[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M78AXDS0W2ME6NHKVNP4DAWM":3,"skill-same:01M78AXDS0W2ME6NHKVNP4DAWM":105},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":14,"repositoryHtmlUrl":36,"repositoryHomepageUrl":37,"ownerHtmlUrl":38,"ownerBlogUrl":39,"ownerDescription":40,"updatedAt":41,"install":42,"resources":55},"01M78AXDS0W2ME6NHKVNP4DAWM","tao-train-nvdinov2",null,"NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels and produces general-purpose visual features. Use when training, exporting, or running inference for a TAO NVDINOv2 backbone. Trigger phrases include \"train NVDINOv2\", \"self-supervised ViT pretraining\", \"DINOv2 backbone\", \"visual representation learning\".","Solizardking\u002Fskillhub-main","Solizardking","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F108096810?v=4","01M786KDB10AYV042N0F9BD5B5","Clawd","01M786NYYXSZK1D05WM8JQPC48",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: tao-train-nvdinov2\ndescription: NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation\n  (teacher-student) without labels and produces general-purpose visual features. Use when training, exporting, or\n  running inference for a TAO NVDINOv2 backbone. Trigger phrases include \"train NVDINOv2\", \"self-supervised ViT pretraining\",\n  \"DINOv2 backbone\", \"visual representation learning\".\nlicense: Apache-2.0\ncompatibility: Requires docker + nvidia-container-toolkit.\nmetadata:\n  version: \"0.1.0\"\n  author: NVIDIA Corporation\nallowed-tools: Read Bash\ntags:\n- self\n- supervised\n- learning\n---\n\n# NVDINOv2\n\nNVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels. Produces general-purpose visual features.\n\nSet train.pretrained_model_path for pretrained ViT weights.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`), read `references\u002Ftao-deploy-nvdinov2.md` first. Deploy spec templates live in this skill's `references\u002F` folder with the `spec_template_deploy_*.yaml` prefix.\n\n## Dataclass Schemas\n\nGenerated TAO Core schemas are packaged in `schemas\u002F\u003Caction>.schema.json`, with `schemas\u002Fmanifest.json` listing available actions. Each generated schema also emits `references\u002Fspec_template_\u003Caction>.yaml` from the schema top-level `default` field. AutoML enablement is declared at the model layer in `references\u002Fskill_info.yaml` via `automl_enabled`. Runnable AutoML still requires `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` to exist and parse. Use the packaged train schema for `automl_default_parameters`, `automl_disabled_parameters`, defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect `~\u002Ftao-core` at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.\n\n## Train Action Policy\n\nThis model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references\u002Fskill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Use `automl_policy: on` by default and only expose `on` \u002F `off` in new launch prompts. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as `automl_policy: off` for this run only. When `automl_policy: on`, `automl_enabled: true`, and both `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow\u002Fapplication overrides for datasets, specs, output directories, GPU\u002Fplatform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.\n\nNon-train actions such as `inference`, `export`, and deploy flows stay in this model skill. The per-run `automl_policy` override does not change model metadata.\n\n## Training Requirements\n\n- **Dataset type:** image_classification\n- **Formats:** ssl\n- **Monitoring metric:** train_loss\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| inference | dataset.test_dataset.images_dir | inference_dataset | images_test.tar.gz | No |\n| train | dataset.train_dataset.images_dir | train_datasets | images_train.tar.gz | No |\n\n### Typical Spec Overrides\n\nData source overrides are **mandatory for train and inference** — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in `spec_overrides`.\n\n```python\nS3_TRAIN = \"s3:\u002F\u002Fbucket\u002Fdata\u002Ftrain\"\nS3_EVAL = \"s3:\u002F\u002Fbucket\u002Fdata\u002Feval\"\n```\n\n**train (mandatory data sources):**\n```python\n{\n    \"train.num_gpus\": 1,\n    \"train.num_epochs\": 10,\n    \"train.checkpoint_interval\": 10,\n    \"dataset.train_dataset.images_dir\": f\"{S3_TRAIN}\u002Fimages_train.tar.gz\",\n}\n```\n\n**local AutoML validation \u002F smoke run:**\nUse this shape when the goal is to confirm Bayesian launch, metric selection,\nbest-model choice, and checkpoint persistence on local Docker. It keeps the\nrun representative while avoiding the much slower ViT-Large default.\n\n```python\n{\n    \"wandb.enable\": False,\n    \"model.backbone.teacher_type\": \"vit_s\",\n    \"model.backbone.student_type\": \"vit_s\",\n    \"model.backbone.img_size\": 224,\n    \"dataset.batch_size\": 8,\n    \"dataset.workers\": 2,\n    \"train.num_epochs\": 1,\n    \"train.checkpoint_interval\": 1,\n    \"train.num_prototypes\": 1024,\n    \"train.precision\": \"32-true\",\n    \"train.use_custom_attention\": False,\n    \"train.num_gpus\": 1,\n    \"dataset.train_dataset.images_dir\": f\"{S3_TRAIN}\u002Fimages_train.tar.gz\",\n}\n```\n\n**export (mandatory checkpoint handoff):**\n```python\n{\n    \"export.checkpoint\": \"\u003Cselected train\u002FAutoML student_epoch_* checkpoint>\",\n    \"export.onnx_file\": \"\u002Fpath\u002Fto\u002Fresults\u002Fnvdinov2.onnx\",\n}\n```\n\n**inference (mandatory data sources):**\n```python\n{\n    \"inference.checkpoint\": \"\u003Cselected train\u002FAutoML student_epoch_* checkpoint>\",\n    \"model.backbone.teacher_type\": \"\u003Csame value used for train>\",\n    \"model.backbone.student_type\": \"\u003Csame value used for train>\",\n    \"model.backbone.img_size\": \"\u003Csame value used for train>\",\n    \"train.use_custom_attention\": \"\u003Csame value used for train>\",\n    \"dataset.test_dataset.images_dir\": f\"{S3_EVAL}\u002Fimages_test.tar.gz\",\n}\n```\n## Eval Dataset\n\nOptional. SSL training does not use labels. Evaluation is downstream task-specific.\n\n## Important Parameters\n\n- **model.backbone.teacher_type**: Teacher ViT variant. Default vit_l (ViT-Large).\n- **model.backbone.student_type**: Student ViT variant. Default vit_l. Typically matches teacher.\n- **model.backbone.img_size**: Input image size. Default 518. Higher resolution produces better features but costs more memory.\n- **model.backbone.patch_size**: ViT patch size. Default 14.\n- **dataset.batch_size**: Per-GPU batch size. Default 4. SSL training is memory-intensive due to dual (teacher+student) forward passes.\n- **train.layerwise_decay**: Layer-wise learning rate decay. Important for ViT fine-tuning.\n- **train.clip_grad_norm**: Gradient clipping. Important for stable SSL training.\n\n## Multi-GPU \u002F Multi-Node\n\n**Launch method:** Lightning-managed (single `python` process, Lightning spawns workers).\n\n| Spec Key | Description | Default |\n|----------|-------------|---------|\n| `train.num_gpus` | Number of GPUs | 1 |\n| `train.gpu_ids` | GPU device indices | [0] |\n| `train.num_nodes` | Number of nodes | 1 |\n\n- Strategy: `auto` (Lightning picks best strategy automatically)\n- `sync_batchnorm` is always enabled — critical for SSL training with teacher-student framework\n- Multi-GPU strongly recommended (4-8 GPUs) for meaningful SSL training\n\n**Multi-node env vars** (set by orchestrator): `WORLD_SIZE`, `NODE_RANK`, `MASTER_ADDR`, `MASTER_PORT`, `NUM_GPU_PER_NODE`.\n\n## Hardware\n\nMinimum 4 GPU(s), recommended 8 GPU(s). 40GB+ (A100 recommended) VRAM per GPU. SSL with ViT-Large teacher+student is very memory-intensive. Requires A100 40GB+ GPUs. Multi-GPU strongly recommended.\n\n## Error Patterns\n\n**CUDA out of memory**: ViT-Large teacher+student with img_size=518 requires 40GB+ GPU memory. Reduce batch_size, img_size, or use smaller ViT variant.\n\n**Inference checkpoint has unexpected Lightning keys**: For downstream\n`inference`, pass the selected AutoML run's `student_epoch_*.pth` checkpoint,\nnot `nvdinov2_model_latest.pth`. The latest file is a training checkpoint and\nthe inference loader reports unexpected keys such as `state_dict`, optimizer\nstate, and scheduler state.\n\n**Export checkpoint has unexpected Lightning keys**: Export also consumes the\nselected `student_epoch_*.pth` checkpoint. Use the full `model_epoch_*.pth`\ncheckpoint only for resume\u002Fretrain via `train.resume_training_checkpoint_path`.\n\n**TensorRT engine passed to PyT inference**: The packaged PyT `nvdinov2 inference`\nimplementation only loads `.pth` or `.tlt` model paths. TAO Deploy\n`gen_trt_engine` builds a TensorRT engine for downstream consumers, but the PyT\ninference action does not run on that engine.\n\n**Separate distill action not available**: The current TAO PyT CLI exposes\n`export`, `inference`, `train`, and `default_specs` for NvDINOv2. Do not launch\nor advertise a standalone `nvdinov2 distill` action.\n\n**AutoML metric not found**: TAO's status KPI reports the final training scalar\nas `train_loss`. Use `train_loss` with minimize direction for AutoML selection.\nSome Lightning progress lines also render the same scalar as\n`train_loss_epoch`; treat that as a fallback alias only, not the primary\nmonitoring metric.\n\n**Slow convergence**: SSL needs many epochs. Default 10 is for quick testing; production runs typically use 100+ epochs.\n\n## Spec Param \u002F Parent Model Inference\n\nModel-specific inference mappings belong in this MD file, not in `config.json`. Generated runners should read this section and apply the mappings with SDK helpers before `create_job()`. This mirrors the old microservices `infer_params.py` flow.\n\nModel-specific handoff mappings:\n\n| Action | Spec Field | Inference Function | Meaning |\n|---|---|---|---|\n| export | `encryption_key` | `key` | encryption key |\n| export | `export.checkpoint` | `parent_model` | selected `student_epoch_*.pth` checkpoint from the parent job results folder |\n| export | `export.onnx_file` | `create_onnx_file` | output ONNX path |\n| export | `results_dir` | `output_dir` | current job results directory |\n| inference | `encryption_key` | `key` | encryption key |\n| inference | `inference.checkpoint` | `parent_model` | selected `student_epoch_*.pth` checkpoint from the parent job results folder |\n| inference | `results_dir` | `output_dir` | current job results directory |\n| train | `encryption_key` | `key` | encryption key |\n| train | `results_dir` | `output_dir` | current job results directory |\n| train | `train.pretrained_model_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\n| train | `train.resume_training_checkpoint_path` | `resume_model` | selected full `model_epoch_*.pth` training checkpoint from the current job results folder |\n\nFor `parent_model` or `parent_model_folder`, pass the upstream train\u002Fexport\u002FAutoML child job id as `parent_job_id`. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to `config.json` and do not patch generated runner scripts to guess checkpoint paths.\n\n## Deployment\n\n- [tao-deploy-nvdinov2](references\u002Ftao-deploy-nvdinov2.md)\n","# NVDINOv2\n\nNVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels. Produces general-purpose visual features.\n\nSet train.pretrained_model_path for pretrained ViT weights.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`), read `references\u002Ftao-deploy-nvdinov2.md` first. Deploy spec templates live in this skill's `references\u002F` folder with the `spec_template_deploy_*.yaml` prefix.\n\n## Dataclass Schemas\n\nGenerated TAO Core schemas are packaged in `schemas\u002F\u003Caction>.schema.json`, with `schemas\u002Fmanifest.json` listing available actions. Each generated schema also emits `references\u002Fspec_template_\u003Caction>.yaml` from the schema top-level `default` field. AutoML enablement is declared at the model layer in `references\u002Fskill_info.yaml` via `automl_enabled`. Runnable AutoML still requires `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` to exist and parse. Use the packaged train schema for `automl_default_parameters`, `automl_disabled_parameters`, defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect `~\u002Ftao-core` at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.\n\n## Train Action Policy\n\nThis model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references\u002Fskill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Use `automl_policy: on` by default and only expose `on` \u002F `off` in new launch prompts. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as `automl_policy: off` for this run only. When `automl_policy: on`, `automl_enabled: true`, and both `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow\u002Fapplication overrides for datasets, specs, output directories, GPU\u002Fplatform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.\n\nNon-train actions such as `inference`, `export`, and deploy flows stay in this model skill. The per-run `automl_policy` override does not change model metadata.\n\n## Training Requirements\n\n- **Dataset type:** image_classification\n- **Formats:** ssl\n- **Monitoring metric:** train_loss\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| inference | dataset.test_dataset.images_dir | inference_dataset | images_test.tar.gz | No |\n| train | dataset.train_dataset.images_dir | train_datasets | images_train.tar.gz | No |\n\n### Typical Spec Overrides\n\nData source overrides are **mandatory for train and inference** — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in `spec_overrides`.\n\n```python\nS3_TRAIN = \"s3:\u002F\u002Fbucket\u002Fdata\u002Ftrain\"\nS3_EVAL = \"s3:\u002F\u002Fbucket\u002Fdata\u002Feval\"\n```\n\n**train (mandatory data sources):**\n```python\n{\n    \"train.num_gpus\": 1,\n    \"train.num_epochs\": 10,\n    \"train.checkpoint_interval\": 10,\n    \"dataset.train_dataset.images_dir\": f\"{S3_TRAIN}\u002Fimages_train.tar.gz\",\n}\n```\n\n**local AutoML validation \u002F smoke run:**\nUse this shape when the goal is to confirm Bayesian launch, metric selection,\nbest-model choice, and checkpoint persistence on local Docker. It keeps the\nrun representative while avoiding the much slower ViT-Large default.\n\n```python\n{\n    \"wandb.enable\": False,\n    \"model.backbone.teacher_type\": \"vit_s\",\n    \"model.backbone.student_type\": \"vit_s\",\n    \"model.backbone.img_size\": 224,\n    \"dataset.batch_size\": 8,\n    \"dataset.workers\": 2,\n    \"train.num_epochs\": 1,\n    \"train.checkpoint_interval\": 1,\n    \"train.num_prototypes\": 1024,\n    \"train.precision\": \"32-true\",\n    \"train.use_custom_attention\": False,\n    \"train.num_gpus\": 1,\n    \"dataset.train_dataset.images_dir\": f\"{S3_TRAIN}\u002Fimages_train.tar.gz\",\n}\n```\n\n**export (mandatory checkpoint handoff):**\n```python\n{\n    \"export.checkpoint\": \"\u003Cselected train\u002FAutoML student_epoch_* checkpoint>\",\n    \"export.onnx_file\": \"\u002Fpath\u002Fto\u002Fresults\u002Fnvdinov2.onnx\",\n}\n```\n\n**inference (mandatory data sources):**\n```python\n{\n    \"inference.checkpoint\": \"\u003Cselected train\u002FAutoML student_epoch_* checkpoint>\",\n    \"model.backbone.teacher_type\": \"\u003Csame value used for train>\",\n    \"model.backbone.student_type\": \"\u003Csame value used for train>\",\n    \"model.backbone.img_size\": \"\u003Csame value used for train>\",\n    \"train.use_custom_attention\": \"\u003Csame value used for train>\",\n    \"dataset.test_dataset.images_dir\": f\"{S3_EVAL}\u002Fimages_test.tar.gz\",\n}\n```\n## Eval Dataset\n\nOptional. SSL training does not use labels. Evaluation is downstream task-specific.\n\n## Important Parameters\n\n- **model.backbone.teacher_type**: Teacher ViT variant. Default vit_l (ViT-Large).\n- **model.backbone.student_type**: Student ViT variant. Default vit_l. Typically matches teacher.\n- **model.backbone.img_size**: Input image size. Default 518. Higher resolution produces better features but costs more memory.\n- **model.backbone.patch_size**: ViT patch size. Default 14.\n- **dataset.batch_size**: Per-GPU batch size. Default 4. SSL training is memory-intensive due to dual (teacher+student) forward passes.\n- **train.layerwise_decay**: Layer-wise learning rate decay. Important for ViT fine-tuning.\n- **train.clip_grad_norm**: Gradient clipping. Important for stable SSL training.\n\n## Multi-GPU \u002F Multi-Node\n\n**Launch method:** Lightning-managed (single `python` process, Lightning spawns workers).\n\n| Spec Key | Description | Default |\n|----------|-------------|---------|\n| `train.num_gpus` | Number of GPUs | 1 |\n| `train.gpu_ids` | GPU device indices | [0] |\n| `train.num_nodes` | Number of nodes | 1 |\n\n- Strategy: `auto` (Lightning picks best strategy automatically)\n- `sync_batchnorm` is always enabled — critical for SSL training with teacher-student framework\n- Multi-GPU strongly recommended (4-8 GPUs) for meaningful SSL training\n\n**Multi-node env vars** (set by orchestrator): `WORLD_SIZE`, `NODE_RANK`, `MASTER_ADDR`, `MASTER_PORT`, `NUM_GPU_PER_NODE`.\n\n## Hardware\n\nMinimum 4 GPU(s), recommended 8 GPU(s). 40GB+ (A100 recommended) VRAM per GPU. SSL with ViT-Large teacher+student is very memory-intensive. Requires A100 40GB+ GPUs. Multi-GPU strongly recommended.\n\n## Error Patterns\n\n**CUDA out of memory**: ViT-Large teacher+student with img_size=518 requires 40GB+ GPU memory. Reduce batch_size, img_size, or use smaller ViT variant.\n\n**Inference checkpoint has unexpected Lightning keys**: For downstream\n`inference`, pass the selected AutoML run's `student_epoch_*.pth` checkpoint,\nnot `nvdinov2_model_latest.pth`. The latest file is a training checkpoint and\nthe inference loader reports unexpected keys such as `state_dict`, optimizer\nstate, and scheduler state.\n\n**Export checkpoint has unexpected Lightning keys**: Export also consumes the\nselected `student_epoch_*.pth` checkpoint. Use the full `model_epoch_*.pth`\ncheckpoint only for resume\u002Fretrain via `train.resume_training_checkpoint_path`.\n\n**TensorRT engine passed to PyT inference**: The packaged PyT `nvdinov2 inference`\nimplementation only loads `.pth` or `.tlt` model paths. TAO Deploy\n`gen_trt_engine` builds a TensorRT engine for downstream consumers, but the PyT\ninference action does not run on that engine.\n\n**Separate distill action not available**: The current TAO PyT CLI exposes\n`export`, `inference`, `train`, and `default_specs` for NvDINOv2. Do not launch\nor advertise a standalone `nvdinov2 distill` action.\n\n**AutoML metric not found**: TAO's status KPI reports the final training scalar\nas `train_loss`. Use `train_loss` with minimize direction for AutoML selection.\nSome Lightning progress lines also render the same scalar as\n`train_loss_epoch`; treat that as a fallback alias only, not the primary\nmonitoring metric.\n\n**Slow convergence**: SSL needs many epochs. Default 10 is for quick testing; production runs typically use 100+ epochs.\n\n## Spec Param \u002F Parent Model Inference\n\nModel-specific inference mappings belong in this MD file, not in `config.json`. Generated runners should read this section and apply the mappings with SDK helpers before `create_job()`. This mirrors the old microservices `infer_params.py` flow.\n\nModel-specific handoff mappings:\n\n| Action | Spec Field | Inference Function | Meaning |\n|---|---|---|---|\n| export | `encryption_key` | `key` | encryption key |\n| export | `export.checkpoint` | `parent_model` | selected `student_epoch_*.pth` checkpoint from the parent job results folder |\n| export | `export.onnx_file` | `create_onnx_file` | output ONNX path |\n| export | `results_dir` | `output_dir` | current job results directory |\n| inference | `encryption_key` | `key` | encryption key |\n| inference | `inference.checkpoint` | `parent_model` | selected `student_epoch_*.pth` checkpoint from the parent job results folder |\n| inference | `results_dir` | `output_dir` | current job results directory |\n| train | `encryption_key` | `key` | encryption key |\n| train | `results_dir` | `output_dir` | current job results directory |\n| train | `train.pretrained_model_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\n| train | `train.resume_training_checkpoint_path` | `resume_model` | selected full `model_epoch_*.pth` training checkpoint from the current job results folder |\n\nFor `parent_model` or `parent_model_folder`, pass the upstream train\u002Fexport\u002FAutoML child job id as `parent_job_id`. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to `config.json` and do not patch generated runner scripts to guess checkpoint paths.\n\n## Deployment\n\n- [tao-deploy-nvdinov2](references\u002Ftao-deploy-nvdinov2.md)\n",{"name":5,"tags":24,"license":15,"metadata":28,"description":7,"allowed-tools":31,"compatibility":32},[25,26,27],"self","supervised","learning",{"author":29,"version":30},"NVIDIA Corporation","0.1.0","Read Bash","Requires docker + nvidia-container-toolkit.","main","be3de0244dc1a57e323d8160787ebb40fb64930e","public\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main","https:\u002F\u002Fskills.x402.wtf","https:\u002F\u002Fgithub.com\u002FSolizardking","https:\u002F\u002Fmusebook.trade","Got a mile high and never came down","2026-10-08 23:20:13",{"source":8,"skillName":5,"commands":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add Solizardking\u002Fskillhub-main --skill tao-train-nvdinov2",{"tool":48,"command":49},"bunx","bunx skills add Solizardking\u002Fskillhub-main --skill tao-train-nvdinov2",{"tool":51,"command":52},"pnpm","pnpm dlx skills add Solizardking\u002Fskillhub-main --skill tao-train-nvdinov2","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002FSKILL.md",true,[56,62,66,70,74,78,82,86,89,93,97,101],{"path":57,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":60,"sourceHtmlUrl":61},"BENCHMARK.md","REFERENCE","参考文档",4064,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002FBENCHMARK.md",{"path":63,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":64,"sourceHtmlUrl":65},"metadata.json",610,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fmetadata.json",{"path":67,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":68,"sourceHtmlUrl":69},"references\u002Fskill_info.yaml",2734,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fskill_info.yaml",{"path":71,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":72,"sourceHtmlUrl":73},"references\u002Fspec_template_deploy_gen_trt_engine.yaml",514,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_deploy_gen_trt_engine.yaml",{"path":75,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fspec_template_distill.yaml",2113,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_distill.yaml",{"path":79,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fspec_template_export.yaml",2314,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_export.yaml",{"path":83,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fspec_template_inference.yaml",2264,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_inference.yaml",{"path":87,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":76,"sourceHtmlUrl":88},"references\u002Fspec_template_train.yaml","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Fspec_template_train.yaml",{"path":90,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Ftao-deploy-nvdinov2.md",3852,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Ftao-deploy-nvdinov2.md",{"path":94,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Ftao-deploy-nvdinov2.skill_info.yaml",1605,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Freferences\u002Ftao-deploy-nvdinov2.skill_info.yaml",{"path":98,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":99,"sourceHtmlUrl":100},"skill-card.md",3819,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fskill-card.md",{"path":102,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":103,"sourceHtmlUrl":104},"verification.json",3035,"https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fpublic\u002Fapi\u002Fskills\u002Fnvidia\u002Ftao-train-nvdinov2\u002Fverification.json",[106,114,121,128,135,143],{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":110,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":19},"01M78E0TGCAMNPS207BA3G07S7","zkrouter","Self-hosted OpenAI-compatible LLM router — smart tier-based routing, ZK-stamped routing decisions, mode overrides, free OpenRouter at install via the Birth bot. Save 60-80% on inference costs by routing to the cheapest capable model across 12+ OpenRouter models.","MIT","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fzkrouter\u002FSKILL.md",[],[],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":110,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":19},"01M78E0R1Z8BDT6EDTHY17MTNB","zk","For custom ZK Solana programs and privacy-preserving applications to prevent double spending. Guide to integrate rent-free nullifier PDAs for double-spend prevention.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fzk\u002FSKILL.md",[],[],{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":110,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":127,"publishedAt":19},"01M78E0P3ZTZ1YWF8MD5MM23QE","zk-omni-messaging","Zero-knowledge omnichain messaging between Robinhood Chain and Solana using CheshireZkOmniMessenger (msgType 4) with nullifier anti-replay and the zk-omni-relayer service.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fzk-omni-messaging\u002FSKILL.md",[],[],{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":110,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":19},"01M78E0JXZ2PNB1896VXAGDBWD","zerion-wallet-data","Use when the user asks to check a wallet's portfolio, token balances, DeFi positions, PnL, transaction history, or NFTs. Provides interpreted on-chain data across 40+ EVM chains and Solana via x402 pay-per-request or API key — no raw RPC decoding needed.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fzerion-wallet-data\u002FSKILL.md",[],[],{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":110,"licenseFileUrl":139,"sourceHtmlUrl":140,"categories":141,"keywords":142,"publishedAt":19},"01M78E0G14FH9GRRTQYMKD5E59","youtube-clipper","YouTube 视频智能剪辑工具。下载视频和字幕，AI 分析生成精细章节（几分钟级别）， 用户选择片段后自动剪辑、翻译字幕为中英双语、烧录字幕到视频，并生成总结文案。 使用场景：当用户需要剪辑 YouTube 视频、生成短视频片段、制作双语字幕版本时。 关键词：视频剪辑、YouTube、字幕翻译、双语字幕、视频下载、clip video","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-clipper\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-clipper\u002FSKILL.md",[],[],{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":110,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":149,"publishedAt":19},"01M78E0C2ZYA99CFFDNB7ZMK5S","yield-optimization","Multi-chain yield optimization via yield.xyz (StakeKit). Discover 2,988+ yield opportunities across 75+ blockchains, build deposit\u002Fwithdrawal transactions with shell scripts, and sign them with a MoonPay wallet. Use when the user wants to earn yield, find the best lending or staking rate, enter\u002Fexit a position, or check portfolio balances.","https:\u002F\u002Fgithub.com\u002FSolizardking\u002Fskillhub-main\u002Fblob\u002Fmain\u002Fskills\u002Fyield-optimization\u002FSKILL.md",[],[]]