1. Introduce ONNX conversion as an extention to MLContext

2. Address minor comments

Remove two best friends

Author: wschin

src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs

@@ -17,12 +17,15 @@ public sealed class ModelOperationsCatalog
 
         public ExplainabilityTransforms Explainability { get; }
 
+        public PortabilityTransforms Portability { get; }
+
         internal ModelOperationsCatalog(IHostEnvironment env)
         {
             Contracts.AssertValue(env);
             Environment = env;
 
             Explainability = new ExplainabilityTransforms(this);
+            Portability = new PortabilityTransforms(this);
         }
 
         public abstract class SubCatalogBase
@@ -33,7 +36,6 @@ protected SubCatalogBase(ModelOperationsCatalog owner)
             {
                 Environment = owner.Environment;
             }
-
         }
 
         /// <summary>
@@ -60,6 +62,17 @@ internal ExplainabilityTransforms(ModelOperationsCatalog owner) : base(owner)
             }
         }
 
+        /// <summary>
+        /// The catalog of model protability operations. Member function of this classes are able to convert the associated object to a protable format,
+        /// so that the fitted pipeline can easily be depolyed to other platforms. Currently, the only supported format is ONNX (https://github.com/onnx/onnx).
+        /// </summary>
+        public sealed class PortabilityTransforms : SubCatalogBase
+        {
+            internal PortabilityTransforms(ModelOperationsCatalog owner) : base(owner)
+            {
+            }
+        }
+
         /// <summary>
         /// Create a prediction engine for one-time prediction.
         /// </summary>

src/Microsoft.ML.Onnx/ProtabilityCatalog.cs

@@ -0,0 +1,32 @@
+using System.Collections.Generic;
+using Microsoft.ML.Core.Data;
+using Microsoft.ML.Data;
+using Microsoft.ML.Model.Onnx;
+using Microsoft.ML.UniversalModelFormat.Onnx;
+
+namespace Microsoft.ML
+{
+    public static class ProtabilityCatalog
+    {
+        /// <summary>
+        /// Convert the specified <see cref="ITransformer"/> to ONNX format. Note that ONNX uses Google's Protobuf so the returned value is a Protobuf object.
+        /// </summary>
+        /// <param name="catalog">A field in <see cref="MLContext"/> which this function associated with.</param>
+        /// <param name="transform">The <see cref="ITransformer"/> that will be converted into ONNX format.</param>
+        /// <param name="inputData">The input of the specified transform.</param>
+        /// <returns></returns>
+        public static ModelProto ConvertToOnnx(this ModelOperationsCatalog.PortabilityTransforms catalog, ITransformer transform, IDataView inputData)
+        {
+            var env = new MLContext(seed: 1);
+            var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.microsoft", OnnxVersion.Stable);
+            var outputData = transform.Transform(inputData);
+            IDataView root = null;
+            IDataView sink = null;
+            LinkedList<ITransformCanSaveOnnx> transforms = null;
+            using (var ch = (env as IChannelProvider).Start("ONNX conversion"))
+                SaveOnnxCommand.GetPipe(ctx, ch, outputData, out root, out sink, out transforms);
+
+            return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, root, sink, transforms, null, null);
+        }
+    }
+}

src/Microsoft.ML.Onnx/SaveOnnxCommand.cs

@@ -114,7 +114,6 @@ public override void Run()
             }
         }
 
-        [BestFriend]
         internal static void GetPipe(OnnxContextImpl ctx, IChannel ch, IDataView end, out IDataView source, out IDataView trueEnd, out LinkedList<ITransformCanSaveOnnx> transforms)
         {
             Contracts.AssertValue(end);
@@ -140,7 +139,6 @@ internal static void GetPipe(OnnxContextImpl ctx, IChannel ch, IDataView end, ou
             Contracts.AssertValue(source);
         }
 
-        [BestFriend]
         internal static ModelProto ConvertTransformListToOnnxModel(OnnxContextImpl ctx, IDataView inputData, IDataView outputData,
             LinkedList<ITransformCanSaveOnnx> transforms, HashSet<string> inputColumnNamesToDrop=null, HashSet<string> outputColumnNamesToDrop=null)
         {

src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs

@@ -1,11 +1,9 @@
-using System.Collections.Generic;
-using System.IO;
+using System.IO;
 using System.Linq;
 using System.Runtime.InteropServices;
 using System.Text.RegularExpressions;
 using Google.Protobuf;
 using Microsoft.ML.Data;
-using Microsoft.ML.Model.Onnx;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.Transforms;
 using Microsoft.ML.UniversalModelFormat.Onnx;
@@ -43,7 +41,7 @@ public void SimpleEndToEndOnnxConversionTest()
 
             // Step 1: Create and train a ML.NET pipeline.
             var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
-            var mlContext = new MLContext();
+            var mlContext = new MLContext(seed: 1, conc: 1);
             var data = mlContext.Data.ReadFromTextFile<AdultData>(trainDataPath,
                 hasHeader: true,
                 separatorChar: ';'
@@ -57,7 +55,7 @@ public void SimpleEndToEndOnnxConversionTest()
             var transformedData = model.Transform(data);
 
             // Step 2: Convert ML.NET model to ONNX format and save it as a file.
-            var onnxModel = TransformerChainOnnxConverter.Convert(model, data);
+            var onnxModel = mlContext.Model.Portability.ConvertToOnnx(model, data);
             var onnxFileName = "model.onnx";
             var onnxModelPath = GetOutputPath(onnxFileName);
             SaveOnnxModel(onnxModel, onnxModelPath, null);
@@ -93,7 +91,7 @@ public void KmeansOnnxConversionTest()
 
             // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
             // as a catalog of available operations and as the source of randomness.
-            var mlContext = new MLContext(seed: 1);
+            var mlContext = new MLContext(seed: 1, conc: 1);
 
             string dataPath = GetDataPath("breast-cancer.txt");
             // Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed).
@@ -113,7 +111,7 @@ public void KmeansOnnxConversionTest()
             var model = pipeline.Fit(data);
             var transformedData = model.Transform(data);
 
-            var onnxModel = TransformerChainOnnxConverter.Convert(model, data);
+            var onnxModel = mlContext.Model.Portability.ConvertToOnnx(model, data);
 
             var onnxFileName = "model.onnx";
             var onnxModelPath = GetOutputPath(onnxFileName);