Packs

2 packs

Docker, Kubernetes, CI/CD and cloud (AWS/Azure/GCP) automation.

24 skills · pack

@pwdev-solucoes

DevOps com execução segura v1.0 — leitura livre, mutação sob confirmação por comando, destrutivo bloqueado por guard script; 19 skills (AWS, Kubernetes, Docker, Linux, Nginx, PostgreSQL, observabilidade, incidente, segurança, Proxmox, automação, performance, backup, Laravel, confiabilidade, FinOps, IA, documentação) e 4 subagentes

19 skills · pack

Results for “docker”

9 skills

tao-run-on-local-docker

Run TAO SDK jobs as Docker containers on a local or remote Docker daemon with NVIDIA GPU support, including preflight checks and credential handling.

orchestra-research

nemo-evaluator-sdk

Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution on local Docker, Slurm HPC, or cloud platforms.

10.4k · bundle

tao-analyze-gaps-visual-changenet

Identifies the weakest samples per ground-truth label in NVIDIA TAO VCN Classify experiments by running a Docker container that performs threshold sweep, weakness scoring, and per-lighting expansion, then surfaces top-K weak samples for downstream augmentation or relabeling.

tao-train-foundation-stereo

Trains, evaluates, exports, and runs inference on FoundationStereo models for stereo depth estimation and 3D reconstruction from stereo image pairs.

More results

tao-port-huggingface-model

Integrate a HuggingFace computer vision model into the NVIDIA TAO Toolkit ecosystem, covering the full pipeline from prerequisites to container testing.

tao-finetune-cosmos-embed

Fine-tune, evaluate, run inference, and export Cosmos-Embed1 video-text embedding models for tasks like text-to-video retrieval and semantic deduplication.

tao-train-depth-anything-v2

Train, evaluate, export, and run inference for monocular depth estimation models using Metric Depth Anything v2 or Relative Depth Anything architectures via the TAO toolkit.

tao-finetune-huggingface-model

Fine-tune HuggingFace CV, VLM, or LLM models on local NVIDIA GPUs using an NGC PyTorch container, with support for full or LoRA training, dataset handling, and optional model push to the Hub.

tao-run-deft-aoi

Automates the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models, including baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until KPI targets are met.