It is a PyTorch library that allows researchers and developers to scale up Transformers efficiently and effectively. It has the implementation of fundamental research to improve modeling generality and capability as well as training stability and efficiency of scaling Transformers.
TorchScale is a tool in the Development & Training Tools category of a tech stack.
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What are some alternatives to TorchScale?
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
PyTorch are some of the popular tools that integrate with TorchScale. Here's a list of all 1 tools that integrate with TorchScale.