Dm-Haiku

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dm-haiku is a neural network library for JAX, offering a simple and flexible way to construct neural networks. It supports various architectures, though Google DeepMind now recommends Flax for new projects.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is dm-haiku?

dm-haiku is a neural network library specifically designed for use with JAX, providing a straightforward and adaptable framework for building neural networks. It enables users to implement a wide range of neural network architectures. While it offers a robust solution for neural network development, Google DeepMind, its creator, has indicated a preference for Flax for new projects. This library is suitable for researchers and developers working on machine learning models within the JAX ecosystem.

Best used for

Building and experimenting with neural network architectures within the JAX framework.

Common actions

Build neural networks

Develop AI models

Implement deep learning

Use JAX

workflowsautomated workflowlow-code/no-codedeepfakecollaboration"AI Agents"github copilotface swappingopen-source

Capabilities

Key features

JAX neural network library
Simple network building
Flexible architecture support
DeepMind creation

Target Audience

Machine Learning EngineersAI ResearchersData ScientistsDevelopers

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Given DeepMind's preference for Flax for new projects, is dm-haiku still actively maintained and a viable choice?

While DeepMind has shifted focus for new projects, dm-haiku remains a robust and functional library. It continues to be a viable choice for existing projects or for developers who prefer its specific API and design philosophy within the JAX ecosystem.

What are the key differences between dm-haiku and Flax, and when should I choose one over the other?

dm-haiku emphasizes a functional, module-based approach, often feeling more 'bare-metal' with JAX. Flax offers a more opinionated, object-oriented API with built-in features for common ML tasks. Choose dm-haiku for maximum flexibility and control, and Flax for quicker development with common patterns.

Can dm-haiku be used for large-scale research projects and production deployments?

Yes, dm-haiku is designed for both research and production. Its integration with JAX allows for efficient computation on accelerators, making it suitable for complex models and large datasets. Its flexibility supports diverse architectural requirements for advanced research.

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