Microsoft.ML.OnnxRuntime 1.8.1

This package contains native shared library artifacts for all supported platforms of ONNX Runtime.

Install-Package Microsoft.ML.OnnxRuntime -Version 1.8.1
dotnet add package Microsoft.ML.OnnxRuntime --version 1.8.1
<PackageReference Include="Microsoft.ML.OnnxRuntime" Version="1.8.1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add Microsoft.ML.OnnxRuntime --version 1.8.1
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
#r "nuget: Microsoft.ML.OnnxRuntime, 1.8.1"
#r directive can be used in F# Interactive, C# scripting and .NET Interactive. Copy this into the interactive tool or source code of the script to reference the package.
// Install Microsoft.ML.OnnxRuntime as a Cake Addin
#addin nuget:?package=Microsoft.ML.OnnxRuntime&version=1.8.1

// Install Microsoft.ML.OnnxRuntime as a Cake Tool
#tool nuget:?package=Microsoft.ML.OnnxRuntime&version=1.8.1
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

Release Notes

Release Def:
Branch: refs/heads/rel-1.8.1
Commit: 96bb4b1ce83efd13b7dba54f707b27303354e480
Build: https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=162931

NuGet packages (9)

Showing the top 5 NuGet packages that depend on Microsoft.ML.OnnxRuntime:

Package Downloads
Aspose.OCR
Aspose.OCR for .NET is a robust optical character recognition API. Developers can easily add OCR functionalities in their applications. API is extensible, easy to use, compact and provides a simple set of classes for controlling character recognition.
FaceONNX
Face analytics library based on deep neural networks and ONNX runtime.
Aspose.Ocr.Cpp
Aspose.OCR for C and C++ is a robust optical character recognition API. Developers can easily add OCR functionalities in their applications. API is extensible, easy to use, compact and provides a simple set of classes for controlling character recognition. It supports commonly used image formats and provides noise removal filters, determine text fields and automatic alignment of the document. The library requires onnxruntime.dll installed in the system.
MxNet.Sharp
C# Binding for the Apache MxNet library. NDArray, Symbolic and Gluon Supported MxNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. MXNet is more than a deep learning project. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.
RusstressNet
Automatic accent (stress) prediction for Russian language

GitHub repositories (5)

Showing the top 5 popular GitHub repositories that depend on Microsoft.ML.OnnxRuntime:

Repository Stars
dotnet/machinelearning
ML.NET is an open source and cross-platform machine learning framework for .NET.
microsoft/psi
Platform for Situated Intelligence
Azure/azure-stream-analytics
Azure Stream Analytics
microsoft/CryptoNets
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
deepakkumar1984/MxNet.Sharp
.NET Standard bindings for Apache MxNet with Imperative, Symbolic and Gluon Interface for developing, training and deploying Machine Learning models in C#. https://mxnet.tech-quantum.com/

Version History

Version Downloads Last updated
1.8.1 7,544 7/7/2021
1.8.0 6,263 6/3/2021
1.7.0 29,436 3/2/2021
1.6.0 18,407 12/10/2020
1.5.2 34,158 10/15/2020
1.5.1 7,343 9/29/2020
1.4.0 46,757 7/17/2020
1.3.0 44,499 5/18/2020
1.2.0 58,497 3/10/2020
1.1.2 6,042 2/21/2020
1.1.1 4,891 1/24/2020
1.1.0 8,265 12/19/2019
1.0.0 65,734 10/30/2019
0.5.1 150,706 10/12/2019
0.5.0 12,486 8/1/2019
0.4.0 57,662 5/2/2019
0.3.1 4,331 4/9/2019
0.3.0 32,826 3/14/2019
0.2.1 35,169 2/1/2019
0.1.5 7,885 12/1/2018