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name: learning-to-learn description: "Routes TensorFlow 1.x learning-to-learn meta-optimizer, optimizer-network, problem-factory, training, and evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

learning-to-learn

Use this repo skill when a task is about Google's TensorFlow implementation of Learning to Learn by Gradient Descent by Gradient Descent: learned optimizers, optimizer RNNs, built-in optimizee problems, or the training/evaluation scripts.

Before doing runtime work

This is a legacy TensorFlow 1.x / Sonnet 1.x repository with root-level Python modules, not an installable package. For runtime checks, use a checkout/source tree that contains the root modules and a compatible Python environment.

Minimal dependency surface:

python -m pip install "tensorflow==1.15.*" "dm-sonnet==1.*" dill mock nose-parameterized "protobuf<3.20"

Minimal source-tree import check:

python -c "import tensorflow as tf, sonnet; import meta, networks, problems, preprocess, util; print(tf.__version__)"

If TensorFlow 2.x is installed or tensorflow.contrib is missing, stop and switch to a TensorFlow 1.x-compatible environment before debugging repo code.

Route by task

Task intent	Read
Build or debug MetaOptimizer, meta_loss, meta_minimize, returned ops, variable interception, net_assignments, second derivatives, or .l2l save/load	sub-skills/meta-optimizer-api/SKILL.md
Choose/configure optimizer networks such as CoordinateWiseDeepLSTM, KernelDeepLSTM, Sgd, Adam, initializers, preprocessing, or network serialization	sub-skills/optimizer-networks/SKILL.md
Choose built-in problems, inspect util.get_config, handle MNIST/CIFAR data side effects, or author custom optimizee loss factories	sub-skills/problem-factories/SKILL.md
Run or adapt training/evaluation command workflows, tiny CPU smokes, saved optimizer directories, Adam-vs-L2L evaluation, or CLI flag behavior	sub-skills/training-evaluation/SKILL.md

Shared references and helpers

• Overview maps the repository architecture and how modules cooperate.
• Troubleshooting covers cross-cutting install/import, TensorFlow/Sonnet, data, and source-tree issues.
• Repository provenance records the source snapshot used to create this skill.
• Environment checker validates imports and optionally performs a tiny source-module graph smoke against a local checkout.

Operating rules

1. Keep make_loss functions side-effect-light; perform dataset downloads, queue setup, and other Python side effects outside the function passed to MetaOptimizer when possible.
2. Treat simple, simple-multi, and quadratic as the safe CPU problem family for smoke checks.
3. Treat mnist, cifar, and cifar-multi as data-backed workflows that may download/cache data or create queues.
4. Use bundled helper scripts to generate/validate commands instead of relying on memory for flags or paths.
5. Do not assume a .l2l file is a TensorFlow checkpoint; it is a pickle payload loaded by networks.factory(..., net_path=...).