Metadata-Version: 2.1
Name: deeptech
Version: 20201010
Summary: A library to help writing ai functions with ease.
Home-page: https://github.com/penguinmenac3/deeptech
Author: Michael Fuerst
Author-email: mail@michaelfuerst.de
License: MIT
Download-URL: https://github.com/penguinmenac3/deeptech/tarball/20201010
Description: # Deeptech
        
        > A library that helps with writing ai functions fast.
        
        It ships with a full [Documentation](docs/README.md) of its API and [Examples](deeptech/examples).
        
        ## Getting Started
        
        Please make sure you have pytorch installed properly as a first step.
        
        ```bash
        pip install deeptech
        ```
        
        Then follow one of the [examples](deeptech/examples) or check out the [api documentation](docs/README.md).
        
        ## Design Principles
        
        The api builds on three core parts: Data, Model or Training. Some parts which are considered core functionality that is shared among them is in the core package.
        
        * **Data** is concerned about loading and preprocessing the data for training, evaluation and deployment.
        * **Model** is concerned with implementing the model. Everything required for the forward pass of the model is here.
        * **Training** contains all required for training a model on data. This includes loss, metrics, optimizers and trainers.
        * *Core* contains functionality that is shared across model, data and training.
        
        ## Tutorials & Examples
        
        Starting with tutorials and examples is usually easiest.
        
        Simple Fashion MNIST Examples:
        
        * [Fasion MNIST: Simple](deeptech/examples/mnist_simple.py)
        * [Fasion MNIST: Custom Model](deeptech/examples/mnist_custom_model.py)
        * [Fasion MNIST: Custom Loss](deeptech/examples/mnist_custom_loss.py)
        * **TODO** [Fasion MNIST: Custom Optimizer](deeptech/examples/mnist_custom_optimizer.py)
        * [Fasion MNIST: Custom Dataset](deeptech/examples/mnist_custom_dataset.py)
        
        
        ### Fashion MNIST
        
        Here is the simplest mnist example, it is so short it can be part of the main readme.
        
        ```python
        from deeptech.data.datasets import FashionMNISTDataset
        from deeptech.model.models import ImageClassifierSimple
        from deeptech.training.trainers import SupervisedTrainer
        from deeptech.training.losses import SparseCrossEntropyFromLogits
        from deeptech.training.optimizers import smart_optimizer
        from deeptech.core import Config, cli
        from torch.optim import SGD
        
        
        class FashionMNISTConfig(Config):
            def __init__(self, training_name, data_path, training_results_path):
                super().__init__(training_name, data_path, training_results_path)
                # Config of the data
                self.data_dataset = FashionMNISTDataset
        
                # Config of the model
                self.model_model = ImageClassifierSimple
                self.model_conv_layers = [32, 32, 32]
                self.model_dense_layers = [100]
                self.model_classes = 10
        
                # Config for training
                self.training_loss = SparseCrossEntropyLossFromLogits
                self.training_optimizer = smart_optimizer(SGD)
                self.training_trainer = SupervisedTrainer
                self.training_epochs = 10
                self.training_batch_size = 32
        
        
        # Run with parameters parsed from commandline.
        # python -m deeptech.examples.mnist_simple --mode=train --input=Datasets --output=Results
        if __name__ == "__main__":
            cli.run(FashionMNISTConfig)
        ```
        
        ## Contributing
        
        Currently there are no guidelines on how to contribute, so the best thing you can do is open up an issue and get in contact that way.
        In the issue we can discuss how you can implement your new feature or how to fix that nasty bug.
        
        To contribute, please fork the repositroy on github, then clone your fork. Make your changes and submit a merge request.
        
        ## Origin of the Name
        
        The name is a tribute to the [deeptech:ai hackathon](https://pioniergarage.de/deeptechai-der-ai-hackathon-in-karlsruhe/).
        When writing the library for fast, accessible ai development, I remembered how helpfull such a library could have been for a hackathon.
        Thus, I decided to name it as a tribute to that hackathon.
        
        And besides, the name does not seem to be used for any company or library and sounds cool, at least to me.
        ;)
        
        
        ## License
        
        This repository is under MIT License. Please see the [full license here](LICENSE).
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
