semvec/pypi-packages huggingface dataset:
XPipe 1.1.1
Standardize your ML projects
License: MIT
Requires Python: >=3.6

Welcome to XPipe's documentation !
##################################

.. 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.