Metadata-Version: 2.1
Name: pyblaze
Version: 2.2.3
Summary: Large-Scale Machine and Deep Learning in PyTorch.
Home-page: https://github.com/borchero/pyblaze
Author: Oliver Borchert
Author-email: borchero@icloud.com
License: License :: OSI Approved :: MIT License
Description: # PyBlaze
        
        ![PyPi](https://img.shields.io/pypi/v/pyblaze?label=version)
        ![License](https://img.shields.io/github/license/borchero/pyblaze?label=license)
        
        PyBlaze is an unobtrusive, high-level library for large-scale machine and deep learning in
        [PyTorch](https://pytorch.org). It is engineered to cut obsolete boilerplate code while preserving
        the flexibility of PyTorch to create just about any deep learning model.
        
        ## Quickstart
        
        Plenty of tutorials are available in the [official documentation](https://pyblaze.borchero.com/).
        The most basic tutorial builds a
        [classifier for CIFAR10](https://pyblaze.borchero.com/examples/classifier.html).
        
        ### Installation
        
        PyBlaze is available on PyPi and can simply be installed as follows:
        
        ```bash
        pip install pyblaze
        ```
        
        ## Library Design
        
        PyBlaze revolves around the concept of an **engine**. An engine is a powerful abstraction for
        combining a model's definition with the algorithm required to optimize its parameters according to
        some data. Engines provided by PyBlaze are focused on generalization: while the engine encapsulates
        the optimization algorithm, the user must explicitly define the optimization objective (usually the
        loss function).
        
        However, engines go far beyond implementing the optimization algorithm. Specifically, they further
        provide the following features:
        
        - **Evaluation**: During training, validation data can be used to evaluate the generalization
          performance of the trained model every so often. Also, arbitrary metrics may be computed.
        
        - **Callbacks**: During training and model evaluation, callbacks serve as hooks called at specific
          events in the process. This makes it possible to easily use some tracking framework, perform
          early stopping, or dynamically adjust parameters over the course of the training. Custom
          callbacks can easily be created.
        
        - **GPU Support**: Training and model evaluation is automatically performed on all available GPUs.
          The same code that works for the CPU works for the GPU ... and also for multiple GPUs.
        
        ### Available Engines
        
        Engines are currently implemented for the following training procedures:
        
        - `pyblaze.nn.MLEEngine`: This is the most central engine as it enables supervised as well as
          unsupervised learning. It can therefore adapt to multiple different problems: classification,
          regression, (variational) autoencoders, ..., depending on the loss only. In order to simplify
          initialization (as configuration requires toggling some settings), there exist some specialized
          MLE engines. Currently, the only one is `pyblaze.nn.AutoencoderEngine`.
        
        - `pyblaze.nn.WGANEngine`: This engine is specifically designed for training Wasserstein GANs.
          This class is required due to the independent training of generator and critic.
        
        Implementing your custom engine is rarely necessary for most common problems. However, when working
        on highly customized machine learning models, it might be a good idea. Usually, it is sufficient to
        implement the `train_batch` and `eval_batch` methods to specify how to perform training and
        evaluation, respectively, for a single batch of data. Consult the documentation of
        `pyblaze.nn.Engine` to read about all methods available for override.
        
        ## License
        
        PyBlaze is licensed under the [MIT License](LICENSE).
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.7
Description-Content-Type: text/markdown
