Metadata-Version: 2.1
Name: glow
Version: 0.10.5
Summary: Toolset for model training and creation of pipelines
Home-page: https://github.com/arquolo/glow
Author: Paul Maevskikh
Author-email: arquolo@gmail.com
License: MIT
Description: # Glow Library
        Collection of tools for easier prototyping with deep learning extensions (PyTorch framework)
        
        ## Overview
        ...
        
        ## Installation
        
        For basic installation use:
        
        ```bash
        pip install glow
        ```
        <details>
        <summary>Specific versions with additional requirements</summary>
        
        ```bash
        pip install glow[nn]  # For cv/neural network extras
        pip install glow[io]  # For I/O extras
        pip install glow[all]  # For all
        ```
        </details>
        Glow is compatible with: Python 3.9+, PyTorch 1.9+.
        Tested on ArchLinux, Ubuntu 18.04/20.04, Windows 10.
        
        ## Structure
        - `glow.*` - Core parts, available out the box
        - `glow.cv.*` - Tools for computer vision tasks
        - `glow.io.*` - I/O wrappers to access data in convenient formats
        - `glow.transforms` - Some custom-made augmentations for data
        - `glow.nn` - Neural nets and building blocks for them
        - `glow.metrics` - Metric to use while training your neural network
        
        ## Core features
        - `glow.mapped` - convenient tool to parallelize computations
        - `glow.memoize` - use if you want to reduce number of calls for any function
        
        ## IO features
        
        ### `glow.io.TiledImage` - ndarray-like reader for multiscale images (svs, tiff, etc...)
        <details>
        
        CTypes-based replacement of [`torchslide`](https://github.com/arquolo/torchslide) (deprecated).
        
        ```python
        from glow.io import read_tiled
        
        slide = read_tiled('test.svs')
        shape: tuple[int, ...] = slide.shape
        scales: tuple[int, ...] = slide.scales
        image: np.ndarray = slide[:2048, :2048]  # Get numpy.ndarray
        ```
        </details>
        
        ### `glow.io.Sound` - playable sound wrapper
        <details>
        
        ```python
        from datetime import timedelta
        
        import numpy as np
        from glow.io import Sound
        
        array: np.ndarray
        sound = Sound(array, rate=44100)  # Wrap np.ndarray
        sound = Sound.load('test.flac')  # Load sound into memory from file
        
        # Get properties
        rate: int = sound.rate
        duration: timedelta = sound.duration
        dtype: np.dtype = sound.dtype
        
         # Plays sound through default device, supports Ctrl-C for interruption
        sound.play()
        ```
        </details>
        
Platform: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Provides-Extra: psutil
Provides-Extra: cv
Provides-Extra: io
Provides-Extra: nn
Provides-Extra: all
