- 426
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Anyscale
United States
เข้าร่วมเมื่อ 31 ม.ค. 2020
Welcome to the Anyscale TH-cam channel! Join us as we explore Ray, an open-source framework designed to accelerate the development and deployment of machine learning applications. From beginner tutorials to advanced insights, our videos cater to all skill levels. Stay up-to-date with the latest advancements and hear from experts through interviews, panel discussions, and keynotes.
Building Scalable AI Infrastructure with Kuberay and Kubernetes | Ray Summit 2024
KubeRay maintainers Andrew Sy Kim from Google and Kai-Hsun Chen from Anyscale present an in-depth look at scaling generative AI workloads using KubeRay and Kubernetes. Their talk addresses how this integration provides a lightweight, flexible solution for diverse infrastructure requirements in AI deployments.
The presentation covers crucial integrations with the Kubernetes ecosystem and cloud providers, focusing on essential features for training and fine-tuning. These include gang scheduling, distributed checkpointing, and retries. The speakers explore KubeRay's capabilities in supporting both online and offline inference through features like Ray Autoscaler and fault tolerance, along with its compatibility with various hardware accelerators including GPUs, TPUs, and CPUs.
The session includes current KubeRay project updates and developments, highlighting Kubernetes community enhancements such as hierarchical scheduling and dynamic resource allocation (DRA). This comprehensive overview demonstrates how KubeRay and Kubernetes work together to scale AI infrastructure across multi-cloud, production environments.
--
Interested in more?
- Watch the full Day 1 Keynote: th-cam.com/video/jwZHJthQvXo/w-d-xo.html
- Watch the full Day 2 Keynote th-cam.com/video/Lury2ad6KG8/w-d-xo.html
--
🔗 Connect with us:
- Subscribe to our TH-cam channel: www.youtube.com/@anyscale
- Twitter: x.com/anyscalecompute
- LinkedIn: linkedin.com/company/joinanyscale/
- Website: www.anyscale.com
The presentation covers crucial integrations with the Kubernetes ecosystem and cloud providers, focusing on essential features for training and fine-tuning. These include gang scheduling, distributed checkpointing, and retries. The speakers explore KubeRay's capabilities in supporting both online and offline inference through features like Ray Autoscaler and fault tolerance, along with its compatibility with various hardware accelerators including GPUs, TPUs, and CPUs.
The session includes current KubeRay project updates and developments, highlighting Kubernetes community enhancements such as hierarchical scheduling and dynamic resource allocation (DRA). This comprehensive overview demonstrates how KubeRay and Kubernetes work together to scale AI infrastructure across multi-cloud, production environments.
--
Interested in more?
- Watch the full Day 1 Keynote: th-cam.com/video/jwZHJthQvXo/w-d-xo.html
- Watch the full Day 2 Keynote th-cam.com/video/Lury2ad6KG8/w-d-xo.html
--
🔗 Connect with us:
- Subscribe to our TH-cam channel: www.youtube.com/@anyscale
- Twitter: x.com/anyscalecompute
- LinkedIn: linkedin.com/company/joinanyscale/
- Website: www.anyscale.com
มุมมอง: 471
วีดีโอ
Scaling Ray to 10K NPUs: Huawei's Hyperscale Journey | Ray Summit 2024
มุมมอง 54221 วันที่ผ่านมา
Huawei's ambitious project of integrating 10,000 Ascend NPUs into a Ray cluster pushes the boundaries of distributed computing. In this technical deep dive, Boyuan Chen, Chong Yin Tan, and Xiaoshuang Liu from Huawei share their experiences and innovations in creating a hyperscale Ray-NPU infrastructure. The presenters detail the challenges of migrating existing business cases to Ray and adding ...
Optimizing vLLM Performance through Quantization | Ray Summit 2024
มุมมอง 93921 วันที่ผ่านมา
At Ray Summit 2024, Michael Goin and Robert Shaw from Neural Magic delve into the world of model quantization for vLLM deployments. Their presentation focuses on vLLM's support for various quantization methods, including FP8, INT8, and INT4, which are crucial for reducing memory usage and enhancing generation speed. In the talk, Goin and Shaw explain the internal mechanisms of how vLLM leverage...
Scaling AI at Autodesk with Ray and Metaflow | Ray Summit 2024
มุมมอง 31821 วันที่ผ่านมา
Autodesk's journey into large-scale 3D generative AI has led to a powerful synergy between Ray and Metaflow. In this session, Thomas Gale and Peter Meltzer from Autodesk, joined by Savin Goyal from Outerbounds, unveil how they've harnessed these tools to process terabytes of data and train advanced 3D models. The presenters dive into their innovative approach of integrating Ray's distributed co...
Scaling LLMs on Google Cloud: Synergy Between Ray, TPU, and GKE | Ray Summit 2024
มุมมอง 65121 วันที่ผ่านมา
As Large Language Models (LLMs) become increasingly central to AI-driven solutions, the challenge of deploying them at scale demands innovative approaches. In this cutting-edge session, Fanhai Lu and Richard Liu from Google unveil a high-performance serving stack that harnesses the combined power of Ray, TPUs, and Google Kubernetes Engine (GKE). The presenters tackle the trifecta of LLM deploym...
Reverb's ML Evolution: From Data Engineering to MLOps | Ray Summit 2024
มุมมอง 16421 วันที่ผ่านมา
As machine learning becomes integral to business operations, data engineering teams often find themselves at the forefront of ML infrastructure development. Sam Hallam from Reverb shares the company's journey in this transformative process, offering valuable insights for organizations navigating similar transitions. Hallam delves into the challenges and learnings encountered while building a sc...
The LLM-Cloud Synergy: NebiusAI's Insider Perspective | Ray Summit 2024
มุมมอง 10021 วันที่ผ่านมา
In the race to build superior AI clouds, NebiusAI has discovered a crucial advantage: an in-house LLM team. Aleksandr Patrushev takes the stage to reveal how this synergy has become a game-changer in cloud service development. Patrushev shares key insights gleaned from NebiusAI's dual role as both LLM developer and cloud provider. He illustrates how firsthand experience in LLM creation directly...
How Rubrik Unlocked AI at Scale with Ray Serve | Ray Summit 2024
มุมมอง 11021 วันที่ผ่านมา
Rubrik's quest for high-performance, real-time AI inference led them to a game-changing solution: Ray Serve. In this technical deep dive, Shaikh Ismail and Shivanshu Agrawal from Rubrik unveil their journey of harnessing Ray's ML model serving library to meet demanding scalability and throughput requirements. The duo explores Ray Serve's unique capabilities that made it stand out among alternat...
Pricing and Packaging Your AI Products for Scale | Ray Summit 2024
มุมมอง 14921 วันที่ผ่านมา
In the fast-paced world of AI, where capabilities evolve at breakneck speed, pricing strategies can make or break a product's success. Metronome CEO Scott Woody takes the stage to demystify the complex art of AI product pricing and packaging. Woody tackles the pressing questions that keep AI entrepreneurs up at night: How can you confidently price products in an industry that's constantly in fl...
Multi-tenant Data Processing with Ray: Phaidra's Approach to Industrial AI | Ray Summit 2024
มุมมอง 11821 วันที่ผ่านมา
Phaidra is reshaping the landscape of industrial and data center optimization with AI-driven controls. In this illuminating session, Brandon Hernandez and Jerry Luo unveil Phaidra's innovative approach to building a multi-tenant data processing platform on Ray for Reinforcement Learning agents. The presenters delve into the architecture of their Ray-based platform, which forms the backbone of t...
Ray at IBM: Transforming Large-Scale Data Processing for AI and Science | Ray Summit 2024
มุมมอง 10621 วันที่ผ่านมา
Dean Wampler from IBM presents how Ray is being utilized for large-scale data processing in AI and scientific research. The session focuses on the Data Prep Kit, an open-source project developed by IBM Research and the AI Alliance, which uses Ray as its core driver for data processing tasks crucial to LLM training and tuning. The presentation demonstrates Ray's capability to enable easy and res...
Fighting Fire with Algorithms: Lockheed's RL-Based Wildfire Solution | Ray Summit 2024
มุมมอง 9421 วันที่ผ่านมา
Lockheed Martin unveils its cutting-edge decision-aid system for wildland firefighting, powered by deep reinforcement learning. This innovative approach leverages rllib's hierarchical and multi-agent abstractions to recommend optimal fire suppression strategies based on complex environmental factors. Dan Jacobson and John Cerillo demonstrate how their team composed a two-level hierarchical agen...
Ray Meets Daft: Supercharging ETL and Analytics | Ray Summit 2024
มุมมอง 20721 วันที่ผ่านมา
The Ray ecosystem expands its horizons with Daft, a powerful Python/Rust library that brings distributed ETL and analytics capabilities to Ray clusters. This lightning talk showcases how Daft transforms Ray into a comprehensive Data and ML/AI solution, scaling effortlessly to meet any challenge. Jay Chia demonstrates the seamless integration of Daft with Ray, highlighting its superior performan...
How Datadog is Transforming Time Series Forecasting with Toto | Ray Summit 2024
มุมมอง 13121 วันที่ผ่านมา
Datadog revolutionizes time series analysis with Toto, a groundbreaking Time Series-Optimized Transformer designed to tackle the intricate challenges of observability metrics. This session unveils how Toto harnesses advanced transformer architecture to achieve unprecedented forecasting accuracy across diverse domains. Emaad Khwaja delves into the unique attributes that distinguish Toto in the r...
From Spark to Ray: CSS's Data Revolution with Daft | Ray Summit 2024
มุมมอง 12621 วันที่ผ่านมา
City Storage Systems (CSS) revolutionizes its machine learning infrastructure by embracing Daft, a powerful DataFrame library seamlessly integrated with Ray. This session unveils how CSS transforms its data processing and ETL workflows, moving beyond traditional Spark clusters to a more unified and efficient Ray-based ecosystem. Ammar Alrashed, Santosh Jha, and Garret Weaver showcase real-world...
Leveraging LLMs and LangGraph @ FlightAware | Ray Summit 2024
มุมมอง 18521 วันที่ผ่านมา
Leveraging LLMs and LangGraph @ FlightAware | Ray Summit 2024
KubeSecRay: Fortifying Multi-Tenant Ray Clusters on Kubernetes | Ray Summit 2024
มุมมอง 9821 วันที่ผ่านมา
KubeSecRay: Fortifying Multi-Tenant Ray Clusters on Kubernetes | Ray Summit 2024
Ray on Kubernetes: Powering Quant Research at Scale | Ray Summit 2024
มุมมอง 12321 วันที่ผ่านมา
Ray on Kubernetes: Powering Quant Research at Scale | Ray Summit 2024
Scaling LLM Inference: AWS Inferentia Meets Ray Serve on EKS | Ray Summit 2024
มุมมอง 11321 วันที่ผ่านมา
Scaling LLM Inference: AWS Inferentia Meets Ray Serve on EKS | Ray Summit 2024
Model Training on Snowflake with Ray | Ray Summit 2024
มุมมอง 11721 วันที่ผ่านมา
Model Training on Snowflake with Ray | Ray Summit 2024
Akash Network: Powering Ray on an Open Source Cloud | Ray Summit 2024
มุมมอง 7221 วันที่ผ่านมา
Akash Network: Powering Ray on an Open Source Cloud | Ray Summit 2024
Building Scalable Cross-Modal Search with Ray | Ray Summit 2024
มุมมอง 17921 วันที่ผ่านมา
Building Scalable Cross-Modal Search with Ray | Ray Summit 2024
Accelerating Princeton's Computational Science with Ray | Ray Summit 2024
มุมมอง 4821 วันที่ผ่านมา
Accelerating Princeton's Computational Science with Ray | Ray Summit 2024
How Vivix Scales Video Ad Classification with Ray | Ray Summit 2024
มุมมอง 12621 วันที่ผ่านมา
How Vivix Scales Video Ad Classification with Ray | Ray Summit 2024
Building Intelligent AI Infrastructure with O.XYZ's ORI | Ray Summit 2024
มุมมอง 16921 วันที่ผ่านมา
Building Intelligent AI Infrastructure with O.XYZ's ORI | Ray Summit 2024
The Evolution of Multi-GPU Inference in vLLM | Ray Summit 2024
มุมมอง 40821 วันที่ผ่านมา
The Evolution of Multi-GPU Inference in vLLM | Ray Summit 2024
Intelligent Data Classification with Ray and vLLM at Apple | Ray Summit 2024
มุมมอง 20321 วันที่ผ่านมา
Intelligent Data Classification with Ray and vLLM at Apple | Ray Summit 2024
How Roblox Scaled Machine Learning by Leveraging Ray for Efficient Batch Inference | Ray Summit 2024
มุมมอง 34621 วันที่ผ่านมา
How Roblox Scaled Machine Learning by Leveraging Ray for Efficient Batch Inference | Ray Summit 2024
Scaling Ray Train to 10K Kubernetes Nodes on GKE | Ray Summit 2024
มุมมอง 29621 วันที่ผ่านมา
Scaling Ray Train to 10K Kubernetes Nodes on GKE | Ray Summit 2024
How Zoox Accelerated Autonomous Driving with Ray | Ray Summit 2024
มุมมอง 14521 วันที่ผ่านมา
How Zoox Accelerated Autonomous Driving with Ray | Ray Summit 2024
Andreesen is brilliant
Will we all be substituted by super intelligent ai?
Hey, regarding the stable diffusion training: is there any code available on how to have inhomogeneous GPU Types and assign different GPU types to different parts of the model?
they've been punching way above their weight. and all they needed was hundreds of billions of dollars in weapons from the u.s. taxpayer.
hello
I only clicked this vid to say: “LASERS”
Great content, thank you!
Where can I read the slides?
He’s a genius
Deep neural networks are deterministic. This is a common misconception and embarrassing to say in this context.
Great interview! Keep coming back to this talk since it released two weeks ago.
Very informative. 🙌🏼💎
bros speaking on chinese or english??
He says a lot. But a lot of bullshit as well. 5 min in, he has already started to pouring out SV bullshit, "led the creation of self driving car..." There is no self driving car, yet.. there are only attempts to create "self driving cars". To anyone who questions me if I know who he is, yeah, I know who he is and I have personally interacted with him.
"Giant Laser"
Deterministic vs Probabilistic computers. Cush!! in the drive train. Air tires made motor sports happen. Springs and shock absorbers gave us traction. If Humanity were wiped out after a bad election - do you think AI would rise to take its place? Would they be kings and diplomats? Would there be - an Architect?
Nice
Came here to hear about Dr Evil
hello, thanks for that video, is it possible to have the link for the code use in the webinar ?
Instacart presentation is fantastic! (51:33 to 1:07:37)
custom speed at 0.85 worked great to make this talk sound normal
th-cam.com/video/E-PIidaqCyU/w-d-xo.htmlsi=sDLdM1RoJHotkSnn
Great video, thank you for sharing, appreciate you
The guy is a tech investor. Of course he is going to say big tech won't do AI well.
*reads comments* *raises eyebrow* *places pinky to lips*
Cease and desist malicious use of AI, energy weapons, satellites: Axis of EVIL / MAGA / Terrorists. My family and I are not your property!
5 minutes in and i' m hearing the excuse of semiconductor not fast enough and I'm already reminded that there was a reason for the slow incremental roll-out of chip improvements. While I'm not a fan of nor promoting stock market - as I find fiat currency abhorrent enough, nevermind the flippant value exchange and gambling on infinite pyramid schemes. The world competition for new I.P. breakthroughs is pushing more intelligence into the hands of more people around the globe and more data and intelligence will come up with better solutions for the world's problems that the mentality of the old fixed donestic protection racquets that seemed to have no ending . Sanctions seemed ignorant and more like closing down intelligence
Mark just affirmed that there are idiot politicians and bureaucrats populating DC. Trump with Elon heading the new Department of Government Efficiency. Problem solved.
Looked at the thumbnail once and I started looking around for Minnie me.
cone head
He is just regurgitating what the experts have said and done. All i see is a business man riding the wave of AI.
7:36 well said
Russian's have used ten's of thousands of drones on Ukrainian tanks, APC's and other vehicles. Russia has also used long and medium range drones en masse, where Ukraine has not.
it means go yourself
He is only one who speaks super fast and super clearly and in a soothing voice. I saw others try to imitate him but end up being very irritating.
1:03:29 >>> RUNWAY presentation Anastassus Germanadis 1:19:00 >>> World Modelling Visual data richer than language "Towards Universal Simulation"
You know you know
Rewrite rhe cell's software
Awesome
This man can be the definition of superficial knowledge. The confidence with which he speaks about things he doesn't understand is amazing.
Cease and desist malicious use of AI, energy weapons, satellites: Axis of EVIL / MAGA / Terrorists. My family and I are not your property!
How do you know that he doesn't understand what he said?
@@hamdaniyusuf_dani No. That's not what the comment was.
@@DivineMisterAdVentures What was the comment about?
Imagine thinking Marc Andreessen doesn't know what he's talking about.
7:25 How is it possible to compute attentions separately block by block? Softmax (attention weight) is calculated based on all of the previous tokens and then those softmax scores are multiplied with all of the previous tokens' value vectors to calculate the attention score for the new token. So it should use all of the previous tokens on other blocks twice. What am I missing here?
I read the paper. Turns out the illustration is not 100% accurate (probably for the sake of making it intuitive). It indeed uses every previous block (in case sliding windows is not used) while computing the attention for the next layer.
reddit is cancer and should be closed
00:09 AI's future capabilities are inspiring 02:19 Evolution of computer industry towards neural networks 05:19 AI's evolution aided by compute power and data availability 06:54 AI will transform various industries significantly. 09:52 Transition from Discovery to Engineering in Biotech 11:24 AI shaping geopolitics and defense 14:32 Innovative warfare technologies impacting geopolitical dynamics 16:06 The future of warfare shifting towards technology and economic strength 19:02 Technology's increasing political influence 20:33 AI's impact on the movie industry and automation concerns 23:22 Importance of keeping technology open and democratic 24:45 Impact of Regulation on AI Innovation 27:27 The AI Revolution at higher levels vs. Robotics Revolution happening soon 28:56 Rapid advancement in hardware capabilities for human-like robots 31:37 Investing across diverse AI approaches 32:56 Value capture uncertainty in AI industry 35:40 Importance of deep domain expertise in starting tech companies 37:03 Successful startups often come from deep understanding of problems and innovative solutions. Crafted by Merlin AI.
Cease and desist malicious use of AI, energy weapons, satellites: Axis of EVIL / MAGA / Terrorists. My family and I are not your property!
Joe Spisak’s session at Ray Summit 2024 provides an in-depth look at Meta's AI roadmap, focusing on the Llama ecosystem’s transformative impact. He explores practical approaches to building scalable generative AI agents and emphasizes the latest Llama models, their real-world applications, and system-level safety considerations. Spisak's insights offer valuable guidance for developers aiming to harness full-stack AI capabilities, from foundational models to advanced applications, equipping attendees to drive future AI innovation.
5D AI designs for emotion and feeling with love upgraded to compassion wisdomly full of knowledges
If sequences of different sizes can be processed in parallel (say request 1 is generating 11th token and request 2 is generating 3rd token), how come those two operations (Query vector of request 1 - say dimension 1x50 - dot product with previous tokens' key vectors matrix 11x50) and (1x50 dot product 3x50) can be batched together?
Very superficial stuff. I am really disappointed in Andreesen whom I used to admire.
If Dr. Evil and Conehead had a baby...
I used Chat to talk to a French person who couldn't speak english on Discord. He had no idea I couldn't speak French.
TH-cam just added AI to it. I only want ONE AI not AI built into every app.