MPS Device Check Readme

Overview

This repository contains a simple Python script that checks for the availability of the Metal Performance Shaders (MPS) device using PyTorch. The script initializes a PyTorch tensor on the MPS device and prints the tensor if the device is available; otherwise, it notifies the user that the MPS device was not found.

Prerequisites

Before running the script, ensure that you have the following prerequisites installed:

Python: The script is written in Python, so you need to have Python installed on your machine.
Pipenv: Pipenv is recommended for managing Python dependencies. If you don't have Pipenv installed, you can install it using the following command:
```
pip install pipenv
```

Usage

Clone the repository:

git clone https://github.com/69hunter/mps-device-check.git

Navigate to the project directory:
```
cd mps-device-check
```
Install dependencies using Pipenv:
```
pipenv install
```
Activate the virtual environment:
```
pipenv shell
```
Run the script:
```
python main.py
```
The script will output either the initialized tensor on the MPS device or a message indicating that the MPS device was not found.

Script Explanation

The script consists of the following steps:

Check for MPS Availability:
```
if torch.backends.mps.is_available():
```
It checks whether the Memory Persistence Service (MPS) is available.
Initialize MPS Device:
```
mps_device = torch.device("mps")
```
If the MPS device is available, it initializes the device.
Create and Print Tensor:
```
x = torch.ones(1, device=mps_device)
print(x)
```
It creates a PyTorch tensor with all elements set to 1 on the MPS device and prints the tensor.
Handle MPS Device Not Found:
```
else:
    print("MPS device not found.")
```
If the MPS device is not available, it prints a message indicating that the MPS device was not found.

Conclusion

This simple script serves as a quick check to see if the Memory Persistence Service (MPS) device is available using PyTorch. Use it as a starting point for more complex scripts or integrate it into your projects that require MPS functionality. The PyTorch library will be automatically installed as part of the Pipenv setup.