Microsoft.ML.OnnxRuntime 1.3.0

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

Install-Package Microsoft.ML.OnnxRuntime -Version 1.3.0
dotnet add package Microsoft.ML.OnnxRuntime --version 1.3.0
<PackageReference Include="Microsoft.ML.OnnxRuntime" Version="1.3.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
paket add Microsoft.ML.OnnxRuntime --version 1.3.0
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

Release Notes

Release Def:
Branch: refs/heads/rel-1.3.0
Commit: eb5da13bb5744c92583e03f8c7a7ddd8198e6771
Build: https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=124920

Showing the top 3 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.
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.

Read more about the GitHub Usage information on our documentation.

Version History

Version Downloads Last updated
1.3.0 2,268 5/18/2020
1.2.0 29,106 3/10/2020
1.1.2 5,559 2/21/2020
1.1.1 1,973 1/24/2020
1.1.0 3,536 12/19/2019
1.0.0 61,884 10/30/2019
0.5.1 93,068 10/12/2019
0.5.0 9,820 8/1/2019
0.4.0 53,067 5/2/2019
0.3.1 3,526 4/9/2019
0.3.0 31,368 3/14/2019
0.2.1 32,001 2/1/2019
0.1.5 7,478 12/1/2018