Keras
Keras is a multi-backend deep learning framework that supports JAX, TensorFlow, PyTorch, and OpenVINO. It provides a high-level UX for building and training models, allowing for accelerated model development and state-of-the-art performance. Keras 3 is designed to be a drop-in replacement for tf.keras and can run on top of any framework, benefiting from the advantages of each. It also allows for writing custom components that can be used in low-level workflows in any framework, making ML code future-proof by avoiding framework lock-in. Keras 3 supports various applications, including computer vision, natural language processing, audio processing, timeseries forecasting, and recommender systems. It provides a simple and easy-to-use API for building and training models, and it is compatible with Linux and macOS systems, with Windows users recommended to use WSL2. Keras 3 is available on PyPI as 'keras' and can be installed using pip, with backend packages such as TensorFlow, JAX, or PyTorch also required. The OpenVINO backend is available for model inference only. Keras 3 provides a range of features, including accelerated model development, state-of-the-art performance, and datacenter-scale training. It also supports backwards compatibility with tf.keras and allows for custom components to be written. Overall, Keras 3 is a powerful and flexible deep learning framework that provides a high-level UX and state-of-the-art performance, making it a popular choice among developers and researchers. Keras 3 is designed to be highly customizable, with support for various backends and custom components. It also provides a range of tools and features for building and training models, including support for GPU acceleration and distributed training. With its high-level UX and state-of-the-art performance, Keras 3 is an ideal choice for developers and researchers looking to build and train deep learning models.
- Category
- AI/ML
- Pricing
- Open Source
Problem solved
Keras 3 solves the problem of framework lock-in, allowing developers to write custom components that can be used in low-level workflows in any framework. It also provides a high-level UX for building and training models, making it easier to develop and deploy deep learning models. Additionally, Keras 3 provides state-of-the-art performance, allowing developers to benefit from the advantages of each framework. Keras 3 is designed to be a drop-in replacement for tf.keras, making it easy for developers to transition to the new framework. Overall, Keras 3 provides a flexible and powerful solution for building and training deep learning models, making it a popular choice among developers and researchers.
Use cases
- Computer vision
- Natural language processing
- Audio processing
- Timeseries forecasting
- Recommender systems
Audience
- Developers
- ML Engineers
- Data Scientists
Pros
- High-level UX
- State-of-the-art performance
- Flexible and customizable
- Supports various backends
- Backwards compatibility with tf.keras
Cons
- Steep learning curve
- Requires significant computational resources
- Limited support for certain backends
Official website: https://github.com/keras-team/keras
Canonical LambdaBase page: https://www.lambdabase.com/tools/keras