Deep Learning and Tensors
The Deep Learning and Tensors environments are designed for specific deep learning libraries and tools. These include environments for PyTorch, JAX, TensorFlow, Hugging Face, and Fast.ai, each equipped with the necessary tools for efficient deep learning and tensor computations. They provide a streamlined experience for tasks such as training neural network models, high-performance numerical computing, and large-scale machine learning.
| Environment | Description | Quickstart |
|---|---|---|
| torch | Environment equipped with PyTorch, a powerful open-source tensor library for training neural network models written for Python. |  |
| jax | Environment set up for JAX, a library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. | |
| tensorflow | Environment designed for TensorFlow, an end-to-end open-source tensor library for deep learning in Python. | |
| huggingface | Environment optimized for Hugging Face tools like transformers, datasets, accelerate, and optimum powered by PyTorch. | |
| fastai | Environment for Fast.ai tools and PyTorch, ideal for training and running inference on neural network models. | |