Permalink
53 lines (40 sloc) 3.98 KB

Join the chat at https://gitter.im/torch/torch7 Build Status

Development Status

Torch is not in active developement. The functionality provided by the C backend of Torch, which are the TH, THNN, THC, THCUNN libraries is actively extended and re-written in the ATen C++11 library (source, mirror). ATen exposes all operators you would expect from torch7, nn, cutorch, and cunn directly in C++11 and includes additional support for sparse tensors and distributed operations. It is to note however that the API and semantics of the backend libraries in Torch-7 are different from the semantice provided by ATen. For example ATen provides numpy-style broadcasting while TH* dont. For information on building the forked Torch-7 libraries in C, refer to "The C interface" in pytorch/aten/src/README.md.

Need help?

Torch7 community support can be found at the following locations. However Torch7 has a much smaller active community than it used to. If you have questions about the C backend of Torch-7, you can try asking in the PyTorch communication channels, as the developers are familiar with it.

Torch Package Reference Manual

Torch is the main package in Torch7 where data structures for multi-dimensional tensors and mathematical operations over these are defined. Additionally, it provides many utilities for accessing files, serializing objects of arbitrary types and other useful utilities.

Torch Packages

  • Tensor Library
    • Tensor defines the all powerful tensor object that provides multi-dimensional numerical arrays with type templating.
    • Mathematical operations that are defined for the tensor object types.
    • Storage defines a simple storage interface that controls the underlying storage for any tensor object.
  • File I/O Interface Library
  • Useful Utilities
    • Timer provides functionality for measuring time.
    • Tester is a generic tester framework.
    • CmdLine is a command line argument parsing utility.
    • Random defines a random number generator package with various distributions.
    • Finally useful utility functions are provided for easy handling of torch tensor types and class inheritance.

Useful Links