Kur

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Kur is an open-source deep learning tool that allows users to design, train, and evaluate AI models without needing to code. It supports popular frameworks like Theano, TensorFlow, and PyTorch, and offers multi-GPU support.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is kur?

Kur is an open-source system designed to simplify the development and application of state-of-the-art deep learning models. It caters to both novices and experienced machine learning practitioners by allowing models to be described using easily understandable specification files, eliminating the need for extensive coding. The tool supports popular deep learning frameworks such as Theano, TensorFlow, and PyTorch, and provides out-of-the-box multi-GPU support for efficient training. Users can quickly explore different model versions using the Jinja2 templating engine. Kur also offers a friendly and extensible API for advanced deep learning architectures and workflows, making it a versatile solution for building sophisticated AI models.

Best used for

Ideal for developers and data scientists who need to quickly design, train, and evaluate deep learning models, especially without writing extensive code. It is particularly valuable for rapid prototyping and exploring different model architectures across various deep learning frameworks.

Common actions

design deep learning models

train AI models

evaluate neural networks

prototype deep learning architectures

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Capabilities

Key features

No-code model design
Multi-GPU support
Theano/TensorFlow/PyTorch compatible
Jinja2 templating engine
Extensible API

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What deep learning frameworks does Kur support?

Kur is designed to be versatile and supports several popular deep learning frameworks. Specifically, it offers compatibility with Theano, TensorFlow, and PyTorch, allowing users to leverage their preferred backend for model development and training.

Can I use Kur for multi-GPU training?

Yes, Kur provides out-of-the-box support for multi-GPU configurations. This feature allows users to distribute their deep learning model training across multiple GPUs, significantly speeding up the process for larger datasets and more complex models.

Does Kur require coding to build models?

One of Kur's key differentiators is its ability to allow users to design, train, and evaluate deep learning models without needing to write extensive code. It uses simple, human-readable specification files to describe models, making it accessible to a wider audience.

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