Tensor 0.4.11
dotnet add package Tensor --version 0.4.11
NuGet\Install-Package Tensor -Version 0.4.11
<PackageReference Include="Tensor" Version="0.4.11" />
paket add Tensor --version 0.4.11
#r "nuget: Tensor, 0.4.11"
// Install Tensor as a Cake Addin
#addin nuget:?package=Tensor&version=0.4.11
// Install Tensor as a Cake Tool
#tool nuget:?package=Tensor&version=0.4.11
Tensor (n-dimensional array) library for F#
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 (comparision, 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 BLAS accelerated operations
- by default Intel MKL is used (shipped with NuGet package)
- other BLASes (OpenBLAS, vendor-specific) can be selected by configuration option
- CUDA GPU: all operations performed locally on GPU and cuBLAS used for matrix operations
Requirements:
- Linux, MacOS or Windows on x64
- Linux requires libgomp.so.1 installed.
Additional algorithms are provided in the Tensor.Algorithm package.
Product | Versions |
---|---|
.NET | net5.0 net5.0-windows net6.0 net6.0-android net6.0-ios net6.0-maccatalyst net6.0-macos net6.0-tvos net6.0-windows net7.0 net7.0-android net7.0-ios net7.0-maccatalyst net7.0-macos net7.0-tvos net7.0-windows |
.NET Core | netcoreapp2.0 netcoreapp2.1 netcoreapp2.2 netcoreapp3.0 netcoreapp3.1 |
.NET Standard | netstandard2.0 netstandard2.1 |
.NET Framework | net461 net462 net463 net47 net471 net472 net48 net481 |
MonoAndroid | monoandroid |
MonoMac | monomac |
MonoTouch | monotouch |
Tizen | tizen40 tizen60 |
Xamarin.iOS | xamarinios |
Xamarin.Mac | xamarinmac |
Xamarin.TVOS | xamarintvos |
Xamarin.WatchOS | xamarinwatchos |
-
.NETStandard 2.0
- FSharp.Core (>= 4.3.4)
- HDF.PInvoke.NETStandard (>= 1.10.200)
- ManagedCuda.NETStandard (>= 9.1.300)
- ManagedCuda-CUBLAS.NETStandard (>= 9.1.300)
- ManagedCuda-NVRTC.NETStandard (>= 9.1.300)
- System.Numerics.Vectors (>= 4.4.0)
- System.Reflection.Emit (>= 4.3.0)
NuGet packages (3)
Showing the top 3 NuGet packages that depend on Tensor:
Package | Downloads |
---|---|
DeepNet
Deep learning library for F#. Provides symbolic model differentiation, automatic differentiation and compilation to CUDA GPUs. Includes optimizers and model blocks used in deep learning. Make sure to set the platform of your project to x64. |
|
RPlotTools
Tools for plotting using R from F#. |
|
Tensor.Algorithm
Data types: - arbitrary precision rational numbers Matrix algebra (integer, rational): - Row echelon form - Smith normal form - Kernel, cokernel 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. |
GitHub repositories
This package is not used by any popular GitHub repositories.
Version | Downloads | Last updated |
---|---|---|
0.4.11 | 5,434 | 5/8/2018 |
0.4.11-v0.4.11-215 | 564 | 5/8/2018 |
0.4.11-symtensor-core-242 | 655 | 11/15/2018 |
0.4.11-symtensor-core-241 | 610 | 11/15/2018 |
0.4.11-symtensor-core-240 | 616 | 11/15/2018 |
0.4.11-symtensor-core-239 | 607 | 11/15/2018 |
0.4.11-symtensor-core-238 | 615 | 11/15/2018 |
0.4.11-symtensor-core-237 | 642 | 11/15/2018 |
0.4.11-symtensor-core-236 | 586 | 11/14/2018 |
0.4.11-symtensor-core-235 | 603 | 11/14/2018 |
0.4.11-symtensor-core-234 | 604 | 11/14/2018 |
0.4.11-symtensor-core-231 | 615 | 11/9/2018 |
0.4.11-symtensor-core-230 | 631 | 11/9/2018 |
0.4.11-symtensor-core-229 | 588 | 11/8/2018 |
0.4.11-symtensor-core-228 | 597 | 11/8/2018 |
0.4.11-symtensor-core-227 | 644 | 10/30/2018 |
0.4.11-symtensor-core-226 | 650 | 10/30/2018 |
0.4.11-symtensor-core-225 | 581 | 10/30/2018 |
0.4.11-develop-216 | 812 | 5/8/2018 |
0.4.10-develop-213 | 812 | 5/8/2018 |
0.4.10-develop-212 | 803 | 5/7/2018 |
0.4.10-develop-211 | 824 | 5/7/2018 |
0.3.0.712-master | 678 | 9/1/2017 |
0.3.0.711-master | 680 | 9/1/2017 |
0.3.0.710-master | 662 | 9/1/2017 |
0.3.0.709-master | 646 | 8/31/2017 |
0.3.0.708-master | 670 | 8/30/2017 |
0.3.0.707-master | 688 | 8/30/2017 |
0.3.0.706-master | 664 | 8/30/2017 |
0.3.0.701-master | 703 | 6/26/2017 |
0.3.0.700-master | 717 | 6/22/2017 |
0.3.0.699-master | 699 | 6/22/2017 |
0.3.0.698-master | 691 | 6/21/2017 |
0.3.0.697-master | 690 | 6/21/2017 |
0.3.0.696-master | 726 | 6/21/2017 |
0.3.0.695-master | 697 | 6/21/2017 |
0.3.0.694-master | 689 | 6/21/2017 |
0.3.0.693-master | 698 | 6/20/2017 |
0.3.0.692-master | 681 | 6/19/2017 |
0.3.0.691-master | 721 | 6/19/2017 |
0.3.0.690-master | 704 | 6/19/2017 |
0.3.0.689-master | 696 | 5/14/2017 |
0.3.0.688 | 6,535 | 5/14/2017 |
0.3.0.686-master | 698 | 5/14/2017 |
0.2.0.591-master | 697 | 4/19/2017 |
0.2.0.565-master | 711 | 4/11/2017 |
0.2.0.556-master | 693 | 3/21/2017 |
0.2.0.551-master | 747 | 3/17/2017 |
0.2.0.540-master | 687 | 3/15/2017 |
0.2.0.536-master | 681 | 3/14/2017 |
0.2.0.519-master | 706 | 3/2/2017 |
0.2.0.516-master | 683 | 3/2/2017 |
0.2.0.499-master | 712 | 2/13/2017 |
0.2.0.494-master | 685 | 2/7/2017 |
0.2.0.479-master | 704 | 2/1/2017 |
0.2.0.463-master | 698 | 1/17/2017 |
0.2.0.431-master | 784 | 12/2/2016 |
0.2.0.422-master | 1,076 | 11/9/2016 |
0.2.0.421-master | 1,011 | 11/9/2016 |
0.2.0.411-master | 754 | 10/26/2016 |
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0.2.0.394-master | 728 | 10/25/2016 |
0.2.0.382-master | 719 | 10/21/2016 |
0.2.0.377-master | 706 | 10/20/2016 |
0.2.0.323-master | 695 | 10/11/2016 |
0.2.0.262-master | 729 | 9/29/2016 |
0.2.0.248-master | 723 | 9/27/2016 |
0.2.0.174-master | 731 | 9/16/2016 |
0.2.0.128-master | 731 | 9/8/2016 |
0.2.0.122-master | 736 | 9/8/2016 |
0.2.0.121-master | 710 | 9/7/2016 |
0.2.0.111-master | 704 | 9/7/2016 |
0.2.0.105-ci | 768 | 9/5/2016 |
0.2.0.97-ci | 758 | 8/30/2016 |
0.2.0.96-ci | 737 | 8/29/2016 |
0.2.0.90-ci | 725 | 8/25/2016 |
0.2.0.89-ci | 710 | 8/24/2016 |
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0.2.0.87-ci | 727 | 8/24/2016 |
0.2.0.86-ci | 718 | 8/23/2016 |
0.2.0.85-ci | 731 | 8/22/2016 |
0.2.0.84-ci | 743 | 8/22/2016 |
0.2.0.83-ci | 744 | 8/22/2016 |
0.2.0.82 | 1,804 | 8/22/2016 |
0.2.0.81-ci | 749 | 8/19/2016 |
0.2.0.80-ci | 749 | 6/27/2016 |
0.2.0.79-ci | 741 | 6/27/2016 |
0.2.0.77-ci | 742 | 6/22/2016 |
0.2.0.76-ci | 751 | 6/22/2016 |
0.2.0.75 | 1,297 | 6/15/2016 |
0.2.0.74-ci | 1,095 | 6/15/2016 |
0.2.0.73 | 1,520 | 6/15/2016 |
0.2.0.72 | 1,529 | 6/15/2016 |
0.2.0.71 | 1,484 | 6/14/2016 |
0.2.0.70 | 1,379 | 6/9/2016 |
0.2.0.69 | 1,348 | 6/9/2016 |
0.2.0.68 | 1,162 | 6/9/2016 |
0.2.0.67 | 1,655 | 6/8/2016 |
0.2.0.66-ci | 741 | 6/8/2016 |
0.2.0.65-ci | 746 | 6/8/2016 |
0.2.0.64-ci | 792 | 6/8/2016 |
0.2.0.63-ci | 723 | 6/7/2016 |
0.2.0.62 | 1,182 | 6/7/2016 |
0.2.0.61 | 1,149 | 6/6/2016 |
0.2.0.60 | 1,139 | 6/6/2016 |
0.2.0.59 | 1,132 | 6/6/2016 |
0.2.0.57 | 1,169 | 6/3/2016 |
0.2.0.56 | 1,137 | 6/3/2016 |
0.2.0.55 | 1,223 | 6/3/2016 |
0.2.0.54 | 1,174 | 6/3/2016 |
0.2.0.53 | 1,511 | 6/3/2016 |
0.2.0.52-ci | 720 | 6/2/2016 |
0.2.0.51-ci | 747 | 6/2/2016 |
0.2.0.50-ci | 752 | 6/2/2016 |
0.2.0.49 | 1,525 | 5/31/2016 |
0.2.0.48-ci | 788 | 5/31/2016 |
0.2.0.46-ci | 761 | 5/31/2016 |
0.2.0.45 | 1,341 | 5/31/2016 |
0.2.0.44 | 1,341 | 5/31/2016 |
0.2.0.43 | 1,339 | 5/31/2016 |
0.2.0.42 | 1,348 | 5/30/2016 |
0.2.0.41 | 1,347 | 5/30/2016 |
0.2.0.40 | 1,376 | 5/30/2016 |
0.2.0.39 | 1,358 | 5/30/2016 |
0.2.0.38 | 1,360 | 5/30/2016 |
0.2.0.37 | 1,297 | 5/30/2016 |
0.2.0.36 | 1,317 | 5/25/2016 |
0.2.0.35 | 1,343 | 5/24/2016 |
0.2.0.34 | 1,373 | 5/24/2016 |
0.2.0.33 | 2,165 | 5/24/2016 |
0.2.0.32-ci | 752 | 5/24/2016 |
0.1.26-ci | 774 | 5/24/2016 |
0.1.24-ci | 764 | 5/24/2016 |
0.1.19-ci | 744 | 5/24/2016 |