Pre-release
This release is a pre-release and may not be stable for production use.
AutoGluon-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 codes, users could 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.
Example
# First install package from terminal:
# pip install -U pip
# pip install -U setuptools wheel
# pip install autogluon.cloud==0.2.0 # You don't need to install autogluon itself locally
from autogluon.cloud import TabularCloudPredictor
import pandas as pd
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)
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Metadata
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Release files for autogluon.cloud 0.2.1b20230512
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| autogluon.cloud-0.2.1b20230512-py3-none-any.whl | Python 3 | none | any | Details |
Release files / autogluon.cloud-0.2.1b20230512-py3-none-any.whl
| Download URL | autogluon.cloud-0.2.1b20230512-py3-none-any.whl |
|---|---|
| Size | 79.8 kB |
| Tags | Python 3 |
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