From 47ef9051e590e285fb0fef1869d4677540c1dab8 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Tue, 3 Mar 2020 15:41:19 -0800
Subject: [PATCH 01/22] Update Onnx version to 1.2

---
 build/Dependencies.props | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/build/Dependencies.props b/build/Dependencies.props
index 46c90c481e..6ca726efe5 100644
--- a/build/Dependencies.props
+++ b/build/Dependencies.props
@@ -16,7 +16,7 @@
     <GoogleProtobufPackageVersion>3.10.1</GoogleProtobufPackageVersion>
     <LightGBMPackageVersion>2.2.3</LightGBMPackageVersion>
     <MicrosoftExtensionsPackageVersion>2.1.0</MicrosoftExtensionsPackageVersion>
-    <MicrosoftMLOnnxRuntimePackageVersion>1.1.2</MicrosoftMLOnnxRuntimePackageVersion>
+    <MicrosoftMLOnnxRuntimePackageVersion>1.2</MicrosoftMLOnnxRuntimePackageVersion>
     <MlNetMklDepsPackageVersion>0.0.0.9</MlNetMklDepsPackageVersion>
     <ParquetDotNetPackageVersion>2.1.3</ParquetDotNetPackageVersion>
     <SystemDrawingCommonPackageVersion>4.5.0</SystemDrawingCommonPackageVersion>

From 7f5c219a4914bea148b66f99cbee38a6b9a34700 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Tue, 3 Mar 2020 15:42:13 -0800
Subject: [PATCH 02/22] Added onnx 1.2 private feed

---
 Directory.Build.props | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/Directory.Build.props b/Directory.Build.props
index 62d5272a9b..f09591fdd4 100644
--- a/Directory.Build.props
+++ b/Directory.Build.props
@@ -19,7 +19,7 @@
       https://dotnetfeed.blob.core.windows.net/dotnet-core/index.json;
       https://dotnet.myget.org/F/dotnet-core/api/v3/index.json;
       https://dotnet.myget.org/F/roslyn-analyzers/api/v3/index.json;
-      https://pkgs.dev.azure.com/dnceng/public/_packaging/MachineLearning/nuget/v3/index.json;
+      \\LOTUS-WIN\nuget\1.2\GPU
     </RestoreSources>
   </PropertyGroup>
 
@@ -42,7 +42,7 @@
     <OutputPath Condition="'$(OutputPath)'==''">$(BaseOutputPath)$(PlatformConfig)\$(MSBuildProjectName)\</OutputPath>
 
     <PackageAssetsPath>$(ObjDir)/packages/</PackageAssetsPath>
-    
+
     <PackageOutputPath Condition="'$(PackageOutputPath)'=='' and '$(NonShippingPackage)' == 'true'">$(BinDir)packages_noship/</PackageOutputPath>
     <PackageOutputPath Condition="'$(PackageOutputPath)'==''">$(BinDir)packages/</PackageOutputPath>
 
@@ -55,7 +55,7 @@
     <ToolsDir Condition="'$(ToolsDir)'==''">$(RepoRoot)Tools/</ToolsDir>
   </PropertyGroup>
 
-  <Import Project="$(ToolsDir)BuildVersion.targets" 
+  <Import Project="$(ToolsDir)BuildVersion.targets"
           Condition="Exists('$(ToolsDir)BuildVersion.targets')" />
 
   <!-- Version properties -->
@@ -87,16 +87,16 @@
     <SourceRevisionId>$(LatestCommit)</SourceRevisionId>
   </PropertyGroup>
 
-  <Import Project="$(ToolsDir)versioning.props" 
+  <Import Project="$(ToolsDir)versioning.props"
           Condition="Exists('$(ToolsDir)versioning.props') and '$(DisableImportVersioningProps)' != 'true'" />
- 
+
   <!-- Language configuration -->
   <PropertyGroup>
     <LangVersion Condition="'$(MSBuildProjectExtension)'=='.csproj'">8.0</LangVersion>
     <LangVersion Condition="'$(MSBuildProjectExtension)'=='.fsproj'">4.7</LangVersion>
     <TreatWarningsAsErrors>true</TreatWarningsAsErrors>
   </PropertyGroup>
-  
+
   <PropertyGroup>
     <RunningOnUnix Condition="'$(OS)'!='Windows_NT'">true</RunningOnUnix>
   </PropertyGroup>

From e09c233b4504302c69d00ed17fbd001c63fb5fae Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Tue, 3 Mar 2020 15:43:00 -0800
Subject: [PATCH 03/22] Added dependencies to GPU package only

---
 .../Microsoft.ML.OnnxTransformer.nupkgproj                      | 2 +-
 .../Microsoft.ML.OnnxTransformer.csproj                         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pkg/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.nupkgproj b/pkg/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.nupkgproj
index c924ef4aba..9bb73233af 100644
--- a/pkg/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.nupkgproj
+++ b/pkg/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.nupkgproj
@@ -8,7 +8,7 @@
   <ItemGroup>
     <ProjectReference Include="../Microsoft.ML/Microsoft.ML.nupkgproj" />
     <PackageReference Include="Google.Protobuf" Version="$(GoogleProtobufPackageVersion)" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)"/>
+    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)"/>
   </ItemGroup>
 
 </Project>
diff --git a/src/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.csproj b/src/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.csproj
index d2f51a9429..13ca564765 100644
--- a/src/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.csproj
+++ b/src/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.csproj
@@ -9,7 +9,7 @@
   <ItemGroup>
     <ProjectReference Include="..\Microsoft.ML.Core\Microsoft.ML.Core.csproj" />
     <ProjectReference Include="..\Microsoft.ML.Data\Microsoft.ML.Data.csproj" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
     <PackageReference Include="Google.Protobuf" Version="$(GoogleProtobufPackageVersion)" />
   </ItemGroup>
 

From 68e719f3869acada04213cecc7671e6baef6005f Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Tue, 3 Mar 2020 15:43:41 -0800
Subject: [PATCH 04/22] Actually use gpuDeviceId when applying ONNX model

---
 src/Microsoft.ML.OnnxTransformer/OnnxUtils.cs | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/src/Microsoft.ML.OnnxTransformer/OnnxUtils.cs b/src/Microsoft.ML.OnnxTransformer/OnnxUtils.cs
index a0fe800860..ca9110260c 100644
--- a/src/Microsoft.ML.OnnxTransformer/OnnxUtils.cs
+++ b/src/Microsoft.ML.OnnxTransformer/OnnxUtils.cs
@@ -179,9 +179,19 @@ public OnnxModel(string modelFile, int? gpuDeviceId = null, bool fallbackToCpu =
 
             if (gpuDeviceId != null)
             {
-                // The onnxruntime v1.0 currently does not support running on the GPU on all of ML.NET's supported platforms.
-                // This code path will be re-enabled when there is appropriate support in onnxruntime
-                throw new NotSupportedException("Running Onnx models on a GPU is temporarily not supported!");
+                try
+                {
+                    _session = new InferenceSession(modelFile,
+                        SessionOptions.MakeSessionOptionWithCudaProvider(gpuDeviceId.Value));
+                }
+                catch(OnnxRuntimeException)
+                {
+                    if (fallbackToCpu)
+                        _session = new InferenceSession(modelFile);
+                    else
+                        // If called from OnnxTransform, is caught and rethrown
+                        throw;
+                }
             }
             else
             {

From 94e19673a86c464012a5a7c2dea89e5e754d48c9 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Tue, 3 Mar 2020 15:43:56 -0800
Subject: [PATCH 05/22] Add gpuDeviceId parameters to OnnxConversionTests

---
 test/Microsoft.ML.Tests/OnnxConversionTest.cs | 56 ++++++++++---------
 1 file changed, 29 insertions(+), 27 deletions(-)

diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
index 9801284e8b..7087698ffd 100644
--- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs
+++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
@@ -35,6 +35,8 @@ namespace Microsoft.ML.Tests
 {
     public class OnnxConversionTest : BaseTestBaseline
     {
+        private int _gpuid = 0;
+
         private class AdultData
         {
             [LoadColumn(0, 10), ColumnName("FeatureVector")]
@@ -89,7 +91,7 @@ public void SimpleEndToEndOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
 
@@ -180,7 +182,7 @@ public void KmeansOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -235,7 +237,7 @@ public void RegressionTrainersOnnxConversionTest()
                     var onnxModelPath = GetOutputPath(onnxFileName);
                     SaveOnnxModel(onnxModel, onnxModelPath, null);
 
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -296,7 +298,7 @@ public void BinaryClassificationTrainersOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3); //compare scores
@@ -329,7 +331,7 @@ public void TestVectorWhiteningOnnxConversionTest()
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("whitened1", "whitened1", transformedData, onnxResult);
@@ -382,7 +384,7 @@ public void PlattCalibratorOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -429,7 +431,7 @@ public void PlattCalibratorOnnxConversionTest2()
             // Compare model scores produced by ML.NET and ONNX's runtime.
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Probability", "Probability", transformedData, onnxResult, 3); //compare probabilities
@@ -462,7 +464,7 @@ public void TextNormalizingOnnxConversionTest()
             if (IsOnnxRuntimeSupported() && !RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ReadOnlyMemory<char>>("NormText", "NormText", transformedData, onnxResult);
@@ -511,7 +513,7 @@ public void LpNormOnnxConversionTest(
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("Features", "Features", transformedData, onnxResult, 3);
@@ -578,7 +580,7 @@ public void KeyToVectorWithBagOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -899,7 +901,7 @@ public void ConcatenateOnnxConversionTest()
                 var onnxModelPath = GetOutputPath(onnxModelName);
                 SaveOnnxModel(onnxModel, onnxModelPath, null);
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<double>("Features", "Features", transformedData, onnxResult);
@@ -951,7 +953,7 @@ public void RemoveVariablesInPipelineTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(data);
                     var onnxResult = onnxTransformer.Transform(data);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -1016,7 +1018,7 @@ public void TokenizingByCharactersOnnxConversionTest(bool useMarkerCharacters)
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ushort>("TokenizedText", "TokenizedText", transformedData, onnxResult);
@@ -1091,7 +1093,7 @@ public void OnnxTypeConversionTest(DataKind fromKind, DataKind toKind)
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 
@@ -1128,7 +1130,7 @@ public void PcaOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("pca", "pca", transformedData, onnxResult);
@@ -1187,7 +1189,7 @@ public void IndicateMissingValuesOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<int>("MissingIndicator", "MissingIndicator", transformedData, onnxResult);
@@ -1230,7 +1232,7 @@ public void ValueToKeyMappingOnnxConversionTest(DataKind valueType)
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<uint>("Key", "Key", mlnetResult, onnxResult);
@@ -1279,7 +1281,7 @@ public void KeyToValueMappingOnnxConversionTest(DataKind valueType)
 
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareResults("Value", "Value", mlnetResult, onnxResult);
@@ -1320,7 +1322,7 @@ public void WordTokenizerOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ReadOnlyMemory<char>>("Tokens", "Tokens", transformedData, onnxResult);
@@ -1384,7 +1386,7 @@ public void NgramOnnxConversionTest(
 
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     var columnName = i == pipelines.Length - 1 ? "Tokens" : "NGrams";
@@ -1452,7 +1454,7 @@ public void OptionalColumnOnnxTest(DataKind dataKind)
             {
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareResults("Label", "Label", outputData, onnxResult);
@@ -1519,7 +1521,7 @@ public void MulticlassTrainersOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<uint>("PredictedLabel", "PredictedLabel", transformedData, onnxResult);
@@ -1552,7 +1554,7 @@ public void CopyColumnsOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("Target", "Target1", transformedData, onnxResult);
@@ -1613,7 +1615,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
             if (IsOnnxRuntimeSupported())
             {
                 // Step 5: Apply Onnx Model
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxResult = onnxEstimator.Fit(reloadedData).Transform(reloadedData);
 
                 // Step 6: Compare results to an onnx model created using the mappedData IDataView
@@ -1625,7 +1627,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
                 string onnxModelPath2 = GetOutputPath("onnxmodel2-kdvt-as-uint32.onnx");
                 using (FileStream stream = new FileStream(onnxModelPath2, FileMode.Create))
                     mlContext.Model.ConvertToOnnx(model, mappedData, stream);
-                var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2);
+                var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2, gpuDeviceId: _gpuid);
                 var onnxResult2 = onnxEstimator2.Fit(originalData).Transform(originalData);
 
                 var stdSuffix = ".output";
@@ -1678,7 +1680,7 @@ public void FeatureSelectionOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("FeatureSelectMIScalarFloat", "FeatureSelectMIScalarFloat", transformedData, onnxResult);
@@ -1726,7 +1728,7 @@ public void SelectColumnsOnnxTest()
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 

From c91dd19c6c6858af08dced526b91132cccf43edb Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Wed, 4 Mar 2020 14:16:00 -0800
Subject: [PATCH 06/22] Use OnnxRuntime.Managed in OnnxTransformer

---
 .../Microsoft.ML.OnnxTransformer.nupkgproj                      | 2 +-
 .../Microsoft.ML.OnnxTransformer.csproj                         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pkg/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.nupkgproj b/pkg/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.nupkgproj
index 9bb73233af..3c7d9f2ccd 100644
--- a/pkg/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.nupkgproj
+++ b/pkg/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.nupkgproj
@@ -8,7 +8,7 @@
   <ItemGroup>
     <ProjectReference Include="../Microsoft.ML/Microsoft.ML.nupkgproj" />
     <PackageReference Include="Google.Protobuf" Version="$(GoogleProtobufPackageVersion)" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)"/>
+    <PackageReference Include="Microsoft.ML.OnnxRuntime.Managed" Version="$(MicrosoftMLOnnxRuntimePackageVersion)"/>
   </ItemGroup>
 
 </Project>
diff --git a/src/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.csproj b/src/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.csproj
index 13ca564765..7612f974ea 100644
--- a/src/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.csproj
+++ b/src/Microsoft.ML.OnnxTransformer/Microsoft.ML.OnnxTransformer.csproj
@@ -9,7 +9,7 @@
   <ItemGroup>
     <ProjectReference Include="..\Microsoft.ML.Core\Microsoft.ML.Core.csproj" />
     <ProjectReference Include="..\Microsoft.ML.Data\Microsoft.ML.Data.csproj" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime.Managed" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
     <PackageReference Include="Google.Protobuf" Version="$(GoogleProtobufPackageVersion)" />
   </ItemGroup>
 

From 509211944b268ee9ba92a307774fedb0ad048223 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Wed, 4 Mar 2020 14:26:50 -0800
Subject: [PATCH 07/22] Added AI Infra for ONNX 1.2 nightly nugets

---
 Directory.Build.props | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Directory.Build.props b/Directory.Build.props
index f09591fdd4..4c30f448cf 100644
--- a/Directory.Build.props
+++ b/Directory.Build.props
@@ -19,7 +19,7 @@
       https://dotnetfeed.blob.core.windows.net/dotnet-core/index.json;
       https://dotnet.myget.org/F/dotnet-core/api/v3/index.json;
       https://dotnet.myget.org/F/roslyn-analyzers/api/v3/index.json;
-      \\LOTUS-WIN\nuget\1.2\GPU
+      https://aiinfra.pkgs.visualstudio.com/_packaging/OnnxRuntime%40Local/nuget/v3/index.json;
     </RestoreSources>
   </PropertyGroup>
 

From 75427b7dc32ef53a6dbf45ad4557e6021b11d783 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Wed, 4 Mar 2020 14:27:08 -0800
Subject: [PATCH 08/22] Added onnxruntime  new references for experiment

---
 .../Microsoft.ML.Functional.Tests.csproj                         | 1 +
 test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj                | 1 +
 2 files changed, 2 insertions(+)

diff --git a/test/Microsoft.ML.Functional.Tests/Microsoft.ML.Functional.Tests.csproj b/test/Microsoft.ML.Functional.Tests/Microsoft.ML.Functional.Tests.csproj
index 2e31450e8d..624384d9b5 100644
--- a/test/Microsoft.ML.Functional.Tests/Microsoft.ML.Functional.Tests.csproj
+++ b/test/Microsoft.ML.Functional.Tests/Microsoft.ML.Functional.Tests.csproj
@@ -40,6 +40,7 @@
 
   <ItemGroup>
     <PackageReference Include="Microsoft.ML.Onnx.TestModels" Version="$(MicrosoftMLOnnxTestModelsVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
   <ItemGroup>
diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
index 0e0414cf5c..39d994e8ff 100644
--- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
+++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
@@ -49,6 +49,7 @@
     <PackageReference Include="Microsoft.ML.TestModels" Version="$(MicrosoftMLTestModelsPackageVersion)" />
     <PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
     <PackageReference Include="System.Data.SqlClient" Version="$(SystemDataSqlClientVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
   <ItemGroup>

From 45b5709da2e7cdb0dad807307200be132b9abc12 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Wed, 4 Mar 2020 14:33:41 -0800
Subject: [PATCH 09/22] Updated ML.Tests dependency for experiment on CI

---
 test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
index 39d994e8ff..583fc6a853 100644
--- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
+++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
@@ -49,7 +49,7 @@
     <PackageReference Include="Microsoft.ML.TestModels" Version="$(MicrosoftMLTestModelsPackageVersion)" />
     <PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
     <PackageReference Include="System.Data.SqlClient" Version="$(SystemDataSqlClientVersion)" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
   <ItemGroup>

From 477bdfe9bb7531fecdeaf0bddd0c29d73dff1770 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Wed, 4 Mar 2020 17:09:50 -0800
Subject: [PATCH 10/22] Reverted mistake I made in Directory.Build.props

---
 Directory.Build.props | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Directory.Build.props b/Directory.Build.props
index 4c30f448cf..299ea06e7d 100644
--- a/Directory.Build.props
+++ b/Directory.Build.props
@@ -19,6 +19,7 @@
       https://dotnetfeed.blob.core.windows.net/dotnet-core/index.json;
       https://dotnet.myget.org/F/dotnet-core/api/v3/index.json;
       https://dotnet.myget.org/F/roslyn-analyzers/api/v3/index.json;
+      https://pkgs.dev.azure.com/dnceng/public/_packaging/MachineLearning/nuget/v3/index.json;
       https://aiinfra.pkgs.visualstudio.com/_packaging/OnnxRuntime%40Local/nuget/v3/index.json;
     </RestoreSources>
   </PropertyGroup>

From 0cc8006db183b77ec66dd81699aad2d63cd5afd0 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Wed, 4 Mar 2020 17:10:14 -0800
Subject: [PATCH 11/22] Update ML.Samples dependency

---
 docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj b/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj
index 812114e7a5..5951c4bbd1 100644
--- a/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj
+++ b/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj
@@ -968,6 +968,7 @@
   <ItemGroup>
     <PackageReference Include="Microsoft.ML.Onnx.TestModels" Version="$(MicrosoftMLOnnxTestModelsVersion)" />
     <PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
     <ItemGroup>

From 081b91c9c8914491acd9edca088aee56d27f51e2 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Thu, 5 Mar 2020 12:54:50 -0800
Subject: [PATCH 12/22] Update ort feed

---
 Directory.Build.props | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Directory.Build.props b/Directory.Build.props
index 299ea06e7d..bfa59b4675 100644
--- a/Directory.Build.props
+++ b/Directory.Build.props
@@ -20,7 +20,7 @@
       https://dotnet.myget.org/F/dotnet-core/api/v3/index.json;
       https://dotnet.myget.org/F/roslyn-analyzers/api/v3/index.json;
       https://pkgs.dev.azure.com/dnceng/public/_packaging/MachineLearning/nuget/v3/index.json;
-      https://aiinfra.pkgs.visualstudio.com/_packaging/OnnxRuntime%40Local/nuget/v3/index.json;
+      https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/nuget/v3/index.json;
     </RestoreSources>
   </PropertyGroup>
 

From fa5a2ceedff3495964d93559609018c0562a6647 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Thu, 5 Mar 2020 13:31:29 -0800
Subject: [PATCH 13/22] Second experiment

---
 .../Microsoft.ML.OnnxTransformerTest.csproj                     | 1 +
 test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj               | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/test/Microsoft.ML.OnnxTransformerTest/Microsoft.ML.OnnxTransformerTest.csproj b/test/Microsoft.ML.OnnxTransformerTest/Microsoft.ML.OnnxTransformerTest.csproj
index bc16e60ad3..4dcc9cdfc9 100644
--- a/test/Microsoft.ML.OnnxTransformerTest/Microsoft.ML.OnnxTransformerTest.csproj
+++ b/test/Microsoft.ML.OnnxTransformerTest/Microsoft.ML.OnnxTransformerTest.csproj
@@ -10,6 +10,7 @@
   </ItemGroup>
   <ItemGroup>
     <PackageReference Include="Microsoft.ML.Onnx.TestModels" Version="$(MicrosoftMLOnnxTestModelsVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
   <ItemGroup>
diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
index 583fc6a853..39d994e8ff 100644
--- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
+++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
@@ -49,7 +49,7 @@
     <PackageReference Include="Microsoft.ML.TestModels" Version="$(MicrosoftMLTestModelsPackageVersion)" />
     <PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
     <PackageReference Include="System.Data.SqlClient" Version="$(SystemDataSqlClientVersion)" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
   <ItemGroup>

From 2ecb7b5669b013426b93c6ee52ee75cbab1326e1 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Thu, 5 Mar 2020 14:17:40 -0800
Subject: [PATCH 14/22] Update ML.Test to depend on Onnxruntime (no GPU)

---
 test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
index 39d994e8ff..583fc6a853 100644
--- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
+++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
@@ -49,7 +49,7 @@
     <PackageReference Include="Microsoft.ML.TestModels" Version="$(MicrosoftMLTestModelsPackageVersion)" />
     <PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
     <PackageReference Include="System.Data.SqlClient" Version="$(SystemDataSqlClientVersion)" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
   <ItemGroup>

From 94741fe8d4baa885645959a0993a1fa9d0e72ca8 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Thu, 5 Mar 2020 14:18:39 -0800
Subject: [PATCH 15/22] Revert "Add gpuDeviceId parameters to
 OnnxConversionTests"

This reverts commit 94e19673a86c464012a5a7c2dea89e5e754d48c9.
---
 test/Microsoft.ML.Tests/OnnxConversionTest.cs | 56 +++++++++----------
 1 file changed, 27 insertions(+), 29 deletions(-)

diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
index 7087698ffd..9801284e8b 100644
--- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs
+++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
@@ -35,8 +35,6 @@ namespace Microsoft.ML.Tests
 {
     public class OnnxConversionTest : BaseTestBaseline
     {
-        private int _gpuid = 0;
-
         private class AdultData
         {
             [LoadColumn(0, 10), ColumnName("FeatureVector")]
@@ -91,7 +89,7 @@ public void SimpleEndToEndOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
 
@@ -182,7 +180,7 @@ public void KmeansOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -237,7 +235,7 @@ public void RegressionTrainersOnnxConversionTest()
                     var onnxModelPath = GetOutputPath(onnxFileName);
                     SaveOnnxModel(onnxModel, onnxModelPath, null);
 
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -298,7 +296,7 @@ public void BinaryClassificationTrainersOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3); //compare scores
@@ -331,7 +329,7 @@ public void TestVectorWhiteningOnnxConversionTest()
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("whitened1", "whitened1", transformedData, onnxResult);
@@ -384,7 +382,7 @@ public void PlattCalibratorOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -431,7 +429,7 @@ public void PlattCalibratorOnnxConversionTest2()
             // Compare model scores produced by ML.NET and ONNX's runtime.
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Probability", "Probability", transformedData, onnxResult, 3); //compare probabilities
@@ -464,7 +462,7 @@ public void TextNormalizingOnnxConversionTest()
             if (IsOnnxRuntimeSupported() && !RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ReadOnlyMemory<char>>("NormText", "NormText", transformedData, onnxResult);
@@ -513,7 +511,7 @@ public void LpNormOnnxConversionTest(
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("Features", "Features", transformedData, onnxResult, 3);
@@ -580,7 +578,7 @@ public void KeyToVectorWithBagOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -901,7 +899,7 @@ public void ConcatenateOnnxConversionTest()
                 var onnxModelPath = GetOutputPath(onnxModelName);
                 SaveOnnxModel(onnxModel, onnxModelPath, null);
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<double>("Features", "Features", transformedData, onnxResult);
@@ -953,7 +951,7 @@ public void RemoveVariablesInPipelineTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(data);
                     var onnxResult = onnxTransformer.Transform(data);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -1018,7 +1016,7 @@ public void TokenizingByCharactersOnnxConversionTest(bool useMarkerCharacters)
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ushort>("TokenizedText", "TokenizedText", transformedData, onnxResult);
@@ -1093,7 +1091,7 @@ public void OnnxTypeConversionTest(DataKind fromKind, DataKind toKind)
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 
@@ -1130,7 +1128,7 @@ public void PcaOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("pca", "pca", transformedData, onnxResult);
@@ -1189,7 +1187,7 @@ public void IndicateMissingValuesOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<int>("MissingIndicator", "MissingIndicator", transformedData, onnxResult);
@@ -1232,7 +1230,7 @@ public void ValueToKeyMappingOnnxConversionTest(DataKind valueType)
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<uint>("Key", "Key", mlnetResult, onnxResult);
@@ -1281,7 +1279,7 @@ public void KeyToValueMappingOnnxConversionTest(DataKind valueType)
 
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareResults("Value", "Value", mlnetResult, onnxResult);
@@ -1322,7 +1320,7 @@ public void WordTokenizerOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ReadOnlyMemory<char>>("Tokens", "Tokens", transformedData, onnxResult);
@@ -1386,7 +1384,7 @@ public void NgramOnnxConversionTest(
 
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     var columnName = i == pipelines.Length - 1 ? "Tokens" : "NGrams";
@@ -1454,7 +1452,7 @@ public void OptionalColumnOnnxTest(DataKind dataKind)
             {
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareResults("Label", "Label", outputData, onnxResult);
@@ -1521,7 +1519,7 @@ public void MulticlassTrainersOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<uint>("PredictedLabel", "PredictedLabel", transformedData, onnxResult);
@@ -1554,7 +1552,7 @@ public void CopyColumnsOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("Target", "Target1", transformedData, onnxResult);
@@ -1615,7 +1613,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
             if (IsOnnxRuntimeSupported())
             {
                 // Step 5: Apply Onnx Model
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                 var onnxResult = onnxEstimator.Fit(reloadedData).Transform(reloadedData);
 
                 // Step 6: Compare results to an onnx model created using the mappedData IDataView
@@ -1627,7 +1625,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
                 string onnxModelPath2 = GetOutputPath("onnxmodel2-kdvt-as-uint32.onnx");
                 using (FileStream stream = new FileStream(onnxModelPath2, FileMode.Create))
                     mlContext.Model.ConvertToOnnx(model, mappedData, stream);
-                var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2, gpuDeviceId: _gpuid);
+                var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2);
                 var onnxResult2 = onnxEstimator2.Fit(originalData).Transform(originalData);
 
                 var stdSuffix = ".output";
@@ -1680,7 +1678,7 @@ public void FeatureSelectionOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("FeatureSelectMIScalarFloat", "FeatureSelectMIScalarFloat", transformedData, onnxResult);
@@ -1728,7 +1726,7 @@ public void SelectColumnsOnnxTest()
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 

From d4501a0b9aa172b917f1295a152820d6061b66d8 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Mon, 9 Mar 2020 18:30:08 -0700
Subject: [PATCH 16/22] Update ORT feed

---
 Directory.Build.props | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Directory.Build.props b/Directory.Build.props
index bfa59b4675..e57bfbba2a 100644
--- a/Directory.Build.props
+++ b/Directory.Build.props
@@ -20,7 +20,7 @@
       https://dotnet.myget.org/F/dotnet-core/api/v3/index.json;
       https://dotnet.myget.org/F/roslyn-analyzers/api/v3/index.json;
       https://pkgs.dev.azure.com/dnceng/public/_packaging/MachineLearning/nuget/v3/index.json;
-      https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/nuget/v3/index.json;
+      https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Rel-Candidate/nuget/v3/index.json;
     </RestoreSources>
   </PropertyGroup>
 

From dd9f56d980051ffb5a83c23f447434b1ce7fcde8 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Mon, 9 Mar 2020 19:27:22 -0700
Subject: [PATCH 17/22] Revert "Update ML.Test to depend on Onnxruntime (no
 GPU)"

This reverts commit 2ecb7b5669b013426b93c6ee52ee75cbab1326e1.
---
 test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
index 583fc6a853..39d994e8ff 100644
--- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
+++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
@@ -49,7 +49,7 @@
     <PackageReference Include="Microsoft.ML.TestModels" Version="$(MicrosoftMLTestModelsPackageVersion)" />
     <PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
     <PackageReference Include="System.Data.SqlClient" Version="$(SystemDataSqlClientVersion)" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
   <ItemGroup>

From 68b5489a4b01ee075d0bdd44a2aa17b9de66b645 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Mon, 9 Mar 2020 19:28:57 -0700
Subject: [PATCH 18/22] Revert "Revert "Add gpuDeviceId parameters to
 OnnxConversionTests""

This reverts commit 94741fe8d4baa885645959a0993a1fa9d0e72ca8.
---
 test/Microsoft.ML.Tests/OnnxConversionTest.cs | 56 ++++++++++---------
 1 file changed, 29 insertions(+), 27 deletions(-)

diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
index 9801284e8b..7087698ffd 100644
--- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs
+++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
@@ -35,6 +35,8 @@ namespace Microsoft.ML.Tests
 {
     public class OnnxConversionTest : BaseTestBaseline
     {
+        private int _gpuid = 0;
+
         private class AdultData
         {
             [LoadColumn(0, 10), ColumnName("FeatureVector")]
@@ -89,7 +91,7 @@ public void SimpleEndToEndOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
 
@@ -180,7 +182,7 @@ public void KmeansOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -235,7 +237,7 @@ public void RegressionTrainersOnnxConversionTest()
                     var onnxModelPath = GetOutputPath(onnxFileName);
                     SaveOnnxModel(onnxModel, onnxModelPath, null);
 
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -296,7 +298,7 @@ public void BinaryClassificationTrainersOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3); //compare scores
@@ -329,7 +331,7 @@ public void TestVectorWhiteningOnnxConversionTest()
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("whitened1", "whitened1", transformedData, onnxResult);
@@ -382,7 +384,7 @@ public void PlattCalibratorOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -429,7 +431,7 @@ public void PlattCalibratorOnnxConversionTest2()
             // Compare model scores produced by ML.NET and ONNX's runtime.
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Probability", "Probability", transformedData, onnxResult, 3); //compare probabilities
@@ -462,7 +464,7 @@ public void TextNormalizingOnnxConversionTest()
             if (IsOnnxRuntimeSupported() && !RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ReadOnlyMemory<char>>("NormText", "NormText", transformedData, onnxResult);
@@ -511,7 +513,7 @@ public void LpNormOnnxConversionTest(
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("Features", "Features", transformedData, onnxResult, 3);
@@ -578,7 +580,7 @@ public void KeyToVectorWithBagOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -899,7 +901,7 @@ public void ConcatenateOnnxConversionTest()
                 var onnxModelPath = GetOutputPath(onnxModelName);
                 SaveOnnxModel(onnxModel, onnxModelPath, null);
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<double>("Features", "Features", transformedData, onnxResult);
@@ -951,7 +953,7 @@ public void RemoveVariablesInPipelineTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(data);
                     var onnxResult = onnxTransformer.Transform(data);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -1016,7 +1018,7 @@ public void TokenizingByCharactersOnnxConversionTest(bool useMarkerCharacters)
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ushort>("TokenizedText", "TokenizedText", transformedData, onnxResult);
@@ -1091,7 +1093,7 @@ public void OnnxTypeConversionTest(DataKind fromKind, DataKind toKind)
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 
@@ -1128,7 +1130,7 @@ public void PcaOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("pca", "pca", transformedData, onnxResult);
@@ -1187,7 +1189,7 @@ public void IndicateMissingValuesOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<int>("MissingIndicator", "MissingIndicator", transformedData, onnxResult);
@@ -1230,7 +1232,7 @@ public void ValueToKeyMappingOnnxConversionTest(DataKind valueType)
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<uint>("Key", "Key", mlnetResult, onnxResult);
@@ -1279,7 +1281,7 @@ public void KeyToValueMappingOnnxConversionTest(DataKind valueType)
 
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareResults("Value", "Value", mlnetResult, onnxResult);
@@ -1320,7 +1322,7 @@ public void WordTokenizerOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ReadOnlyMemory<char>>("Tokens", "Tokens", transformedData, onnxResult);
@@ -1384,7 +1386,7 @@ public void NgramOnnxConversionTest(
 
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     var columnName = i == pipelines.Length - 1 ? "Tokens" : "NGrams";
@@ -1452,7 +1454,7 @@ public void OptionalColumnOnnxTest(DataKind dataKind)
             {
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareResults("Label", "Label", outputData, onnxResult);
@@ -1519,7 +1521,7 @@ public void MulticlassTrainersOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<uint>("PredictedLabel", "PredictedLabel", transformedData, onnxResult);
@@ -1552,7 +1554,7 @@ public void CopyColumnsOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("Target", "Target1", transformedData, onnxResult);
@@ -1613,7 +1615,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
             if (IsOnnxRuntimeSupported())
             {
                 // Step 5: Apply Onnx Model
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxResult = onnxEstimator.Fit(reloadedData).Transform(reloadedData);
 
                 // Step 6: Compare results to an onnx model created using the mappedData IDataView
@@ -1625,7 +1627,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
                 string onnxModelPath2 = GetOutputPath("onnxmodel2-kdvt-as-uint32.onnx");
                 using (FileStream stream = new FileStream(onnxModelPath2, FileMode.Create))
                     mlContext.Model.ConvertToOnnx(model, mappedData, stream);
-                var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2);
+                var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2, gpuDeviceId: _gpuid);
                 var onnxResult2 = onnxEstimator2.Fit(originalData).Transform(originalData);
 
                 var stdSuffix = ".output";
@@ -1678,7 +1680,7 @@ public void FeatureSelectionOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("FeatureSelectMIScalarFloat", "FeatureSelectMIScalarFloat", transformedData, onnxResult);
@@ -1726,7 +1728,7 @@ public void SelectColumnsOnnxTest()
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 

From 60b899beac2c6d87d5b5397a622795d9ada11d55 Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Mon, 9 Mar 2020 20:10:32 -0700
Subject: [PATCH 19/22] Revert "Revert "Update ML.Test to depend on Onnxruntime
 (no GPU)""

This reverts commit dd9f56d980051ffb5a83c23f447434b1ce7fcde8.
---
 test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
index 39d994e8ff..583fc6a853 100644
--- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
+++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj
@@ -49,7 +49,7 @@
     <PackageReference Include="Microsoft.ML.TestModels" Version="$(MicrosoftMLTestModelsPackageVersion)" />
     <PackageReference Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
     <PackageReference Include="System.Data.SqlClient" Version="$(SystemDataSqlClientVersion)" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime.Gpu" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
   <ItemGroup>

From 932d6ed3d123ca53a03b6cebb22e52f6745df75a Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Mon, 9 Mar 2020 20:51:59 -0700
Subject: [PATCH 20/22] Revert "Revert "Revert "Add gpuDeviceId parameters to
 OnnxConversionTests"""

This reverts commit 68b5489a4b01ee075d0bdd44a2aa17b9de66b645.
---
 test/Microsoft.ML.Tests/OnnxConversionTest.cs | 56 +++++++++----------
 1 file changed, 27 insertions(+), 29 deletions(-)

diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
index 7087698ffd..9801284e8b 100644
--- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs
+++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
@@ -35,8 +35,6 @@ namespace Microsoft.ML.Tests
 {
     public class OnnxConversionTest : BaseTestBaseline
     {
-        private int _gpuid = 0;
-
         private class AdultData
         {
             [LoadColumn(0, 10), ColumnName("FeatureVector")]
@@ -91,7 +89,7 @@ public void SimpleEndToEndOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
 
@@ -182,7 +180,7 @@ public void KmeansOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -237,7 +235,7 @@ public void RegressionTrainersOnnxConversionTest()
                     var onnxModelPath = GetOutputPath(onnxFileName);
                     SaveOnnxModel(onnxModel, onnxModelPath, null);
 
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -298,7 +296,7 @@ public void BinaryClassificationTrainersOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3); //compare scores
@@ -331,7 +329,7 @@ public void TestVectorWhiteningOnnxConversionTest()
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("whitened1", "whitened1", transformedData, onnxResult);
@@ -384,7 +382,7 @@ public void PlattCalibratorOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -431,7 +429,7 @@ public void PlattCalibratorOnnxConversionTest2()
             // Compare model scores produced by ML.NET and ONNX's runtime.
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Probability", "Probability", transformedData, onnxResult, 3); //compare probabilities
@@ -464,7 +462,7 @@ public void TextNormalizingOnnxConversionTest()
             if (IsOnnxRuntimeSupported() && !RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ReadOnlyMemory<char>>("NormText", "NormText", transformedData, onnxResult);
@@ -513,7 +511,7 @@ public void LpNormOnnxConversionTest(
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("Features", "Features", transformedData, onnxResult, 3);
@@ -580,7 +578,7 @@ public void KeyToVectorWithBagOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -901,7 +899,7 @@ public void ConcatenateOnnxConversionTest()
                 var onnxModelPath = GetOutputPath(onnxModelName);
                 SaveOnnxModel(onnxModel, onnxModelPath, null);
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(data);
                 var onnxResult = onnxTransformer.Transform(data);
                 CompareSelectedColumns<double>("Features", "Features", transformedData, onnxResult);
@@ -953,7 +951,7 @@ public void RemoveVariablesInPipelineTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(data);
                     var onnxResult = onnxTransformer.Transform(data);
                     CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -1018,7 +1016,7 @@ public void TokenizingByCharactersOnnxConversionTest(bool useMarkerCharacters)
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ushort>("TokenizedText", "TokenizedText", transformedData, onnxResult);
@@ -1093,7 +1091,7 @@ public void OnnxTypeConversionTest(DataKind fromKind, DataKind toKind)
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 
@@ -1130,7 +1128,7 @@ public void PcaOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<float>("pca", "pca", transformedData, onnxResult);
@@ -1189,7 +1187,7 @@ public void IndicateMissingValuesOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<int>("MissingIndicator", "MissingIndicator", transformedData, onnxResult);
@@ -1232,7 +1230,7 @@ public void ValueToKeyMappingOnnxConversionTest(DataKind valueType)
 
             if (IsOnnxRuntimeSupported())
             {
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<uint>("Key", "Key", mlnetResult, onnxResult);
@@ -1281,7 +1279,7 @@ public void KeyToValueMappingOnnxConversionTest(DataKind valueType)
 
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareResults("Value", "Value", mlnetResult, onnxResult);
@@ -1322,7 +1320,7 @@ public void WordTokenizerOnnxConversionTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<ReadOnlyMemory<char>>("Tokens", "Tokens", transformedData, onnxResult);
@@ -1386,7 +1384,7 @@ public void NgramOnnxConversionTest(
 
                 if (IsOnnxRuntimeSupported())
                 {
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     var columnName = i == pipelines.Length - 1 ? "Tokens" : "NGrams";
@@ -1454,7 +1452,7 @@ public void OptionalColumnOnnxTest(DataKind dataKind)
             {
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareResults("Label", "Label", outputData, onnxResult);
@@ -1521,7 +1519,7 @@ public void MulticlassTrainersOnnxConversionTest()
                 if (IsOnnxRuntimeSupported())
                 {
                     // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                     var onnxTransformer = onnxEstimator.Fit(dataView);
                     var onnxResult = onnxTransformer.Transform(dataView);
                     CompareSelectedColumns<uint>("PredictedLabel", "PredictedLabel", transformedData, onnxResult);
@@ -1554,7 +1552,7 @@ public void CopyColumnsOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("Target", "Target1", transformedData, onnxResult);
@@ -1615,7 +1613,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
             if (IsOnnxRuntimeSupported())
             {
                 // Step 5: Apply Onnx Model
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                 var onnxResult = onnxEstimator.Fit(reloadedData).Transform(reloadedData);
 
                 // Step 6: Compare results to an onnx model created using the mappedData IDataView
@@ -1627,7 +1625,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
                 string onnxModelPath2 = GetOutputPath("onnxmodel2-kdvt-as-uint32.onnx");
                 using (FileStream stream = new FileStream(onnxModelPath2, FileMode.Create))
                     mlContext.Model.ConvertToOnnx(model, mappedData, stream);
-                var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2, gpuDeviceId: _gpuid);
+                var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2);
                 var onnxResult2 = onnxEstimator2.Fit(originalData).Transform(originalData);
 
                 var stdSuffix = ".output";
@@ -1680,7 +1678,7 @@ public void FeatureSelectionOnnxTest()
             if (IsOnnxRuntimeSupported())
             {
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
                 CompareSelectedColumns<float>("FeatureSelectMIScalarFloat", "FeatureSelectMIScalarFloat", transformedData, onnxResult);
@@ -1728,7 +1726,7 @@ public void SelectColumnsOnnxTest()
                 // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
                 string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
                 string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
-                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
                 var onnxTransformer = onnxEstimator.Fit(dataView);
                 var onnxResult = onnxTransformer.Transform(dataView);
 

From 2f30fd552f9e09b8bad6d35d0947441e1518e4ab Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Tue, 10 Mar 2020 15:37:36 -0700
Subject: [PATCH 21/22] Removed ORT custom feed

---
 Directory.Build.props | 1 -
 1 file changed, 1 deletion(-)

diff --git a/Directory.Build.props b/Directory.Build.props
index e57bfbba2a..ea17b66f8f 100644
--- a/Directory.Build.props
+++ b/Directory.Build.props
@@ -20,7 +20,6 @@
       https://dotnet.myget.org/F/dotnet-core/api/v3/index.json;
       https://dotnet.myget.org/F/roslyn-analyzers/api/v3/index.json;
       https://pkgs.dev.azure.com/dnceng/public/_packaging/MachineLearning/nuget/v3/index.json;
-      https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Rel-Candidate/nuget/v3/index.json;
     </RestoreSources>
   </PropertyGroup>
 

From 98c0dff0d8e11d5a0602aa34312d9470f585d68d Mon Sep 17 00:00:00 2001
From: Antonio Velazquez <anvelazq@microsoft.com>
Date: Tue, 10 Mar 2020 15:42:25 -0700
Subject: [PATCH 22/22] Removed whitespaces

---
 Directory.Build.props | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/Directory.Build.props b/Directory.Build.props
index ea17b66f8f..62d5272a9b 100644
--- a/Directory.Build.props
+++ b/Directory.Build.props
@@ -42,7 +42,7 @@
     <OutputPath Condition="'$(OutputPath)'==''">$(BaseOutputPath)$(PlatformConfig)\$(MSBuildProjectName)\</OutputPath>
 
     <PackageAssetsPath>$(ObjDir)/packages/</PackageAssetsPath>
-
+    
     <PackageOutputPath Condition="'$(PackageOutputPath)'=='' and '$(NonShippingPackage)' == 'true'">$(BinDir)packages_noship/</PackageOutputPath>
     <PackageOutputPath Condition="'$(PackageOutputPath)'==''">$(BinDir)packages/</PackageOutputPath>
 
@@ -55,7 +55,7 @@
     <ToolsDir Condition="'$(ToolsDir)'==''">$(RepoRoot)Tools/</ToolsDir>
   </PropertyGroup>
 
-  <Import Project="$(ToolsDir)BuildVersion.targets"
+  <Import Project="$(ToolsDir)BuildVersion.targets" 
           Condition="Exists('$(ToolsDir)BuildVersion.targets')" />
 
   <!-- Version properties -->
@@ -87,16 +87,16 @@
     <SourceRevisionId>$(LatestCommit)</SourceRevisionId>
   </PropertyGroup>
 
-  <Import Project="$(ToolsDir)versioning.props"
+  <Import Project="$(ToolsDir)versioning.props" 
           Condition="Exists('$(ToolsDir)versioning.props') and '$(DisableImportVersioningProps)' != 'true'" />
-
+ 
   <!-- Language configuration -->
   <PropertyGroup>
     <LangVersion Condition="'$(MSBuildProjectExtension)'=='.csproj'">8.0</LangVersion>
     <LangVersion Condition="'$(MSBuildProjectExtension)'=='.fsproj'">4.7</LangVersion>
     <TreatWarningsAsErrors>true</TreatWarningsAsErrors>
   </PropertyGroup>
-
+  
   <PropertyGroup>
     <RunningOnUnix Condition="'$(OS)'!='Windows_NT'">true</RunningOnUnix>
   </PropertyGroup>