Metadata-Version: 2.1
Name: XPipe
Version: 1.0.1
Summary: Standardize your ML projects
Home-page: UNKNOWN
Author: Jules Tevissen
License: MIT
Description: Welcome to XPipe's documentation !
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        .. image:: https://img.shields.io/badge/python-%3E%3D%203.5-blue
          
        Introduction
        ************
        
        XPipe is a library that I started developping in December 2020 for my personal use.
        As it might be useful for other people, I decided to publish the code as an open source project.
        
        **Configuration files** are a big concern in data science field. 
        XPipe facilitates your work by automatically loading python objects from a yaml configuration. 
        You can also easily include other yaml files into another.
        
        It is an interesting tool to improve your workflow, make it reproducible and make your configurations more readable.
        
        Getting started
        ***************
        
        .. code-block:: bash
        
          pip install xpipe
        
        
        Documentation (work in progress): https://x-pipe.readthedocs.io/en/latest/#
        
        Configuration files
        *******************
        
        Here is a simple example of how to use yaml configuration files to seamlessly load needed objects to run your experiments.
          
        .. code-block:: yaml
        
          training:
            gpu: !env CUDA_VISIBLE_DEVICES # Get the value of env variable CUDA_VISIBLE_DEVICES
            epochs: 18
            batch_size: 100
        
            optimizer:
              !obj torch.optim.SGD : {lr : 0.001}
        
            scheduler:
              !obj torch.optim.lr_scheduler.MultiStepLR : {milestones: [2, 6, 10, 14]}
        
            loss:
              !obj torch.nn.BCELoss : {}
        
          model: !include "./models/my_model.yaml"
        
          transforms:
            - !obj transforms.Normalize : {}
            - !obj transforms.Noise : {}
            - !obj transforms.RandomFlip : {probability: 0.5}
        
        
        In your `models/my_model.yaml` file, you can define your model and its parameters (assuming that you defined a module 'models' and a class 'Model1' in it).
        
        .. code-block:: yaml
        
          definition: 
            !obj models.Model1 :
              n_hidden: 100
        
        
        Then you can load the configuration file:
        
        .. code-block:: yaml
        
          from xpipe.config import load_config
        
          conf = load_config("experiment.yaml")
          epochs = conf.training.epochs() # 18
        
          # Instantiate your model defined in models/my_model.yaml
          my_model = conf.model.definition()
        
          # Directly instantiate your optimizer and scheduler from configuration
          # Note that you can add argument that are not in the configuration file
          optimizer = conf.training.optimizer(params=my_model.parameters())
          scheduler = conf.training.scheduler(optimizer=optimizer)
        
        
        Try by yourself the exemples in the `examples` folder.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
