diff --git a/.github/workflows/lint_benchmarks.yml b/.github/workflows/lint_benchmarks.yml
deleted file mode 100644
index 3fe367c..0000000
--- a/.github/workflows/lint_benchmarks.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-name: Lint
-
-on:
-    workflow_call:
-
-
-jobs:
-  linter-flake8:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python 3.8
-      uses: actions/setup-python@v2
-      with:
-        python-version: 3.8
-
-    - name: Lint with flake8
-      run: |
-        pip install flake8
-        flake8 .
diff --git a/.github/workflows/test_benchmarks.yml b/.github/workflows/test_benchmarks.yml
deleted file mode 100644
index 4554ca7..0000000
--- a/.github/workflows/test_benchmarks.yml
+++ /dev/null
@@ -1,83 +0,0 @@
-name: Test Benchmark
-
-on:
-  workflow_call:
-    inputs:
-      benchopt_branch:
-        description: Branch of benchopt to install to test the benchmark.
-        default: benchopt@main
-        required: false
-        type: string
-      benchopt_version:
-        description: |
-          If set, use a specific version of benchopt for the tests,
-          thus ignoring the benchopt_branch input.
-        default: git
-        required: false
-        type: string
-
-jobs:
-  test-benchmark:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: ['ubuntu-latest', 'macos-latest']
-        exclude:
-          # Only run OSX test on version==git, not on the release ones.
-          - os: ${{ inputs.benchopt_version == 'git' || 'macos-latest' }}
-    env:
-      CONDA_ENV: 'test_env'
-      BENCHOPT_BRANCH: ${{ inputs.benchopt_branch }}
-      BENCHOPT_VERSION: ${{ inputs.benchopt_version }}
-      BENCHOPT_DEBUG: 1
-      BENCHOPT_CONDA_CMD: mamba
-
-    defaults:
-      run:
-        # Need to use this shell to get conda working properly.
-        # See https://github.com/marketplace/actions/setup-miniconda#important
-        shell: bash -l {0}
-
-    steps:
-    - uses: actions/checkout@v2
-    - name: Setup Conda
-      uses: conda-incubator/setup-miniconda@v2
-      with:
-        auto-update-conda: true
-        miniforge-variant: Mambaforge
-        use-mamba: true
-        channels: conda-forge
-        python-version: 3.9
-        activate-environment: ${{ env.CONDA_ENV }}
-
-    - name: Create/Restore MNE Data Cache
-      id: cache-mne_data
-      uses: actions/cache@v3
-      with:
-        path: ~/mne_data
-        key: ${{ runner.os }}-v3
-
-    - run: conda info
-
-    - name: Install benchopt and its dependencies
-      run: |
-        conda info
-        mamba install -yq pip
-
-        # Get the correct branch of benchopt
-        if [[ "$BENCHOPT_VERSION" == "git" ]]
-        then
-          user=${BENCHOPT_BRANCH%@*}
-          branch=${BENCHOPT_BRANCH##*@}
-          pip install -U git+https://github.com/$user/benchopt@$branch
-        elif [[ "$BENCHOPT_VERSION" == "latest" ]]
-        then
-          pip install -U benchopt
-        else
-          pip install -U benchopt==$BENCHOPT_VERSION
-        fi
-
-    - name: Test
-      run: |
-        benchopt test . --env-name bench_test_env -vl
-        benchopt test . --env-name bench_test_env -vl --skip-install
diff --git a/benchmark_utils/transformation.py b/benchmark_utils/transformation.py
deleted file mode 100644
index 2d0cb27..0000000
--- a/benchmark_utils/transformation.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from benchopt import safe_import_context
-
-
-# Protect the import with `safe_import_context()`. This allows:
-# - skipping import to speed up autocompletion in CLI.
-# - getting requirements info when all dependencies are not installed.
-with safe_import_context() as import_ctx:
-    import numpy as np
-
-    from numpy import concatenate
-    from torch import as_tensor
-    from skorch.helper import to_numpy
-    from braindecode.augmentation import ChannelsDropout, SmoothTimeMask
-
-
-def channels_dropout(
-    X, y, n_augmentation, seed=0, probability=0.5, p_drop=0.2
-):
-    """
-    Function to apply channels dropout to X raw data
-    and concatenate it to the original data.
-
-    ----------
-    X : array-like of shape (n_trials, n_channels, n_times)
-        The input trials.
-    y : array-like of shape (n_trials,)
-        The labels.
-    n_augmentation : int
-        Number of augmentation to apply and increase the size of the dataset.
-    seed : int
-        Random seed.
-    probability : float
-        Probability of applying the tranformation.
-    p_drop : float
-        Probability of dropout a channel.
-
-    Returns
-    -------
-    X_augm : array-like of shape (n_trials * n_augmentation,
-              n_channels, n_times)
-        The augmented trials.
-    y_augm : array-like of shape (n_trials * n_augmentation,)
-        The labels.
-
-    """
-    transform = ChannelsDropout(probability=probability, random_state=seed)
-    X_augm = to_numpy(X)
-    y_augm = y
-    for i in range(n_augmentation):
-        X_tr, _ = transform.operation(
-            as_tensor(X).float(), None, p_drop=p_drop
-        )
-
-        X_tr = X_tr.numpy()
-        X_augm = concatenate((X_augm, X_tr))
-        y_augm = concatenate((y_augm, y))
-
-    return X_augm, y_augm
-
-
-def smooth_timemask(
-    X, y, n_augmentation, sfreq, seed=0, probability=0.5, second=0.1
-):
-    """
-    Function to apply smooth time mask to X raw data
-    and concatenate it to the original data.
-    """
-
-    transform = SmoothTimeMask(
-        probability=probability,
-        mask_len_samples=int(sfreq * second),
-        random_state=seed,
-    )
-
-    X_torch = as_tensor(np.array(X)).float()
-    y_torch = as_tensor(y).float()
-    param_augm = transform.get_augmentation_params(X_torch, y_torch)
-    mls = param_augm["mask_len_samples"]
-    msps = param_augm["mask_start_per_sample"]
-
-    X_augm = to_numpy(X)
-    y_augm = y
-
-    for i in range(n_augmentation):
-        X_tr, _ = transform.operation(
-            X_torch, None, mask_len_samples=mls, mask_start_per_sample=msps
-        )
-
-        X_tr = X_tr.numpy()
-        X_augm = concatenate((X_augm, X_tr))
-        y_augm = concatenate((y_augm, y))
-
-    return X_augm, y_augm