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Graphbook
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The ML workflow framework
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## Overview
-Graphbook is a framework for building efficient, visual DAG-structured ML workflows composed of nodes written in Python. Graphbook provides common ML processing features such as multiprocessing IO and automatic batching, and it features a web-based UI to assemble, monitor, and execute data processing workflows. It can be used to prepare training data for custom ML models, experiment with custom trained or off-the-shelf models, and to build ML-based ETL applications. Custom nodes can be built in Python, and Graphbook will behave like a framework and call lifecycle methods on those nodes.
+Graphbook is a framework for building efficient, visual DAG-structured ML workflows composed of nodes written in Python. Graphbook provides common ML processing features such as multiprocessing IO and automatic batching for PyTorch tensors, and it features a web-based UI to assemble, monitor, and execute data processing workflows. It can be used to prepare training data for custom ML models, experiment with custom trained or off-the-shelf models, and to build ML-based ETL applications. Custom nodes can be built in Python, and Graphbook will behave like a framework and call lifecycle methods on those nodes.
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