TorchSharp 0.96.6
Install-Package TorchSharp -Version 0.96.6
dotnet add package TorchSharp --version 0.96.6
<PackageReference Include="TorchSharp" Version="0.96.6" />
paket add TorchSharp --version 0.96.6
#r "nuget: TorchSharp, 0.96.6"
// Install TorchSharp as a Cake Addin
#addin nuget:?package=TorchSharp&version=0.96.6
// Install TorchSharp as a Cake Tool
#tool nuget:?package=TorchSharp&version=0.96.6
.NET Bindings for Torch. Requires reference to one of libtorch-cpu, libtorch-cuda-11.3, libtorch-cuda-11.3-win-x64 or libtorch-cuda-11.3-linux-x64 version 1.10.0.1 to execute.
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 |
.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 |
MonoAndroid | monoandroid |
MonoMac | monomac |
MonoTouch | monotouch |
Tizen | tizen40 tizen60 |
Xamarin.iOS | xamarinios |
Xamarin.Mac | xamarinmac |
Xamarin.TVOS | xamarintvos |
Xamarin.WatchOS | xamarinwatchos |
-
.NETCoreApp 3.1
- No dependencies.
-
.NETStandard 2.0
- No dependencies.
NuGet packages (7)
Showing the top 5 NuGet packages that depend on TorchSharp:
Package | Downloads |
---|---|
DiffSharp.Backends.Torch
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/ |
|
TorchSharp-cuda-windows
TorchSharp makes PyTorch available for .NET users. This package combines the TorchSharp package with LibTorch 1.10.0 CUDA 11.3 support for Windows. |
|
TorchSharp-cuda-linux
TorchSharp makes PyTorch available for .NET users. This package combines the TorchSharp package with LibTorch 1.10.0 CUDA 11.3 support for Linux. |
|
TorchSharp-cpu
TorchSharp makes PyTorch available for .NET users. This package combines the TorchSharp package with LibTorch 1.10.0 CPU support. |
|
TsBERT
BERT model in TorchSharp with the ability to load pre-trained weights from Google BERT checkpoints. This model produces a single value per input sequence - suitable for tasks such as text classification. |
GitHub repositories
This package is not used by any popular GitHub repositories.
Version | Downloads | Last updated |
---|---|---|
0.96.6 | 56 | 5/14/2022 |
0.96.5 | 402 | 4/19/2022 |
0.96.4 | 106 | 4/19/2022 |
0.96.3 | 632 | 2/23/2022 |
0.96.1 | 224 | 2/22/2022 |
0.96.0 | 1,687 | 2/3/2022 |
0.95.4 | 988 | 12/3/2021 |
0.95.3 | 819 | 11/15/2021 |
0.95.2 | 812 | 11/10/2021 |
0.95.1 | 753 | 11/6/2021 |
0.93.9 | 808 | 10/29/2021 |
0.93.8 | 1,011 | 10/25/2021 |
0.93.6 | 1,019 | 10/20/2021 |
0.93.5 | 2,451 | 10/8/2021 |
0.93.4 | 1,079 | 10/7/2021 |
0.93.3 | 897 | 10/6/2021 |
0.93.1 | 200 | 9/30/2021 |
0.93.0 | 194 | 9/29/2021 |
0.92.52220 | 260 | 9/14/2021 |
0.91.52719 | 2,194 | 7/24/2021 |
0.91.52681 | 3,538 | 6/23/2021 |
0.91.52680 | 265 | 6/22/2021 |
0.91.52672 | 292 | 6/22/2021 |
0.91.52666 | 291 | 6/21/2021 |
0.91.52661 | 176 | 6/20/2021 |
0.91.52660 | 194 | 6/20/2021 |
0.91.52621 | 210 | 6/16/2021 |
0.91.52604 | 227 | 5/27/2021 |
0.91.52577 | 228 | 5/10/2021 |
0.91.52573 | 171 | 5/10/2021 |
0.91.52518 | 3,496 | 3/25/2021 |
0.91.52511 | 194 | 3/25/2021 |
0.91.52508 | 171 | 3/25/2021 |
0.91.52506 | 200 | 3/25/2021 |
0.91.52502 | 195 | 3/25/2021 |
0.91.52475 | 185 | 3/24/2021 |
0.91.52458 | 748 | 3/23/2021 |
0.91.52214-preview-local-Re... | 110 | 9/2/2021 |
0.9.52445 | 213 | 3/19/2021 |
0.3.52431 | 218 | 3/19/2021 |
0.3.52428 | 206 | 3/19/2021 |
0.3.52423 | 171 | 3/18/2021 |
0.3.52363 | 772 | 12/3/2020 |
0.3.52357 | 1,773 | 11/30/2020 |
0.3.52353 | 643 | 11/26/2020 |
0.3.52348 | 802 | 11/20/2020 |
0.3.52345 | 232 | 11/20/2020 |
0.3.52338 | 1,069 | 11/13/2020 |
0.3.52320 | 943 | 11/11/2020 |
0.3.52318 | 243 | 11/11/2020 |
0.3.52312 | 248 | 11/11/2020 |
0.3.52293 | 593 | 11/6/2020 |
0.3.52276 | 6,611 | 9/15/2020 |
0.3.52267 | 2,964 | 6/29/2020 |
0.3.52264 | 311 | 6/27/2020 |
0.3.52259 | 356 | 6/25/2020 |
0.3.52253 | 298 | 6/12/2020 |
0.3.52249 | 492 | 6/8/2020 |
0.3.52235 | 352 | 6/3/2020 |
0.3.52229 | 255 | 6/3/2020 |
0.3.52216 | 442 | 5/24/2020 |
0.3.52209 | 439 | 5/22/2020 |
0.2.0-preview-27930-2 | 508 | 7/31/2019 |
0.2.0-preview-27915-4 | 403 | 7/16/2019 |
0.2.0-preview-27912-2 | 326 | 7/12/2019 |
0.2.0-preview-27829-1 | 314 | 7/5/2019 |
0.1.0 | 813 | 11/13/2018 |