Microsoft.ML.OnnxTransformer 1.5.2

ML.NET component for Microsoft.ML.Scoring library

Install-Package Microsoft.ML.OnnxTransformer -Version 1.5.2
dotnet add package Microsoft.ML.OnnxTransformer --version 1.5.2
<PackageReference Include="Microsoft.ML.OnnxTransformer" Version="1.5.2" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add Microsoft.ML.OnnxTransformer --version 1.5.2
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

Release Notes

https://aka.ms/mlnetreleasenotes

NuGet packages (6)

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

Package Downloads
Microsoft.ML.DnnImageFeaturizer.AlexNet
ML.NET component for pretrained AlexNet image featurization
Microsoft.ML.DnnImageFeaturizer.ResNet18
ML.NET component for pretrained ResNet18 image featurization
Microsoft.ML.DnnImageFeaturizer.ResNet50
ML.NET component for pretrained ResNet50 image featurization
Microsoft.ML.DnnImageFeaturizer.ResNet101
ML.NET component for pretrained ResNet101 image featurization
Microsoft.Psi.Onnx.Gpu
Provides components for running ONNX models.

GitHub repositories (5)

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

Repository Stars
dotnet/machinelearning
ML.NET is an open source and cross-platform machine learning framework for .NET.
dotnet-presentations/dotNETConf
Creative and technical content for running a .NET Conf local event in your community
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.

Version History

Version Downloads Last updated
1.5.2 890 9/11/2020
1.5.1 2,450 7/11/2020
1.5.0 4,561 5/26/2020
1.5.0-preview2 5,945 3/12/2020
1.5.0-preview 3,920 12/26/2019
1.4.0 96,170 11/5/2019
1.4.0-preview2 3,258 10/8/2019
1.4.0-preview 3,166 8/30/2019
1.3.1 9,423 8/6/2019
1.2.0 21,141 7/3/2019
0.13.0 3,410 6/4/2019
0.12.0 1,918 5/2/2019
0.12.0-preview 1,031 4/2/2019
0.11.0 1,568 3/6/2019