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# AutoGluon-Cloud

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```{image} ./_static/autogluon-s.png
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```
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AutoGluon-Cloud: Train and Deploy AutoGluon on the Cloud

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AutoGluon-Cloud aims to provide user tools to train, fine-tune and deploy [AutoGluon](<https://auto.gluon.ai/stable/index.html>) backed models on the cloud. With just a few lines of code, users can train a model and perform inference on the cloud without worrying about MLOps details such as resource management.

Currently, AutoGluon-Cloud supports [Amazon SageMaker](<https://aws.amazon.com/sagemaker/>) as the cloud backend.

## {octicon}`rocket` Quick Examples

:::{dropdown} Tabular
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```python
import pandas as pd
from autogluon.cloud import TabularCloudPredictor

train_data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv")
test_data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv")
test_data.drop(columns=["class"], inplace=True)
predictor_init_args = {
    "label": "class"
}  # args used when creating TabularPredictor()
predictor_fit_args = {
    "train_data": train_data,
    "time_limit": 120
}  # args passed to TabularPredictor.fit()
cloud_predictor = TabularCloudPredictor(cloud_output_path="YOUR_S3_BUCKET_PATH")
cloud_predictor.fit(
    predictor_init_args=predictor_init_args, predictor_fit_args=predictor_fit_args
)
cloud_predictor.deploy()
result = cloud_predictor.predict_real_time(test_data)
cloud_predictor.cleanup_deployment()
# Batch inference
result = cloud_predictor.predict(test_data)
```
:::


:::{dropdown} Multimodal
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```python
import pandas as pd
from autogluon.cloud import MultiModalCloudPredictor

train_data = pd.read_parquet("https://autogluon-text.s3-accelerate.amazonaws.com/glue/sst/train.parquet")
test_data = pd.read_parquet("https://autogluon-text.s3-accelerate.amazonaws.com/glue/sst/dev.parquet")
test_data.drop(columns=["label"], inplace=True)
predictor_init_args = {
    "label": "label"
}  # args used when creating MultiModalPredictor()
predictor_fit_args = {
    "train_data": train_data
}  # args passed to MultiModalPredictor.fit()
cloud_predictor = MultiModalCloudPredictor(cloud_output_path="YOUR_S3_BUCKET_PATH")
cloud_predictor.fit(
    predictor_init_args=predictor_init_args, predictor_fit_args=predictor_fit_args
)
cloud_predictor.deploy()
result = cloud_predictor.predict_real_time(test_data)
cloud_predictor.cleanup_deployment()
# Batch inference
result = cloud_predictor.predict(test_data)
```
:::


:::{dropdown} TimeSeries
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```python
import pandas as pd
from autogluon.cloud import TimeSeriesCloudPredictor

data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly_tiny/train.csv")

predictor_init_args = {
    "target": "target",
    "prediction_length" : 24,
}  # args used when creating TimeSeriesPredictor()
predictor_fit_args = {
    "train_data": data,
    "time_limit": 120,
}  # args passed to TimeSeriesPredictor.fit()
cloud_predictor = TimeSeriesCloudPredictor(cloud_output_path="YOUR_S3_BUCKET_PATH")
cloud_predictor.fit(
    predictor_init_args=predictor_init_args,
    predictor_fit_args=predictor_fit_args,
    id_column="item_id",
    timestamp_column="timestamp",
)
cloud_predictor.deploy()
result = cloud_predictor.predict_real_time(data)
cloud_predictor.cleanup_deployment()
# Batch inference
result = cloud_predictor.predict(data)
```
:::


## {octicon}`package` Installation

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

```bash
pip install -U pip
pip install -U setuptools wheel
pip install --pre autogluon.cloud  # You don't need to install autogluon itself locally
pip install -U sagemaker  # This is required to ensure the information about newly released containers is available.
```

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

Cloud <tutorials/index>
```

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

Versions <versions.rst>
```

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

TabularCloudPredictor <api/autogluon.cloud.TabularCloudPredictor>
MultiModalCloudPredictor <api/autogluon.cloud.MultiModalCloudPredictor>
TimeSeriesCloudPredictor <api/autogluon.cloud.TimeSeriesCloudPredictor>
TimeSeriesFoundationModel <api/autogluon.cloud.TimeSeriesFoundationModel>
```