[RELEASE ONLY] Update viable/strict in the docs to point to release/1.0

CONTRIBUTING.md

@@ -327,7 +327,7 @@ for their help in the PR comments.
 ### Continuous Integration
 See https://hud.pytorch.org/hud/pytorch/executorch/main for the current state of
 the CI (continuous integration) jobs. If `main` is broken, consider rebasing
-your PR onto the `viable/strict` branch, which points to the most recent
+your PR onto the `release/1.0` branch, which points to the most recent
 all-green commit.
 
  

docs/README.md

@@ -40,7 +40,7 @@ To build the documentation locally:
 1. Clone the ExecuTorch repo to your machine.
 
    ```bash
-   git clone -b viable/strict https://github.com/pytorch/executorch.git && cd executorch
+   git clone -b release/1.0 https://github.com/pytorch/executorch.git && cd executorch
    ```
 
 1. If you don't have it already, start either a Python virtual envitonment:

docs/source/getting-started.md

@@ -168,7 +168,7 @@ ExecuTorch provides C++ APIs, which can be used to target embedded or mobile dev
 CMake is the preferred build system for the ExecuTorch C++ runtime. To use with CMake, clone the ExecuTorch repository as a subdirectory of your project, and use CMake's `add_subdirectory("executorch")` to include the dependency. The `executorch` target, as well as kernel and backend targets will be made available to link against. The runtime can also be built standalone to support diverse toolchains. See [Using ExecuTorch with C++](using-executorch-cpp.md) for a detailed description of build integration, targets, and cross compilation.
 
 ```
-git clone -b viable/strict https://github.com/pytorch/executorch.git
+git clone -b release/1.0 https://github.com/pytorch/executorch.git
 ```
 ```python
 # CMakeLists.txt

docs/source/using-executorch-building-from-source.md

@@ -38,7 +38,7 @@ portability details.
 ## Environment Setup
  Clone the ExecuTorch repository from GitHub and create a conda environment as follows. Venv can be used in place on conda.
    ```bash
-   git clone -b viable/strict https://github.com/pytorch/executorch.git
+   git clone -b release/1.0 https://github.com/pytorch/executorch.git
    cd executorch
    conda create -yn executorch python=3.10.0
    conda activate executorch

docs/source/using-executorch-ios.md

@@ -98,7 +98,7 @@ xcode-select --install
 2. Clone ExecuTorch:
 
 ```bash
-git clone -b viable/strict https://github.com/pytorch/executorch.git --depth 1 --recurse-submodules --shallow-submodules && cd executorch
+git clone -b release/1.0 https://github.com/pytorch/executorch.git --depth 1 --recurse-submodules --shallow-submodules && cd executorch
 ```
 
 3. Set up [Python](https://www.python.org/downloads/macos/) 3.10+ and activate a virtual environment:

extension/benchmark/apple/Benchmark/README.md

@@ -24,7 +24,7 @@ It provides a flexible framework for dynamically generating and running performa
 To get started, clone the ExecuTorch repository and cd into the source code directory:
 
 ```bash
-git clone -b viable/strict https://github.com/pytorch/executorch.git --depth 1 --recurse-submodules --shallow-submodules && cd executorch
+git clone -b release/1.0 https://github.com/pytorch/executorch.git --depth 1 --recurse-submodules --shallow-submodules && cd executorch
 ```
 
 This command performs a shallow clone to speed up the process.