From 99bfaa3a979c862872de679638f21938da24d503 Mon Sep 17 00:00:00 2001 From: Satoshi Yoshikawa Date: Tue, 20 Aug 2024 00:47:58 +0900 Subject: [PATCH] Correct Spelling and Proper Capitalization in Documentation (#21790) This pull request addresses several spelling errors and inconsistencies in the capitalization of proper nouns within the documentation. ### Motivation and Context To improve the quality of the documentation, spelling errors and capitalization mistakes have been corrected. This ensures that the content is more accurate and easier to read. --- docs/extensions/build.md | 2 +- docs/extensions/index.md | 2 +- docs/get-started/with-java.md | 2 +- docs/get-started/with-javascript/web.md | 6 +++--- docs/get-started/with-python.md | 6 +++--- docs/performance/device-tensor.md | 4 ++-- docs/performance/transformers-optimization.md | 2 +- docs/reference/compatibility.md | 2 +- docs/tutorials/azureml.md | 4 ++-- docs/tutorials/csharp/bert-nlp-csharp-console-app.md | 4 ++-- docs/tutorials/csharp/fasterrcnn_csharp.md | 4 ++-- docs/tutorials/mobile/superres.md | 2 +- docs/tutorials/tensorflow.md | 2 +- docs/tutorials/web/build-web-app.md | 4 ++-- docs/tutorials/web/ep-webgpu.md | 2 +- docs/tutorials/web/ep-webnn.md | 2 +- docs/tutorials/web/excel-addin-bert-js.md | 4 ++-- 17 files changed, 27 insertions(+), 27 deletions(-) diff --git a/docs/extensions/build.md b/docs/extensions/build.md index 7eae2b884bd77..d7a664d545d8a 100644 --- a/docs/extensions/build.md +++ b/docs/extensions/build.md @@ -62,7 +62,7 @@ check this link https://docs.opensource.microsoft.com/releasing/general-guidance (v6.9.2) ## Commands -Launch **Developer PowerShell for VS 2022** in Windows Tereminal +Launch **Developer PowerShell for VS 2022** in Windows Terminal ``` . $home\miniconda3\shell\condabin\conda-hook.ps1 conda activate base diff --git a/docs/extensions/index.md b/docs/extensions/index.md index 70a5afb2b09d3..1aab3b4602f7c 100644 --- a/docs/extensions/index.md +++ b/docs/extensions/index.md @@ -33,7 +33,7 @@ pip install --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_pa The onnxruntime-extensions package depends on onnx and onnxruntime. -##### on Linux/MacOS +##### on Linux/macOS Please make sure the compiler toolkit like gcc(later than g++ 8.0) or clang are installed before the following command diff --git a/docs/get-started/with-java.md b/docs/get-started/with-java.md index db4ee42710f17..8bcf775f1354d 100644 --- a/docs/get-started/with-java.md +++ b/docs/get-started/with-java.md @@ -20,7 +20,7 @@ The ONNX runtime provides a Java binding for running inference on ONNX models on Java 8 or newer ## Builds -Release artifacts are published to **Maven Central** for use as a dependency in most Java build tools. The artifacts are built with support for some popular plaforms. +Release artifacts are published to **Maven Central** for use as a dependency in most Java build tools. The artifacts are built with support for some popular platforms. ![Version Shield](https://img.shields.io/maven-central/v/com.microsoft.onnxruntime/onnxruntime) diff --git a/docs/get-started/with-javascript/web.md b/docs/get-started/with-javascript/web.md index b4991ddc0a75b..274c902cb2390 100644 --- a/docs/get-started/with-javascript/web.md +++ b/docs/get-started/with-javascript/web.md @@ -72,7 +72,7 @@ See [ONNX Runtime JavaScript API](../../api/js/index.html){:target="_blank"} for - [SessionOptions](https://github.com/microsoft/onnxruntime-inference-examples/blob/main/js/api-usage_session-options) - a demonstration of how to configure creation of an InferenceSession instance. - [ort.env flags](https://github.com/microsoft/onnxruntime-inference-examples/blob/main/js/api-usage_ort-env-flags) - a demonstration of how to configure a set of global flags. -- See also: Typescript declarations for [Inference Session](https://github.com/microsoft/onnxruntime/blob/main/js/common/lib/inference-session.ts), [Tensor](https://github.com/microsoft/onnxruntime/blob/main/js/common/lib/tensor.ts), and [Environment Flags](https://github.com/microsoft/onnxruntime/blob/main/js/common/lib/env.ts) for reference. +- See also: TypeScript declarations for [Inference Session](https://github.com/microsoft/onnxruntime/blob/main/js/common/lib/inference-session.ts), [Tensor](https://github.com/microsoft/onnxruntime/blob/main/js/common/lib/tensor.ts), and [Environment Flags](https://github.com/microsoft/onnxruntime/blob/main/js/common/lib/env.ts) for reference. See [Tutorial: Web](../../tutorials/web/index.md) for tutorials. @@ -98,7 +98,7 @@ The following are video tutorials that use ONNX Runtime Web in web applications: ## Supported Versions -| EPs/Browsers | Chrome/Edge (Windows) | Chrome/Edge (Android) | Chrome/Edge (MacOS) | Chrome/Edge (iOS) | Safari (MacOS) | Safari (iOS) | Firefox (Windows) | Node.js | +| EPs/Browsers | Chrome/Edge (Windows) | Chrome/Edge (Android) | Chrome/Edge (macOS) | Chrome/Edge (iOS) | Safari (macOS) | Safari (iOS) | Firefox (Windows) | Node.js | |--------------|--------|---------|--------|------|---|----|------|-----| | WebAssembly (CPU) | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️\[1] | | WebGPU | ✔️\[2] | ✔️\[3] | ✔️ | ❌ | ❌ | ❌ | ❌ | ❌ | @@ -109,4 +109,4 @@ The following are video tutorials that use ONNX Runtime Web in web applications: - \[2]: WebGPU requires Chromium v113 or later on Windows. Float16 support requires Chrome v121 or later, and Edge v122 or later. - \[3]: WebGPU requires Chromium v121 or later on Windows. - \[4]: WebGL support is in maintenance mode. It is recommended to use WebGPU for better performance. -- \[5]: Requires to launch browser with commandline flag `--enable-features=WebMachineLearningNeuralNetwork`. \ No newline at end of file +- \[5]: Requires to launch browser with commandline flag `--enable-features=WebMachineLearningNeuralNetwork`. diff --git a/docs/get-started/with-python.md b/docs/get-started/with-python.md index c230af0c2dab0..c89d92e4ad432 100644 --- a/docs/get-started/with-python.md +++ b/docs/get-started/with-python.md @@ -7,7 +7,7 @@ nav_order: 1 # Get started with ONNX Runtime in Python {: .no_toc } -Below is a quick guide to get the packages installed to use ONNX for model serialization and infernece with ORT. +Below is a quick guide to get the packages installed to use ONNX for model serialization and inference with ORT. ## Contents {: .no_toc } @@ -128,7 +128,7 @@ onnx_model = onnx.load("ag_news_model.onnx") onnx.checker.check_model(onnx_model) ``` -- Create inference session with `ort.infernnce` +- Create inference session with `ort.InferenceSession` ```python import onnxruntime as ort import numpy as np @@ -170,7 +170,7 @@ output_path = model.name + ".onnx" model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=13, output_path=output_path) output_names = [n.name for n in model_proto.graph.output] ``` -- Create inference session with `rt.infernnce` +- Create inference session with `rt.InferenceSession` ```python providers = ['CPUExecutionProvider'] diff --git a/docs/performance/device-tensor.md b/docs/performance/device-tensor.md index 9506d6f9b953e..93439ff6895ce 100644 --- a/docs/performance/device-tensor.md +++ b/docs/performance/device-tensor.md @@ -109,7 +109,7 @@ Ort::Value ort_value(Ort::Value::CreateTensor(memory_info_dml, dml_resource, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT)); ``` -A [single file sample](https://github.com/ankan-ban/HelloOrtDml/blob/main/Main.cpp) can be found on github which shows how to manage and create copy and execution command queues. +A [single file sample](https://github.com/ankan-ban/HelloOrtDml/blob/main/Main.cpp) can be found on GitHub which shows how to manage and create copy and execution command queues. ### Python API @@ -132,4 +132,4 @@ binding.bind_output("out", "dml") # binding.bind_ortvalue_output("out", dml_array_out) session.run_with_iobinding(binding) -``` \ No newline at end of file +``` diff --git a/docs/performance/transformers-optimization.md b/docs/performance/transformers-optimization.md index 4df78f7fbeb00..79fa26012b79a 100644 --- a/docs/performance/transformers-optimization.md +++ b/docs/performance/transformers-optimization.md @@ -153,7 +153,7 @@ The first command will generate ONNX models (both before and after optimizations If you remove -o parameter, optimizer script is not used in benchmark. -If your GPU (like V100 or T4) has TensorCore, you can append `-p fp16` to the above commands to enable mixed precision. In some decoder-only(e.g GPT2) based generative models, you can enable [strict mode](../execution-providers/CUDA-ExecutionProvider.md#enable_skip_layer_norm_strict_mode) for SkipLayerNormalization Op on CUDA EP to achieve better accuray. However, the performance will drop a bit. +If your GPU (like V100 or T4) has TensorCore, you can append `-p fp16` to the above commands to enable mixed precision. In some decoder-only(e.g GPT2) based generative models, you can enable [strict mode](../execution-providers/CUDA-ExecutionProvider.md#enable_skip_layer_norm_strict_mode) for SkipLayerNormalization Op on CUDA EP to achieve better accuracy. However, the performance will drop a bit. If you want to benchmark on CPU, you can remove -g option in the commands. diff --git a/docs/reference/compatibility.md b/docs/reference/compatibility.md index 21a1dcd505cfb..39ec47064bb75 100644 --- a/docs/reference/compatibility.md +++ b/docs/reference/compatibility.md @@ -18,7 +18,7 @@ nav_order: 2 Newer versions of ONNX Runtime support all models that worked with prior versions, so updates should not break integrations. ## Environment compatibility -ONNX Runtime is not explicitly tested with every variation/combination of environments and dependencies, so this list is not comprehensive. Please use this as starting reference. For specific questions or requests, please [file an issue](https://github.com/microsoft/onnxruntime/issues) on Github. +ONNX Runtime is not explicitly tested with every variation/combination of environments and dependencies, so this list is not comprehensive. Please use this as starting reference. For specific questions or requests, please [file an issue](https://github.com/microsoft/onnxruntime/issues) on GitHub. ### Platforms diff --git a/docs/tutorials/azureml.md b/docs/tutorials/azureml.md index 443184d7cbcfa..cc9665e86ede4 100644 --- a/docs/tutorials/azureml.md +++ b/docs/tutorials/azureml.md @@ -96,7 +96,7 @@ model = BertForQuestionAnswering.from_pretrained(model_name) # behave differently in inference and training mode. model.eval() -# Generate dummy inputs to the model. Adjust if neccessary +# Generate dummy inputs to the model. Adjust if necessary inputs = { 'input_ids': torch.randint(32, [1, 32], dtype=torch.long), # list of numerical ids for the tokenized text 'attention_mask': torch.ones([1, 32], dtype=torch.long), # dummy list of ones @@ -263,7 +263,7 @@ print("ONNX Runtime version: ", onnxruntime.__version__) We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook. -Note that, the following code assumes you have a config.json file containing the subscription information in the same directory as the notebook, or in a sub-directory called .azureml. You can also supply the workspace name, subscription name, and resource group explicity using the Workspace.get() method. +Note that, the following code assumes you have a config.json file containing the subscription information in the same directory as the notebook, or in a sub-directory called .azureml. You can also supply the workspace name, subscription name, and resource group explicitly using the Workspace.get() method. ```python import os diff --git a/docs/tutorials/csharp/bert-nlp-csharp-console-app.md b/docs/tutorials/csharp/bert-nlp-csharp-console-app.md index 488bf8b39c431..c0f44adf63748 100644 --- a/docs/tutorials/csharp/bert-nlp-csharp-console-app.md +++ b/docs/tutorials/csharp/bert-nlp-csharp-console-app.md @@ -37,7 +37,7 @@ To run locally: - [Visual Studio](https://visualstudio.microsoft.com/downloads/) - [VS Code](https://code.visualstudio.com/Download) with the [Jupyter notebook extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter). -- [Anacaonda](https://www.anaconda.com/) +- [Anaconda](https://www.anaconda.com/) To run in the cloud with Azure Machine Learning: @@ -78,7 +78,7 @@ Now that we have downloaded the model we need to export it to an `ONNX` format. - Set the `dynamic_axes` for the dynamic length input because the `sentence` and `context` variables will be of different lengths for each question inferenced. ```python -# Generate dummy inputs to the model. Adjust if neccessary. +# Generate dummy inputs to the model. Adjust if necessary. inputs = { # list of numerical ids for the tokenized text 'input_ids': torch.randint(32, [1, 32], dtype=torch.long), diff --git a/docs/tutorials/csharp/fasterrcnn_csharp.md b/docs/tutorials/csharp/fasterrcnn_csharp.md index 4de72b33cf54b..eddc2542a3a8a 100644 --- a/docs/tutorials/csharp/fasterrcnn_csharp.md +++ b/docs/tutorials/csharp/fasterrcnn_csharp.md @@ -93,7 +93,7 @@ image.ProcessPixelRows(accessor => }); ``` -Here, we're creating a Tensor of the required size `(channels, paddedHeight, paddedWidth)`, accessing the pixel values, preprocessing them and finally assigning them to the tensor at the appropriate indicies. +Here, we're creating a Tensor of the required size `(channels, paddedHeight, paddedWidth)`, accessing the pixel values, preprocessing them and finally assigning them to the tensor at the appropriate indices. ### Setup inputs @@ -117,7 +117,7 @@ var inputs = new Dictionary ``` -To check the input node names for an ONNX model, you can use [Netron](https://github.com/lutzroeder/netron) to visualise the model and see input/output names. In this case, this model has `image` as the input node name. +To check the input node names for an ONNX model, you can use [Netron](https://github.com/lutzroeder/netron) to visualize the model and see input/output names. In this case, this model has `image` as the input node name. ### Run inference diff --git a/docs/tutorials/mobile/superres.md b/docs/tutorials/mobile/superres.md index 1af394fcb2dfa..e6a5b5555640b 100644 --- a/docs/tutorials/mobile/superres.md +++ b/docs/tutorials/mobile/superres.md @@ -58,7 +58,7 @@ After the script runs, you should see two ONNX files in the folder in the locati ```bash pytorch_superresolution.onnx -pytorch_superresolution_with_pre_and_post_proceessing.onnx +pytorch_superresolution_with_pre_and_post_processing.onnx ``` If you load the two models into [netron](https://netron.app/) you can see the difference in inputs and outputs between the two. The first two images below show the original model with its inputs being batches of channel data, and the second two show the inputs and outputs being the image bytes. diff --git a/docs/tutorials/tensorflow.md b/docs/tutorials/tensorflow.md index fe18785466817..b606a4d41b028 100644 --- a/docs/tutorials/tensorflow.md +++ b/docs/tutorials/tensorflow.md @@ -28,4 +28,4 @@ These examples use the [TensorFlow-ONNX converter](https://github.com/onnx/tenso ### TFLite {: .no_toc } -* [TFLite: Image classifciation (mobiledet)](https://github.com/onnx/tensorflow-onnx/blob/master/tutorials/mobiledet-tflite.ipynb) +* [TFLite: Image classification (mobiledet)](https://github.com/onnx/tensorflow-onnx/blob/master/tutorials/mobiledet-tflite.ipynb) diff --git a/docs/tutorials/web/build-web-app.md b/docs/tutorials/web/build-web-app.md index 7d56f91ad0b26..fe1452c82a712 100644 --- a/docs/tutorials/web/build-web-app.md +++ b/docs/tutorials/web/build-web-app.md @@ -111,8 +111,8 @@ Raw input is usually a string (for NLP model) or an image (for image model). The ### Outputs -The output of a model vary, and most need their own post-processing code. Refer to the above tutorial as an example of Javascript post processing. +The output of a model vary, and most need their own post-processing code. Refer to the above tutorial as an example of JavaScript post processing. ## Bundlers -_[This section is coming soon]_ \ No newline at end of file +_[This section is coming soon]_ diff --git a/docs/tutorials/web/ep-webgpu.md b/docs/tutorials/web/ep-webgpu.md index 4be2c68c4db26..7d1508b6cba43 100644 --- a/docs/tutorials/web/ep-webgpu.md +++ b/docs/tutorials/web/ep-webgpu.md @@ -53,7 +53,7 @@ To use WebGPU EP, you just need to make 2 small changes: const session = await ort.InferenceSession.create(modelPath, { ..., executionProviders: ['webgpu'] }); ``` -You might also consider installing the latest nightly build version of ONNX Runtime Web (onnxruntime-web@dev) to benefit from the latest features and improvments. +You might also consider installing the latest nightly build version of ONNX Runtime Web (onnxruntime-web@dev) to benefit from the latest features and improvements. ## WebGPU EP features diff --git a/docs/tutorials/web/ep-webnn.md b/docs/tutorials/web/ep-webnn.md index 0c7c0247ba2d5..fe1c1d729daf0 100644 --- a/docs/tutorials/web/ep-webnn.md +++ b/docs/tutorials/web/ep-webnn.md @@ -72,7 +72,7 @@ To use WebNN EP, you just need to make 3 small changes: ``` 3. If it is dynamic shape model, ONNX Runtime Web offers `freeDimensionOverrides` session option to override the free dimensions of the model. See [freeDimensionOverrides introduction](https://onnxruntime.ai/docs/tutorials/web/env-flags-and-session-options.html#freedimensionoverrides) for more details. -WebNN API and WebNN EP are in actively development, you might consider installing the latest nightly build version of ONNX Runtime Web (onnxruntime-web@dev) to benefit from the latest features and improvments. +WebNN API and WebNN EP are in actively development, you might consider installing the latest nightly build version of ONNX Runtime Web (onnxruntime-web@dev) to benefit from the latest features and improvements. ## Keep tensor data on WebNN MLBuffer (IO binding) diff --git a/docs/tutorials/web/excel-addin-bert-js.md b/docs/tutorials/web/excel-addin-bert-js.md index dbff8e31b8620..eed42aeef2ed6 100644 --- a/docs/tutorials/web/excel-addin-bert-js.md +++ b/docs/tutorials/web/excel-addin-bert-js.md @@ -77,14 +77,14 @@ Now we are ready to jump into the code! ## The `manifest.xml` file - The `manifest.xml` file specifies that all custom functions belong to the `ORT` namespace. You'll use the namespace to access the custom functions in Excel. Update the values in the `mainfest.xml` to `ORT`. + The `manifest.xml` file specifies that all custom functions belong to the `ORT` namespace. You'll use the namespace to access the custom functions in Excel. Update the values in the `manifest.xml` to `ORT`. ```xml ORT ``` -Learn more about the configuration of the [mainfest file here](https://learn.microsoft.com/office/dev/add-ins/develop/configure-your-add-in-to-use-a-shared-runtime#configure-the-manifest). +Learn more about the configuration of the [manifest file here](https://learn.microsoft.com/office/dev/add-ins/develop/configure-your-add-in-to-use-a-shared-runtime#configure-the-manifest). ## The `functions.ts` file