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name: training-and-export description: "Route questions about TF1 dense and sparse training, checkpointing, SavedModel export, inference, TensorBoard events, and legacy queue/distributed notes." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Training and Export

Use this sub-skill when a user asks about:

• dense or sparse training
• checkpoint save/restore and SavedModel export
• local inference from checkpoints or exported models
• supported models, optimizers, losses, and data flags
• TensorBoard event reading
• legacy queue or distributed training variants

Read first

• references/model-overview.md — model families, losses, optimizers, and export outputs.
• references/cli-reference.md — current flags, default modes, and README-era aliases.
• references/workflows.md — train/export/inference/TensorBoard recipes plus advanced notes.
• references/troubleshooting.md — TF1-only runtime, DuplicateFlagError, shape, export, and log issues.
• scripts/build_training_command.py — validates and normalizes dense/sparse commands.
• scripts/read_tensorboard_events.py — path-parameterized TensorBoard event inspection.

What this sub-skill covers

• Dense tf.data training and sparse TFRecords training.
• Checkpointing, SavedModel export, and checkpoint-based inference.
• Supported models, optimizers, losses, and data-layout flags.
• TensorBoard scalar/event reading.
• Legacy queue and distributed notes when the user explicitly asks for them.

Route elsewhere

• Dataset conversion, CSV/LIBSVM/TFRecords creation, and fixture generation belong to the data-preparation sub-skill.
• TensorFlow Serving clients, prediction requests, and HTTP/mobile clients belong to the serving-and-clients sub-skill.
• Cross-repo tuning, benchmark sweeps, or package installation are out of scope unless they are needed to answer a training/export question.

Fast routing rules

• If the question is about flag names or legacy README examples, open references/cli-reference.md first.
• If the question is about model choices or loss/optimizer support, open references/model-overview.md first.
• If the user wants a safe command, use scripts/build_training_command.py to normalize aliases and check combinations before hand-writing the command.
• If the user wants to inspect TensorBoard output, use scripts/read_tensorboard_events.py against the event file or log directory.
• If the user mentions TF2, missing tf.contrib, or DuplicateFlagError, go straight to references/troubleshooting.md.
• Keep dense and sparse trainers in separate processes; both register tf.app.flags at import time.
• Dense uses mode=train|savedmodel|inference; sparse uses mode=train|save_model|inference|inference_with_tfrecords.

Common answers this sub-skill can give

• Which dense or sparse mode to use.
• How to restore from checkpoint and export a SavedModel.
• Which model name fits a given feature layout.
• Which flag names are current versus README-era aliases.
• Why a TensorBoard log or SavedModel export failed.