Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use AiZynthFinder for retrosynthetic planning, configuration, route analysis, custom extensions, and focused development workflows.
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Review the instructions the agent will receive before you install.
Use this skill when a user is working with AiZynthFinder, a retrosynthetic route-finding package for planning molecule syntheses with policy-guided tree search.
>=3.10,<3.13 environment.output.json.gz, HDF5 output, trees.json, checkpoint, route collection, or reaction tree, use route-analysis.For end users, install the public package with base dependencies unless the task needs optional features:
python -m pip install aizynthfinder
python - <<'PY'
from aizynthfinder.aizynthfinder import AiZynthFinder, AiZynthExpander
print(AiZynthFinder, AiZynthExpander)
PY
Use python -m pip install "aizynthfinder[all]" only when the task needs optional MongoDB, molbloom, route-distance, timeout, or similar extras. Use TensorFlow/gRPC-related extras only for TensorFlow Serving or remote model workflows.
Run scripts/check_aizynthfinder_env.py when diagnosing import, version, CLI, or optional-dependency availability without launching a search.
aizynthcli commands, use AiZynthFinder or AiZynthExpander, handle single vs batch SMILES, checkpoints, multiprocessing, clustering flags, and GUI/notebook launch decisions.smiles2stock, download_public_data, and optional dependency choices.output.json.gz, HDF5 tables, trees.json, checkpoints, ReactionTree, RouteCollection, route scores, clustering, images, and result summaries.references/repo-routing-metadata.json is structured metadata used by the managed repo-skills-router during import.download_public_data, MongoDB stock creation, long retrosynthesis batches, GUI launches, or plugin service calls unless the user explicitly asks for those side effects.No reviews yet. Tell others what you thought.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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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.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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