---

name: training description: "Routes checkpoint training, continuation, evaluation, and data/vocab validation for the couplet model." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

training

Use this sub-skill when the user wants to train the couplet model, continue a checkpoint, inspect BLEU or loss, or validate the aligned sentence-pair data layout.

Includes

• Building or reusing the TensorFlow training graph.
• Training from aligned input/target files.
• Continuing a checkpoint with restore_model.
• Evaluating against a held-out set and reading BLEU output.
• Checking the vocabulary order and the line-aligned data format.
• Running the bundled tiny smoke fixture to prove the training path works.

Excludes

• Serving HTTP requests or starting the Flask inference wrapper.
• Ranking or post-processing generated couplets for the API.
• Generic TensorFlow advice that is not specific to this repository.

If the user wants generation or serving, route to ../inference/SKILL.md. If the user only needs install or dependency help, route back to the root references/dependencies.md and references/troubleshooting.md.

Read these files

• ../../references/model-overview.md for the graph and module map.
• ../../references/dependencies.md if the environment needs the verified package set first.
• ../../references/troubleshooting.md for protobuf, checkpoint, and data-layout failures that block training.
• references/workflows.md for the step-by-step train/continue workflow.
• references/data-formats.md for the line and vocabulary layout.
• scripts/train_couplet.py for real training on explicit file paths.
• scripts/train_smoke.py for the tiny end-to-end verification fixture.

Typical questions this route answers

• How do I train the model on my aligned couplet data?
• How do I resume training from a checkpoint?
• Why is BLEU not changing or why are some rows ignored?
• What should the vocabulary file look like?
• How can I prove the training graph still works after an environment change?

Working pattern

1. Verify the runtime environment and install the pinned dependencies if needed.
2. Confirm the vocabulary starts with <s> and </s>.
3. Make sure input and target files are line-aligned and space-tokenized.
4. Run scripts/train_couplet.py for the real dataset or scripts/train_smoke.py for the bundled tiny fixture.
5. Use the checkpoint and eval output to decide whether to continue training or adjust the data/hyperparameters.

High-value reminders

• SeqReader drops tokens that are absent from the vocabulary.
• SeqReader.data_size uses integer division by batch size.
• Model.train can restore the checkpoint when restore_model=True.
• Model.eval restores the saved checkpoint before computing BLEU.
• Changing the vocabulary order requires retraining or at least rebuilding the checkpoint.

When to read the deeper references

• Read data-formats.md if the file layout or tokenization is unclear.
• Read workflows.md if you need the exact command sequence for a training run or continuation run.
• Read troubleshooting.md when TensorFlow import, checkpoint restore, or data alignment fails.