A tensor is an n-dimensional array of an arbitrary data type (for example single or double). Tensors of data type 'T are implemented by the Tensor<'T> type.

A tensor can be either stored in host memory or in the memory of a GPU computing device. Currently only nVidia cards implementing the CUDA API are supported. The API for host and GPU stored tensors is mostly equal, thus a program can make use of GPU accelerated operations without porting effort.

The tensor library provides functionality similar to Numpy's Ndarray and MATLAB arrays, including vector-wise operations, reshaping, slicing, broadcasting, masked assignment, reduction operations and BLAS operations.

This open source library is written in F# and targets the .NET Standard 2.0 platform with Linux, MacOS and Microsoft Windows as supported operating systems.

• Core features
  • n-dimensional arrays (tensors) in host memory or on CUDA GPUs
  • element-wise operations (addition, multiplication, absolute value, etc.)
  • basic linear algebra operations (dot product, SVD decomposition, matrix inverse, etc.)
  • reduction operations (sum, product, average, maximum, arg max, etc.)
  • logic operations (comparison, and, or, etc.)
  • views, slicing, reshaping, broadcasting (similar to NumPy)
  • scatter and gather by indices
  • standard functional operations (map, fold, etc.)
• Data exchange
  • read/write support for HDF5 (.h5)
  • interop with standard F# types (Seq, List, Array, Array2D, Array3D, etc.)
• Performance
  • host: SIMD and Intel MKL accelerated operations
  • CUDA GPU: all operations performed locally on GPU and cuBLAS used for matrix operations

• Matrix algebra (integer, rational)
  • Row echelon form
  • Smith normal form
  • Kernel, co-kernel and (pseudo-)inverse
• Matrix decomposition (floating point)
  • Principal component analysis (PCA)
  • ZCA whitening
• Misc
  • Bezout's identity
  • Loading of NumPy's .npy and .npz files.

Release notes are at http://www.deepml.net/Tensor/articles/ReleaseNotes.html.

The following NuGet packages are available for download.

• Tensor NuGet package
• Tensor.Algorithm NuGet package

Documentation is provided at http://www.deepml.net/Tensor.