XPipe 1.1.1

Standardize your ML projects

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

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.

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).

definition:
  !obj models.Model1 :
    n_hidden: 100

Then you can load the configuration file:

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.