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Tao Finetune Video Clip

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Medium risk

From the author

InternVideo2-CLIP L14 (TAO video_clip) for video-text retrieval, zero-shot classification, embedding extraction, LoRA fine-tuning, ONNX export, and TensorRT deployment. Use when the user asks to "fine-tune IV2CLIP", "run video_clip train/evaluate/inference/export", "build a Video-CLIP TensorRT engine", "InternVideo2-CLIP on KPI chunks", or "TAO video_clip on vadr1_chunks JSON".
— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The tao-finetune-video-clip skill provides a structured interface for working with the InternVideo2-CLIP L14 model within the NVIDIA TAO ecosystem. It supports the full lifecycle of video-text retrieval tasks, including LoRA fine-tuning, model evaluation, inference, and ONNX export. The skill also facilitates high-throughput deployment by enabling the conversion of exported models into TensorRT engines for optimized execution.

What it does

  • Performs LoRA fine-tuning on InternVideo2-CLIP L14 models.
  • Executes zero-shot classification and video-text retrieval tasks.
  • Extracts video and text embeddings for downstream analysis.
  • Exports models to ONNX format for cross-platform compatibility.
  • Generates TensorRT engines for accelerated model deployment.
  • Supports evaluation of retrieval metrics using vadr1_chunks metadata.

When to use it

  • When you need to fine-tune InternVideo2-CLIP on custom video datasets.
  • When you require video-text retrieval or zero-shot classification capabilities.
  • When you need to extract embeddings from video clips or text prompts.
  • When you are preparing a model for production deployment using TensorRT.

Advantages

  • Provides a unified workflow for training, evaluation, and deployment.
  • Includes support for LoRA to enable efficient model adaptation.
  • Enables TensorRT engine generation for optimized inference.
  • Maintains consistency between PyTorch training and deployment environments.

Usage examples

“Fine-tune the InternVideo2-CLIP model using my custom training data.”
The agent initiates the training process using the specified train.yaml configuration and vadr1_chunks metadata.
“Export my trained Video-CLIP model to ONNX.”
The agent runs the export command to generate the combined image-and-text ONNX file along with necessary tokenizer and configuration sidecars.
“Build a TensorRT engine for my exported Video-CLIP model.”
The agent uses the TAO Deploy container to convert the ONNX model into a TensorRT engine optimized for inference.
“Run inference on a set of video clips using the Video-CLIP model.”
The agent processes the input videos and text prompts to generate retrieval results or embeddings based on the provided inference configuration.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/tao-finetune-video-clip/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip tao-finetune-video-clip.zip -d .agents/skills

Documentation by NVIDIA Corporation

InternVideo2-CLIP (TAO video_clip)

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

TAO task video_clip wraps OpenGVLab InternVideo2-CLIP L14. The PyTorch image provides train, evaluate, inference, export, and default_specs. TAO Deploy provides gen_trt_engine, TensorRT evaluate, and TensorRT inference.

Container images and per-action commands are in references/skill_info.yaml and references/tao-deploy-video-clip.skill_info.yaml. Starting specs are in references/spec_template_*.yaml.

Release note: The pinned PyTorch image is the TAO 7.2 release-candidate build validated for Video-CLIP. It includes PyAV 17.1.0 as the primary decoder and ONNXScript 0.7.1 for export, with decord absent. The TAO Deploy image is pinned independently because gen_trt_engine and TensorRT-backed actions do not run in the PyTorch image.

Known-broken images: interim builds cut before tao-pytorch commit 0cc31de4 ship a video_clip package with no model.backbones submodule, so train/evaluate/inference die at import while video_clip --help still exits 0. Images without PyAV also fail at data loading. Run both import checks in the preflight below before pulling data or launching a run.

Train Action Policy

AutoML is not packaged for this model skill. Always use direct video_clip actions even when a higher-level request mentions AutoML. Non-train actions stay in this skill.

Quick Start (local Docker)

Use the pinned TAO container declared in references/skill_info.yaml. Pull with NGC_KEY when the image is not cached locally.

VIDEO_CLIP_IMAGE_DEFAULT="nvcr.io/nvidia/tao/tao-toolkit:7.3.0-pyt"  # versions-key: images.tao_toolkit.pyt
VIDEO_CLIP_IMAGE="${VIDEO_CLIP_IMAGE:-$VIDEO_CLIP_IMAGE_DEFAULT}"
docker pull "$VIDEO_CLIP_IMAGE"

Expected workspace layout (host paths bind-mounted into the container):

workspace/
├── data/
│   ├── train.json              # vadr1_chunks metadata; video_path = /data/videos/<name>.mp4
│   ├── val.json
│   ├── prompts.txt             # one text prompt per line (inference)
│   └── videos/                 # mp4 clips referenced by train.json / val.json
├── model/
│   ├── mobileclip_blt.pt       # MobileCLIP weights for model.text_encoder
│   └── hf/                     # offline InternVideo2_distillation_models snapshot (HF_HUB_OFFLINE=1)
├── specs/
│   ├── train.yaml
│   ├── evaluate.yaml
│   ├── inference.yaml
│   └── export.yaml
├── deploy_specs/
│   ├── gen_trt_engine.yaml
│   ├── evaluate.yaml
│   └── inference.yaml
└── results/

Docker options for all actions (skill-eval CI uses the same $WORKSPACE_DIR bind-mount pattern):

VIDEO_CLIP_IMAGE_DEFAULT="nvcr.io/nvidia/tao/tao-toolkit:7.3.0-pyt"  # versions-key: images.tao_toolkit.pyt
VIDEO_CLIP_IMAGE="${VIDEO_CLIP_IMAGE:-$VIDEO_CLIP_IMAGE_DEFAULT}"
RUN_ROOT="${RUN_ROOT:-$PWD}"
DOCKER_COMMON=(
  --rm --gpus all --shm-size=8g --network=host
  --shm-size=64g
  --ulimit memlock=-1
  --ulimit stack=67108864
  -e WANDB_DISABLED=true
  -e WANDB_MODE=disabled
  -e HF_HUB_OFFLINE=1
  -e HUGGINGFACE_HUB_CACHE=/model/hf
  -e TRANSFORMERS_OFFLINE=1
  -e TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1
  -v "$RUN_ROOT/data:/data:ro"
  -v "$RUN_ROOT/model:/model:ro"
  -v "$RUN_ROOT/specs:/specs:ro"
  -v "$RUN_ROOT/results:/results"
)

Preflight (host):

[ -f "$RUN_ROOT/model/mobileclip_blt.pt" ] || echo "MISSING: MobileCLIP weights"
[ -f "$RUN_ROOT/data/train.json" ] || echo "MISSING: train metadata"
[ -d "$RUN_ROOT/model/hf" ] || echo "MISSING: offline HF snapshot under model/hf"
docker run --rm "$VIDEO_CLIP_IMAGE" video_clip --help >/dev/null || echo "MISSING: video_clip in container"
docker run --rm "$VIDEO_CLIP_IMAGE" \
  python -c "import nvidia_tao_pytorch.multimodal.video_clip.model.adapters.internvideo2clip" \
  >/dev/null 2>&1 || echo "BROKEN IMAGE: video_clip package is incomplete (missing model.backbones) — stop, see Release note"
docker run --rm "$VIDEO_CLIP_IMAGE" python -c "import av; print(av.__version__)" \
  >/dev/null 2>&1 || echo "BROKEN IMAGE: PyAV is missing — use the pinned FC image; do not install decord"
nvidia-smi >/dev/null 2>&1 || echo "note: no GPU visible"

Train:

docker run "${DOCKER_COMMON[@]}" "$VIDEO_CLIP_IMAGE" \
  video_clip train -e /specs/train.yaml results_dir=/results

Evaluate:

docker run "${DOCKER_COMMON[@]}" "$VIDEO_CLIP_IMAGE" \
  video_clip evaluate -e /specs/evaluate.yaml results_dir=/results

Inference:

docker run "${DOCKER_COMMON[@]}" "$VIDEO_CLIP_IMAGE" \
  video_clip inference -e /specs/inference.yaml results_dir=/results

Export:

docker run "${DOCKER_COMMON[@]}" "$VIDEO_CLIP_IMAGE" \
  video_clip export -e /specs/export.yaml results_dir=/results

TensorRT Deploy

Use the independently pinned TAO Deploy image after PyTorch export. Read references/tao-deploy-video-clip.md before running the deploy actions; its templates cover the engine build, retrieval evaluation, and embedding inference contracts.

VIDEO_CLIP_DEPLOY_IMAGE_DEFAULT="nvcr.io/nvidia/tao/tao-toolkit:7.3.0-deploy"  # versions-key: images.tao_toolkit.deploy
VIDEO_CLIP_DEPLOY_IMAGE="${VIDEO_CLIP_DEPLOY_IMAGE:-$VIDEO_CLIP_DEPLOY_IMAGE_DEFAULT}"

# Verify the independently pinned image before staging artifacts or using a GPU.
docker run --rm "$VIDEO_CLIP_DEPLOY_IMAGE" video_clip gen_trt_engine --help >/dev/null || \
  { echo "BROKEN IMAGE: Video-CLIP deploy entrypoint is unavailable" >&2; exit 1; }

docker run --gpus all --rm --shm-size=16g \
  -v "$RUN_ROOT/deploy_specs:/specs:ro" \
  -v "$RUN_ROOT/results/export:/models:ro" \
  -v "$RUN_ROOT/data:/data:ro" \
  -v "$RUN_ROOT/results/deploy:/results" \
  "$VIDEO_CLIP_DEPLOY_IMAGE" \
  video_clip gen_trt_engine -e /specs/gen_trt_engine.yaml

Keep the exported ONNX file, its matching *_config.yaml, and its matching *_tokenizer/ directory together. gen_trt_engine copies the sidecars beside the engine so TensorRT evaluate and inference can reconstruct preprocessing and tokenization.

Quick Start (virtualenv — local dev hosts)

On hosts with a tao-pytorch checkout and tao-cli venv (for example rtdetr-pytorch), run through tao-run-on-virtualenv instead of Docker:

export VENV="${VENV:?set to your tao-cli virtualenv (must contain bin/video_clip)}"
export TAO_PYTORCH_ROOT="${TAO_PYTORCH_ROOT:?set to your tao-pytorch checkout with multimodal/video_clip}"
export PATH="$VENV/bin:$PATH"
export PYTHONPATH="$TAO_PYTORCH_ROOT:$TAO_PYTORCH_ROOT/tao-core:$PYTHONPATH"
export HF_HOME="${HF_HOME:-$PWD/hf_cache}"
export HF_HUB_OFFLINE="${HF_HUB_OFFLINE:-1}"
export WANDB_DISABLED=true
export WANDB_MODE=disabled

Copy a template spec from references/spec_template_*.yaml, fill checkpoint paths and metadata, then run:

video_clip train -e /path/to/train.yaml
video_clip evaluate -e /path/to/evaluate.yaml
video_clip inference -e /path/to/inference.yaml
video_clip export -e /path/to/export.yaml

Credentials

  • NGC_KEY — pull the pinned TAO container from nvcr.io when it is not cached locally.

  • HF_TOKEN (online Hugging Face download only): Hugging Face read token used when model.vision_encoder and model.clip_head are null and the resolver downloads the InternVideo2 snapshot named by model.internvideo2clip_hf_id. For offline CI/eval, stage the complete S3/local snapshot and point both fields at the local files; no Hugging Face token is then required.

Treat tokens as secrets. Export them into the environment or pass them through an --env-file of bare KEY=value lines, rather than inlining values into generated spec YAML, command lines, or anything written under results_dir.

Data format (vadr1_chunks)

Metadata is a top-level JSON list of video records. Each record has video_path, split, and nested chunks[] with caption fields (queries, action_queries, anomaly_queries, dense_caption, scene_caption).

Point dataset.*.video_text.metadata at the user's JSON files (for example /data/train.json and /data/val.json in the templates). Each video_path must be an absolute path resolvable inside the runtime — for Docker, remap host clips to container paths like /data/videos/<name>.mp4 and set data_root: null unless using path_prefix_mapping.

Smoke overrides

For a short functional check (for example 2 epochs, 1 GPU, small batch):

train.num_epochs: 2
train.num_gpus: 1
train.gpu_ids: [0]
train.optim.warmup_steps: 10
dataset.train.batch_size: 2
dataset.val.batch_size: 2
dataset.train.num_workers: 4
dataset.val.num_workers: 4
dataset.train.video_text.caption_fields: [queries, action_queries, anomaly_queries]
dataset.train.video_text.caption_mode: first
dataset.metrics.mode: classification

Use dataset.metrics.mode: retrieval only when dataset.val.video_text.relevance_file is provided.

Inference

  • inference.mode: embeddings writes video_embeddings.h5 and text_embeddings.h5 under results_dir.

  • Provide inference.query.text_file (one prompt per line) and/or inference.query.input_texts.

  • Gallery videos come from dataset.inference.video_text.metadata.

Export

export.encoder_type: combined produces the image-and-text ONNX consumed by the Video-CLIP deploy workflow. Keep export.batch_size: -1 for symbolic/dynamic batch dimensions; a positive value produces a fixed-batch ONNX. Export also writes matching *_config.yaml and *_tokenizer/ sidecars; preserve all three artifacts. Default opset is 23 on the vendor branch. Export requires a trained .pth at export.checkpoint.

LoRA

For vision-LoRA runs, start from tao-pytorch experiment_spec_lora.yaml or add a top-level peft: block (see shipped spec comments). Merge LoRA before export when checkpoints contain lora_* keys.

Common pitfalls

  • PATH must prefer $VENV/bin on virtualenv hosts so child processes resolve the venv Python.

  • evaluate uses dataset.val, not a separate test split — Lightning stage "test" still loads val metadata.

  • Eval precision: match train.precision (typically bf16) or flash-attn paths may fail under fp32 eval.

  • Empty action_queries on normal chunks become literal "Normal" positives during training; exclude Normal/Abnormal in dataset.metrics.exclude_categories for classification eval.

  • No hard-negative / explicit-neg training on the vendor branch unless the spec and branch explicitly enable it.

  • video_clip --help is not a health check. It exits 0 on an image whose video_clip package is missing model.backbones; only the import smoke check in the preflight catches it.

  • PyAV is the primary Video-CLIP decoder in TAO 7.2. The loader can fall back to the image's FFmpeg CLI and OpenCV support. A missing av import is an image defect: use the pinned FC image. Do not add or force-install decord, because it is not part of the supported TAO 7.2 decode contract.

  • TensorRT actions use TAO Deploy. gen_trt_engine, TensorRT evaluate, and TensorRT inference must use the independently pinned deploy image and deploy templates, not the PyTorch image/specs.

  • Deploy sidecars are required. TensorRT evaluation and text inference need the exported *_config.yaml and *_tokenizer/ beside the engine. Keep them with the ONNX input so engine generation can copy them automatically.

  • PyTorch ≥ 2.6 defaults to torch.load(weights_only=True) and rejects the TAO checkpoint’s numpy dtype objects with _pickle.UnpicklingError. TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 is set in DOCKER_COMMON above; keep it for evaluate, inference, and export.

  • model/hf/ is a snapshot, not an HF hub cache. Offline packs stage InternVideo2 weights at repo-relative paths (stage1/L14/L14_dist_1B_stage2/pytorch_model.bin, clip/L14/pytorch_model.bin), while HUGGINGFACE_HUB_CACHE expects a models--<org>--<repo>/snapshots/<sha>/ tree. Leaving model.vision_encoder / model.clip_head at null sends asset resolution to hf_hub_download and fails under HF_HUB_OFFLINE=1 — point both at the files directly.

  • CI / skill-eval: stage weights + remapped JSON under $WORKSPACE_DIR from S3; do not rely on HuggingFace LFS downloads at eval time.

References

Files

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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, Needs Python

The author says: Requires docker + nvidia-container-toolkit and the pinned TAO video_clip PyTorch and Deploy containers (see references/skill_info.yaml and references/tao-deploy-video-clip.skill_info.yaml), or a local tao-pytorch checkout + tao-cli venv for PyTorch virtualenv runs. MobileCLIP + InternVideo2 weights must be on disk for offline eval (HF LFS may be blocked in CI). Metadata JSON uses vadr1_chunks with absolute video_path entries.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • References to credential files

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-finetune-video-clip · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
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