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.
Torch7 community support can be found at the following locations. As of 2019, the Torch-7 community is close to non-existent.
Questions, Support, Install issues: Google groups
Hanging out with other developers and users (strictly no install issues, no large blobs of text): Gitter Chat
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.
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
File is an abstract interface for common file operations.
Disk File defines operations on files stored on disk.
Memory File defines operations on stored in RAM.
Pipe File defines operations for using piped commands.
High-Level File operations defines higher-level serialization functions.
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.
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