# cudnn Published articles for cudnn. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Protect your AI workloads from supply chain attacks DevFeed: [Protect your AI workloads from supply chain attacks]() Original publisher: [Read original article]() Published: 2026-01-30T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [Containers](), [chainguard](), [Vulnerabilities](), [AI Strategy](), [Open Source](), [Security](), [PyTorch](), [Tensorflow](), [MLOps]() Tags: [ai](), [ai-containers](), [chainguard](), [chainguard-containers](), [chainguard-for-ai](), [chainguard-libraries](), [containers](), [cudnn](), [cves](), [data-processing](), [deployment](), [kserve](), [legacy](), [libraries](), [mlops](), [nemo](), [ollama](), [python](), [pytorch](), [security](), [security-vulnerabilities](), [supply-chain](), [supply-chain-attacks](), [zero-cve-containers]() ### AI overview The article discusses security and operational challenges in AI/ML workloads, including complex dependencies, bloated artifacts, infrastructure sprawl, and unremediated CVEs. It presents Chainguard Containers' minimal images for AI workloads as a way to reduce attack surface, storage needs, and deployment overhead, and cites a 50 MB gpu-operator image compared with a 170 MB upstream equivalent. ### Source excerpt Chainguard secures AI adoption with minimal, zero-CVE containers and source-built libraries that prevent supply chain malware while keeping developers fast. ## What's new in TensorFlow 2.18 DevFeed: [What's new in TensorFlow 2.18]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-10-28T19:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [Tensorflow](), [releases](), [LiteRT](), [NumPy](), [CUDA](), [cudnn](), [NCCL](), [Google]() Tags: [announcement](), [cuda](), [cudnn](), [gpu](), [litert](), [migration](), [nccl](), [nvidia](), [nvidia-rtx](), [python](), [release](), [releases](), [reproducible-builds](), [tensorflow](), [tensorflow-core]() ### AI overview TensorFlow 2.18 introduces NumPy 2.0 compatibility updates, transitions TFLite development to the LiteRT repository, and adds hermetic CUDA, cuDNN, and NCCL dependencies for more reproducible source builds. Binary packages add dedicated kernels for compute capability 8.9 GPUs, including NVIDIA RTX 40 series, L4, and L40, while dropping precompiled support for Maxwell GPUs. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.18 has been released! Highlights of this release (and 2.17) include NumPy 2.0, LiteRT repository, CUDA Update, Hermetic CUDA and more. For the full release notes, please click here. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core NumPy 2.0 The upcoming TensorFlow 2.18 release will include support for NumPy 2.0. While the majority of TensorFlow APIs will function seamlessly with NumPy 2.0, this may break some edge cases of usage, e.g., out-of-boundary conversion errors and numpy scalar representation errors. You can consult the following common solutions. Note that NumPy's type promotion rules have been changed (See NEP 50 for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results. Please see the NumPy 2 migration guide. We've updated some TensorFlow tensor APIs to maintain compatibility with NumPy 2.0 while preserving the out-of-boundary conversion behavior in NumPy 1.x. LiteRT Repository We're making some changes to how LiteRT (formerly known as TFLite) is developed. Over the coming months, we'll be gradually transitioning TFLite's codebase to LiteRT. Once the migration is complete, we'll start accepting contributions directly through the LiteRT repository. There will no longer be any binary TFLite releases and developers should switch to LiteRT for the latest updates. Hermetic CUDA If you build TensorFlow from source, Bazel will now download specific versions of CUDA, CUDNN and NCCL distributions, and then use those tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions because the build no longer relies on the locally installed versions. More details are provided here. CUDA Update TensorFlow binary distributions now ship with d ## Build AI-powered applications using OpenLLM and Vultr Cloud GPU DevFeed: [Build AI-powered applications using OpenLLM and Vultr Cloud GPU]() Original publisher: [Read original article]() Author: vultr Published: 2023-12-12T00:00:00Z Content type: tutorial Language: en Sources: [MDN Blog]() Topics: [Artificial Intelligence](), [Large Language Model](), [Cloud](), [GPU](), [API](), [nginx](), [SSL](), [Security](), [CUDA](), [cudnn](), [Python](), [PyTorch]() Tags: [ai](), [api](), [article](), [cloud](), [cuda](), [cudnn](), [gpu](), [guide](), [large-language-model](), [nginx](), [python](), [pytorch](), [security](), [ssl]() ### AI overview This tutorial explains how to deploy the Falcon 7B model with OpenLLM on a Vultr Cloud GPU server and generate API responses for AI-powered applications. It covers server provisioning, dependency installation, persistent OpenLLM service setup, Nginx reverse-proxy configuration, load balancing, and SSL-based HTTPS security. ### Source excerpt Learn how to build AI-powered apps using OpenLLM and Vultr Cloud GPU. This guide shows how to generate API responses using a Large Language Model. It also covers instructions for setting up an Nginx server and implementing SSL security. ## Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning DevFeed: [Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning]() Original publisher: [Read original article]() Author: Simone Margaritelli Published: 2018-11-22T17:15:50Z Content type: tutorial Language: en Sources: [evilsocket]() Topics: [Keras](), [Tensorflow](), [Framework](), [Deep learning](), [Deep neural networks](), [datasets](), [Open Source](), [Code](), [Development](), [Back end](), [Python](), [Shell]() Tags: [cnn](), [code](), [computer-vision](), [convolutional-neural-networks](), [cpu](), [cuda](), [cudnn](), [cybersecurity](), [deep-learning](), [deep-neural-networks](), [dnn](), [ergo](), [how-to](), [image-classification](), [keras](), [machine-learning](), [neural-networks](), [nvidia](), [open-source](), [planes](), [planes-detector](), [planesnet](), [project-release](), [python](), [shell](), [tensorflow](), [tf](), [train](), [training](), [tutorial]() ### AI overview The article introduces Project Ergo, an open-source framework and manager for Keras-based machine-learning projects. It demonstrates prototyping, training, and testing a convolutional neural network with the PlanesNet dataset to build an airplane detector for satellite imagery, including CPU and GPU setup considerations. ### Source excerpt It's been a while that i've been quite intensively playing with Deep Learning both for work related research and personal projects. More specifically, I've been using the Keras framework on top of a TensorFlow backend for all sorts of stuff. From big and complex projects for malware detection, to smaller and simpler experiments about ideas i just wanted to quickly implement and test - it didn't really matter the scope of the project, I always found myself struggling with the same issues: code reuse over tens of crap python and shell scripts, datasets and models that are spread all over my dev and prod servers, no real standard for versioning them, no order, no structure. So a few days ago I started writing what it was initially meant to be just a simple wrapper for the main commands of my training pipelines but quickly became a full-fledged framework and manager for all my Keras based projects. Today I'm pleased to open source and present project Ergo by showcasing an example use-case: we'll prototype, train and test a Convolutional Neural Network on top of the PlanesNet raw dataset in order to build an airplane detector for satellite imagery. ## Most original prize at The 2017 Deep Learning Hackathon DevFeed: [Most original prize at The 2017 Deep Learning Hackathon]() Original publisher: [Read original article]() Author: Suresh Alse (noreply@blogger.com) Published: 2017-03-29T20:38:00Z Content type: article Language: en Sources: [Life Plus Linux]() Topics: [Deep learning](), [Hackathon](), [Development](), [Nvidia](), [Google](), [cudnn]() Tags: [ai](), [cudnn](), [deep-learning](), [deeplearning](), [dev](), [developers](), [engineering](), [hackathon](), [ml](), [nvidia]() ### AI overview The author describes participating in Deepgram's 2017 Deep Learning Hackathon and building Medivh, a tool intended to predict where users might look on a website before deployment by generating heat maps. The project won the Most original prize, which was an Nvidia Titan X Pascal. ### Source excerpt Although I have worked on several deep learning projects in the past, I still consider myself to be a newbie in deep learning because of all the new things that keep coming up and it is so hard to keep up with all that. So, I decided to take part in "The 2017 Deep Learning Hackathon" by Deepgram to work on something I have been wanting to do for a while now. I built something called Medivh - prophet from Warcraft who has seen the future. The idea was to build a tool for web developers to predict how users are going to see / use the site even before deploying. Basically, it generates heat maps on websites which show where the user might look at. Example: I will write another post with all the technical details. Here is the sneak peak of how it was done. Apart from building that, We got an opportunity to interact with people like Bryan Catanzaro - maker of CUDNN and VP at Nvidia, Jiaji Huangform from Baidu, Jonathan Hseu from Google Brain etc. We also got to interact with people from Deepgram and their caffe like framework called Kur which seems pretty good. I think I'll write a review about Kur after playing around with it for some more time. Also this: Shenanigans at the @DeepgramAI #DLhackathon !! #ai #hackathon pic.twitter.com/ympPZpPWFG -- BEAST Pets (@beastpets) March 26, 2017 This is me presenting before the results. @DeepgramAI #deeplearning #hackathon @GPUComputing @awscloud @googlecloud class activation mapping web dev guidance @Adobe engineering #AI pic.twitter.com/sDD4vrIUAf -- Leo K Tam (@LeoKTam) March 26, 2017 For Medivh, I won the "Most original prize" - Nvidia Titan X pascal. What a beauty! ## Running Tensorflow on AWS GPUs DevFeed: [Running Tensorflow on AWS GPUs]() Original publisher: [Read original article]() Author: Nelson Elhage Published: 2017-02-26T23:41:27Z Content type: tutorial Language: en Sources: [Nelson Elhage]() Topics: [Tensorflow](), [Amazon Web Services](), [Amazon EC2](), [CUDA](), [cudnn](), [Ubuntu]() Tags: [aws](), [cuda](), [cudnn](), [ec2](), [gpu](), [tensorflow](), [ubuntu]() ### AI overview A tutorial-style post describing how to prepare an Ubuntu Amazon EC2 GPU environment for TensorFlow model training. It covers creating an AMI with NVIDIA drivers, CUDA, cuDNN, and GPU-enabled TensorFlow, along with kernel-driver installation issues. ### Source excerpt I've been spending some time learning deep learning and tensorflow recently, and as part of that project I wanted to be able to train models using GPUs on EC2. This post contains some notes on what it took to get that working. As many people have commented, the environment setup is often the hardest part of getting a deep learning setup going, so hopefully this will be useful reference to someone. ## Caffe: Brew your first DNN DevFeed: [Caffe: Brew your first DNN]() Original publisher: [Read original article]() Published: 2015-07-29T11:49:40Z Content type: tutorial Language: en Sources: [Alex Korbonits]() Topics: [Deep learning](), [CUDA](), [Python](), [cudnn](), [Homebrew](), [GPU](), [Google]() Tags: [caffe](), [code](), [cuda](), [cudnn](), [deep-learning](), [dnn](), [gpu](), [guide](), [homebrew](), [how-to](), [installation]() ### AI overview A hands-on guide to installing and configuring Caffe to build a deep neural network, including prerequisites such as CUDA, cuDNN, Homebrew, Python, NumPy, and an NVIDIA GPU for GPU mode. It also introduces Caffe's Model Zoo and its use for application-focused experimentation. ### Source excerpt A hands-on guide to Caffe -- how to brew your first deep neural network and run Google Deep Dream.