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Tired of logging all hyperparameter configurations of your model prototyping to disk?

Pyklopp is a tool to initialize, train and evaluate pytorch models (currently for supervised problems). It persists all relevant hyperparameters, timings and model configurations. Your prototyping is reduced to defining your model, the dataset and your desired parameters.


  • pip install pyklopp
  • or by poetry build, pip install dist/xxx.whl

Defining model & dataset

Specify your model in a plain python file, e.g.:

import torch.nn as nn
import torch.nn.functional as F

# Your model can be any pytorch module
# Make sure to not define it locally (e.g. within the get_model()-function)
class LeNet(nn.Module):
    def __init__(self, output_size):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, output_size)

    def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return out

# This is your model-instantiation function
# It receives an assembled configuration keyword argument list and should return an instance of your model
def get_model(**kwargs):
    output_size = int(kwargs['output_size'])

    return LeNet(output_size)

Invoke pyklopp to initialize it: pyklopp init my_model.get_model --save='test/model.pth' --config='{"output_size": 10}' Train it on cifar10:

  • pyklopp train test/model.pth --save='test/trained.pth' --config='{"batch_size": 100}'
  • pyklopp train test/model.pth torchvision.datasets.cifar.CIFAR10 --save 'test/trained.pth' --config='{"dataset_config": {"root": "/media/data/set/cifar10"}}'


# Initializing & Saving:
pyklopp init foo --save='mymodel1/model.pth'
pyklopp init foo --config='{"python_seed_initial": 100}' --save='mymodel2/model.pth'

# Training
pyklopp train path/to/mymodel.pth mnist
pyklopp train path/to/mymodel.pth mnist --config='{"batch_size": 100, "learning_rate": 0.01}'
# - Your model initialization function
import pypaddle.sparse

def init(**kwargs):
    input_size = kwargs['input_size']
    output_size = kwargs['output_size']

    return pypaddle.sparse.MaskedDeepFFN(input_size, output_size, [100, 100])
# - Your dataset loading functions
import pypaddle.util

def train_loader(**kwargs):
    batch_size = kwargs['batch_size']

    mnist_train_loader, mnist_test_loader, _, selected_root = pypaddle.util.get_mnist_loaders(batch_size, '/media/data/set/mnist')
    return mnist_train_loader

def test_loader(**kwargs):
    batch_size = kwargs['batch_size']

    mnist_train_loader, mnist_test_loader, _, selected_root = pypaddle.util.get_mnist_loaders(batch_size, '/media/data/set/mnist')
    return mnist_test_loader


  • Create wheel files in dist/: poetry build
  • Install wheel in current environment with pip: pip install path/to/pyklopp/dist/pyklopp-0.1.0-py3-none-any.whl

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