In January 2020 Amazon Web Services Inc. (AWS) launched an open-source library called AutoGluon the library behind Sagemaker Autopilot.
AutoGluon enables developers to write machine learning-based applications that use image, text or tabular data sets with just a few lines of code.
autogluon.forecasting module automatically processes raw time series data into the appropriate format, and then trains and tunes various state-of-the-art deep learning models to produce accurate forecasts.
Autogluon.tabular with only the optional LightGBM and CatBoost models for example, you can do: pip install autogluon.tabular [lightgbm,catboost] Experimental optional dependency: skex. This will speedup KNN models by 25x in training and inference on CPU.
AutoML packages are: AutoGluon (2020): This popular AutoML open-source toolkit developed by AWS helps in getting a strong predictive performance in various machine learning and deep learning models on text, image, and tabular data.
“AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on deep learning and real-world applications spanning image, text, or tabular data. Intended for both ML beginners and experts, AutoGluon enables you to… “
- quickly prototype deep learning solutions
- automatic hyperparameter tuning, model selection/architecture search
- improve existing bespoke models and data pipelines **
AutoGluon enables you to build machine learning models with only 3 Lines of Code.
Currently, AutoGluon can create models for image classification, object detection, text classification, and supervised learning with tabular datasets.
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to:
- Quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code.
- Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge.
- Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing.
- Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case.
Example using AutoGluon to train and deploy a high-performance model on a tabular dataset:
>>> from autogluon.tabular import TabularDataset, TabularPredictor >>> train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv') >>> test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv') >>> predictor = TabularPredictor(label='class').fit(train_data=train_data) >>> predictions = predictor.predict(test_data)
AutoGluon can be applied for prediction tasks that involve image and text data. For adopting state-of-the-art deep learning models for multimodal prediction problems, you may try autogluon.multimodal:
>>> from autogluon.multimodal import MultiModalPredictor >>> from datasets import load_dataset >>> train_data = load_dataset("glue", 'mrpc')['train'].to_pandas().drop('idx', axis=1) >>> test_data = load_dataset("glue", 'mrpc')['validation'].to_pandas().drop('idx', axis=1) >>> predictor = MultiModalPredictor(label='label').fit(train_data) >>> predictions = predictor.predict(test_data) >>> score = predictor.evaluate(test_data)
Example using AutoGluon to forecast future values of time series:
>>> 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) Installation pip3 install -U pip pip3 install -U setuptools wheel # CPU version of pytorch has smaller footprint - see installation instructions in # pytorch documentation - https://pytorch.org/get-started/locally/ pip3 install torch==1.12.1+cpu torchvision==0.13.1+cpu torchtext==0.13.1 -f https://download.pytorch.org/whl/cpu/torch_stable.html pip3 install autogluon https://auto.gluon.ai/stable/install.html https://github.com/autogluon https://github.com/autogluon/autogluon/releases