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10 changes: 5 additions & 5 deletions extension/apple/Benchmark/README.md
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# Executorch Benchmark App for Apple Platforms
# ExecuTorch Benchmark App for Apple Platforms

## Introduction

The **Benchmark App** is a tool designed to help developers measure the performance of PyTorch models on Apple devices using the Executorch runtime.
The **Benchmark App** is a tool designed to help developers measure the performance of PyTorch models on Apple devices using the ExecuTorch runtime.
It provides a flexible framework for dynamically generating and running performance tests on your models, allowing you to assess metrics such as load times, inference speeds, memory usage, and more.

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Expand All @@ -21,7 +21,7 @@ It provides a flexible framework for dynamically generating and running performa

### Get the Code

To get started, clone the Executorch repository and cd into the source code directory:
To get started, clone the ExecuTorch repository and cd into the source code directory:

```bash
git clone https://github.com/pytorch/executorch.git --depth 1 --recurse-submodules --shallow-submodules
Expand All @@ -32,7 +32,7 @@ This command performs a shallow clone to speed up the process.

### Set Up the Frameworks

The Benchmark App relies on prebuilt Executorch frameworks.
The Benchmark App relies on prebuilt ExecuTorch frameworks.
You have two options:

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## Conclusion

The Executorch Benchmark App provides a flexible and powerful framework for testing and measuring the performance of PyTorch models on Apple devices. By leveraging dynamic test generation, you can easily add your models and resources to assess their performance metrics. Whether you're optimizing existing models or developing new ones, this tool can help you gain valuable insights into their runtime behavior.
The ExecuTorch Benchmark App provides a flexible and powerful framework for testing and measuring the performance of PyTorch models on Apple devices. By leveraging dynamic test generation, you can easily add your models and resources to assess their performance metrics. Whether you're optimizing existing models or developing new ones, this tool can help you gain valuable insights into their runtime behavior.
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