[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71PZ755HXHKPFFQ8PEKKDQZ":3,"skill-same:01M71PZ755HXHKPFFQ8PEKKDQZ":196},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":14,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M71PZ755HXHKPFFQ8PEKKDQZ","tao-train-dino",null,"DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with denoising training, multi-scale features, and optional distillation support. Use when training, evaluating, exporting, distilling, quantizing, or running inference for a TAO DINO detector. Trigger phrases include \"train DINO\", \"DETR object detection\", \"TAO 2D detection\", \"DINO with distillation\".","pattanation\u002FNorvik","pattanation","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235419268?v=4","01M71M5THHYYB15ME8R2E4FCZG","Patta","01M71M5TJM1NDM992M8PDKYR2K",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Ftao-train-dino\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"tao","train","tao train dino","2d","container","anchor","trigger","docker","for","NVIDIA","running","boxes","object detection","distillation","features","read","support","Bash","when","training","2026-11-10 13:02:02",false,"---\nname: tao-train-dino\ndescription: DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with\n  denoising training, multi-scale features, and optional distillation support. Use when training, evaluating, exporting,\n  distilling, quantizing, or running inference for a TAO DINO detector. Trigger phrases include \"train DINO\", \"DETR object\n  detection\", \"TAO 2D detection\", \"DINO with distillation\".\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- object\n- detection\n---\n\n# DINO\n\nDINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with denoising training, multi-scale features, and optional distillation support.\n\nUses pretrained backbone weights (e.g. ResNet-50 ImageNet). Set `model.pretrained_backbone_path` for backbone-only or `train.pretrained_model_path` for full model.\n\n## When To Use\n\nTrain, evaluate, export, distill, quantize, or run inference for a TAO DINO 2D object detector.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and\nTensorRT `inference`), read `references\u002Ftao-deploy-dino.md` first. Deploy spec templates live\nin this skill's `references\u002F` folder with the `spec_template_deploy_*.yaml`\nprefix.\n\n## Reference Map\n\n- `references\u002Fdino-data-specs.md` — dataset contracts, per-action dataset requirements, per-action spec-override examples (train, evaluate, export, deploy\u002Fgen_trt_engine, inference, quantize, distill), data-source arrays, checkpoint inference, and dataset layout.\n- `references\u002Fdino-actions-errors.md` — important parameters, default values, evaluate\u002Fexport defaults, hardware, and the full error-pattern catalog.\n- `references\u002Fdino-tuning-multigpu.md` — full AutoML\u002FHPO notes (metrics, hyperparameters, extractor) and multi-GPU spec consistency.\n- `references\u002Fdino-automl-sdk.md` — AutoML metrics, SDK orchestration internals, data-source gap, and spec-param\u002Fparent-model inference.\n- `references\u002Ftao-deploy-dino.md` — TensorRT deploy workflow.\n- `references\u002Fdetailed-guide.md` — map to the detailed model guide.\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 `evaluate`, `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\nThe agent MUST read this section before generating any training or AutoML script for DINO.\n\n- **Dataset type:** object_detection\n- **Formats:** coco, coco_raw\n- **Accepted dataset intents:** training, evaluation, testing, calibration\n- **Monitoring metric:** mAP50 for quick operational checks; `val_mAP` for\n  COCO\u002Fpaper-style benchmark comparisons.\n\n**Required datasets — MUST resolve both:**\n\n| Dataset | Required | Why |\n|---|---|---|\n| Train dataset URI | Yes | Training data (COCO format) |\n| Validation dataset URI | **Yes — ALWAYS** | DINO unconditionally builds a val dataloader. Omitting `val_data_sources` causes `FileNotFoundError` at startup regardless of the metric or workflow. If the user has no separate eval split, reuse the train URI. |\n\n**Required inputs before generating any training spec:**\n\n1. **Train dataset URI** — S3 path to COCO-format training data\n2. **Validation dataset URI** — S3 path to COCO-format val data (can be same as train)\n3. **`num_classes`** — How many object classes? Default 91 (COCO). Must be >= `max(category_id) + 1`. Too low causes `CUDA error: device-side assert triggered`.\n\nResolve these from the user request or the default profile below. Prompt only\nfor values that are still missing after applying the profile rules.\n\n**Bankable local default profile for DINO AutoML smoke runs:**\n\nUse this profile only when the user asks to run DINO AutoML and does not provide\ndataset or class-count inputs. This profile is intentionally small and local to\nthis skill bank; it is for smoke\u002Fiteration runs, not a production benchmark.\nDo not search previous runners, logs, session state, shell history, or the home\ndirectory to recover these values.\n\n```python\nDINO_AUTOML_PROFILE = {\n    \"train_dataset_uri\": \"s3:\u002F\u002Fnvcf-storage-handling\u002Fdata\u002Ftao_od_synthetic_subset_train_no_convert\",\n    \"validation_dataset_uri\": \"s3:\u002F\u002Fnvcf-storage-handling\u002Fdata\u002Ftao_od_synthetic_subset_val_no_convert\",\n    \"object_classes\": 4,\n    \"dataset_num_classes\": 5,\n    \"image_archive\": \"images.tar.gz\",\n    \"annotation_file\": \"annotations.json\",\n    \"max_recommendations\": 10,\n    \"train_num_epochs\": 10,\n    \"train_checkpoint_interval\": 10,\n    \"train_validation_interval\": 1,\n    \"train_num_gpus\": 1,\n}\n```\n\nIf the user supplies any dataset URI or class-count value, prefer the user value\nand ask for any remaining required DINO value. Do not partially mix a user's\ncustom dataset with this profile's class count unless the user confirms it.\n\n**Do not prompt for image layout for the standard DINO dataset.** The standard\nTAO DINO dataset artifact is `images.tar.gz` plus `annotations.json`. Use\n`images.tar.gz` in the remote `image_dir` spec override. The SDK downloads the\narchive and rewrites the runtime spec to the extracted folder named after the\narchive stem (`images.tar.gz` -> `images`). Only deviate if the user explicitly\nprovides a different image artifact name.\n\n## Core Workflow\n\nDINO supports train, evaluate, export, distill, quantize, and inference. Data-source\noverrides are **mandatory for every action** — DINO's `config.json` has empty\n`data_sources` because the runner cannot auto-resolve array-of-objects spec keys.\nThe agent MUST construct data source paths and include them in `spec_overrides`.\n\nSee `references\u002Fdino-data-specs.md` for the per-action dataset requirements table,\nthe standard dataset artifact (`images.tar.gz` + `annotations.json`) and runtime\nfolder rewrite rules, and the complete per-action `spec_overrides` examples for\ntrain, evaluate, export, deploy\u002Fgen_trt_engine, inference, quantize, and distill —\nincluding checkpoint inference via `parent_model`, the `results_dir\u002Ftrain\u002F`\ncheckpoint location, and the distillation FAN-teacher \u002F student rules.\n\n## Important Parameters And Defaults\n\nKey defaults: `num_epochs=10`, `batch_size=4`, `learning_rate=2e-4`,\n`lr_backbone=2e-5`, `num_classes=91`, `backbone=resnet_50`.\n\n- **dataset.num_classes**: Default 91 (COCO). Must be >= `max(category_id) + 1`. Too low causes `CUDA error: device-side assert triggered`. Set as `\u003Cnum_classes> + 1` in spec overrides.\n- **num_epochs**: default 10 (quick iteration); real datasets typically need 30-50+ epochs for good mAP.\n\nSee `references\u002Fdino-actions-errors.md` for the full parameter list (backbone\noptions, `train.optim.lr`\u002F`lr_steps`, `model.num_queries`, `batch_size`),\ndefault values, evaluate defaults, export defaults (input 960x544, opset 17,\nTRT data types, workspace 1024 MB), and hardware requirements.\n\n## Multi-GPU And AutoML \u002F HPO\n\nWhen increasing `train.num_gpus`, also set `train.gpu_ids` to the same visible\ndevice range, or distributed startup can be inconsistent.\n\nAutoML runs training — all **Training Requirements** above apply. For no-input\nlocal smoke runs, use `DINO_AUTOML_PROFILE`. Recommended metric is `mAP50`\n(`val_mAP` for benchmark comparisons) with `direction=\"maximize\"` and a custom\n`metric_extractor`.\n\nSee `references\u002Fdino-tuning-multigpu.md` for the full multi-GPU spec-consistency\nrule (8-GPU example, NCCL timeout note) and the full AutoML\u002FHPO notes (metric\nselection, `metric_extractor`, recommended hyperparameters, `weight_decay`\nbehavior, dense-dataset resume guidance). See `references\u002Fdino-automl-sdk.md` for\nAutoML metric extractor code, SDK orchestration internals, and parent-model\ninference mappings.\n\n## Error Patterns\n\nCommon failures include CUDA OOM (reduce `batch_size`), missing `val_data_sources`\n(`FileNotFoundError` at startup — always supply val), `num_classes` too low (`CUDA\ndevice-side assert`), and the parent `dino gen_trt_engine` \u002F `dino convert` PyT-CLI\nrestrictions.\n\nSee `references\u002Fdino-actions-errors.md` for the complete error-pattern catalog\nwith diagnostics and fixes.\n\n## Spec Param \u002F Parent Model Inference\n\nModel-specific inference mappings belong in this MD file, not in `config.json`.\nGenerated runners read the mappings and apply them with SDK helpers before\n`create_job()`. For `parent_model`\u002F`parent_model_folder`, pass the upstream\ntrain\u002Fexport\u002FAutoML child job id as `parent_job_id`; the SDK lists the parent\nresult folder, filters checkpoint artifacts, and returns the selected model.\n\nSee `references\u002Fdino-automl-sdk.md` for the full inference-mapping table (per\naction: `parent_model`, `key`, `output_dir`, `ptm_if_no_resume_model`,\n`resume_model`, `create_onnx_file`) and the TensorRT-mapping note. TensorRT\nmappings live in the deploy workflow, not the PyT model skill.\n\n## Optional: running via the TAO SDK\n\nWhen running DINO through the TAO SDK (`script_runner` orchestration, S3 I\u002FO\nwrapping, AutoML), skills read `references\u002Fskill_info.yaml` for input and\nspec-param mappings. See `references\u002Fdino-automl-sdk.md` for SDK orchestration\ninternals, including the data-sources gap and the `[0]`-indexed `inputs`\ndeclarations. Skip this when running locally with `docker run`.\n\n## Deployment\n\n- [tao-deploy-dino](references\u002Ftao-deploy-dino.md)\n","# DINO\n\nDINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with denoising training, multi-scale features, and optional distillation support.\n\nUses pretrained backbone weights (e.g. ResNet-50 ImageNet). Set `model.pretrained_backbone_path` for backbone-only or `train.pretrained_model_path` for full model.\n\n## When To Use\n\nTrain, evaluate, export, distill, quantize, or run inference for a TAO DINO 2D object detector.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and\nTensorRT `inference`), read `references\u002Ftao-deploy-dino.md` first. Deploy spec templates live\nin this skill's `references\u002F` folder with the `spec_template_deploy_*.yaml`\nprefix.\n\n## Reference Map\n\n- `references\u002Fdino-data-specs.md` — dataset contracts, per-action dataset requirements, per-action spec-override examples (train, evaluate, export, deploy\u002Fgen_trt_engine, inference, quantize, distill), data-source arrays, checkpoint inference, and dataset layout.\n- `references\u002Fdino-actions-errors.md` — important parameters, default values, evaluate\u002Fexport defaults, hardware, and the full error-pattern catalog.\n- `references\u002Fdino-tuning-multigpu.md` — full AutoML\u002FHPO notes (metrics, hyperparameters, extractor) and multi-GPU spec consistency.\n- `references\u002Fdino-automl-sdk.md` — AutoML metrics, SDK orchestration internals, data-source gap, and spec-param\u002Fparent-model inference.\n- `references\u002Ftao-deploy-dino.md` — TensorRT deploy workflow.\n- `references\u002Fdetailed-guide.md` — map to the detailed model guide.\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 `evaluate`, `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\nThe agent MUST read this section before generating any training or AutoML script for DINO.\n\n- **Dataset type:** object_detection\n- **Formats:** coco, coco_raw\n- **Accepted dataset intents:** training, evaluation, testing, calibration\n- **Monitoring metric:** mAP50 for quick operational checks; `val_mAP` for\n  COCO\u002Fpaper-style benchmark comparisons.\n\n**Required datasets — MUST resolve both:**\n\n| Dataset | Required | Why |\n|---|---|---|\n| Train dataset URI | Yes | Training data (COCO format) |\n| Validation dataset URI | **Yes — ALWAYS** | DINO unconditionally builds a val dataloader. Omitting `val_data_sources` causes `FileNotFoundError` at startup regardless of the metric or workflow. If the user has no separate eval split, reuse the train URI. |\n\n**Required inputs before generating any training spec:**\n\n1. **Train dataset URI** — S3 path to COCO-format training data\n2. **Validation dataset URI** — S3 path to COCO-format val data (can be same as train)\n3. **`num_classes`** — How many object classes? Default 91 (COCO). Must be >= `max(category_id) + 1`. Too low causes `CUDA error: device-side assert triggered`.\n\nResolve these from the user request or the default profile below. Prompt only\nfor values that are still missing after applying the profile rules.\n\n**Bankable local default profile for DINO AutoML smoke runs:**\n\nUse this profile only when the user asks to run DINO AutoML and does not provide\ndataset or class-count inputs. This profile is intentionally small and local to\nthis skill bank; it is for smoke\u002Fiteration runs, not a production benchmark.\nDo not search previous runners, logs, session state, shell history, or the home\ndirectory to recover these values.\n\n```python\nDINO_AUTOML_PROFILE = {\n    \"train_dataset_uri\": \"s3:\u002F\u002Fnvcf-storage-handling\u002Fdata\u002Ftao_od_synthetic_subset_train_no_convert\",\n    \"validation_dataset_uri\": \"s3:\u002F\u002Fnvcf-storage-handling\u002Fdata\u002Ftao_od_synthetic_subset_val_no_convert\",\n    \"object_classes\": 4,\n    \"dataset_num_classes\": 5,\n    \"image_archive\": \"images.tar.gz\",\n    \"annotation_file\": \"annotations.json\",\n    \"max_recommendations\": 10,\n    \"train_num_epochs\": 10,\n    \"train_checkpoint_interval\": 10,\n    \"train_validation_interval\": 1,\n    \"train_num_gpus\": 1,\n}\n```\n\nIf the user supplies any dataset URI or class-count value, prefer the user value\nand ask for any remaining required DINO value. Do not partially mix a user's\ncustom dataset with this profile's class count unless the user confirms it.\n\n**Do not prompt for image layout for the standard DINO dataset.** The standard\nTAO DINO dataset artifact is `images.tar.gz` plus `annotations.json`. Use\n`images.tar.gz` in the remote `image_dir` spec override. The SDK downloads the\narchive and rewrites the runtime spec to the extracted folder named after the\narchive stem (`images.tar.gz` -> `images`). Only deviate if the user explicitly\nprovides a different image artifact name.\n\n## Core Workflow\n\nDINO supports train, evaluate, export, distill, quantize, and inference. Data-source\noverrides are **mandatory for every action** — DINO's `config.json` has empty\n`data_sources` because the runner cannot auto-resolve array-of-objects spec keys.\nThe agent MUST construct data source paths and include them in `spec_overrides`.\n\nSee `references\u002Fdino-data-specs.md` for the per-action dataset requirements table,\nthe standard dataset artifact (`images.tar.gz` + `annotations.json`) and runtime\nfolder rewrite rules, and the complete per-action `spec_overrides` examples for\ntrain, evaluate, export, deploy\u002Fgen_trt_engine, inference, quantize, and distill —\nincluding checkpoint inference via `parent_model`, the `results_dir\u002Ftrain\u002F`\ncheckpoint location, and the distillation FAN-teacher \u002F student rules.\n\n## Important Parameters And Defaults\n\nKey defaults: `num_epochs=10`, `batch_size=4`, `learning_rate=2e-4`,\n`lr_backbone=2e-5`, `num_classes=91`, `backbone=resnet_50`.\n\n- **dataset.num_classes**: Default 91 (COCO). Must be >= `max(category_id) + 1`. Too low causes `CUDA error: device-side assert triggered`. Set as `\u003Cnum_classes> + 1` in spec overrides.\n- **num_epochs**: default 10 (quick iteration); real datasets typically need 30-50+ epochs for good mAP.\n\nSee `references\u002Fdino-actions-errors.md` for the full parameter list (backbone\noptions, `train.optim.lr`\u002F`lr_steps`, `model.num_queries`, `batch_size`),\ndefault values, evaluate defaults, export defaults (input 960x544, opset 17,\nTRT data types, workspace 1024 MB), and hardware requirements.\n\n## Multi-GPU And AutoML \u002F HPO\n\nWhen increasing `train.num_gpus`, also set `train.gpu_ids` to the same visible\ndevice range, or distributed startup can be inconsistent.\n\nAutoML runs training — all **Training Requirements** above apply. For no-input\nlocal smoke runs, use `DINO_AUTOML_PROFILE`. Recommended metric is `mAP50`\n(`val_mAP` for benchmark comparisons) with `direction=\"maximize\"` and a custom\n`metric_extractor`.\n\nSee `references\u002Fdino-tuning-multigpu.md` for the full multi-GPU spec-consistency\nrule (8-GPU example, NCCL timeout note) and the full AutoML\u002FHPO notes (metric\nselection, `metric_extractor`, recommended hyperparameters, `weight_decay`\nbehavior, dense-dataset resume guidance). See `references\u002Fdino-automl-sdk.md` for\nAutoML metric extractor code, SDK orchestration internals, and parent-model\ninference mappings.\n\n## Error Patterns\n\nCommon failures include CUDA OOM (reduce `batch_size`), missing `val_data_sources`\n(`FileNotFoundError` at startup — always supply val), `num_classes` too low (`CUDA\ndevice-side assert`), and the parent `dino gen_trt_engine` \u002F `dino convert` PyT-CLI\nrestrictions.\n\nSee `references\u002Fdino-actions-errors.md` for the complete error-pattern catalog\nwith diagnostics and fixes.\n\n## Spec Param \u002F Parent Model Inference\n\nModel-specific inference mappings belong in this MD file, not in `config.json`.\nGenerated runners read the mappings and apply them with SDK helpers before\n`create_job()`. For `parent_model`\u002F`parent_model_folder`, pass the upstream\ntrain\u002Fexport\u002FAutoML child job id as `parent_job_id`; the SDK lists the parent\nresult folder, filters checkpoint artifacts, and returns the selected model.\n\nSee `references\u002Fdino-automl-sdk.md` for the full inference-mapping table (per\naction: `parent_model`, `key`, `output_dir`, `ptm_if_no_resume_model`,\n`resume_model`, `create_onnx_file`) and the TensorRT-mapping note. TensorRT\nmappings live in the deploy workflow, not the PyT model skill.\n\n## Optional: running via the TAO SDK\n\nWhen running DINO through the TAO SDK (`script_runner` orchestration, S3 I\u002FO\nwrapping, AutoML), skills read `references\u002Fskill_info.yaml` for input and\nspec-param mappings. See `references\u002Fdino-automl-sdk.md` for SDK orchestration\ninternals, including the data-sources gap and the `[0]`-indexed `inputs`\ndeclarations. Skip this when running locally with `docker run`.\n\n## Deployment\n\n- [tao-deploy-dino](references\u002Ftao-deploy-dino.md)\n",{"name":5,"tags":44,"license":15,"metadata":47,"description":7,"allowed-tools":50,"compatibility":51},[45,46],"object","detection",{"author":48,"version":49},"NVIDIA Corporation","0.1.0","Read Bash","Requires docker + nvidia-container-toolkit.","claude\u002Finstall-gsd-build-foQdV","d86cd127f527124b5fd0fd0bc679a4d79b7d01d7",".agents\u002Fskills\u002Ftao-train-dino","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik","https:\u002F\u002Fnorvik-orpin.vercel.app","https:\u002F\u002Fgithub.com\u002Fpattanation","2026-07-17 11:17:04",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add pattanation\u002FNorvik --skill tao-train-dino",{"tool":65,"command":66},"bunx","bunx skills add pattanation\u002FNorvik --skill tao-train-dino",{"tool":68,"command":69},"pnpm","pnpm dlx skills add pattanation\u002FNorvik --skill 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