Installation¶

For Kumo and TabICLv2, use Python 3.11+, [tfm] (or [kumo], [tabiclv2], [all]) and a separate pinned SDM backend installation. See the Kumo/TabICLv2 instructions. Weights download from Hugging Face on first use, not during package installation.

Choose the models and hardware you want to use:

Setup	What to install
Standard MLJAR	`mljar-supervised`; PyTorch is not required
RealMLP on CPU	CPU PyTorch, then MLJAR with `[realmlp]` or `[all]`
RealMLP on NVIDIA GPU	CUDA PyTorch, then the same MLJAR extra
Kumo / TabICLv2 on CPU or NVIDIA GPU	Python 3.11+, chosen PyTorch build, `[tfm]`, then pinned SDM

TabM mini uses the same CPU/GPU setup. Substitute [tabm] for [realmlp] in the commands below, or use [all] for both. These extras share pytabkit/PyTorch dependencies, so installing any one makes both models available in Compete mode. See TabM mini for usage and tuning.

There are no separate CPU/GPU editions of MLJAR. The PyTorch build controls the available devices for RealMLP, TabM, Kumo, and TabICLv2. Other algorithms use their own device settings.

RealMLP release status

RealMLP and TabM mini are currently available from source. The published PyPI 1.3.2 package does not include the extra. Use the source commands below until a release with RealMLP is published. Installing mljar-supervised[realmlp] from an older release will not add RealMLP.

Create an environment¶

Use Python 3.11 or 3.12 for the RealMLP setup covered by our CI. Create a fresh virtual environment for your chosen CPU or GPU build.

Linux / macOS

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip

Windows PowerShell

py -3.12 -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip

If activation is unavailable, use .\.venv\Scripts\python.exe in place of python in the commands below.

Standard MLJAR¶

python -m pip install mljar-supervised

The existing sklearn Neural Network does not require PyTorch or pytabkit. RealMLP is optional.

Kumo and TabICLv2 on CPU or NVIDIA GPU¶

Use Python 3.11+ and install from source until a PyPI release includes these algorithms. Installation has two parts: MLJAR's optional dependencies and the separately pinned SDM backend. There is no direct GitHub dependency in MLJAR's package metadata; the second command explicitly installs it.

Install your PyTorch build first:

CPU (Linux / Windows)

python -m pip install "torch==2.11.0" --index-url https://download.pytorch.org/whl/cpu

NVIDIA GPU (Linux / Windows)

For a CUDA 12.8-compatible driver:

python -m pip install "torch==2.11.0" --index-url https://download.pytorch.org/whl/cu128

For other drivers, choose a compatible build from the PyTorch installation selector. Keep PyTorch within >=2.7,<3.

Then run both commands:

python -m pip install "mljar-supervised[tfm] @ git+https://github.com/mljar/mljar-supervised.git"
python -m pip install "git+https://github.com/NVIDIA/structured-data-models.git@b97cb914e2bdcf0d165fc2c83b5fdf795aad6a69"

From an existing MLJAR checkout, replace the first command with python -m pip install ".[tfm]".

Extra	Dependencies it adds	Separate SDM install needed?
`[kumo]`, `[tabiclv2]`, `[tfm]`	PyTorch, Hugging Face Hub, psutil	Yes
`[all]`	Those dependencies plus pytabkit for RealMLP/TabM	Yes

The three TFM extras share dependencies; installing one does not restrict which of the two algorithms can be selected. SDM installs its own dependencies, including PyArrow and safetensors. A standard MLJAR installation does not load SDM or download model weights.

Verify the backend and device:

python -m pip check
python -c "from supervised.algorithms.sdm import check_dependencies; check_dependencies(); import torch; print('CUDA available:', torch.cuda.is_available())"

A CPU installation should print False. Select algorithms=["Kumo", "TabICLv2"] explicitly for CPU runs. Automatic Compete selection includes both when the tested backend and CUDA are available. Weights download from Hugging Face on first fit. See Kumo and TabICLv2 for model sizes, tuning, memory limits, and offline use.

RealMLP on CPU¶

Install PyTorch first. The examples pin version 2.11.0 for reproducibility; MLJAR's supported dependency range is torch>=2.7,<3.

Linux / Windows

python -m pip install "torch==2.11.0" --index-url https://download.pytorch.org/whl/cpu

macOS

python -m pip install "torch==2.11.0"

Use a Mac/Python combination with a compatible PyTorch wheel. This RealMLP integration uses CPU on macOS; Apple MPS acceleration is not enabled.

This selects CPU-only PyTorch. Other MLJAR dependencies, such as XGBoost on Linux, may still install NVIDIA libraries.

Then follow Install the RealMLP extra.

RealMLP on NVIDIA GPU¶

For Linux or Windows with an NVIDIA GPU and a CUDA 12.8-compatible driver:

nvidia-smi
python -m pip install "torch==2.11.0" --index-url https://download.pytorch.org/whl/cu128

If this build does not match your hardware or driver, choose a compatible build from the PyTorch installation selector or version-specific commands. Keep the installed torch version within >=2.7,<3. RealMLP needs torch; it does not require torchvision or torchaudio.

Then install the same RealMLP extra below. RealMLP currently supports a single CUDA device, not multi-GPU training.

Install the RealMLP extra¶

After installing your chosen PyTorch build, install from source:

git clone https://github.com/mljar/mljar-supervised.git
cd mljar-supervised
python -m pip install ".[realmlp]"

If you already have this checkout, run the final command from its root directory. For all optional model dependencies, use python -m pip install ".[all]" instead. [realmlp] and [tabm] share pytabkit/PyTorch dependencies. [all] also adds Hugging Face support for Kumo and TabICLv2, but their pinned SDM backend still requires the separate installation below.

After a PyPI release includes RealMLP, the equivalent commands will be:

python -m pip install "mljar-supervised[realmlp]"
# Or all optional model dependencies:
python -m pip install "mljar-supervised[all]"

Installing a compatible PyTorch build first lets pip reuse that build. The extra adds pytabkit and the other RealMLP dependencies.

Verify the installation¶

python -m pip check
python -c "from importlib.metadata import version; import torch; print('MLJAR:', version('mljar-supervised')); print('pytabkit:', version('pytabkit')); print('torch:', torch.__version__); print('CUDA build:', torch.version.cuda); print('CUDA available:', torch.cuda.is_available())"

For CPU-only PyTorch, expect CUDA build: None and CUDA available: False. For the NVIDIA setup, expect CUDA available: True. If it is false, check that this environment has the CUDA build installed and that nvidia-smi sees the GPU.

Try RealMLP¶

RealMLP is available only in Compete mode. AutoML(mode="Compete") includes it automatically when its optional dependencies are installed. Select it explicitly to train only RealMLP:

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from supervised import AutoML

X, y = make_classification(
    n_samples=1000, n_features=20, n_informative=10, random_state=42
)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)
automl = AutoML(
    mode="Compete",
    algorithms=["RealMLP"],
    total_time_limit=120,
    custom_params={"realmlp": {"device": "auto"}},
)
automl.fit(X_train, y_train)
print(automl.predict(X_test[:5]))

The default device="auto" selects CUDA when available, otherwise CPU. To force CPU training, use custom_params={"realmlp": {"device": "cpu"}}. Setting the training device does not change installed packages or remove CUDA downloads. GPU memory failures do not trigger an automatic retry on CPU.

For a short check of binary classification, multiclass classification and regression, run this script from the source checkout. Use a fresh output directory:

python examples/scripts/realmlp_synthetic.py --output AutoML_realmlp_check

The script uses CPU and eight epochs per task. See the RealMLP guide for supported data, tuning parameters, memory limits and explanations.