From 706adb7d163e52599d4fd966cc4e82cc90e99350 Mon Sep 17 00:00:00 2001 From: MaanavD Date: Wed, 24 Jan 2024 13:00:13 -0800 Subject: [PATCH 01/11] Added requested Mac x64/arm64 instructions. --- src/routes/getting-started/table.svelte | 32 +++++++++++++++++++------ 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/src/routes/getting-started/table.svelte b/src/routes/getting-started/table.svelte index 2e8f4cc178582..862803bbf205e 100644 --- a/src/routes/getting-started/table.svelte +++ b/src/routes/getting-started/table.svelte @@ -143,11 +143,11 @@ "Install Nuget package Microsoft.ML.OnnxRuntime", 'mac,C-API,X64,DefaultCPU': - "Download .tgz file from Github", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", 'mac,C++,X64,DefaultCPU': - "Download .tgz file from Github", - + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + 'mac,C#,X64,DefaultCPU': "Download .tgz file from Github", @@ -158,6 +158,24 @@ 'mac,Python,X64,DefaultCPU': 'pip install onnxruntime', + 'mac,Python,X64,CoreML': 'pip install onnxruntime', + + 'mac,Python,ARM64,CoreML': 'pip install onnxruntime', + + 'mac,JS,ARM64,CoreML': 'pip install onnxruntime', + + 'mac,objectivec,X64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployement guide", + + 'mac,objectivec,ARM64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployement guide", + + 'mac,objectivec,X64,CoreML': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployement guide", + + 'mac,objectivec,ARM64,CoreML': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployement guide", + + 'mac,C-API,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + + 'mac,C++,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + 'linux,Python,X64,DefaultCPU': 'pip install onnxruntime', 'linux,Python,ARM64,DefaultCPU': 'pip install onnxruntime', @@ -566,13 +584,13 @@ //mac m1 'mac,C-API,ARM64,CoreML': - "Install Nuget package Microsoft.ML.OnnxRuntime", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", 'mac,C#,ARM64,CoreML': "Install Nuget package Microsoft.ML.OnnxRuntime
Refer to docs for requirements.", 'mac,C++,ARM64,CoreML': - "Download .tgz file from Github", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", 'mac,Java,ARM64,CoreML': "Add a dependency on com.microsoft.onnxruntime:onnxruntime using Maven/Gradle", @@ -586,10 +604,10 @@ "Install Nuget package Microsoft.ML.OnnxRuntime", 'mac,C-API,ARM64,DefaultCPU': - "Install Nuget package Microsoft.ML.OnnxRuntime", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", 'mac,C++,ARM64,DefaultCPU': - "Install Nuget package Microsoft.ML.OnnxRuntime", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", //power 'linux,C-API,Power,DefaultCPU': From e3219291cafacdd548095d1433eb04f3d590477e Mon Sep 17 00:00:00 2001 From: MaanavD Date: Wed, 24 Jan 2024 13:12:49 -0800 Subject: [PATCH 02/11] Fixed spelling error, reintroduced .tgz link. --- src/routes/getting-started/table.svelte | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/src/routes/getting-started/table.svelte b/src/routes/getting-started/table.svelte index 862803bbf205e..904d51574e43d 100644 --- a/src/routes/getting-started/table.svelte +++ b/src/routes/getting-started/table.svelte @@ -143,10 +143,10 @@ "Install Nuget package Microsoft.ML.OnnxRuntime", 'mac,C-API,X64,DefaultCPU': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide or download .tgz file from Github.", 'mac,C++,X64,DefaultCPU': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide or download .tgz file from Github.", 'mac,C#,X64,DefaultCPU': "Download .tgz file from Github", @@ -164,17 +164,17 @@ 'mac,JS,ARM64,CoreML': 'pip install onnxruntime', - 'mac,objectivec,X64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployement guide", + 'mac,objectivec,X64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployment guide", - 'mac,objectivec,ARM64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployement guide", + 'mac,objectivec,ARM64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployment guide", - 'mac,objectivec,X64,CoreML': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployement guide", + 'mac,objectivec,X64,CoreML': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployment guide", - 'mac,objectivec,ARM64,CoreML': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployement guide", + 'mac,objectivec,ARM64,CoreML': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployment guide", - 'mac,C-API,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + 'mac,C-API,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", - 'mac,C++,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + 'mac,C++,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", 'linux,Python,X64,DefaultCPU': 'pip install onnxruntime', @@ -584,13 +584,13 @@ //mac m1 'mac,C-API,ARM64,CoreML': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", 'mac,C#,ARM64,CoreML': "Install Nuget package Microsoft.ML.OnnxRuntime
Refer to docs for requirements.", 'mac,C++,ARM64,CoreML': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide or download .tgz file from Github.", 'mac,Java,ARM64,CoreML': "Add a dependency on com.microsoft.onnxruntime:onnxruntime using Maven/Gradle", @@ -604,10 +604,10 @@ "Install Nuget package Microsoft.ML.OnnxRuntime", 'mac,C-API,ARM64,DefaultCPU': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", 'mac,C++,ARM64,DefaultCPU': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployement guide", + "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", //power 'linux,C-API,Power,DefaultCPU': From 4005b92795fb10628d42230df0d02ecee9a2b8e0 Mon Sep 17 00:00:00 2001 From: MaanavD Date: Wed, 24 Jan 2024 13:52:33 -0800 Subject: [PATCH 03/11] Fixed yolov3 model link. --- docs/tutorials/csharp/yolov3_object_detection_csharp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/csharp/yolov3_object_detection_csharp.md b/docs/tutorials/csharp/yolov3_object_detection_csharp.md index d7f67ee41f41b..be83ecd0eddab 100644 --- a/docs/tutorials/csharp/yolov3_object_detection_csharp.md +++ b/docs/tutorials/csharp/yolov3_object_detection_csharp.md @@ -28,7 +28,7 @@ The source code for this sample is available [here](https://github.com/microsoft 3. Use any sample Image as input to the sample. 4. Download the latest YOLOv3 model from the ONNX Model Zoo. - This example was adapted from [ONNX Model Zoo](https://github.com/onnx/models).Download the latest version of the [YOLOv3](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/yolov3) model from here. + This example was adapted from [ONNX Model Zoo](https://github.com/onnx/models).Download the latest version of the [YOLOv3](https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/validated/vision/object_detection_segmentation/yolov3) model from here. ## Install ONNX Runtime for OpenVINO Execution Provider From 5aecdb3896cbdf7ea67c87826f2fd17621978847 Mon Sep 17 00:00:00 2001 From: MaanavD Date: Wed, 24 Jan 2024 14:04:18 -0800 Subject: [PATCH 04/11] Fixed various model zoo links due to model zoo refactor. --- docs/execution-providers/TensorRT-ExecutionProvider.md | 4 ++-- .../OpenVINO_EP_samples/squeezenet_classification_cpp.md | 2 +- .../tiny_yolo_v2_object_detection_python.md | 2 +- docs/tutorials/csharp/fasterrcnn_csharp.md | 4 ++-- docs/tutorials/csharp/resnet50_csharp.md | 4 ++-- docs/tutorials/iot-edge/rasp-pi-cv.md | 2 +- docs/tutorials/mnist_cpp.md | 2 +- docs/tutorials/web/classify-images-nextjs-github-template.md | 2 +- 8 files changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/execution-providers/TensorRT-ExecutionProvider.md b/docs/execution-providers/TensorRT-ExecutionProvider.md index 89fdb895a05d2..7093095637f83 100644 --- a/docs/execution-providers/TensorRT-ExecutionProvider.md +++ b/docs/execution-providers/TensorRT-ExecutionProvider.md @@ -566,7 +566,7 @@ Please note that there is a constraint of using this explicit shape range featur This example shows how to run the Faster R-CNN model on TensorRT execution provider. -1. Download the Faster R-CNN onnx model from the ONNX model zoo [here](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn). +1. Download the Faster R-CNN onnx model from the ONNX model zoo [here](https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/faster-rcnn). 2. Infer shapes in the model by running the [shape inference script](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/symbolic_shape_infer.py) ```bash @@ -583,7 +583,7 @@ This example shows how to run the Faster R-CNN model on TensorRT execution provi 4. To test on model performance, run `onnxruntime_perf_test` on your shape-inferred Faster-RCNN model - > Download sample test data with model from [model zoo](https://github.com/onnx/models/tree/main/vision/object_detection_segmentation/faster-rcnn), and put test_data_set folder next to your inferred model + > Download sample test data with model from [model zoo](https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/faster-rcnn), and put test_data_set folder next to your inferred model ```bash # e.g. diff --git a/docs/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md b/docs/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md index c2815e3e2ce7b..d0759ac028d55 100644 --- a/docs/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md +++ b/docs/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md @@ -21,7 +21,7 @@ The source code for this sample is available [here](https://github.com/microsoft 3. Use any sample image as input to the sample. 4. Download the latest Squeezenet model from the ONNX Model Zoo. - This example was adapted from [ONNX Model Zoo](https://github.com/onnx/models).Download the latest version of the [Squeezenet](https://github.com/onnx/models/tree/master/vision/classification/squeezenet) model from here. + This example was adapted from [ONNX Model Zoo](https://github.com/onnx/models).Download the latest version of the [Squeezenet](https://github.com/onnx/models/tree/main/validated/vision/classification/squeezenet) model from here. ## Install ONNX Runtime for OpenVINO Execution Provider diff --git a/docs/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md b/docs/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md index d8cd6a9fb5159..e6c6e756a2087 100644 --- a/docs/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md +++ b/docs/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md @@ -17,7 +17,7 @@ The source code for this sample is available [here](https://github.com/microsoft 1. [The Intel® Distribution of OpenVINO toolkit](https://docs.openvinotoolkit.org/latest/index.html) 2. Download the latest tinyYOLOv2 model from the ONNX Model Zoo. - This model was adapted from [ONNX Model Zoo](https://github.com/onnx/models).Download the latest version of the [tinyYOLOv2](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/tiny-yolov2) model from here. + This model was adapted from [ONNX Model Zoo](https://github.com/onnx/models).Download the latest version of the [tinyYOLOv2](https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/tiny-yolov2) model from here. ## Install ONNX Runtime for OpenVINO Execution Provider diff --git a/docs/tutorials/csharp/fasterrcnn_csharp.md b/docs/tutorials/csharp/fasterrcnn_csharp.md index 689f11dabf18a..4de72b33cf54b 100644 --- a/docs/tutorials/csharp/fasterrcnn_csharp.md +++ b/docs/tutorials/csharp/fasterrcnn_csharp.md @@ -28,7 +28,7 @@ The source code for this sample is available [here](https://github.com/microsoft To run this sample, you'll need the following things: 1. Install [.NET Core 3.1](https://dotnet.microsoft.com/download/dotnet-core/3.1) or higher for you OS (Mac, Windows or Linux). -2. Download the [Faster R-CNN](https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/faster-rcnn/model/FasterRCNN-10.onnx) ONNX model to your local system. +2. Download the [Faster R-CNN](https://github.com/onnx/models/blob/main/validated/vision/object_detection_segmentation/faster-rcnn/model/FasterRCNN-10.onnx) ONNX model to your local system. 3. Download [this demo image](/images/demo.jpg) to test the model. You can also use any image you like. ## Get started @@ -68,7 +68,7 @@ image.Save(imageStream, format); ### Preprocess image -Next, we will preprocess the image according to the [requirements of the model](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn#preprocessing-steps): +Next, we will preprocess the image according to the [requirements of the model](https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/faster-rcnn#preprocessing-steps): ```cs var paddedHeight = (int)(Math.Ceiling(image.Height / 32f) * 32f); diff --git a/docs/tutorials/csharp/resnet50_csharp.md b/docs/tutorials/csharp/resnet50_csharp.md index d7cd3bf5a1ddd..c6867bccb7b38 100644 --- a/docs/tutorials/csharp/resnet50_csharp.md +++ b/docs/tutorials/csharp/resnet50_csharp.md @@ -27,7 +27,7 @@ The source code for this sample is available [here](https://github.com/microsoft To run this sample, you'll need the following things: 1. Install [.NET Core 3.1](https://dotnet.microsoft.com/download/dotnet-core/3.1) or higher for you OS (Mac, Windows or Linux). -2. Download the [ResNet50 v2](https://github.com/onnx/models/blob/master/vision/classification/resnet/model/resnet50-v2-7.onnx) ONNX model to your local system. +2. Download the [ResNet50 v2](https://github.com/onnx/models/blob/main/validated/vision/classification/resnet/model/resnet50-v2-7.onnx) ONNX model to your local system. 3. Download [this picture of a dog](/images/dog.jpeg) to test the model. You can also use any image you like. ## Getting Started @@ -74,7 +74,7 @@ Note, we're doing a centered crop resize to preserve aspect ratio. ### Preprocess image -Next, we will preprocess the image according to the [requirements of the model](https://github.com/onnx/models/tree/master/vision/classification/resnet#preprocessing): +Next, we will preprocess the image according to the [requirements of the model](https://github.com/onnx/models/tree/main/validated/vision/classification/resnet#preprocessing): ```cs // We use DenseTensor for multi-dimensional access to populate the image data diff --git a/docs/tutorials/iot-edge/rasp-pi-cv.md b/docs/tutorials/iot-edge/rasp-pi-cv.md index e0847a84513d6..3a9d1db0fb181 100644 --- a/docs/tutorials/iot-edge/rasp-pi-cv.md +++ b/docs/tutorials/iot-edge/rasp-pi-cv.md @@ -95,7 +95,7 @@ In this tutorial we are using the Raspberry Pi [Camera Module](https://www.raspb ``` ## Run inference on the Raspberry Pi with the `inference_mobilenet.py` script -Now that we have validated that the camera is connected and working on the Raspberry Pi, its time to inference the ONNX model provided in the source. The model is a [MobileNet](https://github.com/onnx/models/tree/main/vision/classification/mobilenet) model that performs image classification on 1000 classes. +Now that we have validated that the camera is connected and working on the Raspberry Pi, its time to inference the ONNX model provided in the source. The model is a [MobileNet](https://github.com/onnx/models/tree/main/validated/vision/classification/mobilenet) model that performs image classification on 1000 classes. - Run the inference script with the below command. ```python diff --git a/docs/tutorials/mnist_cpp.md b/docs/tutorials/mnist_cpp.md index 5bb9e4832d6d9..c7332f7ddec36 100644 --- a/docs/tutorials/mnist_cpp.md +++ b/docs/tutorials/mnist_cpp.md @@ -5,7 +5,7 @@ nav_exclude: true # Number recognition with MNIST in C++ {: .no_toc } -This sample uses the MNIST model from the Model Zoo: https://github.com/onnx/models/tree/master/vision/classification/mnist +This sample uses the MNIST model from the Model Zoo: https://github.com/onnx/models/tree/main/validated/vision/classification/mnist ![Screenshot](../../../images/mnist-screenshot.png) diff --git a/docs/tutorials/web/classify-images-nextjs-github-template.md b/docs/tutorials/web/classify-images-nextjs-github-template.md index a558cb87b5e39..cdea907742d73 100644 --- a/docs/tutorials/web/classify-images-nextjs-github-template.md +++ b/docs/tutorials/web/classify-images-nextjs-github-template.md @@ -33,7 +33,7 @@ This application performs inference on device, in the browser using the onnxrunt ## SqueezeNet machine learning model -We will be using [SqueezeNet](https://github.com/onnx/models/tree/master/vision/classification/squeezenet) from the [ONNX Model Zoo](https://github.com/onnx/models). SqueezeNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on the ImageNet dataset which contains images from 1000 different classes. SqueezeNet models are highly efficient in terms of size and speed while providing good accuracies. This makes them ideal for platforms with strict constraints on size, like client side inference. +We will be using [SqueezeNet](https://github.com/onnx/models/tree/main/validated/vision/classification/squeezenet) from the [ONNX Model Zoo](https://github.com/onnx/models). SqueezeNet models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on the ImageNet dataset which contains images from 1000 different classes. SqueezeNet models are highly efficient in terms of size and speed while providing good accuracies. This makes them ideal for platforms with strict constraints on size, like client side inference. > If you need even more model memory and disk efficiency, you can convert the ONNX model to [ORT format](../../reference/ort-format-models) and use an ORT model in your application instead of the ONNX one. You can also also [reduce the size of the ONNX Runtime](../../build/custom.md) binary itself to only include support for the specific models in your application. From 9e1228a27f7664862b4b1c775cdecdf5bcc74acf Mon Sep 17 00:00:00 2001 From: Maanav Dalal Date: Thu, 25 Jan 2024 12:08:51 -0800 Subject: [PATCH 05/11] Update docs/tutorials/csharp/yolov3_object_detection_csharp.md Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> --- docs/tutorials/csharp/yolov3_object_detection_csharp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/csharp/yolov3_object_detection_csharp.md b/docs/tutorials/csharp/yolov3_object_detection_csharp.md index be83ecd0eddab..dce5c44694eea 100644 --- a/docs/tutorials/csharp/yolov3_object_detection_csharp.md +++ b/docs/tutorials/csharp/yolov3_object_detection_csharp.md @@ -28,7 +28,7 @@ The source code for this sample is available [here](https://github.com/microsoft 3. Use any sample Image as input to the sample. 4. Download the latest YOLOv3 model from the ONNX Model Zoo. - This example was adapted from [ONNX Model Zoo](https://github.com/onnx/models).Download the latest version of the [YOLOv3](https://github.com/onnx/models/tree/ddbbd1274c8387e3745778705810c340dea3d8c7/validated/vision/object_detection_segmentation/yolov3) model from here. + This example was adapted from [ONNX Model Zoo](https://github.com/onnx/models). Download the latest version of the [YOLOv3](https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/yolov3) model from here. ## Install ONNX Runtime for OpenVINO Execution Provider From 1df80a79939c4bd7c58d3f128f9288cff4138c9b Mon Sep 17 00:00:00 2001 From: MaanavD Date: Wed, 24 Jan 2024 19:17:34 -0800 Subject: [PATCH 06/11] fixed based on suggestions. --- src/routes/getting-started/table.svelte | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/src/routes/getting-started/table.svelte b/src/routes/getting-started/table.svelte index 904d51574e43d..3c3a76b083c20 100644 --- a/src/routes/getting-started/table.svelte +++ b/src/routes/getting-started/table.svelte @@ -143,7 +143,7 @@ "Install Nuget package Microsoft.ML.OnnxRuntime", 'mac,C-API,X64,DefaultCPU': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide or download .tgz file from Github.", + "Add 'onnxruntime-c' using CocoaPods or download the .tgz file from Github.", 'mac,C++,X64,DefaultCPU': "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide or download .tgz file from Github.", @@ -162,19 +162,19 @@ 'mac,Python,ARM64,CoreML': 'pip install onnxruntime', - 'mac,JS,ARM64,CoreML': 'pip install onnxruntime', + 'mac,JS,ARM64,CoreML': 'npm install onnxruntime-react-native', - 'mac,objectivec,X64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployment guide", + 'mac,objectivec,X64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods.", - 'mac,objectivec,ARM64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployment guide", + 'mac,objectivec,ARM64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods.", - 'mac,objectivec,X64,CoreML': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployment guide", + 'mac,objectivec,X64,CoreML': "Add 'onnxruntime-objc' using CocoaPods.", - 'mac,objectivec,ARM64,CoreML': "Add 'onnxruntime-objc' using CocoaPods and refer to the mobile deployment guide", + 'mac,objectivec,ARM64,CoreML': "Add 'onnxruntime-objc' using CocoaPods.", - 'mac,C-API,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", + 'mac,C-API,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods or download the .tgz file from Github.", - 'mac,C++,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", + 'mac,C++,X64,CoreML': "Add 'onnxruntime-c' using CocoaPods or download the .tgz file from Github.", 'linux,Python,X64,DefaultCPU': 'pip install onnxruntime', @@ -584,13 +584,13 @@ //mac m1 'mac,C-API,ARM64,CoreML': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", + "Add 'onnxruntime-c' using CocoaPods or download the .tgz file from Github.", 'mac,C#,ARM64,CoreML': "Install Nuget package Microsoft.ML.OnnxRuntime
Refer to docs for requirements.", 'mac,C++,ARM64,CoreML': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide or download .tgz file from Github.", + "Add 'onnxruntime-c' using CocoaPods or download the .tgz file from Github.", 'mac,Java,ARM64,CoreML': "Add a dependency on com.microsoft.onnxruntime:onnxruntime using Maven/Gradle", @@ -604,10 +604,10 @@ "Install Nuget package Microsoft.ML.OnnxRuntime", 'mac,C-API,ARM64,DefaultCPU': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", + "Add 'onnxruntime-c' using CocoaPods or download the .tgz file from Github.", 'mac,C++,ARM64,DefaultCPU': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide", + "Add 'onnxruntime-c' using CocoaPods or download the .tgz file from Github.", //power 'linux,C-API,Power,DefaultCPU': From db1989db7c6401c90c2b07efea8dc3933323a4c8 Mon Sep 17 00:00:00 2001 From: MaanavD Date: Mon, 29 Jan 2024 15:36:25 -0800 Subject: [PATCH 07/11] removed incomplete combination. --- src/routes/getting-started/table.svelte | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/routes/getting-started/table.svelte b/src/routes/getting-started/table.svelte index 3c3a76b083c20..967b2e9b593c6 100644 --- a/src/routes/getting-started/table.svelte +++ b/src/routes/getting-started/table.svelte @@ -162,8 +162,6 @@ 'mac,Python,ARM64,CoreML': 'pip install onnxruntime', - 'mac,JS,ARM64,CoreML': 'npm install onnxruntime-react-native', - 'mac,objectivec,X64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods.", 'mac,objectivec,ARM64,DefaultCPU': "Add 'onnxruntime-objc' using CocoaPods.", From 7c80a471ecbaf309ee0b26c431bb5a2a2a1416bd Mon Sep 17 00:00:00 2001 From: MaanavD Date: Mon, 29 Jan 2024 15:41:36 -0800 Subject: [PATCH 08/11] Added docs change for 1.17. --- docs/reference/compatibility.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/reference/compatibility.md b/docs/reference/compatibility.md index 47f2259f19ca1..f4beae013b8d3 100644 --- a/docs/reference/compatibility.md +++ b/docs/reference/compatibility.md @@ -68,6 +68,7 @@ ONNX Runtime supports all opsets from the latest released version of the [ONNX]( | ONNX Runtime version | [ONNX version](https://github.com/onnx/onnx/blob/master/docs/Versioning.md) | ONNX opset version | ONNX ML opset version | ONNX IR version | [Windows ML Availability](https://docs.microsoft.com/en-us/windows/ai/windows-ml/release-notes/)| |------------------------------|--------------------|--------------------|----------------------|------------------|------------------| +| 1.17 | **1.15** | 20 | 4 | 9 | Windows AI 1.16+ | | 1.16 | **1.14.1** | 19 | 3 | 9 | Windows AI 1.16+ | | 1.15 | **1.14** | 19 | 3 | 8 | Windows AI 1.15+ | | 1.14 | **1.13** | 18 | 3 | 8 | Windows AI 1.14+ | From 3abc36c05c598fed6255d27d8aeb2e1e9592f387 Mon Sep 17 00:00:00 2001 From: MaanavD Date: Mon, 29 Jan 2024 15:51:13 -0800 Subject: [PATCH 09/11] Removed mobile tutorial from a combination --- src/routes/getting-started/table.svelte | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/routes/getting-started/table.svelte b/src/routes/getting-started/table.svelte index 967b2e9b593c6..71bb2b073f944 100644 --- a/src/routes/getting-started/table.svelte +++ b/src/routes/getting-started/table.svelte @@ -146,7 +146,7 @@ "Add 'onnxruntime-c' using CocoaPods or download the .tgz file from Github.", 'mac,C++,X64,DefaultCPU': - "Add 'onnxruntime-c' using CocoaPods and refer to the mobile deployment guide or download .tgz file from Github.", + "Add 'onnxruntime-c' using CocoaPods or download the .tgz file from Github.", 'mac,C#,X64,DefaultCPU': "Download .tgz file from Github", From b0e9a02ad8451ac68a74c8c4e9cc0f68369d2ed0 Mon Sep 17 00:00:00 2001 From: MaanavD Date: Mon, 29 Jan 2024 16:04:22 -0800 Subject: [PATCH 10/11] Updated windows ai version --- docs/reference/compatibility.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/reference/compatibility.md b/docs/reference/compatibility.md index f4beae013b8d3..1e6eb3676ecf0 100644 --- a/docs/reference/compatibility.md +++ b/docs/reference/compatibility.md @@ -68,7 +68,7 @@ ONNX Runtime supports all opsets from the latest released version of the [ONNX]( | ONNX Runtime version | [ONNX version](https://github.com/onnx/onnx/blob/master/docs/Versioning.md) | ONNX opset version | ONNX ML opset version | ONNX IR version | [Windows ML Availability](https://docs.microsoft.com/en-us/windows/ai/windows-ml/release-notes/)| |------------------------------|--------------------|--------------------|----------------------|------------------|------------------| -| 1.17 | **1.15** | 20 | 4 | 9 | Windows AI 1.16+ | +| 1.17 | **1.15** | 20 | 4 | 9 | Windows AI 1.17+ | | 1.16 | **1.14.1** | 19 | 3 | 9 | Windows AI 1.16+ | | 1.15 | **1.14** | 19 | 3 | 8 | Windows AI 1.15+ | | 1.14 | **1.13** | 18 | 3 | 8 | Windows AI 1.14+ | From 17247c289b05342bf63d8f2a51944989bfbc4b17 Mon Sep 17 00:00:00 2001 From: MaanavD Date: Mon, 29 Jan 2024 16:09:00 -0800 Subject: [PATCH 11/11] Removed windows AI table column. --- docs/reference/compatibility.md | 48 ++++++++++++++++----------------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/docs/reference/compatibility.md b/docs/reference/compatibility.md index 1e6eb3676ecf0..aadd2b82b345f 100644 --- a/docs/reference/compatibility.md +++ b/docs/reference/compatibility.md @@ -66,30 +66,30 @@ ONNX Runtime supports all opsets from the latest released version of the [ONNX]( * *Operators not supported in the current ONNX spec may be available as a [Contrib Operator](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md)* * [How to add a custom operator/kernel](operators/add-custom-op.md) -| ONNX Runtime version | [ONNX version](https://github.com/onnx/onnx/blob/master/docs/Versioning.md) | ONNX opset version | ONNX ML opset version | ONNX IR version | [Windows ML Availability](https://docs.microsoft.com/en-us/windows/ai/windows-ml/release-notes/)| -|------------------------------|--------------------|--------------------|----------------------|------------------|------------------| -| 1.17 | **1.15** | 20 | 4 | 9 | Windows AI 1.17+ | -| 1.16 | **1.14.1** | 19 | 3 | 9 | Windows AI 1.16+ | -| 1.15 | **1.14** | 19 | 3 | 8 | Windows AI 1.15+ | -| 1.14 | **1.13** | 18 | 3 | 8 | Windows AI 1.14+ | -| 1.13 | **1.12** | 17 | 3 | 8 | Windows AI 1.13+ | -| 1.12 | **1.12** | 17 | 3 | 8 | Windows AI 1.12+ | -| 1.11 | **1.11** | 16 | 2 | 8 | Windows AI 1.11+ | -| 1.10 | **1.10** | 15 | 2 | 8 | Windows AI 1.10+ | -| 1.9 | **1.10** | 15 | 2 | 8 | Windows AI 1.9+ | -| 1.8 | **1.9** | 14 | 2 | 7 | Windows AI 1.8+ | -| 1.7 | **1.8** | 13 | 2 | 7 | Windows AI 1.7+ | -| 1.6 | **1.8** | 13 | 2 | 7 | Windows AI 1.6+ | -| 1.5 | **1.7** | 12 | 2 | 7 | Windows AI 1.5+ | -| 1.4 | **1.7** | 12 | 2 | 7 | Windows AI 1.4+ | -| 1.3 | **1.7** | 12 | 2 | 7 | Windows AI 1.3+ | -| 1.2
1.1 | **1.6** | 11 | 2 | 6 | Windows AI 1.3+ | -| 1.0 | **1.6** | 11 | 2 | 6 | Windows AI 1.3+ | -| 0.5 | **1.5** | 10 | 1 | 5 | Windows AI 1.3+ | -| 0.4 | **1.5** | 10 | 1 | 5 | Windows AI 1.3+ | -| 0.3 | **1.4** | 9 | 1 | 3 | Windows 10 2004+ | -| 0.2 | **1.3** | 8 | 1 | 3 | Windows 10 1903+ | -| 0.1 | **1.3** | 8 | 1 | 3 | Windows 10 1809+ | +| ONNX Runtime version | [ONNX version](https://github.com/onnx/onnx/blob/master/docs/Versioning.md) | ONNX opset version | ONNX ML opset version | ONNX IR version | +|------------------------------|--------------------|--------------------|----------------------|------------------| +| 1.17 | **1.15** | 20 | 4 | 9 | +| 1.16 | **1.14.1** | 19 | 3 | 9 | +| 1.15 | **1.14** | 19 | 3 | 8 | +| 1.14 | **1.13** | 18 | 3 | 8 | +| 1.13 | **1.12** | 17 | 3 | 8 | +| 1.12 | **1.12** | 17 | 3 | 8 | +| 1.11 | **1.11** | 16 | 2 | 8 | +| 1.10 | **1.10** | 15 | 2 | 8 | +| 1.9 | **1.10** | 15 | 2 | 8 | +| 1.8 | **1.9** | 14 | 2 | 7 | +| 1.7 | **1.8** | 13 | 2 | 7 | +| 1.6 | **1.8** | 13 | 2 | 7 | +| 1.5 | **1.7** | 12 | 2 | 7 | +| 1.4 | **1.7** | 12 | 2 | 7 | +| 1.3 | **1.7** | 12 | 2 | 7 | +| 1.2
1.1 | **1.6** | 11 | 2 | 6 | +| 1.0 | **1.6** | 11 | 2 | 6 | +| 0.5 | **1.5** | 10 | 1 | 5 | +| 0.4 | **1.5** | 10 | 1 | 5 | +| 0.3 | **1.4** | 9 | 1 | 3 | +| 0.2 | **1.3** | 8 | 1 | 3 | +| 0.1 | **1.3** | 8 | 1 | 3 | Unless otherwise noted, please use the latest released version of the tools to convert/export the ONNX model. Most tools are backwards compatible and support multiple ONNX versions. Join this with the table above to evaluate ONNX Runtime compatibility.