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Found 11 Skills
Serves AI models on AMD Instinct GPU hardware using vLLM. Use this skill whenever the user wants to run, serve, deploy, start, host, or launch a language model on an AMD GPU, AMD Instinct, MI300X, MI325X, MI350X, or MI355X. Also use when the user mentions vLLM on ROCm, vLLM on AMD, serving on HBM, or asks how to get a model running on AMD data center hardware. Use when the user asks "run Qwen3", "serve DeepSeek", "start a vLLM endpoint", "get a model running on my AMD machine", or any similar phrasing. Handles the full flow: GPU detection, environment validation, vLLM configuration, launch, and health verification. Do not use for NVIDIA GPUs, consumer AMD GPUs (RX series, Radeon), Ryzen AI, NPU, MI250X, or MI100.
Diagnoses why ROCm, the HIP SDK, PyTorch, or llama.cpp is broken on an AMD GPU on Linux, Windows, or WSL2, then applies a low-risk fix with consent or hands back the exact next step. Also routes Lemonade, LM Studio, and Ollama problems to the right upstream channel. Use when the user reports that ROCm or HIP "isn't working", torch.cuda.is_available() is False, rocminfo / hipInfo can't see the GPU, or hits hipErrorNoBinaryForGpu, HSA_STATUS_ERROR_INVALID_ISA, "invalid device function", "no kernel image is available", cannot open /dev/kfd, permission denied on /dev/kfd, "ROCk module is NOT loaded", a missing libamdhip64.so / amdhip64_6.dll / hipblas.dll / vcruntime140_1.dll, an HSA_OVERRIDE_GFX_VERSION page fault, an iGPU+dGPU crash, a container that can't see the GPU, or an amdgpu-install / DKMS failure. Backed by the `rocm` CLI (`rocm examine` / `rocm diagnose` / `rocm fix`); this skill is a thin driver over those commands, not a re-implementation.
AMD ROCm / HIP GPU コンピューティングリファレンス。 hipMalloc, hipLaunchKernelGGL, HIP Graph, HIPRTC, hipify, rocBLAS, MIOpen, RCCL, rocprofv3, amd-smi, rocminfo, CDNA1-4 / RDNA1-4, MI300 / MI350, gfx942, Matrix Core, wave32/64。
Answers questions about what rocprofiler-systems configuration options exist and how to use them — e.g. "what hardware counters are available on my system", "what ROCm APIs can I trace", "what settings control sampling/output", "how do I persist my configuration" — by mapping the question to the right `rocprof-sys-avail` or `rocprof-sys-run --help=<topic>` command. Use when the user wants to discover or explore ROCPROFSYS_* settings, Timemory components, hardware counters, ROCm API tracing domains, or GPU metrics, rather than apply them. Do NOT use this for picking a starting --preset for a workload — that's `choosing-a-profiling-preset`.
Port a CUDA project to ROCm/HIP for AMD GPUs. Strategy selection, the AMD-strict-where-CUDA-is-lenient fault classes, wavefront-size portability, library substitutions, and the validation bar. Use when planning, implementing, reviewing or validating a CUDA-to-HIP port, or when a HIP build compiles but misbehaves at runtime.
Autonomously optimizes end-to-end LLM inference throughput on AMD Instinct GPUs and reports a validated gain, using the Hyperloom multi-agent optimizer. Given a model, framework, workload (TP/EP, concurrency, ISL/OSL, precision), an objective and a time budget, it explores per-workload which levers to pull (serving/config parameters and env, framework enablement and source patches, and hot GPU-kernel rewrites), benchmarks each candidate, and returns the optimization stack that produced the gain. Use when the user wants to make a model serve faster, raise tokens/sec or throughput, optimize or tune vLLM or SGLang on MI300X/MI325X/MI355X, run Hyperloom, run the kernel-agent, quantize-then-optimize with Quark, set up Hyperloom from scratch, or resume a Hyperloom session. Do not use to stand up a server for plain serving, diagnose a broken ROCm install, or run a one-off kernel/benchmark or trace analysis without the optimization loop.
Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
Guides instrumentation with `rocprof-sys-instrument` - choosing runtime instrumentation vs binary rewrite (`-o`), including or excluding functions and modules with regex options, previewing what will be instrumented with a dry run (`--simulate`, `--dump-info`, `--print-*`), tuning granularity (`--min-instructions`, `--min-address-range`, `--instrument-loops`), rewriting libraries, and diagnosing why a function is missing from, or too numerous in, a trace. Use when the user asks about rocprof-sys-instrument, binary rewrite, instrumenting a shared library, function or module include/exclude regexes, dry runs, or the difference between a function and a module. Do NOT use to pick a `--preset` (use `choosing-a-profiling-preset`), to list settings, counters or ROCm domains (use `discovering-profiling-options`), or to attach to an already-running process (a separate tool, `rocprof-sys-attach`).
Profiles a target application using rocprofv3 with program counter sampling enabled and then analyzes the results. Use when the user has a program that runs on an AMD GPU and asks to perform PC sampling, to determine the runtime performance characteristics of their application, to determine stall reasons, or to determine hotspots in the code.
Installs or verifies AMD Quark and ensures the selected Python environment has an accelerator-matched PyTorch. Applies to "install Quark", "set up Quark", "pip install amd-quark", Quark dependency or import failures, ModuleNotFoundError for quark, and Quark kernel compiler errors. Defaults to GPU and requires explicit confirmation before CPU mode. Covers PyPI universal, exactly matched native, local-wheel, and local-source installs. Does not handle standalone PyTorch requests, ONNX Runtime-only setup, or quantization.
Recommends which rocprofiler-systems runtime profiling preset (balanced, trace-gpu, trace-hpc, sys-trace, workload-trace, etc.) to pass to `rocprof-sys-run --preset=<name>` or `rocprof-sys-sample --preset=<name>` based on the user's workload and profiling goal, and returns the ready-to-run command. Use when the user asks "which preset should I use", describes a workload (GPU kernel, MPI, OpenMP, AI/ML training, production overview) and wants profiling guidance, or invokes `rocprof-sys-run`/`rocprof-sys-sample` without picking a preset. Do NOT use for CMake build presets (`cmake --preset debug|release|ci`) used to configure/compile rocprofiler-systems itself — that is a build-configuration concern, not a profiling-run concern.