Tao Train Foundation Stereo
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The tao-train-foundation-stereo skill provides a comprehensive interface for the FoundationStereo architecture within the NVIDIA TAO ecosystem. It enables users to perform the full model lifecycle, including training, evaluation, inference, and export, specifically for predicting disparity maps from stereo image pairs. The skill supports advanced features like AutoML for hyperparameter optimization and integrates with TAO Deploy for optimized TensorRT inference. It is designed to handle various dataset formats, including KITTI, Middlebury, and synthetic data, ensuring consistent performance across different stereo vision tasks.
What it does
- Trains FoundationStereo models for stereo depth estimation.
- Evaluates model performance using stereo-specific metrics like EPE, bp1, and d1.
- Exports trained models to ONNX format for deployment.
- Supports AutoML for automated hyperparameter tuning during training.
- Performs inference on stereo image pairs to generate disparity maps.
- Integrates with TAO Deploy for generating optimized TensorRT engines.
When to use it
- When you need to perform 3D reconstruction from stereo image pairs.
- When training a custom stereo depth estimation model using the FoundationStereo architecture.
- When you need to optimize model performance using AutoML.
- When preparing a stereo depth model for production deployment via TensorRT.
Advantages
- Unified workflow for the entire model lifecycle within the TAO ecosystem.
- Supports multiple standard stereo dataset formats.
- Provides stereo-specific evaluation metrics for accurate performance assessment.
- Includes AutoML capabilities to simplify hyperparameter optimization.
Usage examples
- “Train a new FoundationStereo model using my KITTI dataset.”
- The agent initiates the training process using the FoundationStereo architecture, configured for the KITTI dataset format, and monitors progress via the specified training metrics.
- “Evaluate my trained stereo model on the validation set.”
- The agent runs the evaluation action, calculating stereo-specific metrics like EPE and d1 to assess the model's accuracy on the provided validation data.
- “Export my FoundationStereo model to ONNX.”
- The agent triggers the export workflow, converting the trained PyTorch model into an ONNX file suitable for deployment.
- “Run inference on a pair of stereo images.”
- The agent processes the input stereo image pair through the model and outputs the predicted disparity map for 3D reconstruction.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-train-foundation-stereo/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-train-foundation-stereo.zip -d .agents/skills
Documentation by NVIDIA Corporation
Depth Net Stereo
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first (host preflight, credentials, cross-skill discovery).
Stereo depth estimation using FoundationStereo architecture. Predicts disparity maps from stereo image pairs for 3D reconstruction.
Uses pretrained Depth Anything v2 and EdgeNeXt encoders. Set model.stereo_backbone.depth_anything_v2_pretrained_path and model.stereo_backbone.edgenext_pretrained_path.
The mono and stereo skills both invoke the unified TAO depth_net CLI inside the container; the mono/stereo family is selected via model.model_type (e.g., FoundationStereo).
PyT actions packaged by this model skill: train, evaluate, inference, export, and quantize. The PyT depth_net entrypoint does not accept a gen_trt_engine action in the current TAO image; build TensorRT engines only through the deploy workflow.
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-foundation-stereo.md first. The deploy spec template lives in this skill's references/spec_template_deploy.yaml.
Train Action Policy
This model is AutoML-enabled at the model layer. Before handling any train-stage request, read references/skill_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 / 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/train.schema.json and references/spec_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/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and automl_policy. Use direct model training only when automl_policy: off or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.
Non-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.
Workflow
Prerequisites — data accessibility
Your dataset (left + right images + GT disparity) must be reachable from inside the container:
SDK runner: place files at the S3 paths the runner resolves (the
S3_TRAIN/S3_EVALplaceholders shown in the spec overrides). The runner handles S3 → container-path mounting transparently.Direct
docker run(e.g. local testing): mount the host dataset root read-only at the same in-container path:
docker run ... -v <host_data_root>:<host_data_root>:ro <container> ...The same accessibility requirement applies to the <output_dir> written by all actions.
Step 1 — Annotation file
Per-line annotation file referenced by data_sources[*].data_file:
| Columns | Format | Use |
|---|---|---|
| 2 | <left> <right> | Stereo inference (no GT) |
| 3 | <left> <right> <disparity> | Stereo with GT |
| 4 | <left> <right> <disparity> <occlusion_mask> | Stereo with GT and occlusion mask |
If you already have one, point to it. Otherwise generate via depth_net convert:
depth_net convert -e <convert_spec.yaml>convert_spec.yaml template (stereo):
results_dir: <directory where generated annotation files are written>
data_root: <directory whose immediate children are scene folders that contain your image+depth files; convert walks data_root recursively but expects per-scene subdirectories at one level below>
image_dir_pattern: [<substring matching left image paths>]
right_dir_pattern: [<substring matching right image paths>]
depth_dir_pattern: [<substring matching GT disparity paths>]
nocc_dir_pattern: [] # optional, occlusion mask paths
image_extension: '.png' # always include the leading dot
depth_extension: '.png' # form must match image_extension (the swap is a substring replace)
nocc_extension: ''
split_ratio: 0.0 # 0.0/1.0 = test-only; 0.8 = 80/20 train+valconvert walks data_root recursively, selects paths whose path-string contains all substrings in image_dir_pattern (AND-filter), then derives right / depth / mask paths by replacing image_dir_pattern[0] with the corresponding pattern's first element plus extension swap. Inspect your dataset's directory layout and identify the substrings distinguishing left, right, and GT (e.g. im0 vs im1 vs disp0GT for Middlebury).
Step 2 — Pair model_type and dataset_name based on your data
Prefer the dataset-specific class when your layout matches a supported one — it applies class-specific path conventions, evaluation crops, and (where applicable) occlusion-mask handling. Fall back to GenericDataset only for layouts that do not match any registered class.
| Data category | model_type | dataset_name |
|---|---|---|
| Middlebury data | FoundationStereo | Middlebury |
| KITTI data | FoundationStereo | Kitti |
| ETH3D data | FoundationStereo | Eth3d |
| FSD synthetic data | FoundationStereo | FSD |
| IsaacReal synthetic data | FoundationStereo | IsaacRealDataset |
| Crestereo synthetic data | FoundationStereo | Crestereo |
| Other / non-canonical layout | FoundationStereo | GenericDataset |
Valid dataset_name values for stereo data_sources (case-insensitive): FSD, IsaacRealDataset, Crestereo, Middlebury, Eth3d, Kitti, GenericDataset.
The same dataset_name value applies across train and evaluate actions (all of which use 3-column or 4-column annotations with GT disparity). The deploy-side evaluate action follows the same rule — see references/tao-deploy-foundation-stereo.md. For inference with 2-column annotations (left + right, no GT), use dataset_name: GenericDataset regardless of data layout — the dataset-specific classes (Middlebury / Kitti / Eth3d / FSD / IsaacRealDataset / Crestereo) require 3-column input and reject 2-column annotations at the dataloader level. For inference with 3-column annotations (left + right + GT), the dataset-specific class is fine.
Step 3 — Write spec yaml from the spec overrides
Copy the action block from references/spec-overrides-foundation-stereo.md. Replace:
model.model_typefrom Step 2 (typicallyFoundationStereo)dataset.<...>.data_sources[*].dataset_namefrom Step 2dataset.<...>.data_sources[*].data_filewith the path from Step 1For deploy-side
evaluate: enforcedataset.test_dataset.batch_size: 1(seereferences/tao-deploy-foundation-stereo.md).
Shape consistency: the crop_size in dataset.test_dataset.augmentation.crop_size should match export.input_height / input_width so the trained-model evaluator and the deploy-side TensorRT evaluator operate at the same shape. Note that crop_size is decorative on the pyt evaluate path but authoritative on the deploy evaluate side — see references/troubleshooting-foundation-stereo.md and references/tao-deploy-foundation-stereo.md.
Fresh-install smoke runs are validated at crop_size: [128, 128] with dataset.max_disparity: 128 and model.max_disparity: 128. Avoid 112×112 crops and avoid setting max_disparity smaller than the square crop side for smoke tests: those combinations can fail inside FoundationStereo with feature-map or loss-mask shape mismatches before a checkpoint is produced.
Data source overrides are mandatory for every action. Each data_sources entry is a dict with two mandatory fields: data_file and dataset_name. See references/spec-overrides-foundation-stereo.md for the per-action dataset-requirements table, every action's override block, and the quantize known-issue note.
Step 4 — Run
Create writable home/cache directories inside the mounted output path before using
--user. Some TAO containers do not have an /etc/passwd entry for the host UID,
and PyTorch / matplotlib need writable cache paths when running as that UID.
mkdir -p <output_dir>/home \
<output_dir>/.cache/matplotlib \
<output_dir>/.cache/torchinductor \
<output_dir>/.cache/xdgdocker run --gpus 'device=0' --shm-size 16G --shm-size=8g \
--user "$(id -u):$(id -g)" \
-e USER="$(id -un)" \
-e LOGNAME="$(id -un)" \
-e HOME=<output_dir>/home \
-e MPLCONFIGDIR=<output_dir>/.cache/matplotlib \
-e TORCHINDUCTOR_CACHE_DIR=<output_dir>/.cache/torchinductor \
-e XDG_CACHE_HOME=<output_dir>/.cache/xdg \
-v <data_root>:<data_root>:ro \
-v <output_dir>:<output_dir> \
<container> \
depth_net <action> -e <spec.yaml>Without --user "$(id -u):$(id -g)" the container writes outputs as nobody:nogroup, blocking host-side cleanup / retry.
Step 5 — Verify
Container exit code 0
status.jsonkpiblock populatedFor
train: inspect per-steptrain_lossdirectly (the entrypoint reportsExecution status: PASSeven when loss is NaN)For
evaluate: rely onepe/bp1/bp2/bp3/d1/rmse(the evaluator also emitsabs_rel/sq_rel/rmse_logwhich are non-meaningful for stereo — seereferences/parameters-foundation-stereo.md)For
inference: artifacts underresults_dir
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-foundation-stereo.md first. Deploy spec templates live in this skill's references/ folder with the spec_template_deploy_*.yaml prefix.
Training Requirements
AutoML metric contract: when recommendations are scored with the packaged
evaluateaction, useval/epewith minimize direction.Training monitoring metric:
val/lossUse this only for live training-loss monitoring.AutoML/evaluation KPI:
val/epe, minimized. The packagedevaluateaction writes this key tostatus.json; usingval/lossas the AutoML objective makes trial metric extraction fail. Its Lightning console table labels the same evaluator outputtest/epe, so compare AutoML's recordedval/epevalue with the console'stest/epevalue when verifying a run.Recommended AutoML train parameters:
train.optim.lrandtrain.optim.weight_decay. Do not search validation/test/inference augmentation fields, and do not searchmodel.volume_dimbecause the current implementation does not consume it.Eval dataset: optional. Val dataset configured via
dataset.val_dataset.data_sources(each entry needsdata_fileanddataset_name).
See references/spec-overrides-foundation-stereo.md for the per-action dataset-requirements table and every action's mandatory data-source override block.
Parameters, Metrics, Multi-GPU, Export/TRT, Hardware
See references/parameters-foundation-stereo.md for the full Important Parameters list (incl. model.encoder vits override, model.max_disparity default 416, model.volume_dim no-op note, dataset.baseline, dataset.focal_x, train.precision, export.batch_size), the Evaluation Metrics table, Multi-GPU / Multi-Node launch keys, Export / TRT Defaults (opset_version/on_cpu pairing, NGC 576×960 settings), and Hardware requirements.
Error Patterns and Troubleshooting
See references/troubleshooting-foundation-stereo.md for disparity overflow, smoke-test shape mismatch, missing pretrained paths, the encoder / dataset_name struct errors, the depth_net_stereo: not found entrypoint note, the pyt-vs-deploy crop_size discussion, and the deploy evaluate scalar-conversion failure.
Spec Param / Parent Model Inference
See references/checkpoint-inference-mappings-foundation-stereo.md for the checkpoint-resolution rules (model_epoch_<epoch>_step_<step>.pth, dn_model_latest.pth policy), the absence of parent PyT gen_trt_engine, and the full per-action inference-mapping table from depth_net_stereo.config.json (including parent_model / parent_job_id resolution).
Deployment
Files
- ATTRIBUTION.mdAdded by AutoGineer951 B
- BENCHMARK.md7.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md12.7 KiB
- config/skillspector-baseline.yaml1010 B
- evals/evals.json819 B
- references/checkpoint-inference-mappings-foundation-stereo.md3.9 KiB
- references/parameters-foundation-stereo.md5.6 KiB
- references/skill_info.yaml5.5 KiB
- references/spec-overrides-foundation-stereo.md4.1 KiB
- references/spec_template_deploy.yaml2.0 KiB
- references/spec_template_evaluate.yaml5.5 KiB
- references/spec_template_export.yaml5.6 KiB
- references/spec_template_gen_trt_engine.yaml5.6 KiB
- references/spec_template_inference.yaml5.6 KiB
- references/spec_template_quantize.yaml5.4 KiB
- references/spec_template_train.yaml5.4 KiB
- references/tao-deploy-foundation-stereo.md11.7 KiB
- references/tao-deploy-foundation-stereo.skill_info.yaml3.2 KiB
- references/troubleshooting-foundation-stereo.md2.4 KiB
- schemas/evaluate.schema.json104.5 KiB
- schemas/export.schema.json105.1 KiB
- schemas/gen_trt_engine.schema.json107.0 KiB
- schemas/inference.schema.json104.9 KiB
- schemas/manifest.json29.9 KiB
- schemas/quantize.schema.json101.4 KiB
- schemas/train.schema.json101.4 KiB
- skill-card.md4.4 KiB
- skill.oms.sig9.9 KiB
schemas/gen_trt_engine.schema.json
{
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"default": {
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"dataset_name": "StereoDataset",
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"workers": 8
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],
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},
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"gen_trt_engine": {
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"onnx_file": "???",
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"tensorrt": {
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"model": {
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},
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}
},
"type": "collection"
},
"encryption_key": {
"default": "",
"description": "Key for encrypting model checkpoints",
"title": "Encryption key",
"type": "string"
},
"gen_trt_engine": {
"automl_disabled_parameters": [
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"automl_enabled": false,
"default": {
"batch_size": -1,
"gpu_id": 0,
"onnx_file": "???",
"results_dir": "",
"tensorrt": {
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"layers_precision": [],
"max_batch_size": 1,
"min_batch_size": 1,
"opt_batch_size": 1,
"workspace_size": 1024
},
"timing_cache": "",
"trt_engine": "???",
"verbose": false
},
"description": "Configurable parameters to construct the TensorRT engine builder for a DepthNet experiment.",
"popular": [
"batch_size",
"gpu_id",
"tensorrt"
],
"properties": {
"batch_size": {
"default": -1,
"description": "The batch size of the input Tensor for the engine.\n A value of :code:`-1` implies dynamic tensor shapes.",
"minimum": -1,
"popular": true,
"title": "Batch size",
"type": "int"
},
"gpu_id": {
"default": 0,
"description": "The index of the GPU to build the TensorRT engine.",
"minimum": 0,
"popular": true,
"title": "GPU ID",
"type": "int"
},
"onnx_file": {
"default": "???",
"description": "\n Path to the ONNX model file.\n ",
"title": "ONNX file",
"type": "string"
},
"results_dir": {
"default": "",
"description": "\n Path to where all the assets generated from a task are stored.\n ",
"title": "Results directory",
"type": "string"
},
"tensorrt": {
"automl_disabled_parameters": [
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],
"automl_enabled": false,
"default": {
"data_type": "FP32",
"layers_precision": [],
"max_batch_size": 1,
"min_batch_size": 1,
"opt_batch_size": 1,
"workspace_size": 1024
},
"description": "Hyper parameters to configure the TensorRT Engine builder.",
"popular": [
"min_batch_size",
"max_batch_size",
"opt_batch_size"
],
"properties": {
"data_type": {
"default": "FP32",
"description": "The precision to be set for building the TensorRT engine.",
"enum": [
"FP32",
"FP16"
],
"title": "data type",
"type": "categorical"
},
"layers_precision": {
"automl_enabled": false,
"default": [],
"description": "The list to specify layer precision.",
"title": "layers_precision",
"type": "list"
},
"max_batch_size": {
"default": 1,
"description": "The maximum batch size in the optimization profile for\n the input tensor of the TensorRT engine.",
"minimum": 1,
"popular": true,
"title": "Maximum batch size",
"type": "int"
},
"min_batch_size": {
"default": 1,
"description": "The minimum batch size in the optimization profile for\n the input tensor of the TensorRT engine.",
"minimum": 1,
"popular": true,
"title": "Min batch size",
"type": "int"
},
"opt_batch_size": {
"default": 1,
"description": "The optimum batch size in the optimization profile for\n the input tensor of the TensorRT engine.",
"minimum": 1,
"popular": true,
"title": "Optimum batch size",
"type": "int"
},
"workspace_size": {
"default": 1024,
"description": "The size (in MB) of the workspace TensorRT has\n to run it's optimization tactics and generate the\n TensorRT engine.",
"minimum": 0,
"title": "Max workspace size",
"type": "int"
}
},
"title": "TensorRT hyper params.",
"type": "collection"
},
"timing_cache": {
"default": "",
"description": "Path to a TensorRT timing cache that speeds up engine generation.\n This will be created/read/updated.",
"title": "TensorRT timing cache",
"type": "string"
},
"trt_engine": {
"default": "???",
"description": "Path to the TensorRT engine generated should be stored.\n This only works with :code:`tao-deploy`.",
"title": "TensorRT engine",
"type": "string"
},
"verbose": {
"default": false,
"description": "Flag to enable verbose TensorRT logging.",
"title": "Verbose",
"type": "bool"
}
},
"type": "collection"
},
"model": {
"automl_default_parameters": [
"model.corr_radius",
"model.cv_group",
"model.volume_dim"
],
"automl_disabled_parameters": [
"model.mono_backbone",
"model.stereo_backbone",
"model.hidden_dims"
],
"automl_enabled": false,
"default": {
"corr_levels": 2,
"corr_radius": 4,
"cv_group": 8,
"encoder": "vitl",
"hidden_dims": [
128,
128,
128
],
"low_memory": 0,
"max_disparity": 416,
"mixed_precision": false,
"model_type": "MetricDepthAnything",
"mono_backbone": {
"pretrained_path": "",
"use_bn": false,
"use_clstoken": false
},
"n_downsample": 2,
"n_gru_layers": 3,
"stereo_backbone": {
"depth_anything_v2_pretrained_path": "",
"edgenext_pretrained_path": "",
"use_bn": false,
"use_clstoken": false
},
"train_iters": 22,
"valid_iters": 22,
"volume_dim": 32
},
"description": "Configurable parameters to construct the model for a DepthNet experiment.",
"properties": {
"corr_levels": {
"default": 2,
"description": "The number of levels in the correlation pyramid",
"maximum": 2,
"minimum": 1,
"title": "number of correlation pyramid levels",
"type": "int"
},
"corr_radius": {
"automl_enabled": true,
"default": 4,
"description": "The width of the correlation pyramid",
"maximum": 8,
"minimum": 2,
"title": "correlation pyramid width",
"type": "int"
},
"cv_group": {
"automl_enabled": true,
"default": 8,
"description": "cv group",
"maximum": 16,
"minimum": 4,
"title": "cv group",
"type": "int"
},
"encoder": {
"default": "vitl",
"description": "DepthAnythingV2 Encoder options",
"enum": [
"vits",
"vitb",
"vitl",
"vitg"
],
"type": "categorical"
},
"hidden_dims": {
"automl_enabled": false,
"default": [
128,
128,
128
],
"description": "The hidden dimensions.",
"title": "The hidden dimensions.",
"type": "list"
},
"low_memory": {
"default": 0,
"description": "reduce memory usage",
"maximum": 4,
"minimum": 0,
"title": "reduce memory usage",
"type": "int"
},
"max_disparity": {
"default": 416,
"description": "\n The maximum disparity of the model used in the training of a stereo model\n ",
"title": "max disparity",
"type": "int"
},
"mixed_precision": {
"default": false,
"description": "A flag specifying whether to use mixed precision training",
"title": "Mixed Precision Training",
"type": "bool"
},
"model_type": {
"default": "MetricDepthAnything",
"description": "Network name",
"enum": [
"FoundationStereo",
"MetricDepthAnything",
"RelativeDepthAnything"
],
"type": "categorical"
},
"mono_backbone": {
"automl_enabled": false,
"default": {
"pretrained_path": "",
"use_bn": false,
"use_clstoken": false
},
"description": "Network defined paths for Monocular DepthNet Backbone",
"properties": {
"pretrained_path": {
"default": "",
"description": "Path to load depth anything v2 as an encoder for Monocular DepthNet",
"title": "Pretrained path for mono backbone",
"type": "string"
},
"use_bn": {
"default": false,
"description": "A flag specifying whether to use batch normalization in Monocular DepthNet",
"title": "Batch normalization in Monocular DepthNet",
"type": "bool"
},
"use_clstoken": {
"default": false,
"description": "A flag specifying whether to use class token",
"title": "Class token in Monocular DepthNet",
"type": "bool"
}
},
"title": "Mono backbone configuration",
"type": "collection"
},
"n_downsample": {
"default": 2,
"description": "resolution of the disparity field (1/2^K)",
"maximum": 2,
"minimum": 1,
"title": "disparity field resoultion",
"type": "int"
},
"n_gru_layers": {
"default": 3,
"description": "The number of hidden GRU levels",
"maximum": 3,
"minimum": 1,
"title": "number of hidden GRU levels",
"type": "int"
},
"stereo_backbone": {
"automl_enabled": false,
"default": {
"depth_anything_v2_pretrained_path": "",
"edgenext_pretrained_path": "",
"use_bn": false,
"use_clstoken": false
},
"description": "Network defined paths for Edgenext and Depthanythingv2",
"properties": {
"depth_anything_v2_pretrained_path": {
"default": "",
"description": "Path to load depth anything v2 as an encoder for Stereo DepthNet (FoundationStereo)",
"type": "string"
},
"edgenext_pretrained_path": {
"default": "",
"description": "Path to load edgenext encoder for Stereo DepthNet (FoundationStereo)",
"type": "string"
},
"use_bn": {
"default": false,
"description": "A flag specifying whether to use batch normalization in DepthAnythingV2",
"title": "batch normalization in DepthAnythingV2",
"type": "bool"
},
"use_clstoken": {
"default": false,
"description": "A flag specifying whether to use class token",
"title": "class token in DepthAnythingV2",
"type": "bool"
}
},
"title": "Stereo backbone configuration",
"type": "collection"
},
"train_iters": {
"default": 22,
"description": "Train Iteration",
"minimum": 1,
"title": "train iteration",
"type": "int"
},
"valid_iters": {
"default": 22,
"description": "Validation Iteration",
"minimum": 1,
"title": "Validation iteration",
"type": "int"
},
"volume_dim": {
"automl_enabled": true,
"default": 32,
"description": "Volume dimension",
"maximum": 64,
"minimum": 16,
"title": "volume dimension",
"type": "int"
}
},
"type": "collection"
},
"model_name": {
"default": "",
"description": "Name of model if invoking task via :code:`model_agnostic`",
"title": "Model name",
"type": "string"
},
"quantize": {
"automl_disabled_parameters": [
"quantize.layers",
"quantize.skip_names",
"quantize.backend_kwargs"
],
"automl_enabled": false,
"default": {
"algorithm": "minmax",
"backend": "torchao",
"backend_kwargs": {},
"device": "cuda",
"layers": [],
"mode": "weight_only_ptq",
"model_path": "",
"results_dir": "",
"skip_names": []
},
"description": "Configurable parameters for model quantization.",
"properties": {
"algorithm": {
"default": "minmax",
"description": "Calibration/optimization algorithm. Used by ModelOpt backends (modelopt.pytorch and modelopt.onnx). Ignored by torchao backend.",
"enum": [
"minmax",
"max",
"entropy",
"awq_clip",
"awq_lite",
"awq_full",
"rtn_dq"
],
"title": "Calibration algorithm",
"type": "categorical"
},
"backend": {
"default": "torchao",
"description": "The quantization backend to use",
"enum": [
"modelopt.pytorch",
"torchao",
"modelopt.onnx"
],
"title": "Quantization backend",
"type": "categorical"
},
"backend_kwargs": {
"automl_enabled": false,
"description": "Additional keyword arguments to pass to the backend",
"title": "Backend kwargs",
"type": "collection"
},
"device": {
"default": "cuda",
"description": "Device to use for calibration. Accepts 'cuda' (uses default GPU), 'cpu', 'trt' (TensorRT for ONNX backend), or specific GPU device like 'cuda:0', 'cuda:1', etc. If 'cuda' is specified but no GPU is available, the framework will automatically fall back to 'cpu'. Note: 'trt' is only supported by the modelopt.onnx backend for ONNX Runtime with TensorRT execution provider.",
"pattern": "^(cuda|cpu|trt|cuda:[0-9]+)$",
"title": "Calibration device",
"type": "string"
},
"layers": {
"automl_enabled": false,
"default": [],
"description": "List of per-module quantization configurations. Each entry specifies which modules to quantize and their data types. This is the primary way to configure quantization.",
"title": "Layer quantization configs",
"type": "list"
},
"mode": {
"default": "weight_only_ptq",
"description": "The quantization mode to use",
"enum": [
"static_ptq",
"weight_only_ptq"
],
"title": "Quantization mode",
"type": "categorical"
},
"model_path": {
"default": "",
"description": "Path to the model to be quantized. For ONNX backend, path to ONNX file.",
"title": "Model path",
"type": "string"
},
"results_dir": {
"default": "",
"description": "Path to where all the assets generated from a task are stored.",
"title": "Results directory",
"type": "string"
},
"skip_names": {
"automl_enabled": false,
"default": [],
"description": "List of module/layer names or patterns to exclude from quantization",
"title": "Skip names",
"type": "list"
}
},
"required": [
"model_path",
"results_dir"
],
"type": "collection"
},
"results_dir": {
"default": "",
"description": "\n Path to where all the assets generated from a task are stored.\n ",
"title": "Results directory",
"type": "string"
},
"train": {
"automl_disabled_parameters": [
"train.gpu_ids",
"train.cudnn",
"train.optim",
"train.tile_min_overlap"
],
"automl_enabled": false,
"default": {
"activation_checkpoint": false,
"checkpoint_interval": 1,
"checkpoint_interval_unit": "epoch",
"clip_grad_norm": 0.1,
"cudnn": {
"benchmark": false,
"deterministic": true
},
"dataloader_visualize": false,
"distributed_strategy": "ddp",
"gpu_ids": [
0
],
"inference_tile": false,
"is_dry_run": false,
"log_every_n_steps": 500,
"num_epochs": 10,
"num_gpus": 1,
"num_nodes": 1,
"optim": {
"lr": 0.0001,
"lr_decay": 0.1,
"lr_scheduler": "MultiStepLR",
"lr_step_size": 1000,
"lr_steps": [
1000
],
"min_lr": 1e-07,
"momentum": 0.9,
"monitor_name": "val_loss",
"optimizer": "AdamW",
"warmup_steps": 20,
"weight_decay": 0.0001
},
"precision": "fp32",
"pretrained_model_path": "",
"results_dir": "",
"resume_training_checkpoint_path": "",
"seed": 1234,
"tile_min_overlap": [
16,
16
],
"tile_wtype": "gaussian",
"validation_interval": 1,
"vis_step_interval": 10
},
"description": "Configurable parameters to construct the trainer for a DepthNet experiment.",
"popular": [
"validation_interval",
"checkpoint_interval",
"num_gpus",
"num_nodes",
"num_epochs",
"gpu_ids"
],
"properties": {
"activation_checkpoint": {
"default": false,
"description": "\n A True value instructs train to recompute in backward pass to save GPU memory,\n rather than storing activations.",
"title": "enable activation checkpointing",
"type": "bool"
},
"checkpoint_interval": {
"default": 1,
"description": "The interval (in epochs) at which a checkpoint will be saved. Helps resume training.",
"minimum": 1,
"popular": true,
"title": "Checkpoint interval",
"type": "int"
},
"checkpoint_interval_steps": {
"description": "The number of steps to save the checkpoint.",
"title": "checkpoint interval steps",
"type": "int"
},
"checkpoint_interval_unit": {
"default": "epoch",
"description": "The unit of the checkpoint interval.",
"enum": [
"epoch",
"step"
],
"title": "Checkpoint interval unit",
"type": "categorical"
},
"clip_grad_norm": {
"default": 0.1,
"description": "\n Amount to clip the gradient by L2 Norm.\n A value of 0.0 specifies no clipping.",
"math_cond": "> 0.0",
"title": "clip gradient norm",
"type": "float"
},
"cudnn": {
"automl_enabled": false,
"default": {
"benchmark": false,
"deterministic": true
},
"properties": {
"benchmark": {
"default": false,
"type": "bool"
},
"deterministic": {
"default": true,
"type": "bool"
}
},
"type": "collection"
},
"dataloader_visualize": {
"default": false,
"description": "Whether to visualize the dataloader.",
"title": "dataloader visualize",
"type": "bool"
},
"distributed_strategy": {
"default": "ddp",
"description": "\n The multi-GPU training strategy.\n DDP (Distributed Data Parallel) and Fully Sharded DDP are supported.",
"enum": [
"ddp",
"fsdp"
],
"title": "distributed_strategy",
"type": "categorical"
},
"gpu_ids": {
"automl_enabled": false,
"default": [
0
],
"description": "\n List of GPU IDs to run the training on. The length of this list\n must be equal to the number of gpus in train.num_gpus.",
"popular": true,
"title": "GPU IDs",
"type": "list"
},
"inference_tile": {
"default": false,
"description": "Use tiled inference, particularly for transformers\n which expect fixed size of sequences.\n ",
"title": "tile inference",
"type": "bool"
},
"is_dry_run": {
"default": false,
"description": "\n Whether to run the trainer in Dry Run mode. This serves\n as a good means to validate the spec file and run a sanity check on the trainer\n without actually initializing and running the trainer.",
"title": "Is dry run",
"type": "bool"
},
"log_every_n_steps": {
"default": 500,
"description": "\n Interval steps of logging training results and running validation numbers within 1 epoch",
"title": "log steps",
"type": "int"
},
"num_epochs": {
"default": 10,
"description": "Number of epochs to run the training.",
"maximum": Infinity,
"minimum": 1,
"popular": true,
"title": "Number of epochs",
"type": "int"
},
"num_gpus": {
"default": 1,
"description": "The number of GPUs to run the train job.",
"minimum": 1,
"popular": true,
"title": "Number of GPUs",
"type": "int"
},
"num_nodes": {
"default": 1,
"description": "Number of nodes to run the training on. If > 1, then multi-node is enabled.",
"minimum": 1,
"popular": true,
"title": "Number of nodes",
"type": "int"
},
"optim": {
"automl_default_parameters": [
"train.optim.lr",
"train.optim.momentum",
"train.optim.weight_decay",
"train.optim.lr_step_size",
"train.optim.lr_decay",
"train.optim.min_lr"
],
"automl_disabled_parameters": [
"train.optim.lr_steps"
],
"automl_enabled": false,
"default": {
"lr": 0.0001,
"lr_decay": 0.1,
"lr_scheduler": "MultiStepLR",
"lr_step_size": 1000,
"lr_steps": [
1000
],
"min_lr": 1e-07,
"momentum": 0.9,
"monitor_name": "val_loss",
"optimizer": "AdamW",
"warmup_steps": 20,
"weight_decay": 0.0001
},
"description": "Hyper parameters to configure the optimizer.",
"properties": {
"lr": {
"automl_enabled": true,
"default": 0.0001,
"description": "The initial learning rate for training the model, excluding the backbone.",
"math_cond": "> 0.0",
"maximum": 0.01,
"minimum": 1e-06,
"title": "learning rate",
"type": "float"
},
"lr_decay": {
"automl_enabled": true,
"default": 0.1,
"description": "The decreasing factor for the learning rate scheduler.",
"math_cond": "> 0.0",
"maximum": 1.0,
"minimum": 0.0,
"title": "learning rate decay",
"type": "float"
},
"lr_scheduler": {
"default": "MultiStepLR",
"description": "The learning scheduler:\n * MultiStepLR : Decrease the lr by lr_decay from lr_steps\n * StepLR : Decrease the lr by lr_decay at every lr_step_size.",
"enum": [
"MultiStep",
"StepLR",
"CustomMultiStepLRScheduler",
"LambdaLR",
"PolynomialLR",
"OneCycleLR",
"CosineAnnealingLR"
],
"title": "Learning rate scheduler",
"type": "categorical"
},
"lr_step_size": {
"automl_enabled": true,
"default": 1000,
"description": "The number of steps to decrease the learning rate in the StepLR.",
"math_cond": "> 0",
"maximum": 10000,
"minimum": 1,
"title": "learning rate step size",
"type": "int"
},
"lr_steps": {
"automl_enabled": false,
"default": [
1000
],
"description": "The steps at which the learning rate must be decreased.\n This is applicable only with the MultiStep LR.",
"title": "learning rate decay steps",
"type": "list"
},
"min_lr": {
"automl_enabled": true,
"default": 1e-07,
"description": "The minimum learning rate value for the learning rate scheduler.",
"math_cond": "> 0.0",
"maximum": 0.001,
"minimum": 1e-08,
"title": "minimum learning rate",
"type": "float"
},
"momentum": {
"automl_enabled": true,
"default": 0.9,
"description": "The momentum for the AdamW optimizer.",
"math_cond": "> 0.0",
"maximum": 1.0,
"minimum": 0.0,
"title": "momentum - AdamW",
"type": "float"
},
"monitor_name": {
"default": "val_loss",
"description": "The metric value to be monitored for the :code:`AutoReduce` Scheduler.",
"enum": [
"val_loss",
"train_loss"
],
"title": "monitor_name",
"type": "categorical"
},
"optimizer": {
"default": "AdamW",
"description": "Type of optimizer used to train the network.",
"enum": [
"AdamW",
"SGD"
],
"type": "categorical"
},
"warmup_steps": {
"default": 20,
"description": "The number of steps to perform linear learning rate\" warm-up before engaging a learning rate scheduler",
"maximum": Infinity,
"minimum": 0,
"title": "Warm up steps",
"type": "int"
},
"weight_decay": {
"automl_enabled": true,
"default": 0.0001,
"description": "The weight decay coefficient.",
"math_cond": "> 0.0",
"maximum": 0.01,
"minimum": 1e-06,
"title": "weight decay",
"type": "float"
}
},
"title": "optimizer",
"type": "collection"
},
"precision": {
"default": "fp32",
"description": "Precision to run the training on.",
"enum": [
"bf16",
"fp32",
"fp16"
],
"title": "precision",
"type": "categorical"
},
"pretrained_model_path": {
"default": "",
"description": "Path to a pre-trained DepthNet model to initialize the current training from.",
"type": "string"
},
"results_dir": {
"default": "",
"description": "\n Path to where all the assets generated from a task are stored.\n ",
"title": "Results directory",
"type": "string"
},
"resume_training_checkpoint_path": {
"default": "",
"description": "Path to the checkpoint to resume training from.",
"title": "Resume checkpoint path",
"type": "string"
},
"seed": {
"default": 1234,
"description": "The seed for the initializer in PyTorch. If < 0, disable fixed seed.",
"maximum": Infinity,
"minimum": -1,
"title": "Seed for randomization",
"type": "int"
},
"tile_min_overlap": {
"automl_enabled": false,
"default": [
16,
16
],
"description": "Use tiled inference weight type",
"title": "tile weight type",
"type": "list"
},
"tile_wtype": {
"default": "gaussian",
"description": "Use tiled inference weight type",
"title": "tile weight type",
"type": "string"
},
"validation_interval": {
"default": 1,
"description": "\n The interval (in epochs) at which a evaluation\n will be triggered on the validation dataset.",
"minimum": 1,
"popular": true,
"title": "Validation interval",
"type": "int"
},
"vis_step_interval": {
"default": 10,
"description": "The visualization interval in step.",
"title": "visualization interval",
"type": "int"
}
},
"type": "collection"
},
"wandb": {
"automl_disabled_parameters": [
"wandb.tags"
],
"automl_enabled": false,
"default": {
"enable": true,
"entity": "",
"group": "",
"name": "TAO Toolkit Training",
"project": "TAO Toolkit",
"reinit": false,
"run_id": "",
"save_code": false,
"sync_tensorboard": false,
"tags": [
"tao-toolkit"
]
},
"description": "Configurable parameters to construct the wandb client for a DepthNet experiment.",
"properties": {
"enable": {
"default": true,
"type": "bool"
},
"entity": {
"default": "",
"type": "string"
},
"group": {
"default": "",
"type": "string"
},
"name": {
"default": "TAO Toolkit Training",
"type": "string"
},
"project": {
"default": "TAO Toolkit",
"type": "string"
},
"reinit": {
"default": false,
"type": "bool"
},
"run_id": {
"default": "",
"type": "string"
},
"save_code": {
"default": false,
"type": "bool"
},
"sync_tensorboard": {
"default": false,
"type": "bool"
},
"tags": {
"automl_enabled": false,
"default": [
"tao-toolkit"
],
"type": "list"
}
},
"type": "collection"
}
},
"required": [
"quantize"
],
"type": "object",
"x_tao_schema": {
"action": "gen_trt_engine",
"core_module": "depth_net",
"model": "depth-net-stereo",
"network_arch": "depth_net_stereo",
"schema_action": "gen_trt_engine",
"schema_version": 1,
"source": "tao-core dataclass config"
}
}
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Needs a command-line tool
The author says: Requires docker + nvidia-container-toolkit.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA Corporation
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/tao-train-foundation-stereo · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-train-foundation-stereo.zip