Tengwei Cai, Fengbin Fang |
Mar. 30, 2021 |
1838 |
1 |
Why you should build your AI Applications with Ray |
Ben Lorica, Ion Stoica |
May. 04, 2021 |
1375 |
- |
Autoscaling clusters with Ray |
Ameer Haj Ali, Javier Redondo |
May. 17, 2021 |
2325 |
- |
Anyscale Unveils Ray 2.0 and Anyscale Innovations at Ray Summit 2022; Adds an Additional $99M Funding from Existing Investors Addition, Intel Capital, and Foundation Capital |
- |
Aug. 23, 2022 |
827 |
- |
Ray Summit, the Industry Event for Scalable AI, Unveils New Innovations in Ray and the Anyscale Platform; Features Speakers from Uber, IBM, OpenAI, Shopify, Dow and Numerous Global Organizations |
- |
Aug. 24, 2022 |
557 |
- |
Machine Learning for Developers |
Goku Mohandas |
Jul. 26, 2023 |
688 |
- |
Processing 2 Billion Images for Stable Diffusion Model Training - Definitive Guides with Ray Series |
Max Pumperla, Marwan Sarieddine |
May. 14, 2024 |
4209 |
- |
Introducing Anyscale: The Future Is Distributed |
Robert Nishihara |
Dec. 07, 2021 |
702 |
1 |
Best Machine Learning Talks from Ray Summit 2021 |
Michael Galarnyk |
Jul. 20, 2021 |
746 |
- |
Ray Summit 2022 stories - ML Platforms |
Anyscale Ray Team |
Mar. 03, 2023 |
628 |
- |
How to Speed Up Pandas with Modin |
Michael Galarnyk |
Mar. 03, 2021 |
1041 |
- |
Ray Forward 2022 |
Zhe Zhang |
Aug. 18, 2022 |
1855 |
- |
Introducing Collective Communication Primitive APIs in Ray |
Hao Zhang, Richard Liaw |
May. 28, 2021 |
1494 |
- |
Running and Monitoring Distributed ML with Ray and whylogs |
Anthony Naddeo, Danny Leybzon |
Nov. 22, 2021 |
1523 |
- |
How Nixtla uses Ray to accurately predict more than a million time series in half an hour |
Nixtla Team |
Jun. 13, 2022 |
1152 |
- |
Ray 1.13: Improving support for shuffling terabyte-scale and larger datasets |
Stephanie Wang, Jiao Dong, Dmitri Gekhtman, Sang Cho |
Jun. 09, 2022 |
680 |
- |
LiveEO supercharges their ML infrastructure and accelerates their geospatial workloads practice |
Toby Rahloff, Phi Nguyen, Alex Streed |
Apr. 14, 2023 |
658 |
- |
Serverless Kafka Stream Processing with Ray |
Javier Redondo |
Jul. 13, 2021 |
3413 |
- |
Reinforcement learning sessions at Ray Summit: A guided tour |
Avnish Narayan, Christy Bergman, Jun Gong |
Jun. 23, 2022 |
874 |
- |
Building an end-to-end ML pipeline using Mars and XGBoost on Ray |
Chaokun Yang, Yiming Yu |
Jan. 05, 2022 |
2161 |
- |
Anyscale Endpoints: Embedding endpoint, Llama-2 70B fine-tuning and improved sign-up experience |
Anyscale team |
Nov. 30, 2023 |
376 |
- |
The 2021 Ray Community Pulse Survey is Now Open |
Michael Galarnyk |
May. 12, 2021 |
432 |
- |
Fine-Tuning LLMs: LoRA or Full-Parameter? An in-depth Analysis with Llama 2 |
Artur Niederfahrenhorst, Kourosh Hakhamaneshi, Rehaan Ahmad |
Sep. 06, 2023 |
3597 |
22 |
Announcing Ray 2.3: performance improvements, new features and new platforms |
Richard Liaw, Cade Daniel, Jules S. Damji, Zhe Zhang |
Feb. 24, 2023 |
1329 |
- |
Why I joined Anyscale |
Jaikumar Ganesh |
Nov. 29, 2021 |
791 |
- |
Fast AutoML with FLAML + Ray Tune |
Qingyun Wu, Chi Wang, Antoni Baum, Richard Liaw, Michael Galarnyk |
Aug. 24, 2021 |
1802 |
- |
Building a Self Hosted Question Answering Service using LangChain + Ray in 20 minutes |
Waleed Kadous |
May. 08, 2023 |
1693 |
- |
Ray 1.12: Ray AI Runtime (alpha), usage data collection, and more |
Paige Bailey, Richard Liaw, Jian Xiao, Chandler Gibbons |
Apr. 14, 2022 |
570 |
- |
How to Speed Up XGBoost Model Training |
Michael Galarnyk |
Dec. 15, 2021 |
1068 |
- |
Siemens brings reinforcement learning to energy, transportation and logistics |
Erik Martinez |
May. 03, 2022 |
334 |
- |
How Spotify Built a Robust Ray Platform with a Frictionless Developer Experience |
Anyscale Ray Team |
Nov. 09, 2023 |
1259 |
- |
How continuous batching enables 23x throughput in LLM inference while reducing p50 latency |
Cade Daniel, Chen Shen, Eric Liang, Richard Liaw |
Jun. 22, 2023 |
3568 |
110 |
Building an LLM open source search engine in 100 lines using LangChain and Ray |
Waleed Kadous |
Apr. 18, 2023 |
1780 |
3 |
What is hyperparameter tuning? |
Juan Navas |
Feb. 08, 2022 |
1832 |
- |
10 must-attend Ray Summit sessions: Generative AI, scalable ML workloads, and more |
Jules S. Damji, Ben Lorica |
May. 10, 2023 |
1078 |
- |
Improve Utilization and Simplify Cluster Management with Anyscale Job Queues |
Dominic Catalano, Alexey Kudinkin |
Jul. 23, 2024 |
735 |
- |
Ray Summit 2021 CFP Now Open! |
Zhe Zhang, Ben Lorica |
Jan. 13, 2021 |
230 |
- |
Ray 1.11: Redisless Ray, a docs redesign, and Python 3.9 support |
Mingwei Tian, Chandler Gibbons |
Mar. 09, 2022 |
701 |
1 |
Ray Summit 2024 Call for Proposals is now open |
Anyscale team |
Apr. 19, 2024 |
264 |
- |
Deploying XGBoost models with Ray Serve |
Simon Mo, Chandler Gibbons |
Mar. 02, 2022 |
1524 |
- |
Anyscale and Meta Collaborate to Advance the Llama-2 Ecosystem |
Robert Nishihara, Joe Spisak |
Sep. 07, 2023 |
325 |
- |
Open Source LLMs: Viable for Production or a Low-Quality Toy? |
Anyscale Ray Team |
Nov. 20, 2023 |
855 |
- |
New in KubeRay 0.2.0: Autoscaling (alpha), simplified installation, and more |
Jiaxin Shan, KubeRay Team |
Apr. 19, 2022 |
524 |
- |
How ByteDance Scales Offline Inference with multi-modal LLMs to 200 TB Data |
Amog Kamsetty, Hao Chen, Liguang Xie |
Aug. 14, 2023 |
1872 |
7 |
Ray 2.5 features training and serving for LLMs, Multi-GPU training in RLlib, and enhanced Ray Data support |
Richard Liaw, Jules S. Damji |
Jun. 13, 2023 |
1681 |
- |
Llama, Scaling Up LLMs in an Open Ecosystem |
Anyscale Ray Team |
Oct. 16, 2023 |
1246 |
- |
Build and Scale a Powerful Query Engine with LlamaIndex and Ray |
Jerry Liu, Amog Kamsetty |
Jun. 26, 2023 |
2524 |
- |
Introducing Distributed XGBoost Training with Ray |
Kai Fricke, Richard Liaw, Amog Kamsetty |
Jun. 16, 2021 |
1994 |
- |
Deploy Ray Serve with up to 50% fewer nodes using Anyscale Replica Compaction |
Matt Connor, Akshay Malik, Cindy Zhang |
Jul. 15, 2024 |
883 |
- |
Training 175B Parameter Language Models at 1000 GPU scale with Alpa and Ray |
Jiao Dong, Hao Zhang, Lianmin Zheng, Jun Gong, Jules S. Damji, Phi Nguyen |
Mar. 22, 2023 |
2713 |
- |
Heterogeneous Training Cluster with Ray at Netflix |
Anyscale Ray Team |
Oct. 20, 2023 |
902 |
- |
Reinforcement Learning with RLlib in the Unity Game Engine |
Sven Mika |
Jan. 19, 2021 |
2133 |
- |
Advances in Foundation Models — Technology, Society, and Applications |
Anyscale Ray Team |
Nov. 03, 2023 |
1460 |
- |
Ray 2.6 features streaming for Serve and Train and new Multi-GPU Learner API |
Jules S. Damji, Richard Liaw |
Jul. 25, 2023 |
1426 |
- |
Reinforcement learning with Deep Q Networks |
Misha Laskin |
Mar. 01, 2022 |
1189 |
- |
Comparing LLM performance: Introducing the Open Source Leaderboard for LLM APIs |
Anyscale team |
Dec. 21, 2023 |
1202 |
2 |
Time Series Forecasting using an LSTM version of RNN with PyTorch Forecasting and Torch Lightning |
Christy Bergman, Amog Kamsetty |
Dec. 21, 2021 |
2157 |
- |
Ray Serve: Tackling the cost and complexity of serving AI in production |
Akshay Malik, Edward Oakes, Phi Nguyen |
Sep. 25, 2023 |
2392 |
- |
We Pre-Trained Stable Diffusion Models on 2 billion Images and Didn't Break the Bank - Definitive Guides with Ray Series |
Max Pumperla, Marwan Sarieddine |
May. 21, 2024 |
4553 |
- |
Reinforcement learning based on market simulation at JPMorgan |
Erik Martinez |
May. 03, 2022 |
287 |
- |
Ray 1.10: Windows support beta, enhanced job submission, and more |
Chandler Gibbons |
Feb. 07, 2022 |
447 |
2 |
Now Available: The LLM Router Template |
Amjad Almahairi |
Jul. 19, 2024 |
256 |
- |
Biolexis Boosts Their New AI/ML Drug Discovery Platform Using Anyscale’s Fully-Managed Ray Platform |
Jake Carter, Phi Nguyen |
Nov. 09, 2022 |
622 |
- |
Simplify your ML Development Cycle with Anyscale and Weights & Biases |
Phi Nguyen |
Jan. 31, 2023 |
715 |
- |
Why I Joined Anyscale: Solving Cutting-Edge Problems in a Time of Enormous Change |
Sidney Rabsatt |
Apr. 19, 2023 |
260 |
- |
Announcing Ray 2.4.0: Infrastructure for LLM training, tuning, inference, and serving |
Richard Liaw, Jules S. Damji, Jiajun Yao |
Apr. 27, 2023 |
1692 |
- |
Smart supply chain management with reinforcement learning at Dow |
Erik Martinez |
May. 03, 2022 |
329 |
- |
Anyscale Endpoints Preview: Fast, Cost-Efficient, and Scalable LLM APIs |
Ameer Haj Ali, Robin Singh |
Aug. 03, 2023 |
363 |
- |
Fine-tuning LLMs for longer context and better RAG systems |
Artur Niederfahrenhorst, Kourosh Hakhamaneshi |
Feb. 13, 2024 |
2847 |
1 |
Building Production AI Applications with Ray Serve |
Anyscale Ray Team |
Oct. 24, 2023 |
1213 |
- |
Ray version 1.9 has been released |
Michael Galarnyk |
Dec. 06, 2021 |
395 |
- |
How ThirdAI uses Ray for Parallel Training of Billion-Parameter Neural Networks on Commodity CPUs |
Vihan Lakshman, Pratik Pranav, Siddharth Jain, Tharun Medini |
Aug. 29, 2023 |
1643 |
78 |
Introducing Ray Lightning: Multi-node PyTorch Lightning training made easy |
Amog Kamsetty, Richard Liaw, Will Drevo |
Aug. 19, 2021 |
1851 |
9 |
Ray 2.7 features major stability improvements to Ray AI Libraries and KubeRay and introduces RayLLM |
Jules S. Damji, Richard Liaw |
Sep. 18, 2023 |
1798 |
- |
Large-scale distributed training with TorchX and Ray |
Mark Saroufim, Jules S. Damji |
Mar. 24, 2022 |
1385 |
- |
Ray version 1.8 has been released |
Michael Galarnyk |
Nov. 04, 2021 |
527 |
- |
Optimizing LLM Training with Airbnb's Next-Gen ML Platform |
Anyscale Ray Team |
Oct. 30, 2023 |
1048 |
- |
Data Processing Support in Ray |
Sang Cho, Alex Wu, Clark Zinzow, Eric Liang, Stephanie Wang |
Feb. 16, 2021 |
1178 |
2 |
Accelerating AI: Harnessing Intel(R) Gaudi(R) 3 with Ray 2.10 |
Ramit Hora |
Apr. 09, 2024 |
596 |
- |
Attention Nets and More with RLlib's Trajectory View API |
Sven Mika |
Apr. 21, 2021 |
1475 |
- |
Distributed deep learning with Ray Train is now in Beta |
Matthew Deng, Amog Kamsetty, Richard Liaw, Will Drevo |
Jan. 25, 2022 |
2105 |
- |
Serving PyTorch models with FastAPI and Ray Serve |
Simon Mo, Chandler Gibbons |
Feb. 23, 2022 |
1506 |
2 |
Ray Serve + FastAPI: The best of both worlds |
Phi Nguyen |
Aug. 02, 2022 |
1229 |
- |
Serving ML Models in Production: Common Patterns |
Simon Mo, Edward Oakes, Michael Galarnyk |
Oct. 01, 2021 |
2759 |
- |
Introducing Anyscale’s Unified Log Viewer |
Alan Guo, Gene Su |
Jul. 18, 2024 |
405 |
- |
Cross-modal Search for E-commerce: Building and Scaling a Cross-Modal Image Retrieval App |
Marwan Sarieddine, Natalia Czerep, Mateusz Kwasniak, Artur Zygadło |
Jun. 04, 2024 |
3253 |
- |
Ray breaks the $1/TB barrier as the world’s most cost-efficient sorting system |
Frank Sifei Luan, UC Berkeley |
Jan. 25, 2023 |
1257 |
36 |
Modern Distributed C++ with Ray |
Guyang Song, Yu Qi |
Nov. 11, 2021 |
2651 |
- |
The Emergence of Multi-cloud Native Applications and Platforms |
Ben Lorica, Ion Stoica |
Jan. 05, 2021 |
1463 |
- |
Adtriba Accelerates and Advances Media Mix Modeling Using the Anyscale Fully-Managed Ray Platform |
Houssem Eddine Gharbi, Tim Kreienkamp, Phi Nguyen |
Nov. 08, 2022 |
957 |
- |
Reinventing Multi-Modal Search with Anyscale and MongoDB |
Marwan Sarieddine, Kamil Kaczmarek |
Jul. 25, 2024 |
5145 |
- |
How to tune hyperparameters on XGBoost |
Juan Navas, Richard Liaw |
Feb. 09, 2022 |
1305 |
- |
Infusing AI and ML into integrated circuit design for faster chip delivery, better chip performance |
IBM Research Team |
Jun. 16, 2022 |
1390 |
- |
Parallelizing Python Code |
Dawid Borycki, Michael Galarnyk |
Sep. 02, 2021 |
1668 |
2 |
Retrieval Augmented Generation with Huggingface Transformers and Ray |
Amog Kamsetty |
Feb. 10, 2021 |
1050 |
- |
Practical Data Considerations for Building Production-Ready LLM Applications |
Anyscale Ray Team |
Oct. 19, 2023 |
1116 |
- |
Llama 2 is about as factually accurate as GPT-4 for summaries and is 30X cheaper |
Waleed Kadous |
Aug. 23, 2023 |
2933 |
143 |
New on Anyscale: Debug and Optimize Ray Applications Faster with Structured Logging |
Jiajun Yao, Kai-Hsun Chen |
Jul. 16, 2024 |
449 |
- |
Ray Datasets for large-scale machine learning ingest and scoring |
Clark Zinzow, Alex Wu, Jiajun Yao, Eric Liang, Chen Shen |
Feb. 14, 2022 |
1661 |
2 |
How to Speed up Scikit-Learn Model Training |
Michael Galarnyk |
Feb. 03, 2021 |
911 |
- |
Ray + Arize, Productionize ML for Scale and Usability |
Dat Ngo |
Aug. 22, 2022 |
1828 |
- |
Gang Scheduling Ray Clusters on Kubernetes with Multi-Cluster-App-Dispatcher (MCAD) |
Abhishek Malvankar (IBM Research) and Dmitri Gekhtman (Anyscale) |
Nov. 16, 2022 |
1406 |
1 |
Flexible, cross-language, distributed model inference framework: Ray Serve with Java API |
Tengwei Cai, Yang Liu, Chengxi Luo, Xiaofeng Yang, Simon Mo |
Dec. 13, 2022 |
910 |
- |
Ray Distributed Library Patterns |
Eric Liang, Zhe Zhang |
Jun. 14, 2021 |
1391 |
- |
Biggest takeaways from our RL tutorial: Long-term rewards, offline RL, and more |
Christy Bergman |
Apr. 06, 2022 |
1114 |
- |
Easily Debug Ray Applications with Ray Distributed Debugger |
Anyscale team |
May. 15, 2024 |
624 |
- |
Inference Graphs at LinkedIn Using Ray-Serve |
Anyscale Ray Team |
Nov. 09, 2023 |
1267 |
- |
End-to-end LLM Workflows Guide |
Goku Mohandas |
Jun. 17, 2024 |
4910 |
1 |
Building Context-Aware Reasoning Applications with LangChain and LangSmith |
Anyscale Ray Team |
Oct. 18, 2023 |
1214 |
- |
Writing your First Distributed Python Application with Ray |
Michael Galarnyk |
Aug. 12, 2021 |
2237 |
- |
Ray 2.2: Improved developer experience, performance and stability |
Richard Liaw |
Jan. 23, 2023 |
789 |
- |
Building an LLM-powered GitHub bot to improve your pull requests |
Max Pumperla |
Nov. 15, 2023 |
3491 |
- |
Configuring and Scaling ML with Hydra + Ray |
Richard Liaw, Bill Chambers, Jieru Hu |
Jan. 26, 2021 |
480 |
- |
How Hutom.io uses Ray and PyTorch to Scale Surgical Video Analysis and Review |
Jihun Yoon |
Oct. 26, 2021 |
1575 |
- |
Ray version 1.6 is released |
Asawari Samant |
Aug. 23, 2021 |
799 |
- |
Introducing RLlib Multi-GPU Stack for Cost Efficient, Scalable, Multi-GPU RL Agents Training |
Avnish Narayan, Kourosh Hakhamaneshi |
Jun. 26, 2023 |
1058 |
- |
Building an LLM Router for High-Quality and Cost-Effective Responses |
Amjad Almahairi |
Jul. 01, 2024 |
4430 |
1 |
An informal introduction to reinforcement learning |
Misha Laskin |
Feb. 22, 2022 |
1187 |
3 |
Practical tips for training Deep Q Networks |
Misha Laskin |
Mar. 03, 2022 |
875 |
- |
Don’t Miss: Hands-On Ray Training at Ray Summit 2024 |
Kamil Kaczmarek |
Aug. 13, 2024 |
788 |
- |
7 must-attend Ray Summit sessions: RL-powered traffic control, infra-less ML, and more |
Jules S. Damji, Ben Lorica |
Jun. 01, 2022 |
653 |
- |
Low-latency Generative AI Model Serving with Ray, NVIDIA Triton Inference Server, and NVIDIA TensorRT-LLM |
Neelay Shah, Akshay Malik |
Mar. 13, 2024 |
642 |
- |
What is distributed training? |
Keith Pijanowski, Michael Galarnyk |
Apr. 26, 2022 |
727 |
- |
Considerations for Deploying Machine Learning Models in Production |
Jules S. Damji, Michael Galarnyk |
Nov. 16, 2021 |
1791 |
- |
Many Models Batch Training at Scale with Ray Core |
Jules S. Damji, Antoni Baum |
Jan. 19, 2023 |
2178 |
- |
Monitoring and Debugging Ray workloads: Ray Metrics |
SangBin Cho, Alan Guo, Ricky Xu, Eric Liang |
Nov. 08, 2022 |
1221 |
- |
Fine tuning is for form, not facts |
Waleed Kadous, Kourosh Hakhamaneshi |
Jul. 05, 2023 |
1631 |
- |
Introducing the Anyscale Snowflake Connector |
Eric Greene |
Jul. 20, 2023 |
745 |
- |
Reducing the Cost of Pre-training Stable Diffusion by 3.7x with Anyscale |
Yunxuan Xiao, Hao Chen |
May. 09, 2024 |
2176 |
5 |
Considerations for deploying machine learning models in production: Part 2 |
Jules S. Damji |
Feb. 04, 2022 |
2475 |
- |
Cost Effective Machine Learning with Ray |
Miha Jenko |
Dec. 20, 2021 |
869 |
- |
Ray version 1.7 has been released |
Michael Galarnyk |
Oct. 11, 2021 |
722 |
- |
Riot Games and deep reinforcement learning in gaming |
Erik Martinez |
May. 03, 2022 |
364 |
- |
Why I joined Anyscale: The vision, the tech, and the team |
Sriram Sankar |
Feb. 02, 2022 |
648 |
- |
How Ray solves common production challenges for generative AI infrastructure |
Antoni Baum, Eric Liang, Jun Gong, Kai Fricke, Richard Liaw |
Mar. 20, 2023 |
1494 |
- |
5 reasons to attend this month’s Production RL Summit |
Chandler Gibbons |
Mar. 22, 2022 |
669 |
- |
Streaming distributed execution across CPUs and GPUs |
Eric Liang, Stephanie Wang, Cheng Su |
May. 11, 2023 |
2067 |
- |
From Ray to Chronos: Build end-to-end AI use cases using BigDL on top of Ray |
Wesley Du, Junwei Deng, Kai Huang, Shan Yu, Shane Huang |
Nov. 02, 2021 |
1594 |
- |
Redis in Ray: Past and future |
Mingwei Tian |
Mar. 15, 2022 |
930 |
1 |
Multi-model composition with Ray Serve deployment graphs |
Jiao Dong, Shreyas Krishnaswamy, Simon Mo, Edward Oakes |
May. 18, 2022 |
2554 |
- |
The reinforcement learning framework |
Misha Laskin |
Feb. 24, 2022 |
921 |
- |
An Introduction to Reinforcement Learning with OpenAI Gym, RLlib, and Google Colab |
Michael Galarnyk, Sven Mika |
Aug. 26, 2021 |
2649 |
- |
Ray Summit Series - Scaling Parallel Python Jobs |
Anyscale Ray Team |
Mar. 16, 2023 |
599 |
- |
Foobot optimizes building energy efficiency by training fully autonomous control agents for HVAC systems, bringing energy savings to office buildings, hospitals, schools and commercial buildings. |
Antoine Galataud, Phi Nguyen, Inouk Bourgon, Adrien Lafond |
Dec. 19, 2022 |
751 |
- |
Leveraging the Possibilities of Ray Serve in Implementing a Scalable, Fully Automated Digital Verification Service |
Tanja Bayer |
Nov. 09, 2021 |
1324 |
- |
Forecasting at Scale |
Phi Nguyen, Max Mergenthaler |
Feb. 02, 2023 |
683 |
- |
Introducing the Anyscale Databricks Connector |
Eric Greene |
Jun. 15, 2023 |
632 |
- |
Ray Summit 2023 Call for Proposals is now open |
Jules S. Damji |
Jan. 12, 2023 |
777 |
- |
Fast, flexible, and scalable data loading for ML training with Ray Data |
Stephanie Wang, Scott Lee, Cheng Su, Hao Chen, Eric Liang |
Sep. 15, 2023 |
3238 |
4 |
Executing a distributed shuffle without a MapReduce system |
Stephanie Wang |
Mar. 22, 2021 |
1675 |
- |
Life @ Anyscale: Investing in our communities |
Tyler Faust |
May. 26, 2022 |
271 |
- |
Anyscale and Lambda - Addressing AI Scarcity with Engineering |
Anyscale team |
Nov. 21, 2023 |
585 |
- |
RAG at Scale: 10x Cheaper Embedding Computations with Anyscale and Pinecone |
Scott Lee, Kyle Huang, Cheng Su, Hao Chen |
Jan. 16, 2024 |
995 |
1 |
Ray 2.8 features Ray Data extensions, AWS Neuron cores support, and Dashboard improvements |
Jules S. Damji, Richard Liaw |
Nov. 07, 2023 |
791 |
- |
The Third Generation of Production ML Architectures |
Waleed Kadous |
Sep. 15, 2021 |
2388 |
3 |
Scaling Time Series Forecasting on Ray: ARIMA and Prophet on Ray |
Christy Bergman |
Nov. 23, 2021 |
2725 |
- |
Analyzing memory management and performance in Dask-on-Ray |
Stephanie Wang |
Jun. 29, 2021 |
2784 |
1 |
Sailing to victory with reinforcement learning |
Erik Martinez |
Feb. 28, 2022 |
449 |
- |
Update on Ray CVE-2023-48022: New Verification Tooling Available |
Anyscale team |
Mar. 27, 2024 |
606 |
- |
Update on Ray CVEs CVE-2023-6019, CVE-2023-6020, CVE-2023-6021, CVE-2023-48022, CVE-2023-48023 |
Anyscale team |
Nov. 30, 2023 |
508 |
- |
Simplify your MLOps with Ray & Ray Serve |
Phi Nguyen |
Jul. 26, 2022 |
1167 |
- |
Ray Spotlight Series: Multitenant Serve Applications with Runtime Envs as Containers |
Sam Chan, Cindy Zhang |
Jun. 13, 2024 |
800 |
- |
How to fine tune and serve LLMs simply, quickly and cost effectively using Ray + DeepSpeed + HuggingFace |
Waleed Kadous, Jun Gong, Antoni Baum, Richard Liaw |
Apr. 10, 2023 |
2055 |
- |
Turbocharge LangChain: guide to 20x faster embedding |
Amog Kamsetty, Philipp Moritz |
May. 03, 2023 |
1934 |
- |
Direct Preference Optimization with Synthetic Data on Anyscale |
Franklin Wang, Sumanth Hegde, Kourosh Hakhamaneshi |
Aug. 21, 2024 |
9249 |
1 |
Model Batch Inference in Ray: Actors, ActorPool, and Datasets |
Eric Liang, Jules S. Damji, Zhe Zhang |
Nov. 03, 2022 |
2084 |
4 |
Anyscale Endpoints: JSON Mode, Function calling, New models: Llama Guard and Mistral-7B-OpenOrca |
Endpoints Team |
Dec. 12, 2023 |
186 |
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Loading Llama-2 70b 20x faster with Anyscale Endpoints |
Yi Cheng, Cade Daniel, Chen Shen, Liguang Xie |
Oct. 11, 2023 |
1961 |
5 |
Portkey ♥️ Anyscale Endpoints |
Endpoints Team |
Dec. 12, 2023 |
564 |
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Getting Started with Distributed Machine Learning with PyTorch and Ray |
Michael Galarnyk, Richard Liaw, Robert Nishihara |
Mar. 02, 2021 |
1360 |
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Scaling Model Batch Inference in Ray: Using Actors, ActorPool, and Ray Data |
Eric Liang, Jules S. Damji, Zhe Zhang |
May. 16, 2023 |
1856 |
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Numbers every LLM Developer should know |
Waleed Kadous |
May. 17, 2023 |
1423 |
95 |
Handling files and packages on your cluster with Ray runtime environments |
Archit Kulkarni, Edward Oakes |
May. 05, 2022 |
860 |
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Three ways to speed up XGBoost model training |
Antoni Baum, Chandler Gibbons |
Feb. 17, 2022 |
1609 |
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Deep Dive: Data Ingest in a Third Generation ML Architecture |
Eric Liang, Chen Shen, Clark Zinzow, Waleed Kadous |
Nov. 30, 2021 |
1783 |
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Automatic and optimistic memory scheduling for ML workloads in Ray |
Clarence Ng, Jules S. Damji |
Mar. 02, 2023 |
2423 |
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Ray Summit 2022 Call for Papers is now open |
Jules S. Damji, Chandler Gibbons |
Mar. 23, 2022 |
585 |
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Ray Summit 2022 Stories - Large Language Models |
Anyscale Ray Team |
Feb. 16, 2023 |
680 |
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LLM-based summarization: A case study of human, Llama 2 70b and GPT-4 summarization quality |
Justin Olsson, Waleed Kadous |
Nov. 09, 2023 |
1195 |
1 |
Welcome Keerti |
Robert Nishihara |
Jul. 31, 2024 |
743 |
2 |
Offline Batch Inference: Comparing Ray, Apache Spark, and SageMaker |
Amog Kamsetty, Eric Liang, Jules S. Damji |
May. 04, 2023 |
2042 |
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How Ikigai Labs Serves Interactive AI Workflows at Scale using Ray Serve |
Jaehyun Sim, Amar Shah |
Aug. 19, 2021 |
2040 |
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Introducing Distributed LightGBM Training with Ray |
Antoni Baum, Will Drevo |
Aug. 10, 2021 |
1149 |
22 |
Introducing Elastic Distributed Training on Anyscale |
Matthew Deng, Justin Yu |
Jul. 22, 2024 |
478 |
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Ray & MLflow: Taking Distributed Machine Learning Applications to Production |
Amog Kamsetty, Archit Kulkarni |
Jan. 13, 2021 |
1091 |
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How to distribute hyperparameter tuning using Ray Tune |
Juan Navas, Richard Liaw |
Feb. 15, 2022 |
3064 |
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Why I Joined Anyscale: Powering an Open Source AI Revolution |
Lance Walter |
Apr. 28, 2023 |
799 |
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Anyscale Endpoints: JSON Mode and Function calling Features |
Endpoints Team |
Dec. 12, 2023 |
2050 |
2 |
Announcing Anyscale Private Endpoints and Anyscale Endpoints Fine-tuning |
Matt Connor, Robin Singh |
Oct. 24, 2023 |
467 |
3 |
Cloud Infrastructure for LLM and Generative AI Applications |
Yifei Feng, Sriram Sankar, Siddharth Venkatesh, Ameer Haj Ali |
Sep. 14, 2023 |
1868 |
4 |
Three Key Elements of a Scalable ML Platform |
Phi Nguyen |
Dec. 16, 2022 |
1733 |
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How Ant Group uses Ray to build a Large-Scale Online Serverless Platform |
Tengwei Cai, Yang Liu, Chengxi Luo, Xiaofeng Yang |
Dec. 12, 2022 |
2353 |
3 |
Building RAG-based LLM Applications for Production |
Goku Mohandas, Philipp Moritz |
Oct. 25, 2023 |
10794 |
11 |
Faster stable diffusion fine-tuning with Ray AIR |
Kai Fricke |
Mar. 28, 2023 |
1627 |
- |
Challenges of deploying ML models in production |
Phi Nguyen |
Jul. 14, 2022 |
959 |
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Why I’m joining Anyscale |
Waleed Kadous |
Jan. 04, 2021 |
757 |
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Announcing Aviary: Open Source Multi-LLM Serving |
Waleed Kadous |
May. 31, 2023 |
743 |
24 |
Reproducible Performance Metrics for LLM inference |
Waleed Kadous, Kyle Huang, Wendi Ding, Liguang Xie, Avnish Narayan, Ricky Xu |
Nov. 01, 2023 |
2495 |
2 |
Ray Spotlight: How we delivered Ray weekly releases |
Sam Chan |
Jun. 25, 2024 |
629 |
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Inspecting Sewer Line Safety Using Thousands of Hours of Video |
Lance Walter |
May. 22, 2023 |
814 |
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Training One Million Machine Learning Models in Record Time with Ray |
Eric Liang, Robert Nishihara |
Dec. 17, 2022 |
2124 |
1 |
Blue River Technology Developers Iterate 2.5X Faster with the Anyscale Fully-Managed Ray Platform |
Uday Kanwar, Deb Daipayan |
Feb. 27, 2023 |
608 |
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Scaling Embedding Generation Pipelines From Pandas to Ray Data |
Marwan Sarieddine |
Sep. 04, 2024 |
2154 |
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Fine-tuning Llama-3, Mistral and Mixtral with Anyscale |
Marwan Sarieddine and Kamil Kaczmarek |
Sep. 11, 2024 |
2256 |
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Building a RAG Batch Inference Pipeline with Anyscale and Union |
Kevin Su and Kai-Hsun Chen |
Sep. 12, 2024 |
1665 |
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Roblox Guest Blog: Fast and Efficient Online Model Serving |
Younes Abouelnagah |
Sep. 19, 2024 |
2925 |
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RL for recommender systems |
Michael Galarnyk |
Jul. 20, 2021 |
769 |
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Accelerated Metadata Fetching in Ray Data up to 4.5x Faster on Anyscale |
Balaji Veeramani, Hao Chen, Richard Liaw, Matthew Connor and Praveen Gorthy |
Oct. 01, 2024 |
607 |
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Anyscale on Kubernetes: Simplifying AI Workloads on User-Managed Infrastructure |
Dominic Catalano and Yifei Feng |
Oct. 01, 2024 |
792 |
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Anyscale Now Available on AWS Marketplace and Achieves Generative AI Competency |
The Anyscale Team |
Oct. 01, 2024 |
510 |
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Batch LLM Inference on Anyscale slashes AWS Bedrock costs by up to 6x |
Cody Yu, Scott Lee, Ricky Xu, William Lin, Praveen Gorthy and Richard Liaw |
Oct. 01, 2024 |
1180 |
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Ray Data GA |
Hao Chen, Richard Liaw and Praveen Gorthy |
Oct. 01, 2024 |
1037 |
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Anyscale’s New User Experience: A Comprehensive Overview |
The Anyscale Team |
Oct. 01, 2024 |
1161 |
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Anyscale Now on GCP Marketplace |
The Anyscale Team |
Oct. 01, 2024 |
381 |
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Autoscaling Large AI Models up to 5.1x Faster on Anyscale |
Christopher Chou, Austin Kuo, Richard Liaw, Edward Oakes and Chris Sivanich |
Oct. 01, 2024 |
1260 |
- |
Enterprise Governance and Observability on Anyscale |
The Anyscale Team |
Oct. 01, 2024 |
479 |
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Announcing RayTurbo |
Akshay Malik, Praveen Gorthy and Richard Liaw |
Oct. 01, 2024 |
1453 |
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Ray Summit 2024: Breaking Through the AI Complexity Wall |
The Anyscale Team |
Oct. 03, 2024 |
1600 |
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Ray Compiled Graphs: Optimized AI Workloads with Native GPU Communication |
Sang Cho, Sam Chan and Stephanie Wang |
Oct. 07, 2024 |
1910 |
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Unlocking the Power of Scalable Machine Learning with Anyscale and Astronomer |
The Anyscale Team |
Oct. 29, 2024 |
1063 |
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Anyscale Named a Cool Vendor for AI Engineering by Gartner® |
The Anyscale Team |
Nov. 13, 2024 |
399 |
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