Programming Tutorials

  Data scientists and Windows developers attempting to leverage AMD hardware for machine learning frequently hit a wall when transitioning from Windows to WSL2. You install a high-end Radeon GPU, initialize an Ubuntu subsystem, and install your ML frameworks, only to be met with   No devices found   errors,   rocminfo   failures, or persistent segmentation faults when invoking tensors. Unlike NVIDIA’s tightly integrated CUDA-on-WSL pipeline, achieving stable WSL2 AMD GPU passthrough requires navigating a fragmented driver architecture. This guide details the exact engineering steps to stabilize ROCm on Windows 11, configure your data science WSL2 setup, and correctly bridge your Radeon GPU into a Linux environment. The Root Cause: Paravirtualization Conflicts and WDDM To fix the driver crashes, you must first understand how WSL2 handles hardware acceleration. WSL2 does not use traditional PCIe passthrough (like VFIO in KVM). Instead, Microsoft implements GPU Par...

  There are few things more frustrating in AI engineering than watching a powerful Llama 3 or Mistral model crawl at 0.5 tokens per second. You have an RTX 3090 or a hefty server GPU, yet your Dockerized Ollama instance insists on burning up your CPU cores instead. If you are running Ollama inside a Docker container and it fails to detect your NVIDIA GPU, the issue is rarely with Ollama itself. The problem lies in the isolation layer between the Docker daemon and the host kernel’s graphics drivers. This guide provides the architectural root cause and the specific, copy-paste configurations required to force GPU passthrough on both native Linux and WSL2 environments. The Root Cause: Why Docker Isolates Your GPU To fix the issue, you must understand the "gap" in the architecture. Docker containers share the host's OS kernel but maintain their own user space (filesystem, libraries, and binaries). By default, a container acts as a clean slate. It does not have access to the h...