See the version list below for details.
Install-Package Microsoft.ML.Scoring -Version 1.1.0
dotnet add package Microsoft.ML.Scoring --version 1.1.0
<PackageReference Include="Microsoft.ML.Scoring" Version="1.1.0" />
paket add Microsoft.ML.Scoring --version 1.1.0
#r "nuget: Microsoft.ML.Scoring, 1.1.0"
// Install Microsoft.ML.Scoring as a Cake Addin #addin nuget:?package=Microsoft.ML.Scoring&version=1.1.0 // Install Microsoft.ML.Scoring as a Cake Tool #tool nuget:?package=Microsoft.ML.Scoring&version=1.1.0
Microsoft.ML.Scoring library is a Model Inference Library that can used for scoring DNN models saved in either ONNX or TensorFlow format.. The library is .NET Standard 1.3 compatible library, with API for both managed as well as native application development. The following versions of ONNX and TensorFlow models are supported:
- ONNX Version: 1.2
- CPU only, with MKLDNN acceleration
- TensorFlow Version: 1.5.0
- CPU only, with MKL acceleration
- Model formats: checkpoint and saved model only. Frozen model is not supported.
- For TensorFlow Checkpoint - all files including a checkpoint file, a meta file, and data files should be stored under the same folder. If your model contains TensorFlow lookup operations, please copy your vocabulary file to this folder as well.
- For TensorFlow SavedModel - all files including a pb file, data files and asset files should be stored under the same folder. Please do not import SavedModel files that were previously optimized by the library -this can result in unexpected errors.
Note: This library contains third party software. For more details, refer to ThirdPartyNotices.txt in the package.
This package has no dependencies.
This package is not used by any NuGet packages.
GitHub repositories (1)
Showing the top 1 popular GitHub repositories that depend on Microsoft.ML.Scoring:
Samples for getting started with deep learning across TensorFlow, CNTK, Theano and more.
New additions in the version 1.1.0 release are -- .Net standard C# module; faster ONNX runtime with mkldnn acceleration, supporting ONNX models of version 1.2