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SkillsMP는 intel/skills에서 34개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
수집된 skill 34개 중 34개를 표시합니다.
Create a CUDA-to-XPU migration assessment for an existing AI repo. Identify CUDA-specific assumptions, route to the right XPU skills, produce a migration report. Use when the user has a CUDA repo, notebook, Dockerfile, launch script, HF / vLLM / SGLang…
원문 언어: 영어
Passing dpnp arrays to and from other Python libraries on Intel CPUs and GPUs. Use when dpnp numeric work has to feed pandas, scikit-learn, PyTorch, or TensorFlow, when one of those libraries raises a type error on a dpnp array, when a pipeline mixes device…
원문 언어: 영어
Reading and writing files from dpnp code on Intel CPUs and GPUs. Use when the user needs to load an array into dpnp or save a dpnp result — .npy, .npz, HDF5 via h5py, Zarr, CSV or plain text — when a file is larger than device memory and has to be read in…
원문 언어: 영어
Linear algebra and FFT with dpnp on Intel CPUs and GPUs, backed by oneMKL. Use when a matrix multiply, solve, decomposition, eigenvalue problem, or Fourier transform is the hot part of NumPy code, when the user asks whether dpnp covers a linalg or FFT call,…
원문 언어: 영어
Device memory management for dpnp arrays on Intel CPUs and GPUs. Use when a dpnp script grows in memory until it fails, when a dataset does not fit in device memory, when an array turns out to be on a different device than expected, or when a loop allocates a…
원문 언어: 영어
Porting an existing NumPy or CuPy program to dpnp on Intel CPUs and GPUs. Use when deciding whether a codebase can run on dpnp at all, when a call raises NotImplementedError or AttributeError after the import was swapped, when the user asks whether dpnp…
원문 언어: 영어
NumPy-compatible array operations optimized for Intel hardware. Use when the user wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can run on an Intel CPU or GPU, needs to check dpnp installation or SYCL device selection with dpctl,…
원문 언어: 영어
Random number generation with dpnp on Intel CPUs and GPUs, backed by oneMKL. Use when NumPy random calls move to dpnp, when a seeded dpnp run does not reproduce a NumPy sequence, when a distribution turns out not to be implemented, when results have to be…
원문 언어: 영어
Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises NotImplementedError or an unexpected TypeError, when the import fails or a SYCL runtime library is missing, when no SYCL device is visible, when dpctl reports a device the user did not…
원문 언어: 영어
Profile and fix Linux performance problems using `perf`. Workflows: (A) hardware counters -- IPC, cache-miss, branch mispredictions; (B) hotspot profiling -- which functions and source lines consume CPU, with SIMD and accumulator detection; (C) cache-line…
원문 언어: 영어
Run a GGUF model on an Intel GPU using llama.cpp's SYCL backend (Level Zero) with the official intel.Dockerfile. Covers building the Docker image from source at a pinned tag, launching llama-server with an OpenAI-compatible API, device selection, multi-GPU…
원문 언어: 영어
Deciding whether Intel's MKL extension packages apply to NumPy or SciPy code on Intel CPUs. Use when a user asks whether mkl_fft, mkl_random, or mkl_umath help their code, or points at a snippet, function, file, or codebase using np.fft, scipy.fft, np.random,…
원문 언어: 영어
Estimate whether a Hugging Face decoder-only LLM, MoE, or VLM fits in Intel GPU VRAM for a quantization, context length, concurrency, runtime, and tensor-parallel setting. Use for memory-fit or max-model-len planning before launch. Reports weights, KV cache,…
원문 언어: 영어
Recommend a vLLM-XPU deployment config (quant, KV dtype, DP/TP, max concurrency, max context) for a Hugging Face decoder-only LLM on Intel Arc B-series GPUs using roofline math against published hardware specs. Experimental; predictions are physics-bounded…
원문 언어: 영어
Getting started with Intel oneTBB for C++ parallelism on Intel CPUs. Use when a C++ loop or reduction should run on multiple threads with oneTBB, when the user needs the headers, namespace, or CMake wiring for a first oneTBB program, when a parallel_for body…
원문 언어: 영어
Detect and fix x86/C/C++ performance patterns from source code or profiling output (perf, VTune, flamegraphs). Invoke when the user asks to optimize, review for performance, or write new SIMD/vectorized code — even without profiling data. Trigger on: serial…
원문 언어: 영어
Install, run, parse, and optimize benchmarks from the Phoronix Test Suite (PTS). Use this skill whenever the user mentions "phoronix", "pts/", or "phoronix-test-suite", or asks to run, measure, improve, or optimize a PTS test — e.g., "run pts/mt-dgemm",…
원문 언어: 영어
Benchmark a **running SGLang-XPU server** on an Intel GPU using `sglang.bench_serving`. Measures TTFT, TPOT, ITL, end-to-end latency, and throughput against the OpenAI-compatible endpoint. Use after sglang-xpu-run. Not for vLLM servers (use vllm-xpu-bench) or…
원문 언어: 영어
Serve a Hugging Face safetensors model on an Intel GPU using SGLang's XPU backend with the OpenAI-compatible API. Covers pulling the pre-built `intel/sglang-dev:latest` image, fixing the render-group and UMD/kernel compatibility issues that affect non-root…
원문 언어: 영어
Benchmark a Hugging Face model on an Intel GPU through pure PyTorch + Transformers, **single-process, no HTTP server**. Measures generate() throughput in tokens/sec, time-to-first-token, decode-step latency, and peak XPU memory. Also covers diffusion and…
원문 언어: 영어
Profile a Hugging Face model on Intel GPU at the **PyTorch level** with `torch.profiler` and Kineto. Captures CPU + XPU timeline, exports Chrome trace, identifies hottest kernels and async-overlap gaps. Use when the user asks why a model is slow, which op is…
원문 언어: 영어
Run an arbitrary Hugging Face safetensors model on an Intel GPU using **upstream PyTorch** (>= 2.8) with the built-in `torch.xpu` device. Covers loading from the Hub, picking the right dtype, autocast, multi-GPU with accelerate's `device_map`, and the CUDA ->…
원문 언어: 영어
Benchmark a **running vLLM-XPU OpenAI-compatible server** on an Intel GPU using `vllm bench`. Measures TTFT (time-to-first-token), TPOT (time-per-output-token), ITL (inter-token latency), end-to-end latency, and throughput under concurrency. Covers online…
원문 언어: 영어
Profile a running vLLM-XPU server with torch.profiler around a window of real requests, either via /start_profile and /stop_profile HTTP endpoints or via vllm bench --profile for offline runs. Use to find the dominant op under real concurrent traffic. Not for…
원문 언어: 영어
Serve a Hugging Face safetensors model on an Intel GPU with upstream vLLM-XPU's OpenAI-compatible API. Covers image choice, container launch, the right vllm serve flags (dtype, enforce-eager, model-impl fallback, attention backend, quant + KV-cache pairing),…
원문 언어: 영어
Launch a Docker container with Intel GPU access on Linux. Encodes the correct combination of `--device /dev/dri`, render-group access, `--ipc=host`, `ZE_AFFINITY_MASK` pinning, Hugging Face cache mount, and `--entrypoint /bin/bash` for interactive use. Use…
원문 언어: 영어
Plan an end-to-end Intel XPU model deployment by chaining existing skills. Calls xpu-runtime-preflight (readiness), model-can-it-fit (sizing), model-config-recommend (flags), and the selected runtime skill (vllm-xpu-run / sglang-xpu-run / torch-xpu-run), then…
원문 언어: 영어
Inventory Intel GPUs (Arc, Arc Pro, Data Center GPU Max) on a Linux host. Detect devices, check driver health, list processes using each XPU, run a quick diagnostic, and read live utilisation.
원문 언어: 영어
Before loading a Hugging Face model on Intel XPU, detect its actual type (text generation, text encoder, seq2seq, vision classification, vision-language, audio encoder, audio seq2seq, multimodal VL, diffusion, time-series, reward model, masked LM) so the…
원문 언어: 영어
Execute a single-target CUDA-to-XPU port of a PyTorch repo with libcst-based scan, mechanical rewrite, and CPU FP64 vs target-dtype correctness verify on one forward pass. Use when the request says "port" — "port my repo to XPU", "port my repo at <path> to…
원문 언어: 영어
Profile Intel-XPU workloads at the SYCL / Level Zero kernel level via Intel pti-gpu's unitrace. Captures per-API-call and per-kernel timing, memory transfers, oneCCL / MPI events, and hardware counters PyTorch-level profilers cannot see. Use when a hot op is…
원문 언어: 영어
Run a read-only go/no-go preflight before any Intel GPU/XPU skillpack work. Checks driver health, /dev/dri permissions, render/video groups, Docker, /dev/shm, disk, proxy, and optional container-level XPU visibility. Use when the user asks whether a machine…
원문 언어: 영어
First-time setup for Intel XPU/GPU hosts. Detects what's missing and installs xpu-smi, configures user groups (render), sets up Intel GPU PPA repository, installs Level Zero runtime, installs Docker, and runs a post-setup verification gate. Prompts before…
원문 언어: 영어
What this skill covers, and when an agent should load it. Write it in the words a user would type, not the canonical product name, and say what it is not for. Two or three sentences: the descriptions in this catalog run 450-600 characters, and 1024 is the…
원문 언어: 영어