From 1f13a9f98207642dedc194ca63530a0dadbe68cb Mon Sep 17 00:00:00 2001 From: Faith Xu Date: Mon, 22 Jul 2019 16:48:26 -0700 Subject: [PATCH] Update to include more samples (#1381) * Update to include more samples * Link fix --- README.md | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index fe06b66fbd..0281d3b296 100644 --- a/README.md +++ b/README.md @@ -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)