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name: dm-haiku description: "Use DeepMind dm-haiku as a JAX neural-network library for transforms, modules, state/RNG, layers, advanced JAX interop, and optional Flax interop." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

dm-haiku

Use this repo skill when a task involves the dm-haiku Python package, usually imported as import haiku as hk, or when a JAX neural-network workflow needs Haiku's object-oriented modules plus pure init/apply functions.

Haiku is in maintenance mode: it remains useful for existing Haiku code and research checkouts, but new greenfield projects may be better served by Flax. Do not present Haiku as a full training framework: it provides module, parameter, state, RNG, transform, and model-building utilities that compose with JAX, optimizers, datasets, checkpointing, and accelerators from other libraries.

Quick install and smoke check

1. Install a JAX build that matches the target machine first. CPU JAX is enough for API work; GPU/TPU execution requires the appropriate JAX backend package for that machine.

2. Install Haiku:

   python -m pip install -U dm-haiku
   

3. For optional Flax interop, also install Flax:

   python -m pip install -U flax
   

4. Run the bundled checker from this skill directory if import or backend state is uncertain:

   python scripts/check_haiku_env.py
   python scripts/check_haiku_env.py --require-flax
   

The checker imports haiku, jax, and optionally flax, runs a tiny hk.Linear transform, reports JAX backend/devices, and does not download data or train.

Route by task

• Use sub-skills/core-transforms/SKILL.md when choosing hk.transform, hk.transform_with_state, hk.without_apply_rng, hk.multi_transform, or diagnosing init/apply signatures.
• Use sub-skills/params-state-rng/SKILL.md when writing hk.Module subclasses, direct hk.get_parameter / hk.get_state / hk.set_state code, RNG flows, module naming, or creators/getters/interceptors.
• Use sub-skills/modules-and-networks/SKILL.md when building or debugging Haiku layers, normalization, attention, recurrent cores, hk.nets models, or no-download model smoke tests.
• Use sub-skills/jax-interop-and-advanced/SKILL.md when Haiku code interacts with vmap, scan, grad, control flow, nested transforms/lifting, parameter tree utilities, mixed precision, summaries, or visualization.
• Use sub-skills/flax-interop/SKILL.md when mixing Haiku with Flax through hk.experimental.flax APIs.

Root references and helper

• Read references/troubleshooting.md for install/import, JAX backend, optional dependency, version, example dependency, and maintenance-mode issues that cut across sub-skills.
• Read references/repo-provenance.md before deciding whether this skill is current for a checkout or should be refreshed.
• references/repo-routing-metadata.json contains structured router metadata for managed repo-skill import.
• Run scripts/check_haiku_env.py as the shared environment/import checker before deeper sub-skill smoke scripts.

Operating pattern

For most Haiku tasks, work in this order:

1. Pick the transform boundary first: stateless hk.transform, stateful hk.transform_with_state, or multi-transform. See core-transforms.
2. Build module code inside the transformed function. Keep hk.Module construction, hk.get_parameter, hk.get_state, and hk.next_rng_key inside Haiku contexts. See params-state-rng.
3. Choose modules/networks and validate with synthetic arrays before adding optimizers, datasets, large examples, or distributed JAX. See modules-and-networks.
4. If raw JAX transforms appear inside a Haiku-transformed function, switch to Haiku wrappers or lift nested transforms deliberately. See jax-interop-and-advanced.
5. Treat Flax interop as optional and explicit. Verify the flax dependency and variable collection mapping before mixing codebases. See flax-interop.

Common boundaries

• Haiku does not own optimizers, checkpoint formats, dataset loaders, training launchers, or accelerator installation. Bring those from JAX/Optax/Orbax/TFDS or the user's own stack.
• Full public examples often require external datasets, TensorFlow/TFDS, Optax, RL libraries, or long training. This skill distills their Haiku model patterns into bundled no-download scripts and references; do not require the original checkout for runtime use.
• GPU warnings from JAX usually mean the installed JAX backend is CPU-only. That does not invalidate Haiku API work, but it does mean accelerator performance has not been verified.
• If a task is about editing the dm-haiku repository source itself rather than using Haiku as a package, first decide whether a maintainer/repository-development skill is more appropriate; this skill is optimized for package usage.