Redirect

mergennachin · mergennachin · commit 77e92f35da97 · 2025-03-03T11:59:36.000-05:00
diff --git a/docs/source/getting-started-setup.md b/docs/source/getting-started-setup.md
@@ -1,263 +1,6 @@
-<!---- DO NOT MODIFY Progress Bar Start --->
-<div class="progress-bar-wrapper">
-   <div class="progress-bar-item">
-     <div class="step-number" id="step-1">1</div>
-     <span class="step-caption" id="caption-1"></span>
-   </div>
-   <div class="progress-bar-item">
-     <div class="step-number" id="step-2">2</div>
-     <span class="step-caption" id="caption-2"></span>
-   </div>
-   <div class="progress-bar-item">
-     <div class="step-number" id="step-3">3</div>
-     <span class="step-caption" id="caption-3"></span>
-   </div>
-   <div class="progress-bar-item">
-     <div class="step-number" id="step-4">4</div>
-     <span class="step-caption" id="caption-4"></span>
-   </div>
-</div>
-<!---- DO NOT MODIFY Progress Bar End--->
-
 # Setting Up ExecuTorch
-In this section, we'll learn how to
-* Set up an environment to work on ExecuTorch
-* Generate a sample ExecuTorch program
-* Build and run a program with the ExecuTorch runtime
-
-## System Requirements
-### Operating System
-
-We've tested these instructions on the following systems, although they should
-also work in similar environments.
-
-
-Linux (x86_64)
-- CentOS 8+
-- Ubuntu 20.04.6 LTS+
-- RHEL 8+
-
-macOS (x86_64/M1/M2)
-- Big Sur (11.0)+
-
-Windows (x86_64)
-- Windows Subsystem for Linux (WSL) with any of the Linux options
-
-### Software
-* `conda` or another virtual environment manager
-  - We recommend `conda` as it provides cross-language
-    support and integrates smoothly with `pip` (Python's built-in package manager)
-  - Otherwise, Python's built-in virtual environment manager `python venv` is a good alternative.
-* `g++` version 7 or higher, `clang++` version 5 or higher, or another
-  C++17-compatible toolchain.
-
-Note that the cross-compilable core runtime code supports a wider range of
-toolchains, down to C++17. See the [Runtime Overview](./runtime-overview.md) for
-portability details.
-
-## Quick Setup: Colab/Jupyter Notebook Prototype
-
-To utilize ExecuTorch to its fullest extent, please follow the setup instructions provided below to install from source.
-
-Alternatively, if you would like to experiment with ExecuTorch quickly and easily, we recommend using the following [colab notebook](https://colab.research.google.com/drive/1qpxrXC3YdJQzly3mRg-4ayYiOjC6rue3?usp=sharing) for prototyping purposes. You can install directly via `pip` for basic functionality.
-  ```bash
-  pip install executorch
-  ```
-
-
-## Environment Setup
-
-### Create a Virtual Environment
-
-[Install conda on your machine](https://conda.io/projects/conda/en/latest/user-guide/install/index.html). Then, create a virtual environment to manage our dependencies.
-   ```bash
-   # Create and activate a conda environment named "executorch"
-   conda create -yn executorch python=3.10.0
-   conda activate executorch
-   ```
-
-### Clone and install ExecuTorch requirements
-
-   ```bash
-   # Clone the ExecuTorch repo from GitHub
-   # 'main' branch is the primary development branch where you see the latest changes.
-   # 'viable/strict' contains all of the commits on main that pass all of the necessary CI checks.
-   git clone --branch viable/strict https://github.com/pytorch/executorch.git
-   cd executorch
-
-   # Update and pull submodules
-   git submodule sync
-   git submodule update --init
-
-   # Install ExecuTorch pip package and its dependencies, as well as
-   # development tools like CMake.
-   # If developing on a Mac, make sure to install the Xcode Command Line Tools first.
-   ./install_executorch.sh
-   ```
-
-   Use the [`--pybind` flag](https://github.com/pytorch/executorch/blob/main/install_executorch.sh#L26-L29) to install with pybindings and dependencies for other backends.
-   ```bash
-   ./install_executorch.sh --pybind <coreml | mps | xnnpack>
-
-   # Example: pybindings with CoreML *only*
-   ./install_executorch.sh --pybind coreml
-
-   # Example: pybinds with CoreML *and* XNNPACK
-   ./install_executorch.sh --pybind coreml xnnpack
-   ```
-
-   By default, `./install_executorch.sh` command installs pybindings for XNNPACK. To disable any pybindings altogether:
-   ```bash
-   ./install_executorch.sh --pybind off
-   ```
-
-After setting up your environment, you are ready to convert your PyTorch programs
-to ExecuTorch.
-
-> **_NOTE:_**  Cleaning the build system
->
-> When fetching a new version of the upstream repo (via `git fetch` or `git
-> pull`) it is a good idea to clean the old build artifacts. The build system
-> does not currently adapt well to changes in build dependencies.
->
-> You should also update and pull the submodules again, in case their versions
-> have changed.
->
-> ```bash
-> # From the root of the executorch repo:
-> ./install_executorch.sh --clean
-> git submodule sync
-> git submodule update --init
-> ```
-
-## Create an ExecuTorch program
-
-After setting up your environment, you are ready to convert your PyTorch programs
-to ExecuTorch.
-
-### Export a Program
-ExecuTorch provides APIs to compile a PyTorch [`nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html) to a `.pte` binary consumed by the ExecuTorch runtime.
-1. [`torch.export`](https://pytorch.org/docs/stable/export.html)
-1. [`exir.to_edge`](export-to-executorch-api-reference.md#exir.to_edge)
-1. [`exir.to_executorch`](ir-exir.md)
-1. Save the result as a [`.pte` binary](pte-file-format.md) to be consumed by the ExecuTorch runtime.
-
-
-Let's try this using with a simple PyTorch model that adds its inputs.
-
-Create `export_add.py` in a new directory outside of the ExecuTorch repo.
-
-**Note: It's important that this file does does not live in the directory that's a parent of the `executorch` directory. We need python to import from site-packages, not from the repo itself.**
-
-```
-mkdir -p ../example_files
-cd ../example_files
-touch export_add.py
-```
-
-Add the following code to `export_add.py`:
-```python
-import torch
-from torch.export import export
-from executorch.exir import to_edge
-
-# Start with a PyTorch model that adds two input tensors (matrices)
-class Add(torch.nn.Module):
-  def __init__(self):
-    super(Add, self).__init__()
-
-  def forward(self, x: torch.Tensor, y: torch.Tensor):
-      return x + y
-
-# 1. torch.export: Defines the program with the ATen operator set.
-aten_dialect = export(Add(), (torch.ones(1), torch.ones(1)))
-
-# 2. to_edge: Make optimizations for Edge devices
-edge_program = to_edge(aten_dialect)
-
-# 3. to_executorch: Convert the graph to an ExecuTorch program
-executorch_program = edge_program.to_executorch()
-
-# 4. Save the compiled .pte program
-with open("add.pte", "wb") as file:
-    file.write(executorch_program.buffer)
-
-```
-
-Then, execute it from your terminal.
-```bash
-python3 export_add.py
-```
-
-If it worked you'll see `add.pte` in that directory
-
-See the [ExecuTorch export tutorial](tutorials_source/export-to-executorch-tutorial.py) to learn more about the export process.
-
-
-## Build & Run
-
-After creating a program go back to the executorch directory to execute it using the ExecuTorch runtime.
-```
-cd ../executorch
-```
-
-For now, let's use [`executor_runner`](https://github.com/pytorch/executorch/blob/main/examples/portable/executor_runner/executor_runner.cpp), an example that runs the `forward` method on your program using the ExecuTorch runtime.
-
-### Build Tooling Setup
-The ExecuTorch repo uses CMake to build its C++ code. Here, we'll configure it to build the `executor_runner` tool to run it on our desktop OS.
-  ```bash
-  # Clean and configure the CMake build system. Compiled programs will
-  # appear in the executorch/cmake-out directory we create here.
-  ./install_executorch.sh --clean
-  (mkdir cmake-out && cd cmake-out && cmake ..)
-
-  # Go to work directory.
-  cd ..
-
-  # Build the executor_runner target
-  cmake --build cmake-out --target executor_runner -j9
-  ```
-
-> **_NOTE:_**  Cleaning the build system
->
-> When fetching a new version of the upstream repo (via `git fetch` or `git
-> pull`) it is a good idea to clean the old build artifacts. The build system
-> does not currently adapt well to changes in build dependencies.
->
-> You should also update and pull the submodules again, in case their versions
-> have changed.
->
-> ```bash
-> # From the root of the executorch repo:
-> ./install_executorch.sh --clean
-> git submodule sync
-> git submodule update --init
-> ```
-
-### Run Your Program
-
-Now that we've exported a program and built the runtime, let's execute it!
-
-  ```bash
-  ./cmake-out/executor_runner --model_path ../example_files/add.pte
-  ```
-Our output is a `torch.Tensor` with a size of 1. The `executor_runner` sets all input values to a [`torch.ones`](https://pytorch.org/docs/stable/generated/torch.ones.html) tensor, so when `x=[1]` and `y=[1]`, we get `[1]+[1]=[2]`
-  :::{dropdown} Sample Output
-
-  ```
-Output 0: tensor(sizes=[1], [2.])
-  ```
-  :::
-
-To learn how to build a similar program, visit the [Runtime APIs Tutorial](extension-module.md).
-
-## Next Steps
 
-Congratulations! You have successfully exported, built, and run your first
-ExecuTorch program. Now that you have a basic understanding of ExecuTorch,
-explore its advanced features and capabilities below.
+This page is re-organized into the following pages:
 
-* Build an [Android](demo-apps-android.md) or [iOS](demo-apps-ios.md) demo app
-* Learn more about the [export process](export-overview.md)
-* Dive deeper into the [Export Intermediate Representation (EXIR)](ir-exir.md) for complex export workflows
-* Refer to [advanced examples in executorch/examples](https://github.com/pytorch/executorch/tree/main/examples)
+* [Getting Started with ExecuTorch](getting-started.md)
+* [Building from Source](using-executorch-building-from-source.md)
