[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-nurec-fixer":3,"mdc--3tyv47-key":34,"related-repo-nvidia-nurec-fixer":2264,"related-org-nvidia-nurec-fixer":2337},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"nurec-fixer","harmonize and fine-tune 3D reconstruction frames","Use to run NVIDIA DiffusionHarmonizer (public successor to the older Fixer recipes) to enhance, harmonize, evaluate, or fine-tune novel-view frames from NRE \u002F NuRec \u002F 3DGS \u002F NeRF reconstructions. Do NOT use for training the 3D reconstruction itself (use `nre`) or for sensor-to-NCore conversion (use `ncore`).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,20],{"name":13,"slug":14,"type":15},"Machine Learning","machine-learning","tag",{"name":17,"slug":18,"type":15},"3D","3d",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Simulation","simulation",16,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnurec-skills","2026-07-14T05:32:38.465728","CC-BY-4.0 AND Apache-2.0",4,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Agent skills for Neural Reconstruction Engine","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnurec-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fnurec-fixer","---\nname: nurec-fixer\ndescription: >-\n  Use to run NVIDIA DiffusionHarmonizer (public successor to the\n  older Fixer recipes) to enhance, harmonize, evaluate, or\n  fine-tune novel-view frames from NRE \u002F NuRec \u002F 3DGS \u002F NeRF\n  reconstructions. Do NOT use for training the 3D reconstruction\n  itself (use `nre`) or for sensor-to-NCore conversion (use\n  `ncore`).\nversion: \"0.5.0\"\ntools:\n  - Shell\n  - Read\n  - Write\nlicense: CC-BY-4.0 AND Apache-2.0\ncompatibility: >-\n  Linux + NVIDIA GPU Ampere+ (compute capability >= 8.0; A100,\n  A10, L40, H100, RTX 30\u002F40\u002FPRO, B200, GB200), Docker + NVIDIA\n  Container Toolkit, ~120 GB free disk. HF_TOKEN required for the\n  `nvidia\u002FHarmonizer` checkpoints and the base\n  `nvidia\u002FCosmos-Predict2-0.6B-Text2Image` model; NGC_API_KEY\n  needed when pulling the `nvcr.io\u002Fnvidia\u002Fpytorch:25.10-py3` base\n  image from nvcr.io. Code at github.com\u002FNVIDIA\u002Fharmonizer;\n  runtime is the `harmonizer-cosmos-env` image built from the\n  project `Dockerfile.cosmos`, which layers cosmos-predict2 and\n  the required patches on top of `nvcr.io\u002Fnvidia\u002Fpytorch:25.10-py3`.\ndependencies:\n  - bash\n  - docker\n  - nvidia-container-toolkit\n  - python3\n  - git\n  - huggingface_hub\nmetadata:\n  author: NVIDIA NRS \u003Cnurec-skills@nvidia.com>\n  tags:\n    - nurec\n    - fixer\n    - harmonizer\n    - diffusionharmonizer\n    - reconstruction\n    - autonomous-vehicles\n    - post-processing\n    - evaluation\n  upstream: https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fharmonizer\n  hf_model: https:\u002F\u002Fhuggingface.co\u002Fnvidia\u002FHarmonizer\n  hf_dataset: https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fnvidia\u002FHarmonizer-Dataset\n  container: nvcr.io\u002Fnvidia\u002Fpytorch:25.10-py3\n  paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.24096\n  product_page: https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fsil\u002Fprojects\u002Fdiffusion-harmonizer\u002F\n  model_license: NVIDIA Open Model License Agreement\n---\n\n# NVIDIA DiffusionHarmonizer (NuRec post-processing)\n\n## Purpose\n\nRun NVIDIA DiffusionHarmonizer on rendered images from neural\nreconstructions. DiffusionHarmonizer is a single-step,\ntemporally-aware image diffusion enhancer for NeRF \u002F 3DGS \u002F\nNuRec-style renderings. It improves realism, reduces\nreconstruction artifacts, and harmonizes inserted dynamic\nobjects with the surrounding scene.\n\n## When to Use \u002F When NOT to Use\n\n**Use this skill when** the user has rendered frames from NRE,\nNuRec, 3DGS, NeRF, or a similar reconstruction pipeline and\nwants to enhance, harmonize, evaluate, or optionally fine-tune\nthe DiffusionHarmonizer model.\n\n**Do NOT use this skill when:**\n\n- The user wants to train or render the 3D reconstruction itself\n  (use `nre`).\n- The user wants to convert raw sensor data to NCore V4 (use\n  `ncore`).\n- The user wants a generic photo enhancer. DiffusionHarmonizer\n  is tuned for neural-reconstruction artifacts and\n  object-insertion failures.\n- The user only wants NRE inline rendering with\n  `--enable-difix`. That remains an NRE runtime feature; use the\n  `nre` skill for the complete `serve-grpc` \u002F `render-grpc`\n  command shape.\n\n## What changed from the older Fixer skill\n\nThis skill follows the public `NVIDIA\u002Fharmonizer` release, not\nthe older NGC JIT `.pt` artifact recipe. Use these public\nrelease artifacts:\n\n- Code: \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fharmonizer>\n- Model: `nvidia\u002FHarmonizer` on Hugging Face (the paper checkpoint\n  `models\u002Fdiffusion_harmonizer.pkl`), plus the base\n  `nvidia\u002FCosmos-Predict2-0.6B-Text2Image` model that inference\n  also requires.\n- Checkpoint download: `.\u002Fdownload_checkpoints.sh` from the repo\n  root. It fetches the Harmonizer checkpoints into `models\u002F`\n  (`diffusion_harmonizer.pkl`, `harmonizer_nontemporal.pt`) and\n  the base Cosmos DiT + tokenizer into\n  `src\u002Fcheckpoints\u002Fnvidia\u002FCosmos-Predict2-0.6B-Text2Image\u002F`.\n- Runtime: the `harmonizer-cosmos-env` image built from\n  `Dockerfile.cosmos` (base `nvcr.io\u002Fnvidia\u002Fpytorch:25.10-py3`).\n- Inference entry: `src\u002Finference_pix2pix_turbo_harmonizer.py`,\n  run from inside `\u002Fwork\u002Fsrc` so it can import its sibling\n  modules.\n- Evaluation entry: `src\u002Fevaluate_test_dataset.py`\n- Training entry: `src\u002Ftrain_pix2pix_turbo_harmonizer.py`\n\nDo not use the obsolete standalone recipe that downloads\n`nvidia\u002Fnre\u002Fnurec-fixer:cosmos_3dgut_fixer_harmonizer`, mounts\n`harmonizer_temporal.pt`, or runs `inference_jit_harmonizer.py`\ninside `nvcr.io\u002Fnvidia\u002Fpytorch:24.10-py3` unless the user\nexplicitly asks for that older beta artifact. Do not run\n`inference_pretrained_model.py`; the current README documents\n`inference_pix2pix_turbo_harmonizer.py` as the inference entry\npoint.\n\n## Background\n\nDiffusionHarmonizer is described in *DiffusionHarmonizer:\nBridging Neural Reconstruction and Photorealistic Simulation\nwith Online Diffusion Enhancer*\n([arXiv 2602.24096](https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.24096), CVPR 2026).\nIt distills a pretrained multi-step diffusion model into a\nsingle-step enhancer designed for online simulation and offline\ndata cleanup.\n\nTwo operating modes:\n\n- **Offline:** clean pseudo-training views rendered from a\n  reconstruction, then distill the improved views back into the\n  3D representation.\n- **Online:** enhance frames during simulation\u002Finference by\n  harmonizing color and lighting, reconstructing missing or\n  inconsistent shadows for inserted actors, and reducing residual\n  reconstruction artifacts.\n\nThe public model card describes `DiffusionHarmonizer-cosmos-0.6B`,\na Cosmos Predict2 Diffusion Transformer post-trained at\n`576x1024` input and output resolution.\n\n## Inputs\n\n- **code_dir** — checkout of\n  \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fharmonizer>.\n- **model_dir** — checkpoints fetched by `.\u002Fdownload_checkpoints.sh`\n  from the repo root. It places the paper checkpoint at\n  `models\u002Fdiffusion_harmonizer.pkl` and the required base Cosmos\n  model under\n  `src\u002Fcheckpoints\u002Fnvidia\u002FCosmos-Predict2-0.6B-Text2Image\u002F`.\n- **input_dir** — directory of rendered RGB frames (`.png`,\n  `.jpg`, `.jpeg`) to enhance.\n- **output_dir** — `inference_pix2pix_turbo_harmonizer.py` does\n  not take an output flag; it writes to a sibling folder named\n  `\u003Cinput_dir>_\u003Cmodel_identifier>` next to the input directory.\n- **HF_TOKEN** — Hugging Face token with access to\n  `nvidia\u002FHarmonizer`, `nvidia\u002FCosmos-Predict2-0.6B-Text2Image`,\n  and (if used) `nvidia\u002FHarmonizer-Dataset`. Accept the\n  model\u002Fdataset license terms first.\n- **NGC_API_KEY** — often needed to authenticate `docker pull`\n  from `nvcr.io`. Use only for container pulls, not model\n  download.\n\n## Instructions\n\n1. **Validate the host.** Have the agent execute\n   `scripts\u002Fvalidate_setup.py` via its standard script runner —\n   e.g. `run_script(\"scripts\u002Fvalidate_setup.py\")` or\n   `python scripts\u002Fvalidate_setup.py`. It checks Docker, the\n   NVIDIA Container Toolkit, GPU architecture, `git`, the\n   Hugging Face CLI, token presence, and free disk space — and\n   exits non-zero on any missing prerequisite.\n2. **Clone the code and build (or pull) the runtime image.**\n   Full commands and the Blackwell patch caveat live in\n   [`references\u002Finference.md`](references\u002Finference.md).\n\n   ```bash\n   git clone https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fharmonizer.git\n   cd harmonizer\n   docker build -t harmonizer-cosmos-env -f Dockerfile.cosmos .\n   ```\n\n3. **Download the checkpoints.** From the repo root run the\n   helper, which fetches both the Harmonizer checkpoints and the\n   base Cosmos model into the paths the code expects.\n\n   ```bash\n   export HF_TOKEN=\u003Cyour-hugging-face-token>\n   hf auth login --token \"$HF_TOKEN\"\n   .\u002Fdownload_checkpoints.sh\n   ```\n\n   Verify `models\u002Fdiffusion_harmonizer.pkl` and\n   `src\u002Fcheckpoints\u002Fnvidia\u002FCosmos-Predict2-0.6B-Text2Image\u002F`\n   exist.\n4. **Confirm `input_dir` exists and filenames sort into frame\n   order.** The temporal inference script sorts frames with\n   natural sort and uses previous outputs as references; prefer\n   zero-padded names such as `frame_000001.png`.\n5. **Run inference inside the container** with the repo mounted\n   at `\u002Fwork`, then `cd \u002Fwork\u002Fsrc` and run\n   `inference_pix2pix_turbo_harmonizer.py`. Pin\n   `-u $(id -u):$(id -g)` so outputs are owned by the host user.\n   Output frames land in\n   `\u003Cinput_dir>_\u003Cmodel_identifier>`. Full `docker run` recipe and\n   flag matrix in\n   [`references\u002Finference.md`](references\u002Finference.md).\n6. **Validate that the output frame count matches the input\n   frame count,** spot-check frames, and (if ground truth is\n   available) run paired evaluation with PSNR\u002FLPIPS — see\n   [`references\u002Fevaluation.md`](references\u002Fevaluation.md).\n7. **(Optional) Train or fine-tune.** Download the dataset (or\n   prepare JSON manifests in the documented format), then run\n   `src\u002Ftrain_pix2pix_turbo_harmonizer.py` with the recommended\n   hyperparameters. For fine-tuning, initialize from the released\n   checkpoint with `--pretrained_path\n   \u002Fpath\u002Fto\u002Fdiffusion_harmonizer.pkl`. Full recipe + NuRec\n   data-pair recipes in\n   [`references\u002Ftraining.md`](references\u002Ftraining.md).\n8. **(Optional) Teardown.** Follow\n   [`references\u002Fteardown.md`](references\u002Fteardown.md) to remove\n   images, code clones, model weights, datasets, and outputs.\n\n## Examples\n\n### Example 1 — Enhance a folder of rendered frames\n\nRun `scripts\u002Fvalidate_setup.py`, build the image once (see\n[`references\u002Fwrapper-image.md`](references\u002Fwrapper-image.md)), then\ninvoke `inference_pix2pix_turbo_harmonizer.py` inside the\n`harmonizer-cosmos-env` container with the repo checkout mounted at\n`\u002Fwork`. From `\u002Fwork\u002Fsrc`, point `--input_image` at the rendered\nframes, `--model_path` at `\u002Fwork\u002Fmodels\u002Fdiffusion_harmonizer.pkl`,\nset `--model_identifier`, and pass typical flags\n`--timestep 250 --resolution 1024 --use_sched`. Enhanced frames are\nwritten to `\u003Cinput_dir>_\u003Cmodel_identifier>`. The canonical\n`docker run` command and the full flag matrix live in\n[`references\u002Finference.md`](references\u002Finference.md).\n\n### Example 2 — Quantitative PSNR \u002F LPIPS evaluation\n\nPrepare the paired `test_dataset\u002F{scene}\u002Frender` +\n`test_dataset\u002F{scene}\u002Fgt` layout, then run\n`src\u002Fevaluate_test_dataset.py` inside the container. See\n[`references\u002Fevaluation.md`](references\u002Fevaluation.md) for the\nexact directory shape and the `docker run` command.\n\n### Example 3 — Fine-tune from the public checkpoint\n\nDownload `nvidia\u002FHarmonizer-Dataset`, prepare the\ntraining JSON, then run\n`src\u002Ftrain_pix2pix_turbo_harmonizer.py` with the multi-GPU\n`accelerate launch` recipe in\n[`references\u002Ftraining.md`](references\u002Ftraining.md). For\nfine-tuning add `--pretrained_path\n\u002Fpath\u002Fto\u002Fdiffusion_harmonizer.pkl` and use\n`--fixing_data_weight 3` on the released dataset.\n\n### Example 4 — Non-temporal (frame-by-frame) enhancement\n\n`inference_pix2pix_turbo_harmonizer.py` is temporal by default\nand uses previous enhanced frames as references\n(`--offset_list -1 -2 -3 -4`). When the user wants each frame\nenhanced independently (e.g. unordered images), add\n`--nontemporal` to disable temporal conditioning. Full command +\n`--offset_list` defaults in\n[`references\u002Finference.md`](references\u002Finference.md).\n\n## Consuming the inline Fixer via NRE\n\n`nre --enable-difix` is still the right answer when the user\nwants NRE to enhance frames as part of rendering without a\nseparate harmonizer checkout. That path is owned by the sibling\n`nre` skill. Do not mix the standalone DiffusionHarmonizer\nHF\u002FCosmos workflow with NRE's internal cache flags unless the\nNRE documentation for the user's tag explicitly says they share\nweights.\n\nUse this standalone skill when the user wants the public\nDiffusionHarmonizer code, model card, training\u002Fevaluation\nscripts, or post-processing of frames that already exist on\ndisk.\n\n## Prerequisites\n\n- **OS:** Linux host.\n- **GPU \u002F driver:** NVIDIA GPU Ampere or newer (compute\n  capability `>= 8.0`; A100, A10, L40, H100, RTX 30\u002F40\u002FPRO,\n  B200, GB200).\n- **Container runtime:** Docker with the NVIDIA Container\n  Toolkit.\n- **Tools:** `git`, `python3`, Hugging Face CLI (`hf` or\n  `huggingface-cli`).\n- **Secrets:**\n  - `HF_TOKEN` with the\n    [`nvidia\u002FHarmonizer`](https:\u002F\u002Fhuggingface.co\u002Fnvidia\u002FHarmonizer)\n    and\n    [`nvidia\u002FCosmos-Predict2-0.6B-Text2Image`](https:\u002F\u002Fhuggingface.co\u002Fnvidia\u002FCosmos-Predict2-0.6B-Text2Image)\n    licenses accepted (required to download model weights and\n    the optional dataset).\n  - `NGC_API_KEY` (often required for `docker login nvcr.io`\n    before pulling `nvcr.io\u002Fnvidia\u002Fpytorch:25.10-py3`).\n- **Disk:** at least ~120 GB free for the runtime image, build\n  cache, model weights, optional dataset, and outputs combined.\n- **Source \u002F model \u002F dataset:**\n  - Code: \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fharmonizer>.\n  - Model: \u003Chttps:\u002F\u002Fhuggingface.co\u002Fnvidia\u002FHarmonizer>.\n  - Base model:\n    \u003Chttps:\u002F\u002Fhuggingface.co\u002Fnvidia\u002FCosmos-Predict2-0.6B-Text2Image>.\n  - Optional dataset:\n    \u003Chttps:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fnvidia\u002FHarmonizer-Dataset>.\n\nThe fail-fast check that enforces all of the above is\n`scripts\u002Fvalidate_setup.py`.\n\n## Scripts\n\n| Script | Purpose | Usage |\n|--------|---------|-------|\n| `scripts\u002Fvalidate_setup.py` | Verify Docker, NVIDIA Container Toolkit, GPU architecture, `git`, Hugging Face CLI, token presence, and disk space. No network calls. | `run_script(\"scripts\u002Fvalidate_setup.py\")` or `python scripts\u002Fvalidate_setup.py` |\n| `scripts\u002F.env.example` | Template for `HF_TOKEN` and optional `NGC_API_KEY`. | `cp scripts\u002F.env.example .env && set -a && . .\u002F.env && set +a` |\n\n## References\n\n- [`references\u002Finference.md`](references\u002Finference.md) — container\n  build, raw-base fallback, Blackwell patches, checkpoint\n  download, `inference_pix2pix_turbo_harmonizer.py` flag matrix,\n  non-temporal mode.\n- [`references\u002Fevaluation.md`](references\u002Fevaluation.md) — paired\n  `test_dataset\u002F` layout and `evaluate_test_dataset.py` command.\n- [`references\u002Ftraining.md`](references\u002Ftraining.md) — dataset\n  download, training JSON format, multi-GPU `accelerate launch`\n  recipe, fine-tuning flags, NuRec data-pair recipes.\n- [`references\u002Fwrapper-image.md`](references\u002Fwrapper-image.md) —\n  build and run the project image for repeat inference.\n- [`references\u002Ftroubleshooting.md`](references\u002Ftroubleshooting.md)\n  — extended diagnostic notes.\n- [`references\u002Fteardown.md`](references\u002Fteardown.md) — cleanup\n  inventory for images, code, Hugging Face caches, datasets,\n  and outputs.\n- Public code: \u003Chttps:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fharmonizer>\n- Model card: \u003Chttps:\u002F\u002Fhuggingface.co\u002Fnvidia\u002FHarmonizer>\n- Dataset: \u003Chttps:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fnvidia\u002FHarmonizer-Dataset>\n- Paper: \u003Chttps:\u002F\u002Farxiv.org\u002Fabs\u002F2602.24096>\n- Project page: \u003Chttps:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fsil\u002Fprojects\u002Fdiffusion-harmonizer\u002F>\n\n## Limitations\n\n- **Rendered inputs only.** The model is tuned for\n  neural-reconstruction renderings and object-insertion\n  artifacts, not arbitrary real photos.\n- **Primary model resolution is 576x1024.** The inference script\n  maps resolution key `1024` to `1024x576`. Only `1024`, `960`,\n  and `1360` are supported keys; `1024` matches the model-card\n  operating point.\n- **Temporal references and filename order matter.** The\n  inference script enhances frames in natural-sorted order and\n  feeds previous outputs back as temporal references. Use\n  zero-padded frame numbers, or pass `--nontemporal` for\n  unordered images.\n- **Container builds can be large.** The runtime image, build\n  cache, model weights, and optional dataset can exceed 100 GB.\n- **Training is multi-GPU by default.** The README command\n  assumes 8 GPUs with bf16 mixed precision.\n- **Public code evolves.** The current README documents\n  `inference_pix2pix_turbo_harmonizer.py` as the inference entry\n  point; if a future checkout renames it, prefer the script and\n  flags present in that checkout.\n\n## Troubleshooting (top 5)\n\n| Error \u002F symptom | Most common cause |\n|-----------------|-------------------|\n| `docker: could not select device driver ... gpu` | NVIDIA Container Toolkit missing or Docker is not configured for the NVIDIA runtime. |\n| `docker pull` 401 \u002F 403 from `nvcr.io` | Docker is not authenticated to NGC, or the API key lacks container access. |\n| `hf download ... 401 \u002F 403` | `HF_TOKEN` is missing\u002Fexpired\u002Flacks read scope, or the model\u002Fdataset license has not been accepted. |\n| `diffusion_harmonizer.pkl` missing | Checkpoint download path is wrong or incomplete. Re-run `.\u002Fdownload_checkpoints.sh` from the repo root. |\n| Output files owned by `root` | The `docker run` omitted `-u $(id -u):$(id -g)`. |\n\nFull matrix in\n[`references\u002Ftroubleshooting.md`](references\u002Ftroubleshooting.md).\n\n## Teardown\n\nA full workflow can leave large artifacts on disk: the Cosmos\nimage, project image, build cache, `harmonizer` code checkout,\nHugging Face model weights, optional dataset, evaluation\noutputs, and enhanced frames. Reclaim them with the inventory\nin [`references\u002Fteardown.md`](references\u002Fteardown.md). Do not\nrevoke `HF_TOKEN` or `NGC_API_KEY` as normal cleanup. Rotate a\ntoken only if you suspect it was leaked.\n",{"data":35,"body":67},{"name":4,"description":6,"version":36,"tools":37,"license":26,"compatibility":41,"dependencies":42,"metadata":49},"0.5.0",[38,39,40],"Shell","Read","Write","Linux + NVIDIA GPU Ampere+ (compute capability >= 8.0; A100, A10, L40, H100, RTX 30\u002F40\u002FPRO, B200, GB200), Docker + NVIDIA Container Toolkit, ~120 GB free disk. HF_TOKEN required for the `nvidia\u002FHarmonizer` checkpoints and the base `nvidia\u002FCosmos-Predict2-0.6B-Text2Image` model; NGC_API_KEY needed when pulling the `nvcr.io\u002Fnvidia\u002Fpytorch:25.10-py3` base image from nvcr.io. 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DiffusionHarmonizer is a single-step,\ntemporally-aware image diffusion enhancer for NeRF \u002F 3DGS \u002F\nNuRec-style renderings. 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Use when the sub-skill is unclear or a multi-stage pipeline is needed; do NOT use for non-NuRec tasks or to run any pipeline itself.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2316,2317,2318],{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"2026-07-14T05:32:31.902229",{"slug":2321,"name":2321,"fn":2322,"description":2323,"org":2324,"tags":2325,"stars":23,"repoUrl":24,"updatedAt":2335},"physical-ai-datasets","find and download NVIDIA Physical AI datasets","Use when the user wants to find, download, or pick a NVIDIA Physical AI dataset on Hugging Face for autonomous-vehicle, robotics, spatial intelligence, manipulation, or neural-reconstruction workflows. Catalog of every dataset under huggingface.co\u002Fnvidia with the `PhysicalAI-` prefix, organised by domain (AV, Robotics-Manipulation, Robotics-GR00T, Robotics-mindmap, Robotics-NuRec, Spatial Intelligence, Grasping, Healthcare, Sim-Ready, Material properties), with per-dataset size, format, gating, license, and the downstream sibling skill (`ncore`, `nre`, `asset-harvester`, `nurec-fixer`) or upstream tool (Isaac Sim, CARLA, Isaac-GR00T, Cosmos-*) that consumes it. Do NOT use as a runtime — it routes you elsewhere. Trigger keywords: nvidia physical ai dataset, PhysicalAI- dataset, hf nvidia dataset, NCore dataset, NuRec dataset, GR00T dataset, GraspGen, SimReady, Cosmos-Drive-Dreams, Lyra SDG, Open-H-Embodiment, huggingface-cli download, physical_ai_av, dataset gated, RDS-HQ.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2326,2329,2332,2333,2334],{"name":2327,"slug":2328,"type":15},"Datasets","datasets",{"name":2330,"slug":2331,"type":15},"Hugging Face","hugging-face",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":2289,"slug":2290,"type":15},"2026-07-14T05:32:33.182445",6,{"items":2338,"total":2495},[2339,2357,2375,2386,2398,2412,2425,2437,2450,2461,2475,2484],{"slug":2340,"name":2340,"fn":2341,"description":2342,"org":2343,"tags":2344,"stars":2354,"repoUrl":2355,"updatedAt":2356},"nemoclaw-user-guide","retrieve NemoClaw documentation and configuration","Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2345,2348,2351],{"name":2346,"slug":2347,"type":15},"Documentation","documentation",{"name":2349,"slug":2350,"type":15},"MCP","mcp",{"name":2352,"slug":2353,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":2358,"name":2358,"fn":2359,"description":2360,"org":2361,"tags":2362,"stars":2372,"repoUrl":2373,"updatedAt":2374},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2363,2366,2369],{"name":2364,"slug":2365,"type":15},"Containers","containers",{"name":2367,"slug":2368,"type":15},"Deployment","deployment",{"name":2370,"slug":2371,"type":15},"Python","python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":2376,"name":2376,"fn":2377,"description":2378,"org":2379,"tags":2380,"stars":2372,"repoUrl":2373,"updatedAt":2385},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2381,2384],{"name":2382,"slug":2383,"type":15},"CI\u002FCD","ci-cd",{"name":2367,"slug":2368,"type":15},"2026-07-14T05:25:59.97109",{"slug":2387,"name":2387,"fn":2388,"description":2389,"org":2390,"tags":2391,"stars":2372,"repoUrl":2373,"updatedAt":2397},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2392,2393,2394],{"name":2382,"slug":2383,"type":15},{"name":2367,"slug":2368,"type":15},{"name":2395,"slug":2396,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":2399,"name":2399,"fn":2400,"description":2401,"org":2402,"tags":2403,"stars":2372,"repoUrl":2373,"updatedAt":2411},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2404,2407,2408],{"name":2405,"slug":2406,"type":15},"Debugging","debugging",{"name":2395,"slug":2396,"type":15},{"name":2409,"slug":2410,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":2413,"name":2413,"fn":2414,"description":2415,"org":2416,"tags":2417,"stars":2372,"repoUrl":2373,"updatedAt":2424},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2418,2421],{"name":2419,"slug":2420,"type":15},"Best Practices","best-practices",{"name":2422,"slug":2423,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":2426,"name":2426,"fn":2427,"description":2428,"org":2429,"tags":2430,"stars":2372,"repoUrl":2373,"updatedAt":2436},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2431,2432,2435],{"name":13,"slug":14,"type":15},{"name":2433,"slug":2434,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":2438,"name":2438,"fn":2439,"description":2440,"org":2441,"tags":2442,"stars":2372,"repoUrl":2373,"updatedAt":2449},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2443,2446],{"name":2444,"slug":2445,"type":15},"QA","qa",{"name":2447,"slug":2448,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":2451,"name":2451,"fn":2452,"description":2453,"org":2454,"tags":2455,"stars":2372,"repoUrl":2373,"updatedAt":2460},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2456,2457],{"name":2367,"slug":2368,"type":15},{"name":2458,"slug":2459,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":2462,"name":2462,"fn":2463,"description":2464,"org":2465,"tags":2466,"stars":2372,"repoUrl":2373,"updatedAt":2474},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2467,2470,2471],{"name":2468,"slug":2469,"type":15},"Code Review","code-review",{"name":2395,"slug":2396,"type":15},{"name":2472,"slug":2473,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":2476,"name":2476,"fn":2477,"description":2478,"org":2479,"tags":2480,"stars":2372,"repoUrl":2373,"updatedAt":2483},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2481,2482],{"name":2444,"slug":2445,"type":15},{"name":2447,"slug":2448,"type":15},"2026-07-14T05:25:54.928983",{"slug":2485,"name":2485,"fn":2486,"description":2487,"org":2488,"tags":2489,"stars":2372,"repoUrl":2373,"updatedAt":2494},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2490,2493],{"name":2491,"slug":2492,"type":15},"Automation","automation",{"name":2382,"slug":2383,"type":15},"2026-07-30T05:29:03.275638",496]