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name: persistence-export-and-recording description: "Save, load, checkpoint, summarize, record, pretrain, track, and export Tensorforce agents and models." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Persistence, Export, and Recording

Use this sub-skill when the task involves Tensorforce checkpoints, NumPy/HDF5 weight saves, Agent.load, Agent.save, saver, summarizer, tracked tensors, recorder/pretraining, Runner.save_best_agent, or TensorFlow SavedModel export.

Route by task

• Read save/load/export for checkpoint, NumPy/HDF5, SavedModel, and load patterns.
• Read recording and pretraining for recorder traces, Agent.pretrain, and tracking/summaries.
• Run scripts/save_load_smoke.py to verify a small save/load cycle.
• Run scripts/export_saved_model_smoke.py only when TensorFlow SavedModel export is needed.
• Use troubleshooting for ambiguous formats, changed environment spaces, HDF5/TensorFlow issues, or SavedModel misuse.

Boundaries

This sub-skill assumes an agent/environment workflow already exists. Use agents-and-specifications for Agent.create arguments and runner-and-cli-workflows for training/evaluation loops before persistence. Use this sub-skill once the question is about keeping, restoring, inspecting, recording, or exporting agent state.