MXnet to onnx to ml.net

Cosmin Catalin Sanda

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4   April 2019

TOPICs

  • About me
  • Machine Learning in .NET
  • The ONNX interchange format
  • ML workflow in AWS

ABOUT ME

Cosmin Catalin Sanda

             Data Scientist and Engineer at AudienceProject

Blogging at https://cosminsanda.com

Machine learning in .NET before 2018

...THEN something happened

In early 2018 Microsoft open-sourced ML.NET

  • Heavily supported by Microsoft
  • A great generalist framework
  • No deep learning capabilities
  • ONNX support

What is ONNX

Open source interchange format between ML models

  • Model using one language/library you want and Infer using another language/library you want
  • Performance
  • Some limitations apply 

Typical Workflow

  • Model a regression problem using MXNet
  • Export awesome model to ONNX
  • Use ONNX model for real-time inference in a .NET application

Unfortunately there's a but

ONNX scoring in ML.NET works only on Windows X64 platforms (currently)

ML pipeline components

  • Amazon Sagemaker for building the model
  • Amazon S3 for storing the model
  • AWS EC2 for building the application image using Windows containers
  • AWS Elastic Container Registry for storing the Docker image
  • AWS Elastic Container Service for running the Docker image
  • Remote Desktop Connection client
  • Internet browser

Amazon SAGEMAKER

MXNet Model & DATA

from mxnet.gluon.nn import HybridSequential, Dense, Dropout

net = HybridSequential()
with net.name_scope():
    net.add(Dense(9))
    net.add(Dropout(.25))
    net.add(Dense(16))
    net.add(Dropout(.25))
    net.add(Dense(1))

Kaggle's New York City Taxi Fare Prediction

MXNET to onnx

from mxnet.contrib import onnx as onnx_mxnet

def save(net, model_dir):
    net.export("model", epoch=4)
    onnx_mxnet.export_model(sym="model-symbol.json",
                            params="model-0004.params",
                            input_shape=[(1, 4)],
                            input_type=np.float32,
                            onnx_file_path="{}/model.onnx".format(model_dir),
                            verbose=True)

Model artifact (archive) is uploaded to S3 by Sagemaker

building the .net application

  • Use your own Windows x64 installation
  • Provision a Window EC2 instance

Two options for developing

PROVISIONED tools

  • Docker
  • AWS CLI

Making predictions in ml.net using onnx

MLContext _env = env;
string _onnxFilePath = onnxFilePath;

var pipeline = new ColumnConcatenatingEstimator(_env, "Features", "RateCode", "PassengerCount", "TripTime", "TripDistance")
    .Append(new ColumnSelectingEstimator(_env, "Features"))
    .Append(new OnnxScoringEstimator(_env, _onnxFilePath, "Features", "Estimate"))
    .Append(new ColumnSelectingEstimator(_env, "Estimate"))
    .Append(new CustomMappingEstimator<RawPrediction, FlatPrediction>(_env, contractName: "OnnxPredictionExtractor",
        mapAction: (input, output) =>
        {
            output.Estimate = input.Estimate[0];
        }));

var transformer = pipeline.Fit(data);

Dockerizing

FROM microsoft/dotnet:sdk AS build-env
COPY inference/*.csproj /src/
COPY inference/*.cs /src/
WORKDIR /src
RUN dotnet restore --verbosity normal
RUN dotnet publish -c Release -o /app/ -r win10-x64

FROM microsoft/dotnet:aspnetcore-runtime
COPY --from=build-env /app/ /app/
COPY models/model.onnx /models/
COPY lib/* /app/
WORKDIR /app
CMD ["dotnet", "inference.dll", "--onnx", "/models/model.onnx"]

RUNNING inference on AWS

  • Create Docker registry in AWS ECR
  • Push local image to ECR
  • Create Windows Docker cluster in AWS ECS
  • Configure task and run the application

KEY takeAways

  • ONNX gives flexibility and power when using deep learning libraries
  • Docker works for Windows native apps
  • ML.NET is a great framework for doing cross-platform development on .NET Core

MXNet to ONNX to ML.NET

By Cosmin Cătălin Sanda

MXNet to ONNX to ML.NET

Learn how to use the power of MXNet modes when doing inference in ML.NET

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