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A scikit-learn compatible neural network library that wraps PyTorch.

Resources

• Documentation
• Source Code
• Installation

Examples

To see more elaborate examples, look here.

import numpy as np
from sklearn.datasets import make_classification
from torch import nn

from skorch import NeuralNetClassifier


X, y = make_classification(1000, 20, n_informative=10, random_state=0)
X = X.astype(np.float32)
y = y.astype(np.int64)

class MyModule(nn.Module):
    def __init__(self, num_units=10, nonlin=nn.ReLU()):
        super(MyModule, self).__init__()

        self.dense0 = nn.Linear(20, num_units)
        self.nonlin = nonlin
        self.dropout = nn.Dropout(0.5)
        self.dense1 = nn.Linear(num_units, num_units)
        self.output = nn.Linear(num_units, 2)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, X, **kwargs):
        X = self.nonlin(self.dense0(X))
        X = self.dropout(X)
        X = self.nonlin(self.dense1(X))
        X = self.softmax(self.output(X))
        return X


net = NeuralNetClassifier(
    MyModule,
    max_epochs=10,
    lr=0.1,
    # Shuffle training data on each epoch
    iterator_train__shuffle=True,
)

net.fit(X, y)
y_proba = net.predict_proba(X)

In an sklearn Pipeline:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler


pipe = Pipeline([
    ('scale', StandardScaler()),
    ('net', net),
])

pipe.fit(X, y)
y_proba = pipe.predict_proba(X)

With grid search:

from sklearn.model_selection import GridSearchCV


# deactivate skorch-internal train-valid split and verbose logging
net.set_params(train_split=False, verbose=0)
params = {
    'lr': [0.01, 0.02],
    'max_epochs': [10, 20],
    'module__num_units': [10, 20],
}
gs = GridSearchCV(net, params, refit=False, cv=3, scoring='accuracy', verbose=2)

gs.fit(X, y)
print("best score: {:.3f}, best params: {}".format(gs.best_score_, gs.best_params_))

skorch also provides many convenient features, among others:

• Learning rate schedulers (Warm restarts, cyclic LR and many more)
• Scoring using sklearn (and custom) scoring functions
• Early stopping
• Checkpointing
• Parameter freezing/unfreezing
• Progress bar (for CLI as well as jupyter)
• Automatic inference of CLI parameters
• Integration with GPyTorch for Gaussian Processes

Installation

skorch requires Python 3.6 or higher.

conda installation

You need a working conda installation. Get the correct miniconda for your system from here.

To install skorch, you need to use the conda-forge channel:

conda install -c conda-forge skorch

We recommend to use a conda virtual environment.

Note: The conda channel is not managed by the skorch maintainers. More information is available here.

pip installation

To install with pip, run:

python -m pip install -U skorch

Again, we recommend to use a virtual environment for this.

From source

If you would like to use the most recent additions to skorch or help development, you should install skorch from source.

Using conda

To install skorch from source using conda, proceed as follows:

git clone https://github.com/skorch-dev/skorch.git
cd skorch
conda env create
source activate skorch
python -m pip install .

If you want to help developing, run:

git clone https://github.com/skorch-dev/skorch.git
cd skorch
conda env create
source activate skorch
python -m pip install -e .

py.test  # unit tests
pylint skorch  # static code checks

Using pip

For pip, follow these instructions instead:

git clone https://github.com/skorch-dev/skorch.git
cd skorch
# create and activate a virtual environment
python -m pip install -r requirements.txt
# install pytorch version for your system (see below)
python -m pip install .

If you want to help developing, run:

git clone https://github.com/skorch-dev/skorch.git
cd skorch
# create and activate a virtual environment
python -m pip install -r requirements.txt
# install pytorch version for your system (see below)
python -m pip install -r requirements-dev.txt
python -m pip install -e .

py.test  # unit tests
pylint skorch  # static code checks

PyTorch

PyTorch is not covered by the dependencies, since the PyTorch version you need is dependent on your OS and device. For installation instructions for PyTorch, visit the PyTorch website. skorch officially supports the last four minor PyTorch versions, which currently are:

• 1.9.1
• 1.10.2
• 1.11.0
• 1.12.0

However, that doesn't mean that older versions don't work, just that they aren't tested. Since skorch mostly relies on the stable part of the PyTorch API, older PyTorch versions should work fine.

In general, running this to install PyTorch should work (assuming CUDA 11.1):

# using conda:
conda install pytorch cudatoolkit==11.1 -c pytorch
# using pip
python -m pip install torch

External resources

• @jakubczakon: blog post "8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem" 2020
• @BenjaminBossan: talk 1 "skorch: A scikit-learn compatible neural network library" at PyCon/PyData 2019
• @githubnemo: poster for the PyTorch developer conference 2019
• @thomasjpfan: talk 2 "Skorch: A Union of Scikit learn and PyTorch" at SciPy 2019
• @thomasjpfan: talk 3 "Skorch - A Union of Scikit-learn and PyTorch" at PyData 2018

Communication

• GitHub issues: bug reports, feature requests, install issues, RFCs, thoughts, etc.
• Slack: We run the #skorch channel on the PyTorch Slack server, for which you can request access here.