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# AutoGluon

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```{image} ./_static/autogluon-s.png
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Fast and Accurate ML in 3 Lines of Code

```{button-link} tutorials/tabular/tabular-quick-start.html
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Get Started
```

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Quick Prototyping 
: Build machine learning solutions on raw data in a few lines of code.

State-of-the-art Techniques
: Automatically utilize SOTA models without expert knowledge.

Easy to Deploy
: Move from experimentation to production with cloud predictors and pre-built containers.

Customizable
: Extensible with custom feature processing, models, and metrics.

## {octicon}`rocket` Quick Examples

:::{dropdown} Tabular
:animate: fade-in-slide-down
:open:
:color: primary

Predict the `class` column in a data table:

```python
from autogluon.tabular import TabularDataset, TabularPredictor

data_root = 'https://autogluon.s3.amazonaws.com/datasets/Inc/'
train_data = TabularDataset(data_root + 'train.csv')
test_data = TabularDataset(data_root + 'test.csv')

predictor = TabularPredictor(label='class').fit(train_data=train_data)
predictions = predictor.predict(test_data)
```
:::


::::{dropdown} Multimodal
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:::{tab} Text Classification
```python
from autogluon.multimodal import MultiModalPredictor
from autogluon.core.utils.loaders import load_pd

data_root = 'https://autogluon-text.s3-accelerate.amazonaws.com/glue/sst/'
train_data = load_pd.load(data_root + 'train.parquet')
test_data = load_pd.load(data_root + 'dev.parquet')

predictor = MultiModalPredictor(label='label').fit(train_data=train_data)
predictions = predictor.predict(test_data)
```
:::

:::{tab} Image Classification

```python
from autogluon.multimodal import MultiModalPredictor
from autogluon.multimodal.utils.misc import shopee_dataset

train_data, test_data = shopee_dataset('./automm_shopee_data')

predictor = MultiModalPredictor(label='label').fit(train_data=train_data)
predictions = predictor.predict(test_data)
```
:::

:::{tab} NER
```python
from autogluon.multimodal import MultiModalPredictor
from autogluon.core.utils.loaders import load_pd

data_root = 'https://automl-mm-bench.s3.amazonaws.com/ner/mit-movies/'
train_data = load_pd.load(data_root + 'train.csv')
test_data = load_pd.load(data_root + 'test.csv')

predictor = MultiModalPredictor(problem_type="ner", label="entity_annotations")

predictor.fit(train_data)
predictor.evaluate(test_data)

sentence = "Game of Thrones is an American fantasy drama television series created" +
           "by David Benioff"
prediction = predictor.predict({ 'text_snippet': [sentence]})
```
:::

:::{tab} Matching
```python
from autogluon.multimodal import MultiModalPredictor, utils
import ir_datasets
import pandas as pd

dataset = ir_datasets.load("beir/fiqa/dev")
docs_df = pd.DataFrame(dataset.docs_iter()).set_index("doc_id")

predictor = MultiModalPredictor(problem_type="text_similarity")

doc_embedding = predictor.extract_embedding(docs_df)
q_embedding = predictor.extract_embedding([
  "what happened when the dot com bubble burst?"
])

similarity = utils.compute_semantic_similarity(q_embedding, doc_embedding)
```
:::

:::{tab} Object Detection
```ipython
# Install mmcv-related dependencies
!mim install "mmcv==2.1.0"
!pip install "mmdet==3.2.0"

from autogluon.multimodal import MultiModalPredictor
from autogluon.core.utils.loaders import load_zip

data_zip = "https://automl-mm-bench.s3.amazonaws.com/object_detection_dataset/" + \
           "tiny_motorbike_coco.zip"
load_zip.unzip(data_zip, unzip_dir=".")

train_path = "./tiny_motorbike/Annotations/trainval_cocoformat.json"
test_path = "./tiny_motorbike/Annotations/test_cocoformat.json"

predictor = MultiModalPredictor(
  problem_type="object_detection",
  sample_data_path=train_path
)

predictor.fit(train_path)
score = predictor.evaluate(test_path)

pred = predictor.predict({"image": ["./tiny_motorbike/JPEGImages/000038.jpg"]})
```
:::

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:::{dropdown} Time Series
:animate: fade-in-slide-down
:color: primary

Forecast future values of time series:

```python
from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor

data = TimeSeriesDataFrame('https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly/train.csv')

predictor = TimeSeriesPredictor(target='target', prediction_length=48).fit(data)
predictions = predictor.predict(data)
```
:::


## {octicon}`package` Installation

![](https://img.shields.io/pypi/pyversions/autogluon)
![](https://img.shields.io/pypi/v/autogluon.svg)
![](https://img.shields.io/pypi/dm/autogluon)

Install AutoGluon using [pip](https://pip.pypa.io/en/stable/installation/):

```bash
pip install autogluon
```

AutoGluon supports Linux, MacOS, and Windows. See {doc}`./install` for detailed instructions.

## Managed Service

Looking for a managed AutoML service? We highly recommend checking out [Amazon SageMaker Canvas](https://aws.amazon.com/sagemaker/canvas/)! Powered by AutoGluon, it allows you to create highly accurate machine learning models without any machine learning experience or writing a single line of code.

## Community

[![](https://img.shields.io/discord/1043248669505368144?logo=discord&style=flat)](https://discord.gg/wjUmjqAc2N)
[![Twitter](https://img.shields.io/twitter/follow/autogluon?style=social)](https://twitter.com/autogluon)

Get involved in the AutoGluon community by joining our [Discord](https://discord.gg/wjUmjqAc2N)!


```{toctree}
---
caption: Get Started
maxdepth: 1
hidden:
---

Install <install>
Tabular Quick Start <tutorials/tabular/tabular-quick-start>
Multimodal Quick Start <tutorials/multimodal/multimodal_prediction/multimodal-quick-start>
Time Series Quick Start <tutorials/timeseries/forecasting-quick-start>
```

```{toctree}
---
caption: Tutorials
maxdepth: 3
hidden:
---

Tabular <tutorials/tabular/index>
Multimodal <tutorials/multimodal/index>
Time Series <tutorials/timeseries/index>
tutorials/cloud_fit_deploy/index
<!-- EDA <tutorials/eda/index> -->
```

```{toctree}
---
caption: Resources
maxdepth: 2
hidden:
---

Cheat Sheets <cheatsheet.rst>
Versions <https://auto.gluon.ai/stable/versions.html>
What's New <whats_new/index>
GitHub <https://github.com/autogluon/autogluon>
Tabular FAQ <tutorials/tabular/tabular-faq.md>
Multimodal FAQ <tutorials/multimodal/multimodal-faq.md>
Time Series FAQ <tutorials/timeseries/forecasting-faq.md>
```


```{toctree}
---
caption: API
maxdepth: 1
hidden:
---

TabularPredictor <api/autogluon.tabular.TabularPredictor>
TabularDataset <api/autogluon.core.TabularDataset>
Tabular Models <api/autogluon.tabular.models.rst>
MultiModalPredictor <api/autogluon.multimodal.MultiModalPredictor>
TimeSeriesPredictor <api/autogluon.timeseries.TimeSeriesPredictor>
TimeSeriesDataFrame <api/autogluon.timeseries.TimeSeriesDataFrame>
Feature Generators <api/autogluon.features.rst>
FeatureMetadata <api/autogluon.common.features.feature_metadata.FeatureMetadata>
Search Spaces <api/autogluon.common.space.rst>
```