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Merge branch 'master' of https://github.com/dotnet/machinelearning into ImageClassificationVBuf
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‎Microsoft.ML.sln

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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.AutoML.Samples", "docs\samples\Microsoft.ML.AutoML.Samples\Microsoft.ML.AutoML.Samples.csproj", "{A6924919-9E37-4023-8B7F-E85C8E3CC9B3}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Samples.GPU", "docs\samples\Microsoft.ML.Samples.GPU\Microsoft.ML.Samples.GPU.csproj", "{3C8F910B-7F23-4D25-B521-6D5AC9570ADD}"
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EndProject
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@@ -1697,6 +1723,7 @@ Global
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{3C8F910B-7F23-4D25-B521-6D5AC9570ADD} = {DA452A53-2E94-4433-B08C-041EDEC729E6}
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EndGlobalSection
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GlobalSection(ExtensibilityGlobals) = postSolution
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SolutionGuid = {41165AF1-35BB-4832-A189-73060F82B01D}

‎docs/api-reference/tensorflow-usage.md

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## Using TensorFlow based APIs
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In order to run any TensorFlow based ML.Net APIs you must first add a NuGet dependency
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on the TensorFlow redist library. There are currently two versions you can use. One which is
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compiled for GPU support, and one which has CPU support only.
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### CPU only
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CPU based TensorFlow is currently supported on:
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* Linux
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* MacOS
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* Windows
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To get TensorFlow working on the CPU only all that is to take a NuGet dependency on
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SciSharp.TensorFlow.Redist v1.14.0
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### GPU support
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GPU based TensorFlow is currently supported on:
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* Windows
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#### Prerequisites
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You must have at least one CUDA compatible GPU, for a list of compatible GPUs see
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[Nvidia's Guide](https://developer.nvidia.com/cuda-gpus).
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Install [CUDA v10.0](https://developer.nvidia.com/cuda-10.0-download-archive) and [CUDNN v7.6.4](https://developer.nvidia.com/rdp/cudnn-download)
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following [Nvidia's Install guide](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html).
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#### Usage
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To use TensorFlow with GPU support take a NuGet dependency on the following package depending on your OS:
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Windows -> SciSharp.TensorFlow.Redist-Windows-GPU
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No code modification should be necessary to leverage the GPU for TensorFlow operations.
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#### Troubleshooting
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If you are not able to use your GPU after adding the GPU based TensorFlow NuGet,
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make sure that there is only a dependency on the GPU based version. If you have
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a dependency on both NuGets, the CPU based TensorFlow will run instead.

‎docs/samples/Microsoft.ML.Samples.GPU/Microsoft.ML.Samples.GPU.csproj

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>netcoreapp2.1</TargetFramework>
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<OutputType>Exe</OutputType>
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<SignAssembly>false</SignAssembly>
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<!--This ensures that we can never make the mistake of adding this as a friend assembly. Please don't remove.-->
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<PublicSign>false</PublicSign>
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<RootNamespace>Samples</RootNamespace>
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</PropertyGroup>
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<ItemGroup>
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<Compile Include="..\Microsoft.ML.Samples\Program.cs" Link="Program.cs" />
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<Compile Include="..\Microsoft.ML.Samples\Dynamic\ImageClassification\*.cs">
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<Link>Dynamic\ImageClassification\%(FileName)</Link>
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</Compile>
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<Compile Include="..\Microsoft.ML.Samples\Dynamic\TensorFlow\*.cs">
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<Link>Dynamic\TensorFlow\%(FileName)</Link>
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</Compile>
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\src\Microsoft.ML.Dnn\Microsoft.ML.Dnn.csproj" />
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<ProjectReference Include="..\..\..\src\Microsoft.ML.TensorFlow\Microsoft.ML.TensorFlow.csproj" />
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<ProjectReference Include="..\..\..\src\Microsoft.ML.SamplesUtils\Microsoft.ML.SamplesUtils.csproj" />
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<NativeAssemblyReference Include="CpuMathNative" />
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<NativeAssemblyReference Include="FastTreeNative" />
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<NativeAssemblyReference Include="MatrixFactorizationNative" />
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<NativeAssemblyReference Include="LdaNative" />
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<NativeAssemblyReference Include="SymSgdNative" />
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<NativeAssemblyReference Include="MklProxyNative" />
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</ItemGroup>
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<ItemGroup>
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<Service Include="{508349b6-6b84-4df5-91f0-309beebad82d}" />
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</ItemGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.ML.Onnx.TestModels" Version="$(MicrosoftMLOnnxTestModelsVersion)" />
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<PackageReference Include="SciSharp.TensorFlow.Redist-Windows-GPU" Version="$(TensorFlowVersion)" />
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</ItemGroup>
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<ItemGroup>
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<Content Include="$(ObjDir)DnnImageModels\ResNet18Onnx\ResNet18.onnx">
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<Link>DnnImageModels\ResNet18Onnx\ResNet18.onnx</Link>
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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</Content>
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</ItemGroup>
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<ItemGroup>
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<Content Include="$(ObjDir)DnnImageModels\ResNetPrepOnnx\ResNetPreprocess.onnx">
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<Link>DnnImageModels\ResNetPrepOnnx\ResNetPreprocess.onnx</Link>
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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</Content>
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</ItemGroup>
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<ItemGroup>
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<Folder Include="Dynamic\" />
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</ItemGroup>
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</Project>

‎docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj

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<AutoGen>True</AutoGen>
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<DependentUpon>LbfgsLogisticRegressionWithOptions.tt</DependentUpon>
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</None>
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<PackageReference Include="Microsoft.ML.TensorFlow.Redist" Version="0.10.0" />
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</ItemGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.ML.Onnx.TestModels" Version="$(MicrosoftMLOnnxTestModelsVersion)" />
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
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</ItemGroup>
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<ItemGroup>

‎src/Microsoft.ML.Dnn/DnnCatalog.cs

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/// <summary>
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/// Retrain the dnn model on new data.
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/// usage of this API requires additional NuGet dependencies on TensorFlow redist, see linked document for more information.
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/// <format type="text/markdown">
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/// <![CDATA[
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/// [!include[io](~/../docs/samples/docs/api-reference/tensorflow-usage.md)]
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/// ]]>
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/// </format>
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/// </summary>
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/// usage of this API requires additional NuGet dependencies on TensorFlow redist, see linked document for more information.
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/// <format type="text/markdown">
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/// <![CDATA[
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/// [!include[io](~/../docs/samples/docs/api-reference/tensorflow-usage.md)]
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/// ]]>
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/// </format>
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/// <param name="featuresColumnName">The name of the input features column.</param>

‎src/Microsoft.ML.Dnn/Microsoft.ML.Dnn.csproj

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="1.14.0" />
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<PackageReference Include="System.IO.FileSystem.AccessControl" Version="$(SystemIOFileSystemAccessControl)" />
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<PackageReference Include="System.Security.Principal.Windows" Version="$(SystemSecurityPrincipalWindows)" />
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<PackageReference Include="TensorFlow.NET" Version="$(TensorflowDotNETVersion)" />

‎src/Microsoft.ML.TensorFlow/TensorflowCatalog.cs

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/// usage of this API requires additional NuGet dependencies on TensorFlow redist, see linked document for more information.
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/// <format type="text/markdown">
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/// <![CDATA[
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/// [!include[io](~/../docs/samples/docs/api-reference/tensorflow-usage.md)]
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/// ]]>
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/// </format>
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/// </summary>
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‎test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj

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<ItemGroup>
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<PackageReference Include="Microsoft.ML.TestModels" Version="$(MicrosoftMLTestModelsPackageVersion)" />
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
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<NativeAssemblyReference Include="FastTreeNative" />
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<NativeAssemblyReference Include="LdaNative" />
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<NativeAssemblyReference Include="MklImports" />
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<NativeAssemblyReference Condition="'$(OS)' == 'Windows_NT'" Include="libiomp5md"/>
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<NativeAssemblyReference Condition="'$(OS)' == 'Windows_NT'" Include="libiomp5md" />
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<!-- TensorFlow is 64-bit only -->

‎test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj

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<PackageReference Include="Microsoft.ML.Onnx.TestModels" Version="$(MicrosoftMLOnnxTestModelsVersion)" />
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<PackageReference Include="Microsoft.ML.TestDatabases" Version="$(MicrosoftMLTestDatabasesPackageVersion)" />
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<PackageReference Include="Microsoft.ML.TestModels" Version="$(MicrosoftMLTestModelsPackageVersion)" />
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
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<PackageReference Include="System.Data.SqlClient" Version="$(SystemDataSqlClientVersion)" />
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</ItemGroup>
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</Project>

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