AutoGluon-Cloud

AutoGluon-Cloud: Train and Deploy AutoGluon on the Cloud

AutoGluon-Cloud aims to provide user tools to train, fine-tune and deploy AutoGluon 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 AWS SageMaker as the cloud backend.

Quick Examples

Tabular

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")
predictor_init_args = {
    "label": "class"
}  # init args you would pass to AG TabularPredictor
predictor_fit_args = {
    "train_data": train_data,
    "time_limit": 120
}  # fit args you would pass to AG TabularPredictor
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)

Multimodal

import pandas as pd
from autogluon.cloud import MultiModalCloudPredictor

train_data = pd.read_csv("https://autogluon-text.s3-accelerate.amazonaws.com/glue/sst/train.parquet")
test_data = pd.read_csv("https://autogluon-text.s3-accelerate.amazonaws.com/glue/sst/dev.parquet")
predictor_init_args = {
    "label": "label"
}  # init args you would pass to AG MultiModalPredictor
predictor_fit_args = {
    "train_data": train_data
}  # fit args you would pass to AG MultiModalPredictor
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)

TimeSeries

import pandas as pd
from autogluon.cloud import TimeSeriesCloudPredictor

data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/cloud/timeseries_train.csv")
id_column="item_id"
timestamp_column="timestamp"
target="target"

predictor_init_args = {
    "target": target
}  # init args you would pass to AG TimeSeriesCloudPredictor
predictor_fit_args = {
    "train_data": data,
    "time_limit": 120
}  # fit args you would pass to AG TimeSeriesCloudPredictor
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=id_column,
    timestamp_column=timestamp_column
)
cloud_predictor.deploy()
result = cloud_predictor.predict_real_time(
    test_data=data,
    id_column=id_column,
    timestamp_column=timestamp_column,
    target=target
)
cloud_predictor.cleanup_deployment()
# Batch inference
result = cloud_predictor.predict(
    test_data=data,
    id_column=id_column,
    timestamp_column=timestamp_column,
    target=target
)

Installation

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