.. _sec_tabulargpu:

Training models with GPU support
================================


Training with GPU can significantly speed up base algorithms, and is a
necessity for text and vision models where training without GPU is
infeasibly slow. CUDA toolkit is required for GPU training. Please refer
to the `official documentation <https://docs.nvidia.com/cuda/>`__ for
the installation instructions.

.. code:: python

   predictor = TabularPredictor(label=label).fit(
       train_data,
       ag_args_fit={'num_gpus': 1}
   )

To enable GPU acceleration on only specific models, the same parameter
can be passed into model ``hyperparameters``:

.. code:: python

   hyperparameters = {
       'GBM': [
           {'ag_args_fit': {'num_gpus': 0}},  # Train with CPU
           {'ag_args_fit': {'num_gpus': 1}}   # Train with GPU
       ]
   }
   predictor = TabularPredictor(label=label).fit(
       train_data, 
       hyperparameters=hyperparameters, 
   )

Multi-modal
-----------

In :ref:`sec_tabularprediction_multimodal` tutorial we presented how
to train an ensemble which can utilize tabular, text and images. If
available GPUs don’t have enough VRAM to fit the default model, or it is
needed to speedup testing, different backends can be used:

Regular configuration is retrieved like this:

.. code:: python

    from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
    hyperparameters = get_hyperparameter_config('multimodal')
    hyperparameters




.. parsed-literal::
    :class: output

    {'NN_TORCH': {},
     'GBM': [{},
      {'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}},
      'GBMLarge'],
     'CAT': {},
     'XGB': {},
     'AG_TEXT_NN': {'presets': 'medium_quality_faster_train'},
     'AG_IMAGE_NN': {},
     'VW': {}}



Text models
~~~~~~~~~~~

Text model preset to use can be set via:

.. code:: python

   hyperparameters['AG_TEXT_NN'] = ['<preset>']

Available text model presets:

.. code:: python

    from autogluon.text.text_prediction.presets import list_text_presets
    list_text_presets()




.. parsed-literal::
    :class: output

    ['default',
     'medium_quality_faster_train',
     'high_quality',
     'best_quality',
     'multilingual']



Vision models
~~~~~~~~~~~~~

Text model preset to use can be set via:

.. code:: python

   hyperparameters['AG_IMAGE_NN'] = {'model': '<model>'}

The list of available text model presets is:

.. code:: python

    from autogluon.vision.predictor.predictor import _get_supported_models
    _get_supported_models()[:10]  # there're more, we just show a few




.. parsed-literal::
    :class: output

    ['adv_inception_v3',
     'bat_resnext26ts',
     'beit_base_patch16_224',
     'beit_base_patch16_224_in22k',
     'beit_base_patch16_384',
     'beit_large_patch16_224',
     'beit_large_patch16_224_in22k',
     'beit_large_patch16_384',
     'beit_large_patch16_512',
     'botnet26t_256']



Enabling GPU for LightGBM
-------------------------

The default installation of LightGBM does not support GPU training,
however GPU support can be enabled via a special install. If
``num_gpus`` is set, the following warning will be displayed:

::

   Warning: GPU mode might not be installed for LightGBM, GPU training raised an exception. Falling back to CPU training...Refer to LightGBM GPU documentation: https://github.com/Microsoft/LightGBM/tree/master/python-package#build-gpu-versionOne possible method is:  pip uninstall lightgbm -y   pip install lightgbm --install-option=--gpu

If the suggested commands do not work, uninstall existing lightgbm
``pip uninstall -y lightgbm`` and install from sources following the
instructions in the `official
guide <https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html>`__.
The optional `Install Python
Interface <https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html#install-python-interface-optional>`__
section is also required to make it work with AutoGluon.

Troubleshooting
---------------

::

   OSError: libcudnn.so.X: cannot open shared object file: No such file or directory
   OSError: libcudart.so.XX.Y: cannot open shared object file: No such file or directory

This might happen when installed cuda is not matching MXNet library. To
resolve it, get the cuda version installed in system: ``nvcc --version``
or ``nvidia-smi``. Then install matching ``mxnet-cuXXX`` package (CUDA
``11.0`` -> ``mxnet-cu110``, etc.)

::

   pip install 'mxnet-cu110<2.0.0'