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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.\n\nCurrently, AutoGluon-Cloud supports [Amazon SageMaker](https://aws.amazon.com/sagemaker/) as the cloud backend.\n\n## Installation\n```bash\npip install -U pip\npip install -U setuptools wheel\npip install autogluon.cloud\n```\n\n## Example\n```python\n\nfrom autogluon.cloud import TabularCloudPredictor\nimport pandas as pd\ntrain_data = pd.read_csv(\"https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv\")\ntest_data = pd.read_csv(\"https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv\")\ntest_data.drop(columns=['class'], inplace=True)\npredictor_init_args = {\"label\": \"class\"}  # init args you would pass to AG TabularPredictor\npredictor_fit_args = {\"train_data\": train_data, \"time_limit\": 120}  # fit args you would pass to AG TabularPredictor\ncloud_predictor = TabularCloudPredictor(cloud_output_path='YOUR_S3_BUCKET_PATH')\ncloud_predictor.fit(predictor_init_args=predictor_init_args, predictor_fit_args=predictor_fit_args)\ncloud_predictor.deploy()\nresult = cloud_predictor.predict_real_time(test_data)\ncloud_predictor.cleanup_deployment()\n# Batch inference\nresult = cloud_predictor.predict(test_data)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fautogluon%2Fautogluon-cloud","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fautogluon%2Fautogluon-cloud","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fautogluon%2Fautogluon-cloud/lists"}