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name: seq2seq-couplet description: "Routes training, inference, and Flask serving workflows for the TensorFlow seq2seq couplet project." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

seq2seq-couplet

Use this skill for the TensorFlow seq2seq couplet repository. It covers the project's two user-facing workflows:

• training or continuing a checkpoint,
• generating couplets from a trained checkpoint or serving the HTTP API.

Read first

• references/repo-provenance.md to check whether this skill still matches the current checkout.
• references/dependencies.md for the verified runtime dependency set and the legacy GPU notes.
• references/model-overview.md for the architecture and module map.
• references/troubleshooting.md for the common install, data, checkpoint, and backend failures.
• references/licensing.md before redistributing or modifying the bundled runtime copy.

Install and verify

1. Use Python 3.7 for the verified runtime set.
2. Run scripts/install_runtime_deps.py inside the target environment if the runtime dependencies are missing.
3. Run scripts/check_env.py to verify the bundled runtime copy, TensorFlow smoke, and route definitions. Add --repo-root <checkout> only when you need to compare a live checkout against the bundled copy.

The verified dependency set is CPU-friendly and does not require CUDA for the bundled workflows. Legacy GPU acceleration needs TensorFlow 1.15 together with CUDA 10.0 and cuDNN 7, which are not bundled or required here.

Route map

If the user wants...	Go here
Train the couplet model, continue a checkpoint, inspect loss or BLEU, or validate the line/vocab layout	sub-skills/training/SKILL.md
Generate couplets from a checkpoint, inspect beam scores, or expose the Flask API	sub-skills/inference/SKILL.md
Diagnose TensorFlow, protobuf, CUDA, checkpoint, or path issues	references/troubleshooting.md

Shared helpers

• scripts/couplet_runtime.py holds the common path handling, tiny-fixture, and inference helpers used by the bundled scripts.
• scripts/install_runtime_deps.py installs the verified runtime package set into the active Python environment.
• scripts/check_env.py confirms imports and the TensorFlow smoke without importing the legacy long-running server module.

Working rules

• Prefer the bundled wrappers over the legacy source scripts; they use the self-contained runtime copy by default and accept explicit file paths instead of hard-coded paths.
• Keep training and inference vocab files aligned. The first two vocabulary entries must be <s> and </s> in that order.
• If a script mentions the legacy source behavior, treat it as evidence, not as a runtime dependency.
• Use the sub-skill references for step-by-step workflows; keep this root skill as the router.

Fast orientation

• reader.py tokenizes on spaces, appends </s> to the input, and prepends <s> plus </s> to the target side.
• seq2seq.py builds a bidirectional LSTM encoder, Bahdanau attention decoder, and beam-search inference path.
• model.py manages the training, evaluation, and inference graphs.
• server.py is the legacy Flask surface; the bundled inference scripts provide the same behavior with explicit paths and safer startup.

Common first checks

• If imports fail with protobuf descriptor errors or the beam-search graph raises a NumPy symbolic-tensor error, read the troubleshooting reference and reinstall the pinned dependency set.
• If TensorFlow prints missing libcudart or libcudnn warnings, the CPU path is still usable; the legacy GPU path is simply unavailable.
• If a checkpoint load fails, make sure the output directory was created by the training workflow and that the vocab file has not changed.

For details on the per-workflow commands, continue into the relevant sub-skill.