nvidia
55 Skills com documentação substancial e fontes verificadas. Abra um cartão para ler as instruções originais e conferir o pacote e a instalação.
nvidiarag-eval
- Filesystem RAG benchmarks: corpus/, train.json, evaluaterag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.
- Instalações / 1h
- 1
- GitHub Stars
- 3,4 mil
- Atualizado
- 22 de set.
nvidiatao-finetune-clip
CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. Use when fine-tuning or training CLIP, running zero-shot classification, computing image embeddings, or deploying CLIP to ONNX/TensorRT. This is a single-action model skill; do not use it for an iterative weak-attribute improvement loop that keeps retraining and evaluating until progress stops, which belongs to tao-run-deft-pas.
- Instalações / 1h
- 1
- GitHub Stars
- 3,5 mil
- Atualizado
- 25 de set.
nvidiatao-port-huggingface-model
Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). Use when the user asks to "integrate a HuggingFace model into TAO", "add an HF model to TAO Toolkit", "wire a HuggingFace ViT/DETR/ SegFormer into tao-pytorch", "build a TAO trainer + deploy pipeline for an HF CV model", or pastes a HuggingFace model URL/ID and wants it turned into a TAO model. Covers the full 7-phase loop: prerequisites check,
- Instalações / 1h
- 1
- GitHub Stars
- 3,5 mil
- Atualizado
- 25 de set.
nvidiatao-run-inference-service
Start, query, and stop a network-specific TAO inference microservice ({networkarch}-inference-microservice) by delegating container execution to the appropriate platform skill. Handles container image resolution, job-payload JSON construction, and the service registry. Use when the user wants to run inference on a TAO model checkpoint using a microservice container, deploy a TAO inference endpoint, or stop a running inference container.
- Instalações / 1h
- 1
- GitHub Stars
- 3,5 mil
- Atualizado
- 25 de set.
nvidiatao-run-on-brev
Run a TAO training/evaluation/inference container on an NVIDIA Brev GPU instance. Instance provisioning (create/search/stop/delete/login) is delegated to the official brev-cli agent skill or the Brev MCP server; this skill covers only the TAO-specific part — running the container over brev exec via the four-verb docker contract. Trigger phrases include "run on Brev", "Brev GPU instance", "TAO on Brev", "submit job to Brev".
- Instalações / 1h
- 1
- GitHub Stars
- 3,5 mil
- Atualizado
- 25 de set.
nvidiatilegym-converting-cutile-to-julia
Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents. Handles kernel syntax translation, 0-indexed to 1-indexed conversion, broadcasting differences, memory layout (row-major to column-major), type system mapping, and launch API differences. Use when converting, porting, or translating cuTile Python kernels to Julia cuTile.jl, or debugging/optimizing existing Julia cuTile translations.
- Instalações / 1h
- 1
- GitHub Stars
- 3,3 mil
- Atualizado
- 19 de set.
nvidiawarp-compile-time-optimizer
- Use when compile time or startup time is the problem in code that uses Warp: a request to improve, optimize, or cut compile times; an app that is slow to start or stalls at the first wp.launch; seconds of compiling before real work begins; JIT modules recompiling on every run or every CI job. Only applies when the code being optimized uses Warp kernels. Not for steady-state kernel runtime, memory, correctness, building Warp itself from source, or nvcc/C++ build times.
- Instalações / 1h
- 1
- GitHub Stars
- 3,5 mil
- Atualizado
- 25 de set.