# pycuts
<div style="display: flex; gap: 7px;">
<a href="https://pypi.org/project/pycuts/" target="_blank"><img alt="PyPI" src="https://img.shields.io/pypi/v/pycuts"></a>
<a href="https://huggingface.co/spaces/dwancin/" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-%23097EFF?style=flat&logoColor=black"></a>
<a href="https://github.com/dwancin/pycuts" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/Repository-white?logo=github&logoColor=black"></a>
</div>
**pycuts** is a small Python library that provides a collection of shortcut functions for common operations across various libraries, particularly for PyTorch and Hugging Face Hub environments.
## Installation
You can install pycuts directly from PyPI:
```bash
pip install pycuts
```
## Shortcuts
| **Function** | **Return Type** | **Description** |
|-----------------------|-----------------------|------------------------------------------------------------------------|
| `device()` | `torch.device` | Determines the best device to use (`cuda`, `mps`, or `cpu`). |
| `gpu()` | `bool` | Returns `True` if a GPU ("cuda" or "mps") is available, otherwise `False`. |
| `torch_dtype()` | `torch.dtype` | Determines the appropriate tensor precision based on the device. |
| `synchronize()` | `None` | Waits for all kernels in all streams on the given device to complete. |
| `empty_cache()` | `None` | Clears the GPU memory to prevent out-of-memory errors. |
| `device_count()` | `int` | Returns the number of available devices (e.g., number of GPUs). |
| `manual_seed(seed)` | `None` | Sets the random seed for reproducible behavior across CPU/GPU. |
| `is_spaces()` | `bool` | Returns `True` if running in a Hugging Face Space, otherwise `False`. |
| `is_zero_gpu_space()` | `bool` | Returns `True` if running in a zero-GPU Hugging Face Space. |
## Examples
```python
import pycuts
print(f"current device is: {pycuts.device()}")
```
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"description": "# pycuts\n\n<div style=\"display: flex; gap: 7px;\">\n <a href=\"https://pypi.org/project/pycuts/\" target=\"_blank\"><img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/pycuts\"></a>\n <a href=\"https://huggingface.co/spaces/dwancin/\" target=\"_blank\"><img alt=\"Hugging Face\" src=\"https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-%23097EFF?style=flat&logoColor=black\"></a>\n <a href=\"https://github.com/dwancin/pycuts\" target=\"_blank\"><img alt=\"Static Badge\" src=\"https://img.shields.io/badge/Repository-white?logo=github&logoColor=black\"></a>\n</div>\n\n**pycuts** is a small Python library that provides a collection of shortcut functions for common operations across various libraries, particularly for PyTorch and Hugging Face Hub environments.\n\n## Installation\n\nYou can install pycuts directly from PyPI:\n\n```bash\npip install pycuts\n```\n\n## Shortcuts\n\n| **Function** | **Return Type** | **Description** |\n|-----------------------|-----------------------|------------------------------------------------------------------------|\n| `device()` | `torch.device` | Determines the best device to use (`cuda`, `mps`, or `cpu`). |\n| `gpu()` | `bool` | Returns `True` if a GPU (\"cuda\" or \"mps\") is available, otherwise `False`. |\n| `torch_dtype()` | `torch.dtype` | Determines the appropriate tensor precision based on the device. |\n| `synchronize()` | `None` | Waits for all kernels in all streams on the given device to complete. |\n| `empty_cache()` | `None` | Clears the GPU memory to prevent out-of-memory errors. |\n| `device_count()` | `int` | Returns the number of available devices (e.g., number of GPUs). |\n| `manual_seed(seed)` | `None` | Sets the random seed for reproducible behavior across CPU/GPU. |\n| `is_spaces()` | `bool` | Returns `True` if running in a Hugging Face Space, otherwise `False`. |\n| `is_zero_gpu_space()` | `bool` | Returns `True` if running in a zero-GPU Hugging Face Space. |\n\n## Examples\n\n```python\nimport pycuts\nprint(f\"current device is: {pycuts.device()}\")\n```\n",
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