Getting started

Quick start

Take your favorite OS, terminal, environment and run:

git clone

First you need to install the library. It has been tested with python 3.6+, with the latest versions of pytorch-lightning.

If you want to create a new conda environment, run:

conda env create -n adaenv python=3.7
conda activate adaenv

Install the library (with developer mode if you want to develop your own models later on, otherwise you can skip the -e):

pip install -e adalib

Note: on Windows, it could be necessary to first install pytorch and torchvision with conda:

conda install -c pytorch pytorch
conda install -c pytorch torchvision
pip install -e adalib

Run on of the scripts:

cd scripts

By default, this script launches experiments with all kinds of methods on a blobs dataset – it doesn’t take any parameter, you can change it easily from the script. It may take a few minutes to finish.

Most parameters are available and can be changed through configuration files, which are all grouped in the configs folder:

  • datasets
  • network layers and training parameters
  • methods (Source, DANN, CDAN…), and their specific parameters.

Advanced options

The script runs the same kind of experiments allowing for more variants (semi-supervised, unbalanced, with gpus and MLFlow logging). You can run it without parameters or with -h to get help.


You can log results to MLFlow. Start a MLFlow server in another terminal:

conda activate adaenv
mlflow ui --port=31014

Streamlit application

Optionally, you can use the streamlit app. First install streamlit with

pip install streamlit

then launch the app like this:

streamlit run

This will start a web app with a default port = 8501. It should look like this in your browser:


Quick run on other datasets

Run experiments on Office31

The default configuration file office31.json is configured for domain adaptation from Amazon to Webcam.

python -d ../configs/datasets/office31.json -g 0

-g 0 tell PyTorch to use the GPU with id 0, remove this parameter altogether to train on CPU. You can list several GPU ids with -g 0 1 … (note this can cause errors with some pytorch-lightning versions). The above command will train networks with the default configuration in ../configs/office31_network.json, which defines the architecture (eg ResNet50 for the feature extractor) and the learning hyperparameters (epochs, learning rate, …).

You can edit the content of the ../configs/datasets/office31.json config file to use other domain configurations (eg Webcam to DSLR). Please note it is important to change the “dataset_name” field to organize the output directory structure.

"dataset_group": "office31",
"dataset_name": "Amazon to Webcam",
"source": "amazon",
"target": "webcam",
"size_type": "source",
"weight_type": "natural"

You can edit ../configs/office31_network.json to change any network parameter. The configuration contains two subsets of parameters:

  • train_params define the hyperparameters to be used for training: learning rate, optimizer, number of epochs.
  • archi_params define the “static” architecture of the network, with three blocks for feature extrator, task classifier and critic network.
    The critic network parameters may be ignored by some methods.


    "train_params": {
        "adapt_lambda": true,
        "adapt_lr": true,
        "lambda_init": 1,
        "nb_adapt_epochs": 150,
        "nb_init_epochs": 50,
        "init_lr": 1e-3,
        "batch_size": 32,
        "optimizer": {
        "type": "SGD",
        "optim_params": {
            "momentum": 0.9,
            "weight_decay": 0.0005,
            "nesterov": true
    "archi_params": {
        "feature": {
            "name": "ResNet50",
            "feature_dim": 128
        "task": {
            "name": "feedforward",
            "n_classes": 31
        "critic": {
            "name": "feedforward",
            "hidden_size": [

Run experiments on Digits

This is very similar to Office31. The default configuration file digits.json is configured for domain adaptation from MNIST to USPS. You can easily change the domains by editing this configuration file.

python -d ../configs/datasets/digits.json -g 0

This will use by default the network configuration in ../configs/digits_network.json.