复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
SkillAtlasSkill 详情
tao-train-segformer
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 80Security 92
项目 README
来源文件：README.md
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Quickstart
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Install One Skill Without Prompts
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Install for a Specific Agent
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
Keep Skills Up to Date
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Browse the Catalog
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Skill Catalog
Getting Help & Contributing
Where to file an issue depends on what's broken:
- Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
- Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
- Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
- Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Verifying Skills
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md— the skill instructions consumed by the agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
Roadmap
- ✅ Public skills catalog with NVIDIA-verified skills across multiple products
- ✅ Automated sync pipeline with skills mirrored from product repos daily
- ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
- ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
- ✅ Skills universal evaluation criteria and task-specific criteria
- ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
- ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
- ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
- 🔲 Syndication to additional MCP hubs and partner channels
Repository Structure
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig— detached OMS-format signature (verifiable againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
License
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
其他
低风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 未检测到明显外部权限要求。
- 未检测到高风险命令。
- 扫描发现：0 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-train-segformer" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-train-segformer" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-train-segformer" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-train-segformer" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-train-segformer" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: tao-train-segformer
description: SegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature
extraction, efficient for real-time segmentation tasks. Use when training, evaluating, exporting, quantizing, or running
inference for a TAO SegFormer model. Trigger phrases include "train SegFormer", "semantic segmentation", "lightweight
transformer segmenter", "real-time semantic segmentation".
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit.
metadata:
version: "0.1.0"
author: NVIDIA Corporation
allowed-tools: Read Bash
tags:
- segmentationSegFormer
SegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature extraction. Efficient for real-time segmentation tasks.
Set model.backbone.pretrained_backbone_path for backbone weights.
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-segformer.md first. Deploy spec templates live in this skill's references/ folder with the spec_template_deploy_*.yaml prefix.
Dataclass Schemas
Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spec_template_<action>.yaml from the schema top-level default field. AutoML enablement is declared at the model layer in references/skill_info.yaml via automl_enabled. Runnable AutoML still requires schemas/train.schema.json and references/spec_template_train.yaml to exist and parse. Use the packaged train schema for automl_default_parameters, automl_disabled_parameters, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ~/tao-core at runtime; maintainers regenerate schemas/templates before packaging the skill bank.
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.
Supported Actions
The packaged SegFormer PyT CLI supports train, evaluate, export, inference, quantize, and default_specs. This model skill exposes train, evaluate, export, inference, and quantize; resume/retrain is performed through train with train.resume_training_checkpoint_path.
The parent PyT CLI does not expose gen_trt_engine. Use models/segformer/deploy for TensorRT engine generation, TensorRT evaluation, and TensorRT inference.
Training Requirements
- Dataset type: segmentation
- Formats: unet
- Monitoring metric: val_miou, maximize
Per-Action Dataset Requirements
| Action | Spec Key | Source | Files | List? |
|---|---|---|---|---|
| evaluate | dataset.segment.root_dir | eval_dataset | extracted root containing images/<split> and masks/<split> | No |
| export | dataset.segment.root_dir | train_datasets | extracted root containing images/<split> and masks/<split> | No |
| inference | dataset.segment.root_dir | inference_dataset | extracted root containing images/<split> and masks/<split> | No |
| quantize | dataset.segment.root_dir | train_datasets | extracted root containing images/<split> and masks/<split> | No |
| quantize | dataset.segment.quant_calibration_dataset.images_dir | calibration_dataset | extracted image directory | No |
| train | dataset.segment.root_dir | train_datasets | extracted root containing images/<split> and masks/<split> | No |
Typical Spec Overrides
Data source overrides are mandatory for every action — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in spec_overrides.
SEG_TRAIN_ROOT = "/data/segformer/train"
SEG_EVAL_ROOT = "/data/segformer/eval"
SEG_INFER_ROOT = "/data/segformer/infer"
CAL_IMAGES = f"{SEG_TRAIN_ROOT}/images/train"
train (mandatory data sources):
{
"train.num_gpus": 1,
"train.num_epochs": 10,
"train.checkpoint_interval": 10,
"train.validation_interval": 10,
"dataset.segment.batch_size": 4,
"dataset.segment.root_dir": SEG_TRAIN_ROOT,
}
evaluate (mandatory data sources):
{
"evaluate.batch_size": 4,
"dataset.segment.root_dir": SEG_EVAL_ROOT,
"evaluate.checkpoint": CHECKPOINT,
}
inference (mandatory data sources):
{
"dataset.segment.batch_size": 1,
"dataset.segment.root_dir": SEG_INFER_ROOT,
"inference.checkpoint": CHECKPOINT,
}
export (mandatory data sources):
{
"dataset.segment.root_dir": SEG_TRAIN_ROOT,
"export.checkpoint": CHECKPOINT,
"export.input_height": 256,
"export.input_width": 256,
"export.onnx_file": ONNX_FILE,
}
quantize (mandatory data sources):
{
"dataset.segment.root_dir": SEG_TRAIN_ROOT,
"dataset.segment.quant_calibration_dataset.images_dir": CAL_IMAGES,
"quantize.model_path": CHECKPOINT,
}
If the source dataset is delivered as separate images/*.tar.gz and
masks/*.tar.gz archives, extract them before launch so root_dir contains
directories such as images/train, images/val, images/test, masks/train,
and masks/val. Do not point dataset.segment.root_dir at an archive staging
folder that still contains only tarballs.
Eval Dataset
Optional. Validation data is typically part of the root_dir structure.
Important Parameters
- dataset.segment.num_classes: Number of segmentation classes. Default 2 (binary). Must match the number of classes in your mask annotations.
- model.backbone.type: Default fan_small_12_p4_hybrid. Supported includes FAN variants, SegFormer MIT variants, and others.
- dataset.segment.root_dir: Root directory of the segmentation dataset.
- dataset.segment.img_size: Input image size. Default 256. Increase for finer segmentation at the cost of memory.
- train.optim.lr: Learning rate. Default 6e-5.
- model.freeze_backbone: Whether to freeze the backbone during training. Useful for fine-tuning with limited data.
- dataset.segment.batch_size: Per-GPU batch size. Default 8.
- dataset.segment.label_transform: Use the string
"None"when no label transform is desired. Do not set this to JSON/YAML null; strict schema merge treats the field as a string enum. - dataset.segment.palette: For grayscale masks, use one integer per RGB
entry, for example
rgb: [85]. Preserve the dataset's actual label ids and class names rather than normalizing them unless the user explicitly asks for a conversion.
Multi-GPU / Multi-Node
Launch method: Lightning-managed (single python process, Lightning spawns workers).
| Spec Key | Description | Default |
|---|---|---|
train.num_gpus | Number of GPUs | 1 |
train.gpu_ids | GPU device indices | [0] |
train.num_nodes | Number of nodes | 1 |
train.sync_batchnorm | Sync BN across GPUs | configurable |
train.use_distributed_sampler | Use distributed sampler | configurable |
- Multi-GPU strategy:
ddp_find_unused_parameters_true - No fsdp support
Multi-node env vars (set by orchestrator): WORLD_SIZE, NODE_RANK, MASTER_ADDR, MASTER_PORT, NUM_GPU_PER_NODE.
Hardware
Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ (V100 or A100) VRAM per GPU. SegFormer is relatively lightweight. Default img_size=256 is memory-friendly. Increase img_size for higher resolution at the cost of memory and speed.
Error Patterns
CUDA out of memory: Reduce batch_size or img_size. SegFormer memory scales quadratically with image size.
num_classes mismatch: Ensure dataset.segment.num_classes matches the actual number of classes in your mask annotations.
TensorBoard unsupported for segmentation training: Keep train.tensorboard.enabled: false. The SegFormer training entrypoint asserts that TensorBoard visualization is not supported for segmentation, so do not enable TensorBoard just to extract AutoML metrics; use log parsing or a post-train evaluator instead.
AutoML metric extraction: SegFormer train status files report val_miou alongside val_loss, val_acc, and other validation KPIs. Default AutoML train launches must optimize val_miou with direction: maximize; do not optimize val_loss for default model invocations.
For AutoML or long segmentation sweeps, read val_miou from
results_dir/train/status.json first. If the wrapper reports a terminal
failure but the structured status file reached the configured training budget
and contains finite val_miou, report the recovered metric with the wrapper
failure noted instead of discarding the measurement.
For high-resolution custom segmentation targets, keep dataset paths as per-run
inputs. Do not add customer/user-specific roots to this reusable skill. When the
user asks for a fixed full-budget search, remember that bracket algorithms
(asha, bohb, dehb, hyperband, hyperband_es, pbt) may intentionally
lower train.num_epochs for some recommendations; use Bayesian/BFBO or lock the
budget if every recommendation must run the full epoch count.
Checkpoint handoff: For evaluate/export/inference/quantize/resume, use the checkpoint resolver on the best AutoML child job's results_dir/train/ folder and select the action-appropriate model_epoch_*.pth checkpoint, such as model_epoch_000_step_00010.pth. SegFormer may also write segformer_model_latest.pth, but that should only be used when a caller explicitly requests latest. Preserve dataset.segment.num_classes, dataset.segment.img_size, and dataset.segment.root_dir overrides for downstream actions.
Resume/retrain checkpoint: Resume uses train.resume_training_checkpoint_path.
Pass the exact resolved checkpoint from the previous train output, not a guessed
model.pth path. A resumed one-epoch run should produce the next checkpoint in
the new results directory, for example model_epoch_001_step_00020.pth.
Export / TensorRT shape alignment: Keep export.input_height and
export.input_width aligned with dataset.segment.img_size unless the trained
model and deploy specs have been validated at another resolution. The packaged
fresh-install path is validated at 256x256, matching the default SegFormer
dataset and deploy templates.
Parent segformer gen_trt_engine rejected by the PyT CLI: In the validated 7.0.0 PyT container, segformer gen_trt_engine is not a valid parent-model subtask. Use the SegFormer deploy workflow (references/tao-deploy-segformer.md) for TensorRT engine generation, TensorRT evaluation, and TensorRT inference.
Spec Param / Parent Model Inference
Model-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.
Inference mappings from TAO Core segformer.config.json:
| Action | Spec Field | Inference Function | Meaning |
|---|---|---|---|
| evaluate | encryption_key | key | encryption key |
| evaluate | evaluate.checkpoint | parent_model | model file inferred from the parent job results folder |
| evaluate | evaluate.trt_engine | parent_model | model file inferred from the parent job results folder |
| evaluate | results_dir | output_dir | current job results directory |
| export | encryption_key | key | encryption key |
| export | export.checkpoint | parent_model | model file inferred from the parent job results folder |
| export | export.onnx_file | create_onnx_file | output ONNX path |
| export | results_dir | output_dir | current job results directory |
| inference | encryption_key | key | encryption key |
| inference | inference.checkpoint | parent_model | model file inferred from the parent job results folder |
| inference | inference.trt_engine | parent_model | model file inferred from the parent job results folder |
| inference | results_dir | output_dir | current job results directory |
| quantize | encryption_key | key | encryption key |
| quantize | quantize.model_path | parent_model | model file inferred from the parent job results folder |
| quantize | results_dir | output_dir | current job results directory |
| train | encryption_key | key | encryption key |
| train | model.backbone.pretrained_backbone_path | ptm_if_no_resume_model | PTM when no resume checkpoint exists |
| train | results_dir | output_dir | current job results directory |
| train | train.pretrained_model_path | ptm_if_no_resume_model | PTM when no resume checkpoint exists |
| train | train.resume_training_checkpoint_path | resume_model | model file inferred from the current job results folder |
For parent_model or parent_model_folder, pass the upstream train/export/AutoML 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.
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