What is Torch?
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
Torch is a tool in the Machine Learning Tools category of a tech stack.
Torch is an open source tool with 8.9K GitHub stars and 2.4K GitHub forks. Here’s a link to Torch's open source repository on GitHub
Who uses Torch?
Companies
Developers
76 developers on StackShare have stated that they use Torch.
Torch Integrations
Python, Databricks, Flair, Comet.ml, and SQLFlow are some of the popular tools that integrate with Torch. Here's a list of all 7 tools that integrate with Torch.
Torch's Features
- A powerful N-dimensional array
- Lots of routines for indexing, slicing, transposing
- Amazing interface to C, via LuaJIT
- Linear algebra routines
- Neural network, and energy-based models
- Numeric optimization routines
- Fast and efficient GPU support
- Embeddable, with ports to iOS and Android backends
Torch Alternatives & Comparisons
What are some alternatives to Torch?
PyTorch
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.
TensorFlow
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.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
