Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
The English summary is being prepared. Showing the original description.
Router for Alpamayo R1 multimodal driving inference and trajectory sampling.
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Use this skill for Alpamayo R1 inference on PhysicalAI-AV clips: load one clip, build the multimodal prompt, run the model on CUDA, inspect reasoning traces, and compare predicted trajectories to ground truth.
Use a Python 3.12 CUDA environment with the package dependencies installed, then perform a minimal import check from outside the checkout:
python -m pip install -e <repo-checkout>
python -I -c "import alpamayo_r1, torch; print(alpamayo_r1.__name__); print(torch.cuda.is_available())"
If the editable install or import fails, open references/troubleshooting.md before changing the workflow.
sub-skills/inference/SKILL.md — the end-to-end driving inference workflow, including dataset loading, prompt creation, model sampling, output interpretation, and the bundled smoke script.references/repo-provenance.md — source commit, version, and evidence paths used to build this skill.references/repo-routing-metadata.json — structured router metadata consumed during repo-skill import.references/troubleshooting.md — cross-cutting install/import/backend guidance.sub-skills/inference/SKILL.md.sub-skills/inference/scripts/run_inference_smoke.py for a runnable sample.references/troubleshooting.md.No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
The English summary is being prepared. Showing the original description.
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
The English summary is being prepared. Showing the original description.
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
The English summary is being prepared. Showing the original description.
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
The English summary is being prepared. Showing the original description.
Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
The English summary is being prepared. Showing the original description.
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
The English summary is being prepared. Showing the original description.