Tanuki: Alignment-as-Code for LLM Applications

Nov 28, 2023 ·

Tanuki.py is a Python library for general-purpose deep learning, developed by the Tanuki team at OpenAI. It provides tools for building and training neural networks, and for creating complex models. The aim of Tanuki.py is to provide an easy to use and accessible platform for users of all experience levels. It is designed to be flexible, fast, and efficient, allowing for experiments with different architectures and hyperparameters.

The library offers an intuitive API which allows developers to quickly build neural networks from scratch. It comes with basic layers such as convolutional, recurrent, or pooling layers and more complex structures like autoencoders and generative adversarial networks (GANs). It also supports model saving and loading, allowing for quick experimentation.

The library also provides a suite of optimizers, datasets, metrics, callbacks and other useful features. For example, it includes several popular optimizers such as Adam, RMSProp, SGD and L-BFGS; datasets such as MNIST, CIFAR10 and ImageNet; and metrics such as accuracy and precision. In addition, its integration with TensorBoard helps visualize the progress of experiments in real time.

Finally, Tanuki.py enables researchers to leverage the power of GPUs for accelerated training and inference. It has built-in support for the NVIDIA CUDA computing platform, so users can benefit from GPU computing.

Overall, Tanuki.py is a powerful and user-friendly library for creating deep learning models. Its simple and intuitive API allows developers to quickly prototype their own models, while its robust set of features enable them to experiment with more complex architectures and hyperparameters. Furthermore, its integration with TensorBoard helps researchers keep track of the progress of their experiments in real time. Finally, its support for GPU computing helps speed up training and inference times.