Compiler infrastructures such as MLIR rest on a set of design principles: IR abstractions, interfaces, match-and-rewrite, flow analysis, type conversion, staged lowering, and so on. These concepts have proven themselves in practice. Good designs typically arrive through engineering knowledge, intuition and experience. Many of them, however, have correspondences in formal theory. MLIR's match-and-rewrite engine has correspondence to a \emph{term-... Read more ›
LeetCode for Machine Learning. Practice ML coding problems with a real Python execution environment. Read more ›
A step towards generalizing the transformer architecture Read more ›
Introduction Part 1 measured the dual GH200 workstation as a memory system. Part 2 used those measurements to explain why DeepSeek V4 Flash can be fast in vLLM when the model layout fits the hardware: keep hot weights in HBM, avoid unnecessary Hopper-to-Hopper traffic, and use MTP only where the acceptance rate pays for the draft work. GLM-5.2 starts at 2.39 output tok/s on this machine and a... Read more ›
An AMD engineer has contributed to the upstream FFmpeg library an ONNX Runtime back-end for its DNN filter. The FFmpeg Deep Neural Network (DNN) filters allow for running AI models natively inside the video processing pipeline for upscaling, object detection, background segmentation, and more. This ONNX Runntime back-end support is notable in that it expands the GPU and NPU capabilities with FFmpeg... Read more ›
Hyperparameter selection is a critical step in the deployment of modern artificial intelligence systems, given the need to tune degrees of freedom such as inference-time parameters, implementation-level settings, and thresholds driving decision rules. Despite its practical importance, hyperparameter selection is typically performed using best-effort empirical methods such as grid search or Bayesian optimization, which provide no formal statistic... Read more ›
Four browser environments that exposed WebGPU, and what the measurements say about whether a small LLM run completes. Read more ›
A patch release, mostly of bugfixes. Note: one of these includes a behavior change, which is that the primary server function encodings now respect the Axum/Actix request body size limits, rather t... Read more ›
The Open Source Technology Improvement Fund is proud to share the results of our security audit of Kubeflow. Kubeflow functions for building and deploying customizable machine learning workflows in Kubernetes, and has many subprojects able to be implemented individually or in combination. Thanks to ADA Logics and the Cloud Native Computing Foundation, Kubeflow underwent a custom security engagement that audited 6 projects in the Kubeflow ecosystem. Read more ›
Learn about Distributed Training in TensorFlow. Explore the basics of parallel computing and distributed strategies for training… Read more ›
Position encoding recently has shown effective in the transformer architecture. It enables valuable supervision for dependency modeling between elements at different positions of the sequence. In this paper, we first investigate various methods to integrate positional information into the learning process of transformer-based language models. Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information. Specifically, the proposed RoP... Read more ›
Impact of Linux Kernel vulnerabilities on B&R products apeterson Jun 23, 2026 Release DateJune 23, 2026 DescriptionSummaryB&R is aware of publicly reported vulnerabilities affecting the Linux kernel versions shipped with the products listed as affected in the advisory. Successful local exploitation of these vulnerabilities could allow an attacker to escalate privileges on the affected system. Public proof-of-concept exploits are available for the vulnerabilities described herein. At the time ... Read more ›
Deep learning has become an important tool in computational pathology, enabling automated analysis of histopathological images. While convolutional neural networks (CNNs) have traditionally dominated this field, transformer-based and hybrid architectures have recently demonstrated promising performance. However, comprehensive comparisons of these approaches for colorectal histopathology remain limited. This study evaluated twelve ImageNet-pretra... Read more ›
The use of neural networks (NNs) is rapidly increasing, including in safety- and security-critical domains. To provide formal guarantees about NN behavior, many verification methods rely on optimizable linear relaxations of activation functions. However, existing techniques depend on hand-crafted relaxations for each activation function. Extension to state-of-the-art activation functions therefore requires substantial manual effort. In contrast,... Read more ›
Dell Technologies has introduced the PowerEdge XE8812, a new liquid-cooled server platform designed for large-scale inference and high-performance computing workloads. The system joins the Dell AI Factory with the NVIDIA portfolio. It is built around the NVIDIA Vera Rubin NVL4 architecture, offering up to 144 GPUs per rack in a dense rack-scale configuration. The announcement The post appeared first on <a href=" Read more ›
If you are learning Machine Learning, you have probably lived this exact scenario: You spend hours cleaning a dataset, you build a PyTorch… Read more ›
If you told someone you were an “AI Engineer” a few years ago, they probably assumed you were elbow-deep in PyTorch, wrangling massive… Read more ›
Architect a robust MLOps pipeline from scratch using Python, Prefect, MLflow, and Flask to power real-time e-commerce tech. Read more ›
The unified AI inference stack - from custom GPU kernels to production cloud serving on NVIDIA and AMD. 2x performance. Top open models. Open source stack. Read more ›
The choice of loss function and optimizer is an important decision, that shapes further model training. Yet automated architecture search pipelines (AutoML) benefits significantly more from the optimal pairing selection and vice versa. This paper investigates whether a single recipe is sufficient for heterogeneous architecture pools, or whether the optimal pairing varies across structurally diverse models. We conduct a systematic empirical study... Read more ›