Microsoft.ML.TensorFlow
4.0.0-preview.24378.1
Prefix Reserved
This is a prerelease version of Microsoft.ML.TensorFlow.
There is a newer version of this package available.
See the version list below for details.
See the version list below for details.
dotnet add package Microsoft.ML.TensorFlow --version 4.0.0-preview.24378.1
NuGet\Install-Package Microsoft.ML.TensorFlow -Version 4.0.0-preview.24378.1
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="Microsoft.ML.TensorFlow" Version="4.0.0-preview.24378.1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add Microsoft.ML.TensorFlow --version 4.0.0-preview.24378.1
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
#r "nuget: Microsoft.ML.TensorFlow, 4.0.0-preview.24378.1"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install Microsoft.ML.TensorFlow as a Cake Addin #addin nuget:?package=Microsoft.ML.TensorFlow&version=4.0.0-preview.24378.1&prerelease // Install Microsoft.ML.TensorFlow as a Cake Tool #tool nuget:?package=Microsoft.ML.TensorFlow&version=4.0.0-preview.24378.1&prerelease
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
About
ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.
For more information, see the ML.NET documentation.
This package adds the ability to load and run pre-trained TensorFlow models as part of your ML.NET pipeline.
Key Features
- Any type of TensorFlow model can be run with all the data interop handled automatically.
How to Use
See Machine Learning Samples for an assortment of samples that show how to get started using ML.NET.
Main Types
Some of the types provided by this library are:
Microsoft.ML.Transforms.TensorFlowModelMicrosoft.ML.Transforms.TensorFlowTransformerMicrosoft.ML.Transforms.TensorFlowEstimator
Additional Documentation
Related Packages
- Main Package: Microsoft.ML
- Core data abstraction: Microsoft.ML.DataView
- LightGBM Model Support: Microsoft.ML.LightGbm
- Fast Tree: Microsoft.ML.FastTree
- Image analytics: Microsoft.ML.ImageAnalytics
- Recommender: Microsoft.ML.Recommender
- Time series: Microsoft.ML.TimeSeries
- Automatic model selection / tuning: Microsoft.ML.AutoML
- Exporting Onnx Models: Microsoft.ML.OnnxConverter
- Loading Onnx models: Microsoft.ML.OnnxTransformer
- Text tokenizers: Microsoft.ML.Tokenizers
Feedback & Contributing
Microsoft.ML is released as open source under the MIT license. Bug reports and contributions are welcome at the GitHub repository.
| Product | Versions Compatible and additional computed target framework versions. |
|---|---|
| .NET | net5.0 was computed. net5.0-windows was computed. net6.0 was computed. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
| .NET Core | netcoreapp2.0 was computed. netcoreapp2.1 was computed. netcoreapp2.2 was computed. netcoreapp3.0 was computed. netcoreapp3.1 was computed. |
| .NET Standard | netstandard2.0 is compatible. netstandard2.1 was computed. |
| .NET Framework | net461 was computed. net462 was computed. net463 was computed. net47 was computed. net471 was computed. net472 was computed. net48 was computed. net481 was computed. |
| MonoAndroid | monoandroid was computed. |
| MonoMac | monomac was computed. |
| MonoTouch | monotouch was computed. |
| Tizen | tizen40 was computed. tizen60 was computed. |
| Xamarin.iOS | xamarinios was computed. |
| Xamarin.Mac | xamarinmac was computed. |
| Xamarin.TVOS | xamarintvos was computed. |
| Xamarin.WatchOS | xamarinwatchos was computed. |
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.
-
.NETStandard 2.0
- Google.Protobuf (>= 3.19.6)
- Microsoft.ML (>= 4.0.0-preview.24378.1)
- System.IO.FileSystem.AccessControl (>= 4.5.0)
- System.Security.Principal.Windows (>= 4.5.0)
- TensorFlow.NET (>= 0.20.1)
NuGet packages (6)
Showing the top 5 NuGet packages that depend on Microsoft.ML.TensorFlow:
| Package | Downloads |
|---|---|
|
Microsoft.ML.Vision
Microsoft.ML.Vision contains high level APIs for vision tasks like image classification. |
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FenixAlliance.ACL.Dependencies
Application Component for the Alliance Business Suite. |
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Deszolate.MachineLearning
Simple machine learning wrapper for Microsoft.ML |
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MLImageLib
Package Description |
|
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Test.Modules.ImageClassification
Package Description |
GitHub repositories (4)
Showing the top 4 popular GitHub repositories that depend on Microsoft.ML.TensorFlow:
| Repository | Stars |
|---|---|
|
dotnet/samples
Sample code referenced by the .NET documentation
|
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Webreaper/Damselfly
Damselfly is a server-based Photograph Management app. The goal of Damselfly is to index an extremely large collection of images, and allow easy search and retrieval of those images, using metadata such as the IPTC keyword tags, as well as the folder and file names. Damselfly includes support for object/face detection.
|
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microsoft/TailwindTraders-Backend
Tailwind Traders Backend Services
|
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jeffprosise/ML.NET
Code samples utilizing Microsoft's ML.NET machine-learning library
|
| Version | Downloads | Last updated |
|---|---|---|
| 4.0.0 | 1,367 | 11/13/2024 |
| 4.0.0-preview.24522.7 | 421 | 10/23/2024 |
| 4.0.0-preview.24378.1 | 2,228 | 7/29/2024 |
| 4.0.0-preview.24271.1 | 1,124 | 5/21/2024 |
| 4.0.0-preview.24162.2 | 1,518 | 3/13/2024 |
| 3.0.1 | 67,117 | 1/18/2024 |
| 3.0.0 | 18,978 | 11/27/2023 |
| 3.0.0-preview.23511.1 | 855 | 10/13/2023 |
| 3.0.0-preview.23266.6 | 9,670 | 5/17/2023 |
| 3.0.0-preview.22621.2 | 2,177 | 12/22/2022 |
| 2.0.1 | 95,265 | 2/1/2023 |
| 2.0.1-preview.22573.9 | 1,487 | 11/24/2022 |
| 2.0.0 | 46,881 | 11/8/2022 |
| 2.0.0-preview.22551.1 | 935 | 11/1/2022 |
| 2.0.0-preview.22313.1 | 6,618 | 6/14/2022 |
| 1.7.1 | 692,443 | 3/9/2022 |
| 1.7.0 | 92,621 | 11/9/2021 |
| 1.7.0-preview.final | 485 | 10/22/2021 |
| 1.6.0 | 58,699 | 7/15/2021 |
| 1.5.5 | 85,142 | 3/4/2021 |
| 1.5.4 | 25,059 | 12/17/2020 |
| 1.5.2 | 69,005 | 9/11/2020 |
| 1.5.1 | 18,388 | 7/11/2020 |
| 1.5.0 | 30,222 | 5/26/2020 |
| 1.5.0-preview2 | 19,977 | 3/12/2020 |
| 1.5.0-preview | 20,183 | 12/26/2019 |
| 1.4.0 | 143,366 | 11/5/2019 |
| 1.4.0-preview2 | 4,082 | 10/8/2019 |
| 1.4.0-preview | 3,401 | 8/30/2019 |
| 1.3.1 | 11,326 | 8/6/2019 |
| 1.2.0 | 25,165 | 7/3/2019 |
| 0.13.0 | 5,564 | 6/4/2019 |
| 0.12.0 | 8,435 | 5/2/2019 |
| 0.12.0-preview | 2,383 | 4/2/2019 |
| 0.11.0 | 3,627 | 3/6/2019 |
| 0.10.0 | 3,205 | 2/5/2019 |
| 0.9.0 | 2,207 | 1/8/2019 |
| 0.8.0 | 2,359 | 12/4/2018 |
| 0.7.0 | 3,796 | 11/6/2018 |
| 0.6.0 | 4,427 | 10/2/2018 |
| 0.5.0 | 3,044 | 9/5/2018 |