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.