Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use AutoKeras for Keras-based AutoML task APIs, custom AutoModel graphs, tuner search, and model export workflows.
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See how to installReview the instructions the agent will receive before you install.
Use this skill when a user asks how to operate AutoKeras: Keras-based AutoML for image, text, structured-data, custom search-space, tuner, and export workflows. This skill is self-contained; do not rely on the original repository checkout at runtime.
AutoKeras 3.0.0 uses Keras 3. Choose and install a Keras backend before importing Keras or AutoKeras. The repository's public install guidance and CI support a PyTorch backend path for ordinary CPU checks.
python -m pip install autokeras
python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
KERAS_BACKEND=torch python -c "import keras, autokeras as ak; print(ak.__version__, keras.backend.backend())"
Run scripts/check_autokeras_env.py when setup, backend, import, or public API visibility is uncertain.
ImageClassifier, ImageRegressor, TextClassifier, TextRegressor, StructuredDataClassifier, and StructuredDataRegressor workflows, including data shapes and task-specific troubleshooting.AutoModel graphs, nodes, blocks, heads, multimodal inputs, multitask outputs, and graph/data ordering failures.max_trials, objectives, callbacks, search directories, overwrite, export_model(), and reloading saved .keras models.refresh-repo-skill.max_trials=1, epochs=1, small synthetic/local data, and an explicit scratch directory.KERAS_BACKEND before importing Keras or AutoKeras.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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