The "CUDA out of memory" error in Stable Diffusion WebUI is often caused by configuration issues rather than insufficient GPU hardware, particularly due to PyTorch's memory allocator failing to releas…
This article describes a free, open-source AI engineering curriculum on GitHub maintained by Rohit Ghumare and contributors. The curriculum consists of 435 lessons across 20 phases, teaching algorithm…
The PyTorch Docathon 2026, held from May 5 to May 19, resulted in over 150 merged pull requests after more than 260 registrants and 30 active participants contributed fixes, API documentation, and Exe…
AI-driven approach to improve failure detection and diagnosis in Apache Airflow, a tool for orchestrating ETL pipelines. The method combines large language models (LLMs) for classifying log messages, …
Testing the MiniMax M2.7 model via API on three real-world workflows—refactoring a PyTorch project, drafting knowledge-base notes, and scaffolding a Kaggle competition entry—using Claude Code as the a…
Google Cloud announced a performance boost for AI/ML workloads on PyTorch by integrating its Colossus-powered Rapid Storage directly via the fsspec interface and gcsfs. The new Rapid Bucket solution b…
PyTorch's `torch.load()` function uses the Python pickle format for serialization, which inherently executes arbitrary code when loading a file, making it trivially exploitable for remote code executi…
This week's top GPU news highlights three key developments: the release of Fournex, an open-source tool that analyzes Nsight Compute data to identify and suggest fixes for PyTorch/CUDA bottlenecks; a …
PyTorch 2.11 now enables direct installation of CUDA-enabled PyTorch wheels on aarch64 Linux from PyPI, eliminating the need for custom package indexes and workarounds that previously complicated depl…
The ExecuTorch MLX delegate now enables GPU-accelerated inference for PyTorch models on Apple Silicon Macs through Apple's MLX framework. The new backend achieves 3-6x higher throughput on generative …
PyTorch introduced a "fake export" method that allows developers to verify the exportability of large deep learning models using `torch.export` APIs without requiring actual GPU memory. The approach u…
PyTorch 2.12 introduces a new device-agnostic `torch.accelerator.Graph` API that unifies graph capture and replay across CUDA, XPU, and out-of-tree backends. The release delivers up to 100x faster bat…
Arm has released a set of hands-on Jupyter labs demonstrating how to deploy AI models on edge devices using ExecuTorch, an extension of the PyTorch ecosystem designed for local inference on constraine…
Convergent infrastructure requirements for the foundation model lifecycle—including tightly coupled accelerator compute, high-bandwidth networking, and distributed storage—and highlights the growing r…