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Update to include more samples (#1381)

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* Update to include more samples

* Link fix
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Faith Xu 2019-07-22 16:48:26 -07:00 committed by GitHub
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@ -26,6 +26,7 @@
* [Extensibility Options](#extensibility-options)
**[Contributions and Feedback](#contribute)**
**[License](#license)**
***
## Key Features
@ -72,7 +73,7 @@ Additional dockerfiles for some features can be found [here](https://github.com/
* [Python](https://aka.ms/onnxruntime-python)
* [C](docs/C_API.md)
* [C#](docs/CSharp_API.md)
* [C++](onnxruntime/core/session/inference_session.h)
* [C++](https://github.com/microsoft/onnxruntime/blob/master/include/onnxruntime/core/session/onnxruntime_cxx_api.h)
### Official Builds
| | CPU (MLAS+Eigen) | CPU (MKL-ML) | GPU (CUDA)
@ -105,7 +106,7 @@ Dockerfiles are available [here](https://github.com/microsoft/onnxruntime/tree/f
* The [ONNX Model Zoo](https://github.com/onnx/models) has popular ready-to-use pre-trained models.
* To export or convert a trained ONNX model trained from various frameworks, see [ONNX Tutorials](https://github.com/onnx/tutorials). Versioning comptability information can be found under [Versioning](docs/Versioning.md#tool-compatibility)
* Other services that can be used to create ONNX models include:
* [AutoML from AzureML SDK](aka.ms/automatedmldocs)
* [AutoML from AzureML SDK](https://aka.ms/automatedmldocs)
* [Custom Vision](https://www.customvision.ai/)
* [E2E training on Azure Machine Learning Services](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-onnx)
@ -118,23 +119,28 @@ ONNX Runtime can be deployed to the cloud for model inferencing using [Azure Mac
### Python
* [Basic Inferencing Sample](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/simple_onnxruntime_inference.ipynb)
* [Inferencing (Resnet50)](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/resnet50_modelzoo_onnxruntime_inference.ipynb)
* [Inferencing samples](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem/inference_demos) using [ONNX-Ecosystem Docker image](https://github.com/onnx/onnx-docker/tree/master/onnx-ecosystem)
* [Train, Convert, and Inference a SKL pipeline](https://microsoft.github.io/onnxruntime/auto_examples/plot_train_convert_predict.html#sphx-glr-auto-examples-plot-train-convert-predict-py)
* [Convert and Inference a Keras model](https://microsoft.github.io/onnxruntime/auto_examples/plot_dl_keras.html#sphx-glr-auto-examples-plot-dl-keras-py)
* [ONNX Runtime Server: SSD Single Shot MultiBox Detector](https://github.com/onnx/tutorials/blob/master/tutorials/OnnxRuntimeServerSSDModel.ipynb)
* [Running ONNX model tests](https://github.com/microsoft/onnxruntime/blob/master/docs/Model_Test.md)
**Deployment with AzureML**
* Inferencing: [Inferencing Facial Expression Recognition](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb), [Inferencing MNIST Handwritten Digits](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb), [ Resnet50 Image Classification](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb), [TinyYolo](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb)
* [Train and Inference MNIST from Pytorch](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb)
* [FER+ on Azure Kubernetes Service with TensorRT](https://github.com/microsoft/onnxruntime/blob/master/docs/python/notebooks/onnx-inference-byoc-gpu-cpu-aks.ipynb)
### C#
* [Inferencing Tutorial](https://github.com/microsoft/onnxruntime/blob/master/docs/CSharp_API.md#getting-started)
### C
* [Inferencing (SqueezeNet)](https://github.com/microsoft/onnxruntime/blob/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp)
### C++
* [Inferencing (SqueezeNet)](https://github.com/microsoft/onnxruntime/blob/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp)
### C/C++
* [Basic Inferencing (SqueezeNet) - C](https://github.com/microsoft/onnxruntime/blob/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp)
* [Basic Inferencing (SqueezeNet) - C++](https://github.com/microsoft/onnxruntime/blob/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp)
* [Inferencing (MNIST) - C++](https://github.com/microsoft/onnxruntime/tree/master/samples/c_cxx/MNIST)
# Technical Design Details
* [High level architectural design](docs/HighLevelDesign.md)