Tensor 0.4.11-symtensor-core-229
See the version list below for details.
dotnet add package Tensor --version 0.4.11-symtensor-core-229
NuGet\Install-Package Tensor -Version 0.4.11-symtensor-core-229
<PackageReference Include="Tensor" Version="0.4.11-symtensor-core-229" />
paket add Tensor --version 0.4.11-symtensor-core-229
#r "nuget: Tensor, 0.4.11-symtensor-core-229"
// Install Tensor as a Cake Addin #addin nuget:?package=Tensor&version=0.4.11-symtensor-core-229&prerelease // Install Tensor as a Cake Tool #tool nuget:?package=Tensor&version=0.4.11-symtensor-core-229&prerelease
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 Compatible and additional computed target framework versions. |
---|---|
.NET | net5.0 was computed. net5.0-windows was computed. net6.0 was computed. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
.NET Core | netcoreapp2.0 was computed. netcoreapp2.1 was computed. netcoreapp2.2 was computed. netcoreapp3.0 was computed. netcoreapp3.1 was computed. |
.NET Standard | netstandard2.0 is compatible. netstandard2.1 was computed. |
.NET Framework | net461 was computed. net462 was computed. net463 was computed. net47 was computed. net471 was computed. net472 was computed. net48 was computed. net481 was computed. |
MonoAndroid | monoandroid was computed. |
MonoMac | monomac was computed. |
MonoTouch | monotouch was computed. |
Tizen | tizen40 was computed. tizen60 was computed. |
Xamarin.iOS | xamarinios was computed. |
Xamarin.Mac | xamarinmac was computed. |
Xamarin.TVOS | xamarintvos was computed. |
Xamarin.WatchOS | xamarinwatchos was computed. |
-
.NETStandard 2.0
- FSharp.Core (>= 4.5.2)
- 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 | 8,485 | 5/8/2018 |
0.4.11-v0.4.11-215 | 650 | 5/8/2018 |
0.4.11-symtensor-core-242 | 1,709 | 11/15/2018 |
0.4.11-symtensor-core-241 | 1,744 | 11/15/2018 |
0.4.11-symtensor-core-240 | 1,736 | 11/15/2018 |
0.4.11-symtensor-core-239 | 1,594 | 11/15/2018 |
0.4.11-symtensor-core-238 | 1,729 | 11/15/2018 |
0.4.11-symtensor-core-237 | 1,814 | 11/15/2018 |
0.4.11-symtensor-core-236 | 1,624 | 11/14/2018 |
0.4.11-symtensor-core-235 | 1,692 | 11/14/2018 |
0.4.11-symtensor-core-234 | 1,674 | 11/14/2018 |
0.4.11-symtensor-core-231 | 1,756 | 11/9/2018 |
0.4.11-symtensor-core-230 | 1,750 | 11/9/2018 |
0.4.11-symtensor-core-229 | 1,568 | 11/8/2018 |
0.4.11-symtensor-core-228 | 1,815 | 11/8/2018 |
0.4.11-symtensor-core-227 | 1,702 | 10/30/2018 |
0.4.11-symtensor-core-226 | 1,846 | 10/30/2018 |
0.4.11-symtensor-core-225 | 1,686 | 10/30/2018 |
0.4.11-develop-216 | 2,056 | 5/8/2018 |
0.4.10-develop-213 | 1,933 | 5/8/2018 |
0.4.10-develop-212 | 1,865 | 5/7/2018 |
0.4.10-develop-211 | 2,010 | 5/7/2018 |
0.3.0.712-master | 1,430 | 9/1/2017 |
0.3.0.711-master | 1,495 | 9/1/2017 |
0.3.0.710-master | 1,447 | 9/1/2017 |
0.3.0.709-master | 1,424 | 8/31/2017 |
0.3.0.708-master | 1,445 | 8/30/2017 |
0.3.0.707-master | 1,393 | 8/30/2017 |
0.3.0.706-master | 1,477 | 8/30/2017 |
0.3.0.701-master | 1,497 | 6/26/2017 |
0.3.0.700-master | 1,439 | 6/22/2017 |
0.3.0.699-master | 1,436 | 6/22/2017 |
0.3.0.698-master | 1,421 | 6/21/2017 |
0.3.0.697-master | 1,482 | 6/21/2017 |
0.3.0.696-master | 1,531 | 6/21/2017 |
0.3.0.695-master | 1,470 | 6/21/2017 |
0.3.0.694-master | 1,450 | 6/21/2017 |
0.3.0.693-master | 1,507 | 6/20/2017 |
0.3.0.692-master | 1,433 | 6/19/2017 |
0.3.0.691-master | 1,457 | 6/19/2017 |
0.3.0.690-master | 1,468 | 6/19/2017 |
0.3.0.689-master | 1,437 | 5/14/2017 |
0.3.0.688 | 9,195 | 5/14/2017 |
0.3.0.686-master | 1,390 | 5/14/2017 |
0.2.0.591-master | 1,402 | 4/19/2017 |
0.2.0.565-master | 1,360 | 4/11/2017 |
0.2.0.556-master | 1,390 | 3/21/2017 |
0.2.0.551-master | 1,424 | 3/17/2017 |
0.2.0.540-master | 1,337 | 3/15/2017 |
0.2.0.536-master | 1,369 | 3/14/2017 |
0.2.0.519-master | 1,393 | 3/2/2017 |
0.2.0.516-master | 1,363 | 3/2/2017 |
0.2.0.499-master | 1,389 | 2/13/2017 |
0.2.0.494-master | 1,368 | 2/7/2017 |
0.2.0.479-master | 1,389 | 2/1/2017 |
0.2.0.463-master | 1,398 | 1/17/2017 |
0.2.0.431-master | 1,417 | 12/2/2016 |
0.2.0.422-master | 1,730 | 11/9/2016 |
0.2.0.421-master | 1,673 | 11/9/2016 |
0.2.0.411-master | 1,470 | 10/26/2016 |
0.2.0.400-master | 1,396 | 10/26/2016 |
0.2.0.394-master | 1,378 | 10/25/2016 |
0.2.0.382-master | 1,421 | 10/21/2016 |
0.2.0.377-master | 1,436 | 10/20/2016 |
0.2.0.323-master | 1,354 | 10/11/2016 |
0.2.0.262-master | 1,379 | 9/29/2016 |
0.2.0.248-master | 1,440 | 9/27/2016 |
0.2.0.174-master | 1,426 | 9/16/2016 |
0.2.0.128-master | 1,443 | 9/8/2016 |
0.2.0.122-master | 1,412 | 9/8/2016 |
0.2.0.121-master | 1,403 | 9/7/2016 |
0.2.0.111-master | 1,383 | 9/7/2016 |
0.2.0.105-ci | 1,452 | 9/5/2016 |
0.2.0.97-ci | 1,463 | 8/30/2016 |
0.2.0.96-ci | 1,380 | 8/29/2016 |
0.2.0.90-ci | 1,434 | 8/25/2016 |
0.2.0.89-ci | 1,380 | 8/24/2016 |
0.2.0.88-ci | 1,429 | 8/24/2016 |
0.2.0.87-ci | 1,407 | 8/24/2016 |
0.2.0.86-ci | 1,412 | 8/23/2016 |
0.2.0.85-ci | 1,397 | 8/22/2016 |
0.2.0.84-ci | 1,426 | 8/22/2016 |
0.2.0.83-ci | 1,401 | 8/22/2016 |
0.2.0.82 | 2,928 | 8/22/2016 |
0.2.0.81-ci | 1,433 | 8/19/2016 |
0.2.0.80-ci | 1,456 | 6/27/2016 |
0.2.0.79-ci | 1,426 | 6/27/2016 |
0.2.0.77-ci | 1,412 | 6/22/2016 |
0.2.0.76-ci | 1,434 | 6/22/2016 |
0.2.0.75 | 2,221 | 6/15/2016 |
0.2.0.74-ci | 1,785 | 6/15/2016 |
0.2.0.73 | 2,396 | 6/15/2016 |
0.2.0.72 | 2,416 | 6/15/2016 |
0.2.0.71 | 2,430 | 6/14/2016 |
0.2.0.70 | 2,259 | 6/9/2016 |
0.2.0.69 | 2,216 | 6/9/2016 |
0.2.0.68 | 2,070 | 6/9/2016 |
0.2.0.67 | 2,739 | 6/8/2016 |
0.2.0.66-ci | 1,444 | 6/8/2016 |
0.2.0.65-ci | 1,433 | 6/8/2016 |
0.2.0.64-ci | 1,477 | 6/8/2016 |
0.2.0.63-ci | 1,419 | 6/7/2016 |
0.2.0.62 | 2,059 | 6/7/2016 |
0.2.0.61 | 2,028 | 6/6/2016 |
0.2.0.60 | 2,009 | 6/6/2016 |
0.2.0.59 | 1,985 | 6/6/2016 |
0.2.0.57 | 2,086 | 6/3/2016 |
0.2.0.56 | 2,053 | 6/3/2016 |
0.2.0.55 | 2,100 | 6/3/2016 |
0.2.0.54 | 2,089 | 6/3/2016 |
0.2.0.53 | 2,569 | 6/3/2016 |
0.2.0.52-ci | 1,401 | 6/2/2016 |
0.2.0.51-ci | 1,427 | 6/2/2016 |
0.2.0.50-ci | 1,456 | 6/2/2016 |
0.2.0.49 | 2,525 | 5/31/2016 |
0.2.0.48-ci | 1,503 | 5/31/2016 |
0.2.0.46-ci | 1,433 | 5/31/2016 |
0.2.0.45 | 2,248 | 5/31/2016 |
0.2.0.44 | 2,291 | 5/31/2016 |
0.2.0.43 | 2,222 | 5/31/2016 |
0.2.0.42 | 2,217 | 5/30/2016 |
0.2.0.41 | 2,287 | 5/30/2016 |
0.2.0.40 | 2,250 | 5/30/2016 |
0.2.0.39 | 2,331 | 5/30/2016 |
0.2.0.38 | 2,262 | 5/30/2016 |
0.2.0.37 | 2,246 | 5/30/2016 |
0.2.0.36 | 2,271 | 5/25/2016 |
0.2.0.35 | 2,253 | 5/24/2016 |
0.2.0.34 | 2,271 | 5/24/2016 |
0.2.0.33 | 3,251 | 5/24/2016 |
0.2.0.32-ci | 1,446 | 5/24/2016 |
0.1.26-ci | 1,467 | 5/24/2016 |
0.1.24-ci | 1,408 | 5/24/2016 |
0.1.19-ci | 1,442 | 5/24/2016 |