Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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DeepPavlov package routing for config-driven NLP workflows, model-family selection, and serving.
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See how to installReview the instructions the agent will receive before you install.
Use this repo skill when the task involves the DeepPavlov Python package, its
deeppavlov.configs tree, or the python -m deeppavlov command family.
references/installation-and-cli.md
for install, CLI modes, and the offline smoke helper.references/model-overview.md to route
to the right sub-skill for the user’s task family.references/troubleshooting.md when
the failure is package-wide rather than family-specific.references/repo-provenance.md if you
need the source baseline used to create this skill.sub-skills/pipelines/SKILL.mdUse this for config parsing, build_model, train_model, evaluate_model,
train_evaluate_model_from_config, parse_config, deep_download, nested
configs, registries, custom components, cross-validation, and parameter
search.
sub-skills/text-models/SKILL.mdUse this for classifiers, NER, entity extraction, spelling correction, sentence segmentation, morpho-syntax parsing, relation extraction, multitask models, and embedding extraction.
sub-skills/retrieval-qa/SKILL.mdUse this for document retrieval, ranking, FAQ, SQuAD, ODQA, and KBQA.
sub-skills/serving/SKILL.mdUse this for riseapi, risesocket, /probe, /api, socket framing,
service settings, TLS flags, metrics, and runtime request validation.
After installing DeepPavlov in the target environment, run the offline smoke script:
python scripts/smoke_deeppavlov_pipeline.py
Expected result: the tiny lowercasing/tokenization pipeline prints a tokenized output for the default text.
pipelines.text-models or retrieval-qa.serving.python -m deeppavlov --helppython scripts/smoke_deeppavlov_pipeline.pypython -m pip checkreferences/repo-provenance.md for the source baseline.references/repo-routing-metadata.json for router placement and usage
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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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