# PyTorch Published articles for PyTorch. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Hands-On Evolution of Deep Learning - Geoffrey Hinton's AI Legacy DevFeed: [Hands-On Evolution of Deep Learning - Geoffrey Hinton's AI Legacy]() Original publisher: [Read original article]() Author: Beau Carnes Published: 2026-09-17T18:00:11Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Artificial Intelligence](), [Deep neural networks](), [Deep learning](), [PyTorch](), [Algorithms]() Tags: [ai](), [algorithm](), [deep-learning](), [experiments](), [neural](), [neural-networks](), [pytorch](), [tutorial](), [youtube]() ### AI overview A 27-hour video course traces Geoffrey Hinton's contributions to deep learning, from Boltzmann Machines and backpropagation through AlexNet, dropout, knowledge distillation, capsule networks, and the Forward-Forward Algorithm. It explains the motivation and core ideas behind the research and includes PyTorch implementations, visualizations, and experiments. ### Source excerpt Geoffrey Hinton is known as the "the Godfather of AI". We just published a course on the freeCodeCamp.org YouTube channel that will help you master modern neural networks by recreating the groundbreak ## Fault tolerant distributed training on Amazon EKS using NVRx DevFeed: [Fault tolerant distributed training on Amazon EKS using NVRx]() Original publisher: [Read original article]() Author: Aravind Neelakantan Published: 2026-09-16T18:59:25Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [NCCL](), [Amazon Elastic Kubernetes Service](), [PyTorch](), [distributed-training](), [Resilience](), [GPU]() Tags: [advanced-300](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [async](), [benchmarks](), [distributed-training](), [gpu](), [pytorch](), [resiliency](), [technical-how-to]() ### AI overview This tutorial integrates NVIDIA Resiliency Extension (NVRx) with PyTorch FSDP training on Amazon EKS. It covers asynchronous checkpointing, in-process restart, and in-job restart, and reports H100 benchmarks at 2- to 8-node scale with 99%+ training efficiency and recovery measured in seconds. ### Source excerpt Integrate NVIDIA Resiliency Extension (NVRx) into PyTorch FSDP training on Amazon EKS to overlap checkpoint I/O with training and recover from GPU faults in seconds. This post covers async checkpointing, in-process restart, and ft_launcher in-job restart, with H100 benchmarks at 2 to 8 nodes showing 99%+ training efficiency and second-scale recovery. ## On-Device AI Series (Part 5): LiteRT-LM DevFeed: [On-Device AI Series (Part 5): LiteRT-LM]() Original publisher: [Read original article]() Author: Oğuzhan Aslan Published: 2026-09-14T05:59:12Z Content type: tutorial Language: en Sources: [ProAndroidDev - Medium]() Topics: [LiteRT](), [On-device AI](), [Large Language Model](), [Inference](), [Streaming](), [GPU](), [LLM evaluation / benchmarking](), [PyTorch]() Tags: [ai](), [android](), [android-development](), [benchmarking](), [gpu](), [inference](), [litert](), [llama](), [llm](), [multimodal](), [on-device](), [on-device-ai](), [programming](), [pytorch](), [qwen](), [software-development](), [streaming]() ### AI overview This tutorial explains LiteRT-LM for running large language models on-device. It covers the Engine/Session API, streaming output, system prompts, tool calling, multimodal inputs, thinking mode, and CPU-versus-GPU benchmarking. The article also discusses tradeoffs involving privacy, network independence, latency, memory, sampling configuration, and model capability compared with cloud APIs. ### Source excerpt Put your phone in airplane mode. Open the app, type a question, and watch the answer arrive one token at a time -- no spinner waiting on a network round-trip, no API key, no per-token bill, and nothing you typed ever leaving the device. LiteRT-LM removes the genuinely hard parts of running an LLM on-device -- KV-cache management, token streaming, backend selection -- but it doesn't remove your job so much as relocate it. What's left on your plate is a short, specific list: sizing a combined input+output token budget, owning your own sampling defaults, hand-building system prompts and tool calling out of raw text, and one native-library collision that presents as a SIGSEGV rather than a build error. Know those going in and the API itself is a clean three-step pattern. We'll get there in that order: Why you'd choose this runtime and what it costs you versus the cloud. The Engine/Session model you need to read the code at all. Real implementation samples -- streaming, system prompts and tool calling, multimodal inputs, thinking mode, and CPU-vs-GPU benchmarking. The anti-patterns to avoid. A developer-friendliness rating on the same rubric as Parts 1-4. Why Use LiteRT-LM? You reach for LiteRT-LM instead of hand-rolling generation on top of raw LiteRT when: You need multi-turn conversation, not single-shot inference -- session state and KV-cache bookkeeping are handled for you, and resetting a conversation is a session swap, not a model reload. You need streaming output -- token-by-token delivery for a responsive chat UI, instead of a blocking call that returns everything at once. You're choosing between CPU and GPU per device -- the explicit backend parameter turns that into a runtime decision instead of a build-time guess. You want a pre-converted model without doing your own PyTorch-to-LiteRT conversion work -- the Model Zoo covers Gemma, Qwen, Llama, and more out of the box. You're willing to own sampling -- the engine won't pick sane decoding defaults for you; that's on the ## HeyGen x Google Cloud: Bringing Avatar IV to TPUs DevFeed: [HeyGen x Google Cloud: Bringing Avatar IV to TPUs]() Original publisher: [Read original article]() Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Google](), [Cloud](), [Artificial Intelligence](), [Optimization](), [AI Infrastructure](), [Transformer](), [PyTorch](), [Compiler](), [Streaming](), [real-time](), [Hardware](), [API]() Tags: [ai](), [ai-infrastructure](), [api](), [cloud](), [code](), [compiler](), [engineering](), [generation](), [google](), [hardware](), [model](), [optimization](), [performance](), [pytorch](), [real-time](), [streaming](), [time](), [tpu](), [transformers](), [video]() ### AI overview HeyGen and Google Cloud describe porting the 18B+ parameter Avatar IV talking-head video generation pipeline to an eight-chip Trillium TPU host. Using torchax, JAX, XLA, FSDP sharding, Ulysses sequence parallelism, and custom Pallas kernels, the team improved performance by 1.86x for real-time chunked streaming while preserving output quality through strict quality gates. ### Source excerpt HeyGen ported their 18B+ parameter Avatar IV video generation model to Google Cloud's Trillium (v6e) TPUs via torchax and XLA, utilizing FSDP and Ulysses sequence parallelism across an eight-chip mesh. To achieve a 1.86x speedup for real-time streaming, the engineering team pipelined exposed all-to-all collectives, aligned sparse attention block sizes to eliminate mask padding, and bypassed softmax serial dependencies using a precomputed Cauchy-Schwarz upper bound. These custom Pallas kernel and compiler optimizations were deployed only after passing rigorous two-tier quality gates to guarantee byte-identical or mathematically equivalent pixel outputs. ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers DevFeed: [Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers]() Original publisher: [Read original article]() Author: Ananth Raghavendra Published: 2026-09-09T15:51:29Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Inference](), [model-deployment](), [Amazon Elastic Kubernetes Service](), [Containers](), [vlm](), [GPU](), [PyTorch]() Tags: [amazon-eks](), [containers](), [gpu](), [inference](), [intermediate-200](), [pytorch](), [technical-how-to](), [vulnerabilities]() ### AI overview The article introduces AWS Ray Serve Deep Learning Containers as a maintained, pre-tested inference option for teams moving from unmaintained TorchServe workloads. It demonstrates deploying a vision-language model on Amazon EKS using a single GPU node. ### Source excerpt TorchServe is no longer maintained, leaving teams to own the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported, pre-tested container with the framework, GPU drivers, and serving layer already assembled. This post walks through deploying a vision-language model on Amazon EKS using the Ray Serve DLC on a single GPU node. ## Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod DevFeed: [Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod]() Original publisher: [Read original article]() Author: Paulo Aragão Published: 2026-09-08T19:12:51Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Language models](), [Training AI Models](), [compression and generalization](), [LLM evaluation / benchmarking]() Tags: [amazon](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [announcements](), [architecture](), [benchmark](), [chain-of-thought](), [distributed-training](), [intermediate-200](), [pytorch](), [transformer-architecture]() ### AI overview Pathway describes BDH, a brain-inspired architecture that performs reasoning in latent space rather than producing chain-of-thought token traces. The article covers its recurrent internal memory, its contrast with transformer limitations, and scaling training with Amazon SageMaker HyperPod. ### Source excerpt Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark. ## Cloud Native Computing Foundation Announces Karmada Graduation DevFeed: [Cloud Native Computing Foundation Announces Karmada Graduation]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T02:06:17Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Kubernetes](), [Orchestration](), [Cloud Native Ecosystem](), [Inference](), [autoscaling](), [GPU](), [cloud-infrastructure]() Tags: [ai](), [ai-infrastructure](), [announcements](), [autoscaling](), [china](), [cloud](), [critical-infrastructure](), [ecosystem](), [enterprise](), [global](), [gpu](), [inference](), [infrastructure](), [kubernetes](), [open-source](), [orchestration](), [production](), [pytorch](), [release](), [resilience](), [scale]() ### AI overview Karmada has graduated from the CNCF after reaching production maturity as an open-source engine for orchestrating applications across multiple Kubernetes clusters, clouds, and regions. Its v1.19 release adds multi-component scheduling for distributed AI training jobs, while the project supports hybrid-cloud capacity, multi-region resilience, and multi-cluster AI infrastructure. ### Source excerpt Multi-cluster, multi-cloud Kubernetes orchestration project reaches production maturity as global enterprises scale AI training and inference across hybrid infrastructure Key Highlights SHANGHAI, China - KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China 2026 -... ## Fast model loading for AI inference on Amazon EKS DevFeed: [Fast model loading for AI inference on Amazon EKS]() Original publisher: [Read original article]() Author: Sajjan Gundapuneedi Published: 2026-09-01T15:48:15Z Content type: article Language: en Sources: [Containers]() Topics: [Inference Performance](), [Amazon Elastic Kubernetes Service](), [Amazon S3](), [Language models]() Tags: [advanced-300](), [ai](), [ai-inference](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [best-practices](), [cuda](), [gpu](), [inference](), [kubernetes](), [language-models](), [pytorch](), [s3](), [sglang](), [startup](), [vllm]() ### AI overview The article analyzes cold-start delays for AI inference pods on Amazon EKS. It finds that startup bottlenecks vary by model size: torch.compile dominates for smaller models, while loading weights from S3 to GPU memory dominates for larger models. Configuration changes to Run:ai Model Streamer reduce model-loading time on repeat launches. ### Source excerpt When you scale AI inference on Amazon EKS, every new pod must load model weights into GPU memory before serving traffic. We investigated where cold-start time goes and found two configuration-only changes to Run:ai Model Streamer that cut model startup time by 80-93% on subsequent launches, with no code changes. ## 9 GPU Concepts Every AI Engineer Should Know DevFeed: [9 GPU Concepts Every AI Engineer Should Know]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-31T15:19:52Z Content type: tutorial Language: en Sources: [Dr. Ashish Bamania]() Topics: [GPU](), [PyTorch](), [Large Language Model](), [Artificial Intelligence]() Tags: [ai-engineer](), [artificial-intelligence](), [coding](), [compute](), [cpu](), [data-science](), [gpu](), [guide](), [internals](), [llms](), [machine-learning](), [nvidia](), [programming](), [pytorch](), [technology]() ### AI overview A practical guide to nine GPU concepts relevant to AI engineers. It introduces GPU computation hardware, memory, program execution, and multi-GPU connections, including streaming multiprocessors in NVIDIA GPUs and their role in parallel computation. ### Source excerpt A simple and practical guide to GPU internals that AI engineers actually need. Continue reading on Level Up Coding " ## Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect DevFeed: [Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-28T17:06:28Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering](), [C++](), [Orchestration]() Tags: [ai](), [ai-inference](), [ai-models](), [api](), [applications](), [c-plus-plus](), [data-center-cloud](), [deployment](), [developer-tools-techniques](), [edge-computing](), [hugging-face](), [inference](), [integration](), [kernel](), [model](), [nvidia](), [open](), [open-source](), [orchestration](), [python](), [pytorch](), [tensorrt]() ### AI overview The article explains NVIDIA TensorRT Model Connect, a collection of modifiable reference implementations for deploying supported open models from a Hugging Face ID or local checkpoint to native C++ inference. It describes a two-phase deployment bundle workflow, semantic and module-level C++ APIs, and custom GPU-kernel integration. ### Source excerpt Open AI models are evolving faster than ever, but bringing them into native applications can still require model-specific conversion, preprocessing,... ## PyTorch vs. TensorFlow: Differences, Performance, and How to Choose DevFeed: [PyTorch vs. TensorFlow: Differences, Performance, and How to Choose]() Original publisher: [Read original article]() Author: NICOLAS PIRO, DATA SCIENTIST AND AI DEVELOPER @ TOPTAL Published: 2026-08-27T04:00:00Z Content type: comparison Language: en Sources: [Toptal Blog]() Topics: [Training AI Models]() Tags: [deep-learning](), [deployment](), [experimentation](), [infrastructure](), [performance](), [pytorch](), [tensorflow](), [tooling]() ### AI overview A comparison of PyTorch and TensorFlow for deep-learning experimentation, model design, production workflows, tooling, and infrastructure considerations. ### Source excerpt This comprehensive guide explores how PyTorch and TensorFlow shape deep-learning work in 2026, from experimentation and model design to production workflows, ecosystem tooling, and infrastructure considerations. ## CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access DevFeed: [CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-25T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [Python](), [GPU](), [C++](), [toolchain](), [PyTorch](), [RAPIDS](), [releases]() Tags: [c-plus-plus](), [cuda](), [data-science](), [developer-tools-techniques](), [gpu](), [numba](), [python](), [pytorch](), [rapids](), [release](), [toolchain]() ### AI overview CUDA Python 1.0 makes the CUDA platform accessible from Python through stable, versioned components, including runtime access, low-level CUDA C API bindings, parallel algorithms, component discovery, and NVIDIA math libraries. The release establishes a shared foundation for Python GPU libraries and introduces semantic-versioning commitments for public APIs. ### Source excerpt For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain, and... ## How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit DevFeed: [How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-18T18:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [ALCHEMI](), [AI-assisted coding](), [Agent Skills](), [PyTorch](), [CUDA](), [GPU](), [Python](), [AI Development]() Tags: [agent-skills](), [agentic-ai-generative-ai](), [ai-agent](), [ai-coding](), [alchemi](), [coding](), [computational-chemistry-materials-science](), [compute](), [cuda](), [developer-tools-techniques](), [featured](), [gpu](), [python](), [pytorch](), [simulation-modeling-design]() ### AI overview This article presents an end-to-end workflow for using AI coding agents with NVIDIA ALCHEMI Toolkit to build GPU-accelerated atomistic materials simulations. It explains how ALCHEMI agent skills and reference files provide API knowledge, describes the Python, PyTorch, CUDA and NVIDIA GPU environment, and reports validation across 45 generated pipelines. ### Source excerpt Atomistic simulation requires three things: knowledge of the science, compute-efficient implementation of simulations, and accessible interfaces to the... ## Making Knowledge Distillation Cheap Enough to Run at Scale DevFeed: [Making Knowledge Distillation Cheap Enough to Run at Scale]() Original publisher: [Read original article]() Author: Antonio Tiene; Iker García-Ferrero; Ali Hashemi; Bakbergen Ryskulov Published: 2026-08-10T10:05:36Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [LLM Techniques](), [Large Language Model](), [Post-training optimization](), [Model Routing](), [GPU](), [Machine learning](), [PyTorch](), [gpt-oss](), [Nvidia]() Tags: [blog](), [caching](), [compute](), [cost](), [gpt](), [gpt-oss](), [gpu](), [hugging-face](), [language-models](), [large-language-models](), [long-context](), [machine-learning](), [memory](), [performance](), [pytorch](), [qwen](), [research](), [training]() ### AI overview This article presents an efficient approach to knowledge distillation for large language models. It caches the teacher model's top-K logits offline and uses a fused, memory-efficient chunked KL-divergence loss, avoiding simultaneous teacher-student residency and full vocabulary-by-sequence-length matrices. The changes reduce VRAM use and training cost, enabling long-context distillation on a single GPU and making larger-scale experimentation more practical. ### Source excerpt A Blog post by Multiverse Computing on Hugging Face ## Run High-Performance Core Math at Scale with NVIDIA nvmath-python DevFeed: [Run High-Performance Core Math at Scale with NVIDIA nvmath-python]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-30T22:43:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Python](), [CUDA](), [math](), [GPU](), [NumPy](), [cpu](), [PyTorch](), [pip]() Tags: [agentic-ai-generative-ai](), [c-plus-plus](), [ci-cd](), [cpu](), [cuda](), [cuda-x](), [data-science](), [developer-tools-techniques](), [device-apis](), [featured](), [gpu](), [multi-gpu](), [nvidia](), [performance](), [python](), [pytorch](), [scale]() ### AI overview NVIDIA nvmath-python 1.0 provides a Pythonic interface to CUDA-X and NVPL math libraries, enabling optimized numerical operations on CPUs, CUDA GPUs, and distributed multi-GPU, multi-node systems. The article covers its sparse-tensor approach, flexible installation options, and interoperability with NumPy, CuPy, and PyTorch. ### Source excerpt NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users... ## Profiling in PyTorch (Part 3): Attention is all you profile DevFeed: [Profiling in PyTorch (Part 3): Attention is all you profile]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Sergio Paniego; Sayak Paul; Rémi Ouazan Reboul Published: 2026-07-10T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [jobs](), [cpu]() Tags: [artificial-intelligence](), [cudnn](), [flash](), [gpu](), [jobs](), [kernel](), [profile](), [pytorch](), [torch](), [traces](), [xformers]() ### AI overview A PyTorch profiling tutorial examines naive attention, identifying its primitive operations and the CPU and GPU kernels they launch. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 10 Years of Meta's Commitment to Python DevFeed: [10 Years of Meta's Commitment to Python]() Original publisher: [Read original article]() Author: Chris Wiltz Published: 2026-06-30T16:00:46Z Content type: opinion Language: en Sources: [Meta AI Research](), [Meta ML Applications]() Topics: [Meta](), [Python](), [Open Source](), [Programming language](), [Software](), [PyTorch]() Tags: [ai-research](), [culture](), [data-infrastructure](), [devinfra](), [engineering](), [meta](), [ml-applications](), [open-source](), [production-engineering](), [programming](), [python](), [pytorch](), [software]() ### AI overview Meta reflects on its 10th consecutive year sponsoring the Python Software Foundation and explains Python's importance across its engineering stack, including products, infrastructure, and AI research. ### Source excerpt This year marks Meta's 10th consecutive year as a sponsor of the Python Software Foundation (PSF), the charitable organization dedicated to advancing, supporting, and protecting the open-source Python programming language and the community that sustains it. Python is one of the world's most influential programming languages, and we use it across our engineering stack, from [...] Read More... The post 10 Years of Meta's Commitment to Python appeared first on Engineering at Meta. ## Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP DevFeed: [Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Rémi Ouazan Reboul; Sergio Paniego; Pedro Cuenca; Sayak Paul Published: 2026-06-11T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [cpu](), [jobs]() Tags: [cpu](), [cuda](), [deep-learning](), [gpu](), [jobs](), [kernels](), [liger](), [mlp](), [open-source](), [profile](), [pytorch](), [torch](), [traces]() ### AI overview A PyTorch profiling tutorial that moves from nn.Linear to a fused multilayer perceptron, explaining GPU kernels, CPU launch overhead, and tensor-transpose behavior in profiler traces. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The next chapter in flood resilience: Open sourcing Google's hydrology framework DevFeed: [The next chapter in flood resilience: Open sourcing Google's hydrology framework]() Original publisher: [Read original article]() Published: 2026-06-03T18:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Artificial Intelligence](), [AI Models](), [Resilience](), [Open Source](), [GitHub](), [Earth AI](), [Python](), [PyTorch](), [Google AI]() Tags: [ai](), [ai-models](), [architecture](), [climate-sustainability](), [data](), [earth-ai](), [generative-ai](), [github](), [google](), [open-source](), [open-source-models-datasets](), [python](), [pytorch](), [research](), [resilience](), [sustainability](), [training]() ### AI overview Google Research is open-sourcing a Python and PyTorch hydrology framework for AI-based riverine flood forecasting, enabling meteorological and hydrological agencies to use local data, train models, and improve flood warnings. ### Source excerpt Climate & Sustainability ## Executable UDFs are now in public beta on ClickHouse Cloud DevFeed: [Executable UDFs are now in public beta on ClickHouse Cloud]() Original publisher: [Read original article]() Author: San Tran; Jia Xu; Zach Naimon; Ilya Andreev; Hanzi Jiang; Kevin Zhang Published: 2026-05-30T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Python](), [SQL](), [Cloud](), [Inference](), [real-time](), [PyTorch](), [data](), [model-deployment](), [Databases](), [Embeddings](), [Next.js]() Tags: [clickhouse](), [cloud](), [data](), [deployment](), [embeddings](), [inference](), [model](), [next-js](), [python](), [pytorch](), [real-time](), [sql]() ### AI overview ClickHouse Cloud has released executable user-defined functions in public beta. Developers can upload Python functions and invoke them from SQL, including in materialized views, while ClickHouse manages sandboxed processes near the data. The article demonstrates real-time anomaly detection on equity trade ticks with a PyTorch autoencoder and a Next.js front end. ### Source excerpt Today we're excited to announce that executable UDFs are now available in public beta on ClickHouse Cloud. You can write a function in Python, upload it as a zip to your cluster, and call it from SQL like any built-in. ClickHouse manages a pool of long-li ## Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler DevFeed: [Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Sayak Paul; Sergio Paniego; Rémi Ouazan Reboul; Pedro Cuenca Published: 2026-05-29T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [GPU](), [AI, ML & Data Engineering](), [cpu](), [jobs]() Tags: [cpu](), [cuda](), [gpu](), [guide](), [how-to](), [jobs](), [kernel](), [kernels](), [llm](), [open-source](), [profile](), [python](), [pytorch](), [torch](), [traces](), [training](), [tutorials]() ### AI overview A beginner-focused tutorial on using torch.profiler to inspect PyTorch execution, interpret CPU and GPU traces, and connect Python operations to CUDA kernels. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Running the Latest vLLM on the NVIDIA DGX Spark DevFeed: [Running the Latest vLLM on the NVIDIA DGX Spark]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-05-21T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [vllm](), [DGX Spark](), [Docker](), [Dockerfile](), [GitHub Actions](), [NCCL](), [CUDA](), [PyTorch](), [GitHub](), [NGC](), [GPU](), [Ubuntu]() Tags: [ai](), [cuda](), [dgx-spark](), [docker](), [docker-image](), [github](), [github-actions](), [gpu](), [homelab](), [nccl](), [ngc](), [nvidia-dgx](), [pytorch](), [self-hosted](), [ubuntu](), [vllm]() ### AI overview This tutorial presents a reproducible Docker image pipeline for running current vLLM releases on the NVIDIA DGX Spark's GB10 ARM64 platform. It explains why NVIDIA's NGC images can lag upstream and describes a GitHub Actions build on a self-hosted Spark runner with pinned inputs and generated lockfiles. ### Source excerpt When I built my local AI cluster on a pair of ASUS Ascent GX10s, the hard part was not serving a model. The hard part was getting a working vLLM image with current components. NVIDIA's official image was already over a month behind by the time I needed it, and waiting on their release schedule was not an option. If you saw that post, you know the GX10 is an ARM64 machine built around NVIDIA's ... ## PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend DevFeed: [PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend]() Original publisher: [Read original article]() Author: AlexZhang; Cuicheng; Jun Zhang; Manhui Lin; Yue Zhang Published: 2026-05-18T15:12:46Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [document ai](), [Inference](), [hugging face](), [PyTorch](), [Retrieval Augmented Generation (RAG)]() Tags: [document-ai](), [hugging-face](), [inference](), [ocr](), [python](), [pytorch](), [rag](), [rocm](), [transformers]() ### AI overview PaddleOCR 3.5 adds Transformers as a supported inference backend for OCR and document parsing models, while PaddleOCR continues to manage the underlying pipelines. The release simplifies integration with Hugging Face-centered environments and downstream document workflows such as RAG, search, analytics, and automation. ### Source excerpt PaddleOCR continues to provide OCR model series such as PP-OCRv5 and document parsing model series such as PaddleOCR-VL 1.5, while Transformers becomes one of the supported backends for running them. Try the live demo on Hugging Face Spaces: PaddleOCR 3.5 introduces a more flexible inference-engine interface. Developers can select the backend through the parameter and pass backend-specific options through . [Next page]()