The choice of deep learning frameworks increasingly reflects how AI projects are built, from experimentation to large-scale deployment. Hiring decisions now focus on how well candidates can apply ...

JAX is one of the fastest-growing tools in machine learning, and this video breaks it down in just 100 seconds. We explain how JAX uses XLA, JIT compilation, and auto-vectorization to turn ordinary ...

In this article, we are going to explore the machine learning technologies that every machine learning engineer needs to know beyond Pytorch and Tensorflow. As everyone knows, PyTorch and TensorFlow ...

Three critical security flaws have been disclosed in an open-source utility called Picklescan that could allow malicious actors to execute arbitrary code by loading untrusted PyTorch models, ...

This paper investigates the combined potential of neuromorphic and edge computing to develop a flexible machine learning (ML) system designed for processing data from dynamic vision sensors. We build ...

A crafted inference request in Triton’s Python backend can trigger a cascading attack, giving remote attackers control over AI-serving environments, researchers say. A surprising attack chain in ...

Abstract: Jupyter notebooks have become central in data science, integrating code, text and output in a flexible environment. With the rise of machine learning (ML), notebooks are increasingly used ...

The Torch-MLIR project aims to provide first class compiler support from the PyTorch ecosystem to the MLIR ecosystem. This project is participating in the LLVM Incubator process: as such, it is not ...

With the introduction of torch.compile() in PyTorch 2.0, the once purely eager-mode framework has taken a massive leap into the world of compiler-level optimizations. Under the hood, PyTorch now ...