#Ray Core Distributed Computing Framework

I built a local, private, uncensored distributed #AI cluster using four #RaspberryPi CM5 Lite modules with 16 GB each. They are mounted on a #Sipeed #NanoCluster board, which integrates a gigabit internal switch and runs from a single 65 W power supply. The setup delivers 16 cores and 64 GB RAM across the cluster. Using Distributed #LLaMA, the model is partitioned across the nodes, with synchronized workloads handled over the internal fabric. I am currently testing a small model at about 30 tokens per second, and with quantization performance can be pushed significantly further. #artificialintelligenceai #llm #thinkpadx1carbon #archlinux #hyprland #cluster

🚀There is an emerging open source AI compute stack: PyTorch + vLLM + Ray + Kubernetes As AI workloads have evolved from classical ML to deep learning to generative AI, the software stack for running and scaling them has grown drastically in complexity. Teams need a unified way to connect AI workloads to compute resources efficiently. TLDR on the stack: 🔹PyTorch powers model development and training 🔹vLLM delivers fast, efficient inference for large models 🔹Ray distributes and scales compute across nodes and clusters 🔹Kubernetes orchestrates containers and resources With Ray’s recent move to the Linux Foundation, all four of these open source projects now sit under the same umbrella - forming a unified stack that’s becoming the foundation of modern AI infrastructure in production

𝐏𝐲𝐭𝐡𝐨𝐧 𝐩𝐨𝐰𝐞𝐫𝐬 𝐚𝐥𝐦𝐨𝐬𝐭 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐢𝐧 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠, 𝐛𝐮𝐭 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐝𝐨 𝐧𝐨𝐭 𝐤𝐧𝐨𝐰 𝐭𝐡𝐞 𝐞𝐱𝐚𝐜𝐭 𝐭𝐨𝐨𝐥𝐬 𝐭𝐡𝐞𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐥𝐞𝐚𝐫𝐧 𝐟𝐢𝐫𝐬𝐭. If you skip this, you will miss a complete cheatsheet of the Python ecosystem that every AI engineer relies on. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐢𝐧 𝐜𝐥𝐞𝐚𝐫, 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐭𝐞𝐫𝐦𝐬: 𝟏. 𝐂𝐨𝐫𝐞 𝐍𝐮𝐦𝐞𝐫𝐢𝐜𝐚𝐥 𝐚𝐧𝐝 𝐌𝐋 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬. NumPy handles vectorized math. SciPy powers scientific computing. Pandas manages tabular data. Scikit supports classic ML. XGBoost and LightGBM help with high-performance boosting. 𝟐. 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐌𝐨𝐝𝐞𝐫𝐧 𝐀𝐈 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬. PyTorch is the most widely used DL framework today. TensorFlow supports scalable model serving. JAX enables fast numerical computing. Keras simplifies model building. Hugging Face provides transformers and model hubs. 𝟑. 𝐋𝐋𝐌, 𝐍𝐋𝐏, 𝐚𝐧𝐝 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬. Transformers deliver pretrained LLM. Sentence Transformers provide embeddings for search. Tokenizers handle fast text prep. Instructor enables structured outputs. vLLM powers high throughput inference. 𝟒. 𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐞𝐚𝐫𝐜𝐡 𝐚𝐧𝐝 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥. FAISS delivers fast search on CPU or GPU. HNSWlib supports lightweight ANN search. Annoy is memory efficient. Milvus scales vector databases. Chroma offers simple RAG retrieval. 𝟓. 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐚𝐧𝐝 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. Ray enables distributed compute. Dask handles dataframe scaling. Apache Beam supports batch and streaming. Prefect manages workflows. Hydra handles ML configuration. 𝟔. 𝐒𝐞𝐫𝐯𝐢𝐧𝐠, 𝐎𝐩𝐬, 𝐚𝐧𝐝 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐓𝐨𝐨𝐥𝐢𝐧𝐠. FastAPI powers ML backends. BentoML supports packaging and deployment. MLflow handles tracking and model registry. Pytest supports testing. Ruff provides formatting and linting. This cheatsheet is everything you need to navigate the Python ecosystem. 𝐖𝐡𝐢𝐜𝐡 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐈 𝐞𝐱𝐩𝐚𝐧𝐝 𝐢𝐧𝐭𝐨 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐭𝐮𝐭𝐨𝐫𝐢𝐚𝐥 𝐧𝐞𝐱𝐭? ♻️ Repost this to help your network get started ➕ Follow Jothi Moorthy for more

What is a CDN? This is one of the core concepts to know if you want to design global systems which perform at scale. #csmajors #softwareengineering #coding #programming

Today, we delve into RAFT and PAXOS: We break down their core mechanisms, from leader election to log replication, and explore their real-world applications in etcd, Consul, and Google Chubby. Discover why Raft's focus on understandability has made it a practical favorite, while Paxos remains a foundational, albeit complex, powerhouse. Which algorithm truly leads the pack? Find out! #DistributedSystems #ConsensusAlgorithms #Raft #Paxos #SystemDesign #TechExplained #SoftwareEngineering #FaultTolerance #DataConsistency #DistributedComputing

Linux has knocked out all the competitors from supercomputers. Credits: The reel is based on: - An image of the Frontier supercomputer, which is distributed under the Creative Commons Attribution 2.0 Generic license. - An image of the IBM Sequoia supercomputer, which is in the public domain. - The video “What’s A FLOP?,” which is distributed under the Creative Commons Attribution 3.0 Unported license. License: This reel is distributed under the Creative Commons Attribution-ShareAlike 4.0 International license. #CusDebMagazine #TechJournal #TechMagazine #linux #supercomputer #supercomputers #top50

Apache Spark: 5 Features You NEED NOW ⚡ In this video, we will discuss 5 essential features of Apache Spark that make it a powerhouse for big data processing and analysis. Apache Spark is the leading open-source, distributed processing system used for big data workloads. But what makes it so powerful? We'll explore 5 key Apache Spark features that make it a must-know for any aspiring data professional: * Distributed Computing Framework: Learn how Spark leverages clusters of machines to process massive datasets in a distributed environment. * Versatile Language Support: Discover how Spark supports Java, Python, Scala, R, and SQL, allowing you to choose your preferred language for Spark development. * Blazing Fast Performance: Understand the power of in-memory computing in Spark and how it accelerates your data processing workloads. * Run Anywhere: Explore the flexible deployment options of Spark, from single machines to on-premise clusters, cloud-managed services, and Kubernetes. * Unified Compute Engine: Dive into Spark's capabilities as a unified compute engine, supporting batch processing, real-time processing, machine learning, and graph processing. This versatility makes Spark a Swiss Army knife for modern data engineering projects! Topics Covered: * Distributed Computing Framework * Versatile Language Support (Java, Python, Scala, R, SQL) * Blazing Fast Performance (In-Memory Computing) * Run Anywhere Deployment Options * Unified Compute Engine (Batch, Real-time, Machine Learning, Graph) Whether you're dealing with historical batch processing, high-speed real-time processing, complex machine learning, or intricate graph processing, Apache Spark has you covered! Want to master Apache Spark and gain hands-on experience? Check out our Udemy course: https://www.udemy.com/course/apache-spark-and-databricks-for-beginners/?couponCode=ITV202502 Don't forget to like, comment, and subscribe for more data engineering insights! Which Spark feature do you find most useful? Share your thoughts in the comments!

The random load balancing strategy picks any server, but it doesn’t account for load. Do you think it’s the best approach for high-traffic systems? 🤔 #loadbalancing #randomstrategy #systemdesign #scalability #distributedcomputing #bytemonk #bytemonkbytes

Follow (us) 👉 @tappsly to learn something new everyday! DM for credit or removal request (no copyright intended) All rights and credits reserved to the respected owner(s) The power of a graphics card (GPU) is its ability to handle immense, simultaneous calculations for visuals and complex tasks. ​It is measured by: ​Real-World Performance: Primarily Frames Per Second (FPS) in games, which indicates smoothness. ​Core Count: Thousands of tiny processors (CUDA Cores/Stream Processors) that handle parallel work. More cores means more parallel processing. ​VRAM (Video RAM): Dedicated high-speed memory for storing textures and data. More VRAM is essential for high resolutions (4K) and high-quality settings. ​Specialized Cores: Dedicated units for advanced features like Ray Tracing (realistic lighting) and AI acceleration (e.g., DLSS). ​A powerful GPU balances all these components to deliver high-resolution, high-speed visual experiences. Disclaimer: All the videos, songs, images, and graphics used in this video belong to their respective copyright owners and I or this channel does not claim any right over them This content doesn't belong to me, it is edited and shared only for the purpose of awareness and if the content owner of this content has any issue we request you to directly message me. #technology #viral #future #ai

Amazon Bedrock AgentCore - the agentic platform by AWS is now Generally Available 📣 Move AI agents from prototype to production faster without infrastructure management 🕰️ 🛠️ Here's what makes AgentCore a game-changer for developers ⬇️: 🔧 Runtime: Execute dynamic agent workloads with serverless consumption-based offering supporting up to 8 hours 🔗 Gateway: Transform APIs into agent-ready tools with 1-click integration 🧠 Memory: Maintain short and long-term memory context with industry leading accuracy 🔐 Identity: Enables agents to securely access and operate across enterprise applications on behalf of users 🌐 Browser: Interact with web content through VM-isolated browser runtime 💻 Code Interpreter: Execute code in managed environments with pre-built runtimes 📊 Observability: Monitor and control operations with OTEL-compatible CloudWatch integration Whether you're using Strands Agents SDK, LangChain, or any other framework with your favorite LLM, AgentCore handles the infrastructure complexity so you can innovate with confidence See how you can deploy an agent in less than 60 seconds 🔗 Learn more via link in bio Follow @awsdevelopers for more cloud content ————————— 🏷 #AmazonBedrock #generativeAI #MachineLearning #Serverless #CloudComputing

Comment “CLOUD” to get the links! 🔥 Trying to work in modern tech without understanding Cloud Computing is like building software for a single laptop in a world that runs on distributed systems. If you want scalability, reliability, and real-world engineering skills, this mini roadmap is your entry point. ⚡ Cloud Computing Explained A clear, high-level breakdown of what cloud computing actually is, why it exists, and how companies really use it. ⏱ Cloud Computing in 2 Minutes A fast, simplified overview to lock in the core ideas: servers, regions, scalability, and on-demand infrastructure. ☁ What is Cloud Storage? Understand object storage, why it replaced traditional servers, and how data is stored and accessed at scale. 💡 With these Cloud resources you will: 🚀 Think beyond “my code runs locally” and start thinking in distributed systems 🧠 Understand the foundations behind AWS, Azure, and GCP 🏗 Bridge the gap between writing code and deploying real, scalable applications ☁ Level up for Backend, Cloud, DevOps, and Production Engineering roles If you want to move from “I built an app” to “I deployed a system that scales,” Cloud Computing isn’t optional, it’s foundational. 📌 Save this post so you always have a Cloud roadmap. 💬 Comment “CLOUD” and I’ll send you all the links! 👉 Follow for more Backend Engineering, Cloud, System Design, and Career Growth.

Top 10 Open-Source ML Frameworks / Libraries for beginners: ✅ TensorFlow/Keras: Open-source libraries for deep learning and neural networks, known for scalability and flexibility. ✅ scikit-learn: A Python library for machine learning that offers simple and efficient tools for data analysis and modeling. ✅ PyTorch/PyTorch Lightning: A deep learning framework offering dynamic computation graphs and a more research-friendly environment (Lightning simplifies PyTorch). ✅ XGBoost/LightGBM/CatBoost: High-performance gradient boosting libraries, ideal for structured/tabular data and competitive machine learning. ✅ Hugging Face Transformers: A library for natural language processing tasks, featuring state-of-the-art models like BERT and GPT. ✅ Ray: Distributed computing framework for scaling Python workloads, with Ray Tune optimizing hyperparameters in machine learning. ✅ ChatGPT: An AI-powered conversational assistant built on large language models for natural language understanding and generation ✅ OpenCV: An open-source computer vision library for real-time image processing and computer vision applications. ✅ MLflow: An open-source platform for managing the end-to-end machine learning lifecycle, from experimentation to deployment. ✅ Streamlit: A tool for creating interactive data science applications and visualizations with Python in just a few lines of code. Follow @growdataskills for more 🙌 ➡️ Visit - www.growdataskills.com (Link in Bio as well) 🫂 Join our high quality, affordable, demanding tech skills packaged and Industry level project driven Data BootCAMPs to crack Data Engineering, Data Analytics and Data Science role in top tech companies with High Salary 💯 ✅ Dedicated Placement Assistance and Doubt Support ✅ Call/WhatsApp for any query (+91) 9893181542 ✅ 15 LPA Average Salary By Alumnis ✅ Highly qualified mentors from Microsoft, Amazon, EY, Accenture #ml #machinelearing #ai #frameworks #bigdata