Training PyTorch Models using Airflow

Airflow

Apache Airflow is an open-source platform for authoring, scheduling and monitoring data and computing workflows. It was first developed by Airbnb and is now under the Apache Software Foundation. Airflow uses Python to create workflows that can be easily scheduled and monitored. Airflow can help you move data from one source to a destination, filter datasets, apply data policies, manipulation, monitoring and even call microservices to trigger database management tasks. It can be used for batch jobs, organizing, monitoring, and executing workflows automatically. Airflow has been used by many companies for various use cases such as ETL pipelines, machine learning workflows, data warehousing, and more.

PyTorch Models

PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing. It was originally developed by Meta AI and now part of the Linux Foundation umbrella. PyTorch is designed to provide good flexibility and high speeds for deep neural network implementation. PyTorch uses dynamic computation graphs which makes it different from other deep learning frameworks. It has become a popular choice for deep learning tasks such as computer vision, natural language processing, and speech recognition. PyTorch has gained popularity for its simplicity, ease of use, dynamic computational graph, efficient memory usage, flexibility, speed, native ONNX model exports, which can be used to speed up inference. It also shares many commands with numpy which reduces the barrier to learning it.

Open source orchestrators like Airflow are one of the primary means by which companies train PyTorch models in production. Airflow offers a mechanism to schedule and monitor these jobs as part of more complex workflow graphs. Kaspian has a native operator for Airflow; this operator makes it easy to either swap to or get started with training pipelines that utilize Kaspian's flexible compute layer, with native support for autoscaling, GPU acceleration, and more.

Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.