Metadata-Version: 2.1
Name: tf-argonaut
Version: 0.2.1
Summary: Tensorflow Experimentation Pipeline
Home-page: https://github.com/felixnext/tf-argonaut
Author: Felix Geilert
License: Apache License 2.0
Description: # ⛵ tf-argonaut
        
        Library for creating visual experiment pipelines in tensorflow. Allows to test out different network concepts against standardized datasets.
        
        > The Argonauts were a band of heros and adventures that sailed on their ship Argo through the mediterranean and navigated numerous adventures.
        > Like one of them this library is designed to turn TF into your own argo and navigate your experiments.
        
        `Argonaut` was originally build to allow easy research experimentation of multi-task settings against common datasets (thereby reducing the overhead required for experimentation).
        
        ## 👶 Getting Started
        
        To install the library simply use PyPi:
        ```
        pip3 install tf-argonaut
        ```
        
        Alternatively you can install the library directly through `setup.py`:
        ```
        pip3 install .
        ```
        
        You can then import the library:
        ```python
        import argonaut as argo
        ```
        
        At its core, `argonaut` allows you to run experiments with a single line of code and a configuration file (see in folder `examples/simple_multitask`):
        
        ```python
        argo.run_experiment("Baseline", "experiment.json", name="SimpleExample")
        ```
        
        ## 📜 Concepts
        
        Concepts include:
        
        * Experiments
        * Pipeline
        * Datasets
        * Callbacks
        
        ### Tools
        
        The library also contains multiple tools that allow to inspect data and quickly start training processes.
        
        ## 💾‍ Coding Examples
        
        TODO: more advanced coding examples
        
        `Argonaut` also comes with various pre-defined models (although you can also easily plug in every keras model, given right input and output structrue).
        In particular these models include:
        
        TODO
        
        ### Debugging
        
        TODO: integrate options for TF2 debugging
        
        ## ⚙ Configuration
        
        Experiments allow you to specify most of the hyper-parameters through a configuration `json` file. See the detailed [configuration guide](config.md) for more details.
        
        ## License
        
        This library is provided under the Apache License.
        
        **Pull Requests to improve code quality and add new functionality are more then welcome!**
Keywords: tensorflow experimentation computervision
Platform: UNKNOWN
Description-Content-Type: text/markdown
