```{note}
Before diving in, make sure you understand the concepts in the [ExecuTorch Overview](intro-overview.md)
```
# 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.
::::{grid} 3
:::{grid-item-card} Linux (x86_64)
:class-card: card-prerequisites
- CentOS 8+
- Ubuntu 20.04.6 LTS+
- RHEL 8+
:::
:::{grid-item-card} macOS (x86_64/M1/M2)
:class-card: card-prerequisites
- Big Sur (11.0)+
:::
:::{grid-item-card} Windows (x86_64)
:class-card: card-prerequisites
- 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 8 or higher, `clang++` version 8 or higher, or another
C++17-compatible toolchain that supports GNU C-style [statement
expressions](https://gcc.gnu.org/onlinedocs/gcc/Statement-Exprs.html) (`({ ...
})` syntax).
Note that the cross-compilable core runtime code supports a wider range of
toolchains, down to C++11. 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. 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.
## 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
git clone --branch v0.3.0 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_requirements.sh
```
Use the [`--pybind` flag](https://github.com/pytorch/executorch/blob/main/install_requirements.sh#L26-L29) to install with pybindings and dependencies for other backends.
```bash
./install_requirements.sh --pybind
```
After setting up your environment, you are ready to convert your PyTorch programs
to ExecuTorch.
## 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`](https://pytorch.org/executorch/stable/export-to-executorch-api-reference.html#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 a file called `export_add.py` with the following code:
```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
```
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, we can use the ExecuTorch runtime to execute it.
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.
(rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake ..)
# Build the executor_runner target
cmake --build cmake-out --target executor_runner -j9
```
### 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 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.
* 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)