.. _sec_tabularprediction_multimodal:

Multimodal Data Tables: Tabular, Text, and Image
================================================


**Tip**: Prior to reading this tutorial, it is recommended to have a
basic understanding of the TabularPredictor API covered in
:ref:`sec_tabularquick`.

In this tutorial, we will train a multi-modal ensemble using data that
contains image, text, and tabular features.

Note: A GPU is required for this tutorial in order to train the image
and text models. Additionally, GPU installations are required for MXNet
and Torch with appropriate CUDA versions.

The PetFinder Dataset
---------------------

We will be using the `PetFinder
dataset <https://www.kaggle.com/c/petfinder-adoption-prediction>`__. The
PetFinder dataset provides information about shelter animals that appear
on their adoption profile with the goal to predict the adoption rate of
the animal. The end goal is for rescue shelters to use the predicted
adoption rate to identify animals whose profiles could be improved so
that they can find a home.

Each animal's adoption profile contains a variety of information, such
as pictures of the animal, a text description of the animal, and various
tabular features such as age, breed, name, color, and more.

To get started, we first need to download the dataset. Datasets that
contain images require more than a CSV file, so the dataset is packaged
in a zip file in S3. We will first download it and unzip the contents:

.. code:: python

    download_dir = './ag_petfinder_tutorial'
    zip_file = 'https://automl-mm-bench.s3.amazonaws.com/petfinder_kaggle.zip'

.. code:: python

    from autogluon.core.utils.loaders import load_zip
    load_zip.unzip(zip_file, unzip_dir=download_dir)


.. parsed-literal::
    :class: output

    Downloading ./ag_petfinder_tutorial/file.zip from https://automl-mm-bench.s3.amazonaws.com/petfinder_kaggle.zip...


.. parsed-literal::
    :class: output

    100%|██████████| 2.00G/2.00G [03:14<00:00, 10.3MiB/s]


Now that the data is download and unzipped, let's take a look at the
contents:

.. code:: python

    import os
    os.listdir(download_dir)




.. parsed-literal::
    :class: output

    ['petfinder_processed', 'file.zip']



'file.zip' is the original zip file we downloaded, and
'petfinder\_processed' is a directory containing the dataset files.

.. code:: python

    dataset_path = download_dir + '/petfinder_processed'
    os.listdir(dataset_path)




.. parsed-literal::
    :class: output

    ['test.csv', 'dev.csv', 'test_images', 'train_images', 'train.csv']



Here we can see the train, test, and dev CSV files, as well as two
directories: 'test\_images' and 'train\_images' which contain the image
JPG files.

Note: We will be using the dev data as testing data as dev contains the
ground truth labels for showing scores via ``predictor.leaderboard``.

Let's take a peek at the first 10 files inside of the 'train\_images'
directory:

.. code:: python

    os.listdir(dataset_path + '/train_images')[:10]




.. parsed-literal::
    :class: output

    ['ca587cb42-1.jpg',
     'ae00eded4-4.jpg',
     '6e3457b81-2.jpg',
     'acb248693-1.jpg',
     '0bd867d1b-1.jpg',
     'fa53dd6cd-1.jpg',
     '9726ab93e-1.jpg',
     '39818f12c-2.jpg',
     '90ce48a71-2.jpg',
     '2ece6b26b-1.jpg']



As expected, these are the images we will be training with alongside the
other features.

Next, we will load the train and dev CSV files:

.. code:: python

    import pandas as pd
    
    train_data = pd.read_csv(f'{dataset_path}/train.csv', index_col=0)
    test_data = pd.read_csv(f'{dataset_path}/dev.csv', index_col=0)

.. code:: python

    train_data.head(3)




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Type</th>
          <th>Name</th>
          <th>Age</th>
          <th>Breed1</th>
          <th>Breed2</th>
          <th>Gender</th>
          <th>Color1</th>
          <th>Color2</th>
          <th>Color3</th>
          <th>MaturitySize</th>
          <th>...</th>
          <th>Quantity</th>
          <th>Fee</th>
          <th>State</th>
          <th>RescuerID</th>
          <th>VideoAmt</th>
          <th>Description</th>
          <th>PetID</th>
          <th>PhotoAmt</th>
          <th>AdoptionSpeed</th>
          <th>Images</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>10721</th>
          <td>1</td>
          <td>Elbi</td>
          <td>2</td>
          <td>307</td>
          <td>307</td>
          <td>2</td>
          <td>5</td>
          <td>0</td>
          <td>0</td>
          <td>3</td>
          <td>...</td>
          <td>1</td>
          <td>0</td>
          <td>41336</td>
          <td>e9a86209c54f589ba72c345364cf01aa</td>
          <td>0</td>
          <td>I'm looking for people to adopt my dog</td>
          <td>e4b90955c</td>
          <td>4.0</td>
          <td>4</td>
          <td>train_images/e4b90955c-1.jpg;train_images/e4b9...</td>
        </tr>
        <tr>
          <th>13114</th>
          <td>2</td>
          <td>Darling</td>
          <td>4</td>
          <td>266</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
          <td>0</td>
          <td>0</td>
          <td>2</td>
          <td>...</td>
          <td>1</td>
          <td>0</td>
          <td>41401</td>
          <td>01f954cdf61526daf3fbeb8a074be742</td>
          <td>0</td>
          <td>Darling was born at the back lane of Jalan Alo...</td>
          <td>a0c1384d1</td>
          <td>5.0</td>
          <td>3</td>
          <td>train_images/a0c1384d1-1.jpg;train_images/a0c1...</td>
        </tr>
        <tr>
          <th>13194</th>
          <td>1</td>
          <td>Wolf</td>
          <td>3</td>
          <td>307</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
          <td>2</td>
          <td>0</td>
          <td>2</td>
          <td>...</td>
          <td>1</td>
          <td>0</td>
          <td>41332</td>
          <td>6e19409f2847326ce3b6d0cec7e42f81</td>
          <td>0</td>
          <td>I found Wolf about a month ago stuck in a drai...</td>
          <td>cf357f057</td>
          <td>7.0</td>
          <td>4</td>
          <td>train_images/cf357f057-1.jpg;train_images/cf35...</td>
        </tr>
      </tbody>
    </table>
    <p>3 rows × 25 columns</p>
    </div>



Looking at the first 3 examples, we can tell that there is a variety of
tabular features, a text description ('Description'), and an image path
('Images').

For the PetFinder dataset, we will try to predict the speed of adoption
for the animal ('AdoptionSpeed'), grouped into 5 categories. This means
that we are dealing with a multi-class classification problem.

.. code:: python

    label = 'AdoptionSpeed'
    image_col = 'Images'

Preparing the image column
--------------------------

Let's take a look at what a value in the image column looks like:

.. code:: python

    train_data[image_col].iloc[0]




.. parsed-literal::
    :class: output

    'train_images/e4b90955c-1.jpg;train_images/e4b90955c-2.jpg;train_images/e4b90955c-3.jpg;train_images/e4b90955c-4.jpg'



Currently, AutoGluon only supports one image per row. Since the
PetFinder dataset contains one or more images per row, we first need to
preprocess the image column to only contain the first image of each row.

.. code:: python

    train_data[image_col] = train_data[image_col].apply(lambda ele: ele.split(';')[0])
    test_data[image_col] = test_data[image_col].apply(lambda ele: ele.split(';')[0])
    
    train_data[image_col].iloc[0]




.. parsed-literal::
    :class: output

    'train_images/e4b90955c-1.jpg'



AutoGluon loads images based on the file path provided by the image
column.

Here we update the path to point to the correct location on disk:

.. code:: python

    def path_expander(path, base_folder):
        path_l = path.split(';')
        return ';'.join([os.path.abspath(os.path.join(base_folder, path)) for path in path_l])
    
    train_data[image_col] = train_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))
    test_data[image_col] = test_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))
    
    train_data[image_col].iloc[0]




.. parsed-literal::
    :class: output

    '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/ag_petfinder_tutorial/petfinder_processed/train_images/e4b90955c-1.jpg'



.. code:: python

    train_data.head(3)




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Type</th>
          <th>Name</th>
          <th>Age</th>
          <th>Breed1</th>
          <th>Breed2</th>
          <th>Gender</th>
          <th>Color1</th>
          <th>Color2</th>
          <th>Color3</th>
          <th>MaturitySize</th>
          <th>...</th>
          <th>Quantity</th>
          <th>Fee</th>
          <th>State</th>
          <th>RescuerID</th>
          <th>VideoAmt</th>
          <th>Description</th>
          <th>PetID</th>
          <th>PhotoAmt</th>
          <th>AdoptionSpeed</th>
          <th>Images</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>10721</th>
          <td>1</td>
          <td>Elbi</td>
          <td>2</td>
          <td>307</td>
          <td>307</td>
          <td>2</td>
          <td>5</td>
          <td>0</td>
          <td>0</td>
          <td>3</td>
          <td>...</td>
          <td>1</td>
          <td>0</td>
          <td>41336</td>
          <td>e9a86209c54f589ba72c345364cf01aa</td>
          <td>0</td>
          <td>I'm looking for people to adopt my dog</td>
          <td>e4b90955c</td>
          <td>4.0</td>
          <td>4</td>
          <td>/var/lib/jenkins/workspace/workspace/autogluon...</td>
        </tr>
        <tr>
          <th>13114</th>
          <td>2</td>
          <td>Darling</td>
          <td>4</td>
          <td>266</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
          <td>0</td>
          <td>0</td>
          <td>2</td>
          <td>...</td>
          <td>1</td>
          <td>0</td>
          <td>41401</td>
          <td>01f954cdf61526daf3fbeb8a074be742</td>
          <td>0</td>
          <td>Darling was born at the back lane of Jalan Alo...</td>
          <td>a0c1384d1</td>
          <td>5.0</td>
          <td>3</td>
          <td>/var/lib/jenkins/workspace/workspace/autogluon...</td>
        </tr>
        <tr>
          <th>13194</th>
          <td>1</td>
          <td>Wolf</td>
          <td>3</td>
          <td>307</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
          <td>2</td>
          <td>0</td>
          <td>2</td>
          <td>...</td>
          <td>1</td>
          <td>0</td>
          <td>41332</td>
          <td>6e19409f2847326ce3b6d0cec7e42f81</td>
          <td>0</td>
          <td>I found Wolf about a month ago stuck in a drai...</td>
          <td>cf357f057</td>
          <td>7.0</td>
          <td>4</td>
          <td>/var/lib/jenkins/workspace/workspace/autogluon...</td>
        </tr>
      </tbody>
    </table>
    <p>3 rows × 25 columns</p>
    </div>



Analyzing an example row
------------------------

Now that we have preprocessed the image column, let's take a look at an
example row of data and display the text description and the picture.

.. code:: python

    example_row = train_data.iloc[1]
    
    example_row




.. parsed-literal::
    :class: output

    Type                                                             2
    Name                                                       Darling
    Age                                                              4
    Breed1                                                         266
    Breed2                                                           0
    Gender                                                           1
    Color1                                                           1
    Color2                                                           0
    Color3                                                           0
    MaturitySize                                                     2
    FurLength                                                        1
    Vaccinated                                                       2
    Dewormed                                                         2
    Sterilized                                                       2
    Health                                                           1
    Quantity                                                         1
    Fee                                                              0
    State                                                        41401
    RescuerID                         01f954cdf61526daf3fbeb8a074be742
    VideoAmt                                                         0
    Description      Darling was born at the back lane of Jalan Alo...
    PetID                                                    a0c1384d1
    PhotoAmt                                                       5.0
    AdoptionSpeed                                                    3
    Images           /var/lib/jenkins/workspace/workspace/autogluon...
    Name: 13114, dtype: object



.. code:: python

    example_row['Description']




.. parsed-literal::
    :class: output

    'Darling was born at the back lane of Jalan Alor and was foster by a feeder. All his siblings had died of accident. His mother and grandmother had just been spayed. Darling make a great condo/apartment cat. He love to play a lot. He would make a great companion for someone looking for a cat to love.'



.. code:: python

    example_image = example_row['Images']
    
    from IPython.display import Image, display
    pil_img = Image(filename=example_image)
    display(pil_img)



.. figure:: output_tabular-multimodal_e625cb_24_0.jpg


The PetFinder dataset is fairly large. For the purposes of the tutorial,
we will sample 500 rows for training.

Training on large multi-modal datasets can be very computationally
intensive, especially if using the ``best_quality`` preset in AutoGluon.
When prototyping, it is recommended to sample your data to get an idea
of which models are worth training, then gradually train with larger
amounts of data and longer time limits as you would with any other
machine learning algorithm.

.. code:: python

    train_data = train_data.sample(500, random_state=0)

Constructing the FeatureMetadata
--------------------------------

Next, let's see what AutoGluon infers the feature types to be by
constructing a FeatureMetadata object from the training data:

.. code:: python

    from autogluon.tabular import FeatureMetadata
    feature_metadata = FeatureMetadata.from_df(train_data)
    
    print(feature_metadata)


.. parsed-literal::
    :class: output

    ('float', [])        :  1 | ['PhotoAmt']
    ('int', [])          : 19 | ['Type', 'Age', 'Breed1', 'Breed2', 'Gender', ...]
    ('object', [])       :  4 | ['Name', 'RescuerID', 'PetID', 'Images']
    ('object', ['text']) :  1 | ['Description']


Notice that FeatureMetadata automatically identified the column
'Description' as text, so we don't need to manually specify that it is
text.

In order to leverage images, we need to tell AutoGluon which column
contains the image path. We can do this by specifying a FeatureMetadata
object and adding the 'image\_path' special type to the image column. We
later pass this custom FeatureMetadata to TabularPredictor.fit.

.. code:: python

    feature_metadata = feature_metadata.add_special_types({image_col: ['image_path']})
    
    print(feature_metadata)


.. parsed-literal::
    :class: output

    ('float', [])              :  1 | ['PhotoAmt']
    ('int', [])                : 19 | ['Type', 'Age', 'Breed1', 'Breed2', 'Gender', ...]
    ('object', [])             :  3 | ['Name', 'RescuerID', 'PetID']
    ('object', ['image_path']) :  1 | ['Images']
    ('object', ['text'])       :  1 | ['Description']


Specifying the hyperparameters
------------------------------

Next, we need to specify the models we want to train with. This is done
via the ``hyperparameters`` argument to TabularPredictor.fit.

AutoGluon has a predefined config that works well for multimodal
datasets called 'multimodal'. We can access it via:

.. 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': {}}



This hyperparameter config will train a variety of Tabular models as
well as finetune an Electra BERT text model, and a ResNet image model.

Fitting with TabularPredictor
-----------------------------

Now we will train a TabularPredictor on the dataset, using the feature
metadata and hyperparameters we defined prior. This TabularPredictor
will leverage tabular, text, and image features all at once.

.. code:: python

    from autogluon.tabular import TabularPredictor
    predictor = TabularPredictor(label=label).fit(
        train_data=train_data,
        hyperparameters=hyperparameters,
        feature_metadata=feature_metadata,
        time_limit=900,
    )


.. parsed-literal::
    :class: output

    No path specified. Models will be saved in: "AutogluonModels/ag-20220601_000416/"
    Beginning AutoGluon training ... Time limit = 900s
    AutoGluon will save models to "AutogluonModels/ag-20220601_000416/"
    AutoGluon Version:  0.4.2b20220531
    Python Version:     3.9.13
    Operating System:   Linux
    Train Data Rows:    500
    Train Data Columns: 24
    Label Column: AdoptionSpeed
    Preprocessing data ...
    AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).
    	5 unique label values:  [2, 3, 4, 0, 1]
    	If 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
    Train Data Class Count: 5
    Using Feature Generators to preprocess the data ...
    Fitting AutoMLPipelineFeatureGenerator...
    	Available Memory:                    22509.88 MB
    	Train Data (Original)  Memory Usage: 0.51 MB (0.0% of available memory)
    	Stage 1 Generators:
    		Fitting AsTypeFeatureGenerator...
    			Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
    	Stage 2 Generators:
    		Fitting FillNaFeatureGenerator...
    	Stage 3 Generators:
    		Fitting IdentityFeatureGenerator...
    		Fitting IdentityFeatureGenerator...
    			Fitting RenameFeatureGenerator...
    		Fitting CategoryFeatureGenerator...
    			Fitting CategoryMemoryMinimizeFeatureGenerator...
    		Fitting TextSpecialFeatureGenerator...
    			Fitting BinnedFeatureGenerator...
    			Fitting DropDuplicatesFeatureGenerator...
    		Fitting TextNgramFeatureGenerator...
    			Fitting CountVectorizer for text features: ['Description']
    			CountVectorizer fit with vocabulary size = 170
    		Fitting IdentityFeatureGenerator...
    		Fitting IsNanFeatureGenerator...
    	Stage 4 Generators:
    		Fitting DropUniqueFeatureGenerator...
    	Unused Original Features (Count: 1): ['PetID']
    		These features were not used to generate any of the output features. Add a feature generator compatible with these features to utilize them.
    		Features can also be unused if they carry very little information, such as being categorical but having almost entirely unique values or being duplicates of other features.
    		These features do not need to be present at inference time.
    		('object', []) : 1 | ['PetID']
    	Types of features in original data (raw dtype, special dtypes):
    		('float', [])              :  1 | ['PhotoAmt']
    		('int', [])                : 18 | ['Type', 'Age', 'Breed1', 'Breed2', 'Gender', ...]
    		('object', [])             :  2 | ['Name', 'RescuerID']
    		('object', ['image_path']) :  1 | ['Images']
    		('object', ['text'])       :  1 | ['Description']
    	Types of features in processed data (raw dtype, special dtypes):
    		('category', [])                    :   2 | ['Name', 'RescuerID']
    		('category', ['text_as_category'])  :   1 | ['Description']
    		('float', [])                       :   1 | ['PhotoAmt']
    		('int', [])                         :  17 | ['Age', 'Breed1', 'Breed2', 'Gender', 'Color1', ...]
    		('int', ['binned', 'text_special']) :  24 | ['Description.char_count', 'Description.word_count', 'Description.capital_ratio', 'Description.lower_ratio', 'Description.digit_ratio', ...]
    		('int', ['bool'])                   :   1 | ['Type']
    		('int', ['text_ngram'])             : 171 | ['__nlp__.about', '__nlp__.active', '__nlp__.active and', '__nlp__.adopt', '__nlp__.adopted', ...]
    		('object', ['image_path'])          :   1 | ['Images']
    		('object', ['text'])                :   1 | ['Description_raw_text']
    	0.5s = Fit runtime
    	23 features in original data used to generate 219 features in processed data.
    	Train Data (Processed) Memory Usage: 0.58 MB (0.0% of available memory)
    Data preprocessing and feature engineering runtime = 0.51s ...
    AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
    	To change this, specify the eval_metric parameter of Predictor()
    Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 400, Val Rows: 100
    Fitting 9 L1 models ...
    Fitting model: LightGBM ... Training model for up to 899.49s of the 899.49s of remaining time.
    	0.34	 = Validation score   (accuracy)
    	1.13s	 = Training   runtime
    	0.01s	 = Validation runtime
    Fitting model: LightGBMXT ... Training model for up to 898.35s of the 898.35s of remaining time.
    	0.34	 = Validation score   (accuracy)
    	0.76s	 = Training   runtime
    	0.01s	 = Validation runtime
    Fitting model: CatBoost ... Training model for up to 897.57s of the 897.57s of remaining time.
    	0.3	 = Validation score   (accuracy)
    	2.57s	 = Training   runtime
    	0.01s	 = Validation runtime
    Fitting model: XGBoost ... Training model for up to 894.98s of the 894.98s of remaining time.
    	0.35	 = Validation score   (accuracy)
    	1.59s	 = Training   runtime
    	0.01s	 = Validation runtime
    Fitting model: NeuralNetTorch ... Training model for up to 893.37s of the 893.37s of remaining time.
    	0.35	 = Validation score   (accuracy)
    	1.72s	 = Training   runtime
    	0.02s	 = Validation runtime
    Fitting model: VowpalWabbit ... Training model for up to 891.62s of the 891.62s of remaining time.
    	0.24	 = Validation score   (accuracy)
    	0.59s	 = Training   runtime
    	0.03s	 = Validation runtime
    Fitting model: LightGBMLarge ... Training model for up to 890.72s of the 890.72s of remaining time.
    	0.37	 = Validation score   (accuracy)
    	2.35s	 = Training   runtime
    	0.01s	 = Validation runtime
    Fitting model: TextPredictor ... Training model for up to 888.36s of the 888.35s of remaining time.
    Global seed set to 0
    Auto select gpus: [0]
    Using 16bit native Automatic Mixed Precision (AMP)
    GPU available: True, used: True
    TPU available: False, using: 0 TPU cores
    IPU available: False, using: 0 IPUs
    HPU available: False, using: 0 HPUs
    LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
    
      | Name              | Type                | Params
    ----------------------------------------------------------
    0 | model             | MultimodalFusionMLP | 13.7 M
    1 | validation_metric | Accuracy            | 0     
    2 | loss_func         | CrossEntropyLoss    | 0     
    ----------------------------------------------------------
    13.7 M    Trainable params
    0         Non-trainable params
    13.7 M    Total params
    27.305    Total estimated model params size (MB)
    Epoch 0, global step 1: 'val_accuracy' reached 0.24000 (best 0.24000), saving model to '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/TextPredictor/epoch=0-step=1.ckpt' as top 3
    Epoch 0, global step 4: 'val_accuracy' reached 0.28000 (best 0.28000), saving model to '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/TextPredictor/epoch=0-step=4.ckpt' as top 3
    Epoch 1, global step 5: 'val_accuracy' reached 0.25000 (best 0.28000), saving model to '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/TextPredictor/epoch=1-step=5.ckpt' as top 3
    Epoch 1, global step 8: 'val_accuracy' reached 0.27000 (best 0.28000), saving model to '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/TextPredictor/epoch=1-step=8.ckpt' as top 3
    Epoch 2, global step 9: 'val_accuracy' reached 0.30000 (best 0.30000), saving model to '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/TextPredictor/epoch=2-step=9.ckpt' as top 3
    Epoch 2, global step 12: 'val_accuracy' reached 0.28000 (best 0.30000), saving model to '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/TextPredictor/epoch=2-step=12.ckpt' as top 3
    Epoch 3, global step 13: 'val_accuracy' was not in top 3
    Epoch 3, global step 16: 'val_accuracy' was not in top 3
    Epoch 4, global step 17: 'val_accuracy' was not in top 3
    Epoch 4, global step 20: 'val_accuracy' was not in top 3
    Epoch 5, global step 21: 'val_accuracy' reached 0.30000 (best 0.30000), saving model to '/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/TextPredictor/epoch=5-step=21.ckpt' as top 3
    Epoch 5, global step 24: 'val_accuracy' was not in top 3
    Epoch 6, global step 25: 'val_accuracy' was not in top 3
    Epoch 6, global step 28: 'val_accuracy' was not in top 3
    Epoch 7, global step 29: 'val_accuracy' was not in top 3
    Auto select gpus: [0]
    /var/lib/jenkins/miniconda3/envs/autogluon-tutorial-tabular-v3/lib/python3.9/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
      rank_zero_warn(
    HPU available: False, using: 0 HPUs
    Auto select gpus: [0]
    /var/lib/jenkins/miniconda3/envs/autogluon-tutorial-tabular-v3/lib/python3.9/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
      rank_zero_warn(
    HPU available: False, using: 0 HPUs
    Auto select gpus: [0]
    /var/lib/jenkins/miniconda3/envs/autogluon-tutorial-tabular-v3/lib/python3.9/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
      rank_zero_warn(
    HPU available: False, using: 0 HPUs
    Auto select gpus: [0]
    /var/lib/jenkins/miniconda3/envs/autogluon-tutorial-tabular-v3/lib/python3.9/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
      rank_zero_warn(
    HPU available: False, using: 0 HPUs
    	0.3	 = Validation score   (accuracy)
    	51.77s	 = Training   runtime
    	0.58s	 = Validation runtime
    Fitting model: ImagePredictor ... Training model for up to 835.89s of the 835.89s of remaining time.
    /var/lib/jenkins/miniconda3/envs/autogluon-tutorial-tabular-v3/lib/python3.9/site-packages/gluoncv/__init__.py:40: UserWarning: Both `mxnet==1.9.1` and `torch==1.10.2+cu102` are installed. You might encounter increased GPU memory footprint if both framework are used at the same time.
      warnings.warn(f'Both `mxnet=={mx.__version__}` and `torch=={torch.__version__}` are installed. '
    ImagePredictor sets accuracy as default eval_metric for classification problems.
    The number of requested GPUs is greater than the number of available GPUs.Reduce the number to 1
    modified configs(<old> != <new>): {
    root.img_cls.model   resnet101 != resnet50
    root.misc.num_workers 4 != 8
    root.misc.seed       42 != 716
    root.train.early_stop_max_value 1.0 != inf
    root.train.batch_size 32 != 16
    root.train.early_stop_baseline 0.0 != -inf
    root.train.epochs    200 != 15
    root.train.early_stop_patience -1 != 10
    }
    Saved config to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/ImagePredictor/e8ac7ebc/.trial_0/config.yaml
    Model resnet50 created, param count:                                         23518277
    AMP not enabled. Training in float32.
    Disable EMA as it is not supported for now.
    Start training from [Epoch 0]
    [Epoch 0] training: accuracy=0.185000
    [Epoch 0] speed: 81 samples/sec	time cost: 4.703040
    [Epoch 0] validation: top1=0.220000 top5=1.000000
    [Epoch 0] Current best top-1: 0.220000 vs previous -inf, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/ImagePredictor/e8ac7ebc/.trial_0/best_checkpoint.pkl
    [Epoch 1] training: accuracy=0.265000
    [Epoch 1] speed: 91 samples/sec	time cost: 4.208941
    [Epoch 1] validation: top1=0.300000 top5=1.000000
    [Epoch 1] Current best top-1: 0.300000 vs previous 0.220000, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220601_000416/models/ImagePredictor/e8ac7ebc/.trial_0/best_checkpoint.pkl
    [Epoch 2] training: accuracy=0.320000
    [Epoch 2] speed: 91 samples/sec	time cost: 4.215491
    [Epoch 2] validation: top1=0.260000 top5=1.000000
    [Epoch 3] training: accuracy=0.335000
    [Epoch 3] speed: 91 samples/sec	time cost: 4.219242
    [Epoch 3] validation: top1=0.200000 top5=1.000000
    [Epoch 4] training: accuracy=0.350000
    [Epoch 4] speed: 90 samples/sec	time cost: 4.246616
    [Epoch 4] validation: top1=0.230000 top5=1.000000
    [Epoch 5] training: accuracy=0.352500
    [Epoch 5] speed: 90 samples/sec	time cost: 4.240837
    [Epoch 5] validation: top1=0.230000 top5=1.000000
    [Epoch 6] training: accuracy=0.425000
    [Epoch 6] speed: 89 samples/sec	time cost: 4.277815
    [Epoch 6] validation: top1=0.240000 top5=1.000000
    [Epoch 7] training: accuracy=0.400000
    [Epoch 7] speed: 89 samples/sec	time cost: 4.279178
    [Epoch 7] validation: top1=0.170000 top5=1.000000
    [Epoch 8] training: accuracy=0.415000
    [Epoch 8] speed: 89 samples/sec	time cost: 4.301166
    [Epoch 8] validation: top1=0.220000 top5=1.000000
    [Epoch 9] training: accuracy=0.415000
    [Epoch 9] speed: 89 samples/sec	time cost: 4.301456
    [Epoch 9] validation: top1=0.220000 top5=1.000000
    [Epoch 10] training: accuracy=0.410000
    [Epoch 10] speed: 88 samples/sec	time cost: 4.327386
    [Epoch 10] validation: top1=0.230000 top5=1.000000
    [Epoch 11] training: accuracy=0.430000
    [Epoch 11] speed: 88 samples/sec	time cost: 4.344148
    [Epoch 11] validation: top1=0.220000 top5=1.000000
    [Epoch 12] EarlyStop after 10 epochs: no better than 0.3
    Applying the state from the best checkpoint...
    	0.3	 = Validation score   (accuracy)
    	61.3s	 = Training   runtime
    	0.94s	 = Validation runtime
    Auto select gpus: [0]
    /var/lib/jenkins/miniconda3/envs/autogluon-tutorial-tabular-v3/lib/python3.9/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
      rank_zero_warn(
    HPU available: False, using: 0 HPUs
    Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 769.91s of remaining time.
    	0.37	 = Validation score   (accuracy)
    	0.2s	 = Training   runtime
    	0.0s	 = Validation runtime
    AutoGluon training complete, total runtime = 130.31s ... Best model: "WeightedEnsemble_L2"
    TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20220601_000416/")


After the predictor is fit, we can take a look at the leaderboard and
see the performance of the various models:

.. code:: python

    leaderboard = predictor.leaderboard(test_data)


.. parsed-literal::
    :class: output

    Auto select gpus: [0]
    /var/lib/jenkins/miniconda3/envs/autogluon-tutorial-tabular-v3/lib/python3.9/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
      rank_zero_warn(
    HPU available: False, using: 0 HPUs


.. parsed-literal::
    :class: output

                     model  score_test  score_val  pred_time_test  pred_time_val   fit_time  pred_time_test_marginal  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
    0        LightGBMLarge    0.323775       0.37        0.015434       0.006781   2.350454                 0.015434                0.006781           2.350454            1       True          7
    1  WeightedEnsemble_L2    0.323775       0.37        0.317460       0.007168   2.554141                 0.302025                0.000387           0.203686            2       True         10
    2       NeuralNetTorch    0.319773       0.35        0.062715       0.019647   1.723534                 0.062715                0.019647           1.723534            1       True          5
    3             CatBoost    0.319106       0.30        0.026970       0.013039   2.572865                 0.026970                0.013039           2.572865            1       True          3
    4           LightGBMXT    0.315772       0.34        0.038583       0.006673   0.757124                 0.038583                0.006673           0.757124            1       True          2
    5       ImagePredictor    0.313104       0.30       11.259770       0.937059  61.302684                11.259770                0.937059          61.302684            1       True          9
    6        TextPredictor    0.293431       0.30       11.519300       0.583328  51.770657                11.519300                0.583328          51.770657            1       True          8
    7              XGBoost    0.292431       0.35        0.044904       0.012723   1.591348                 0.044904                0.012723           1.591348            1       True          4
    8             LightGBM    0.289763       0.34        0.020977       0.006173   1.127079                 0.020977                0.006173           1.127079            1       True          1
    9         VowpalWabbit    0.278760       0.24        0.663475       0.027602   0.588581                 0.663475                0.027602           0.588581            1       True          6


That's all it takes to train with image, text, and tabular data (at the
same time) using AutoGluon!

For an in-depth tutorial on text + tabular multimodal functionality,
refer to :ref:`sec_tabularprediction_text_multimodal`.

For more tutorials, refer to :ref:`sec_tabularquick` and
:ref:`sec_tabularadvanced`.