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Merge remote-tracking branch 'upstream/master' into dnn-image-feat
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‎Directory.Build.props

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<!-- Taken from https://github.com/dotnet/sdk/blob/073c98b92c81066c6c2e17c3674adbb6e833409a/src/Tasks/Microsoft.NET.Build.Tasks/targets/Microsoft.NET.Sdk.props#L41-L47 -->
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<PropertyGroup Condition="'$(Configuration)' == 'Debug-Intrinsics'">
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<DebugSymbols>true</DebugSymbols>
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<DefineConstants>$(DefineContants);DEBUG</DefineConstants>
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<Optimize>false</Optimize>
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</PropertyGroup>
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<PropertyGroup Condition="'$(Configuration)' == 'Release-Intrinsics'">

‎Microsoft.ML.sln

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@@ -17,8 +17,6 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.CpuMath", "src
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.PipelineInference", "src\Microsoft.ML.PipelineInference\Microsoft.ML.PipelineInference.csproj", "{2D7391C9-8254-4B8F-BF26-FADAF8F02F44}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.InferenceTesting", "test\Microsoft.ML.InferenceTesting\Microsoft.ML.InferenceTesting.csproj", "{E278EC99-E6EE-49FE-92E6-0A309A478D98}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Data", "src\Microsoft.ML.Data\Microsoft.ML.Data.csproj", "{AD92D96B-0E96-4F22-8DCE-892E13B1F282}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Onnx", "src\Microsoft.ML.Onnx\Microsoft.ML.Onnx.csproj", "{65D0603E-B96C-4DFC-BDD1-705891B88C18}"
@@ -183,14 +181,6 @@ Global
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{2D7391C9-8254-4B8F-BF26-FADAF8F02F44}.Release|Any CPU.Build.0 = Release|Any CPU
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{2D7391C9-8254-4B8F-BF26-FADAF8F02F44}.Release-Intrinsics|Any CPU.ActiveCfg = Release-Intrinsics|Any CPU
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{2D7391C9-8254-4B8F-BF26-FADAF8F02F44}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
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{E278EC99-E6EE-49FE-92E6-0A309A478D98}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{E278EC99-E6EE-49FE-92E6-0A309A478D98}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{E278EC99-E6EE-49FE-92E6-0A309A478D98}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug-Intrinsics|Any CPU
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{E278EC99-E6EE-49FE-92E6-0A309A478D98}.Debug-Intrinsics|Any CPU.Build.0 = Debug-Intrinsics|Any CPU
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{E278EC99-E6EE-49FE-92E6-0A309A478D98}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{E278EC99-E6EE-49FE-92E6-0A309A478D98}.Release|Any CPU.Build.0 = Release|Any CPU
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{E278EC99-E6EE-49FE-92E6-0A309A478D98}.Release-Intrinsics|Any CPU.ActiveCfg = Release-Intrinsics|Any CPU
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{E278EC99-E6EE-49FE-92E6-0A309A478D98}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
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{AD92D96B-0E96-4F22-8DCE-892E13B1F282}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{AD92D96B-0E96-4F22-8DCE-892E13B1F282}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{AD92D96B-0E96-4F22-8DCE-892E13B1F282}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug-Intrinsics|Any CPU
@@ -560,7 +550,6 @@ Global
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{EC743D1D-7691-43B7-B9B0-5F2F7018A8F6} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}
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{46F2F967-C23F-4076-858D-33F7DA9BD2DA} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{2D7391C9-8254-4B8F-BF26-FADAF8F02F44} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{E278EC99-E6EE-49FE-92E6-0A309A478D98} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}
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{AD92D96B-0E96-4F22-8DCE-892E13B1F282} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{65D0603E-B96C-4DFC-BDD1-705891B88C18} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{707BB22C-7E5F-497A-8C2F-74578F675705} = {09EADF06-BE25-4228-AB53-95AE3E15B530}

‎README.md

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@@ -16,11 +16,11 @@ Along with these ML capabilities, this first release of ML.NET also brings the f
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[![NuGet Status](https://img.shields.io/nuget/v/Microsoft.ML.svg?style=flat)](https://www.nuget.org/packages/Microsoft.ML/)
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ML.NET runs on Windows, Linux, and macOS - any platform where x64 [.NET Core](https://github.com/dotnet/core) or later is available. In addition, .NET Framework on Windows x64 is also supported.
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ML.NET runs on Windows, Linux, and macOS using [.NET Core](https://github.com/dotnet/core), or Windows using .NET Framework. 64 bit is supported on all platforms. 32 bit is supported on Windows, except for TensorFlow, LightGBM, and ONNX related functionality.
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The current release is 0.6. Check out the [release notes](docs/release-notes/0.6/release-0.6.md) to see what's new.
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The current release is 0.7. Check out the [release notes](docs/release-notes/0.7/release-0.7.md) and [blog post](https://blogs.msdn.microsoft.com/dotnet/2018/11/08/announcing-ml-net-0-7-machine-learning-net/) to see what's new.
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First, ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64-bit process.
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First, ensure you have installed [.NET Core 2.1](https://www.microsoft.com/net/learn/get-started) or later. ML.NET also works on the .NET Framework 4.6.1 or later, but 4.7.2 or later is recommended.
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Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:
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```
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| | x64 Debug | x64 Release |
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|:---|----------------:|------------------:|
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|**Linux**|[![x64-debug](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|[![x64-release](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|
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|**macOS**|[![x64-debug](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|[![x64-release](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|
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|**Windows**|[![x64-debug](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|[![x64-release](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|
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|**Linux**|[![x64-debug](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master&jobname=Linux&configuration=Build_Debug)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|[![x64-release](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master&jobname=Linux&configuration=Build_Release)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|
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|**macOS**|[![x64-debug](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master&jobname=macOS&configuration=Build_Debug)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|[![x64-release](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master&jobname=macOS&configuration=Build_Release)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|
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|**Windows**|[![x64-debug](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master&jobname=Windows_x64&configuration=Build_Debug)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|[![x64-release](https://dnceng.visualstudio.com/public/_apis/build/status/dotnet/machinelearning/MachineLearning-CI?branchName=master&jobname=Windows_x64&configuration=Build_Release)](https://dnceng.visualstudio.com/DotNet-Public/_build/latest?definitionId=104&branch=master)|
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## Contributing
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(You can find a sample of the legacy API [here](test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs)):
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```C#
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var env = new LocalEnvironment();
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var reader = TextLoader.CreateReader(env, ctx => (
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Target: ctx.LoadFloat(2),
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FeatureVector: ctx.LoadFloat(3, 6)),
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separator: ',',
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hasHeader: true);
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var data = reader.Read(new MultiFileSource(dataPath));
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var classification = new MulticlassClassificationContext(env);
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var learningPipeline = reader.MakeNewEstimator()
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.Append(r => (
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r.Target,
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Prediction: classification.Trainers.Sdca(r.Target.ToKey(), r.FeatureVector)));
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var mlContext = new MLContext();
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var reader = mlContext.Data.TextReader(new TextLoader.Arguments
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{
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Column = new[] {
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new TextLoader.Column("SentimentText", DataKind.Text, 1),
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new TextLoader.Column("Label", DataKind.Bool, 0),
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},
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HasHeader = true,
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Separator = ","
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});
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var data = reader.Read(dataPath);
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var learningPipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
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.Append(mlContext.BinaryClassification.Trainers.FastTree());
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var model = learningPipeline.Fit(data);
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```
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Now from the model we can make inferences (predictions):
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```C#
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var predictionFunc = model.MakePredictionFunction<SentimentInput, SentimentPrediction>(env);
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var predictionFunc = model.MakePredictionFunction<SentimentData, SentimentPrediction>(mlContext);
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var prediction = predictionFunc.Predict(new SentimentData
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{
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SentimentText = "Today is a great day!"
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Console.WriteLine("prediction: " + prediction.Prediction);
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```
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A cookbook that shows how to use these APIs for a variety of existing and new scenarios can be found [here](docs/code/MlNetCookBook.md).
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‎ROADMAP.md

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* _Ranking_ - problem where the goal is to automatically sort (rank) instances within a group based on ranked examples in training data
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* _Anomaly Detection_ - is also known as _outlier detection_. It is a task to identify items, events or observations which do not conform to an expected pattern in the dataset.
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* _Quantile Regression_ is a type of regression analysis. Whereas regression results in estimates that approximate the conditional mean of the response variable given certain values of the predictor variables, quantile regression aims at estimating either the conditional median or other quantiles of the response variable
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* Additional Data source support (*)
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* Additional Data Source support (*)
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* Apache Parquet
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* Native Binary high-performance format
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‎build/Dependencies.props

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<SystemReflectionEmitLightweightPackageVersion>4.3.0</SystemReflectionEmitLightweightPackageVersion>
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<SystemThreadingTasksDataflowPackageVersion>4.8.0</SystemThreadingTasksDataflowPackageVersion>
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<SystemComponentModelCompositionVersion>4.5.0</SystemComponentModelCompositionVersion>
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<MathNumericPackageVersion>4.6.0</MathNumericPackageVersion>
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</PropertyGroup>
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<!-- Other/Non-Core Product Dependencies -->
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<!-- Test-only Dependencies -->
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<PropertyGroup>
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<BenchmarkDotNetVersion>0.11.1</BenchmarkDotNetVersion>
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<BenchmarkDotNetVersion>0.11.3</BenchmarkDotNetVersion>
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<MicrosoftMLTestModelsPackageVersion>0.0.3-test</MicrosoftMLTestModelsPackageVersion>
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</PropertyGroup>
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‎build/ci/phase-template.yml

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_phaseName: ${{ parameters.name }}
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_arch: ${{ parameters.architecture }}
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queue:
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timeoutInMinutes: 40
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timeoutInMinutes: 45
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parallel: 99
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matrix:
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Build_Debug:

‎docs/building/unix-instructions.md

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On macOS a few components are needed which are not provided by a default developer setup:
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* cmake 3.10.3
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* gcc
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* libomp
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* libgdiplus
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* gettext
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* All the requirements necessary to run .NET Core 2.0 applications. To view macOS prerequisites click [here](https://docs.microsoft.com/en-us/dotnet/core/macos-prerequisites?tabs=netcore2x).
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One way of obtaining CMake and gcc is via [Homebrew](https://brew.sh):
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One way of obtaining CMake and other required libraries is via [Homebrew](https://brew.sh):
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```sh
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$ brew install cmake
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$ brew install gcc
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$ brew install cmake libomp mono-libgdiplus gettext && brew link gettext --force
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```

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