Training PyTorch Models with Azure ABS Data

Azure ABS

Azure Data Lake Storage is primarily designed to work with Hadoop and all frameworks that use the Hadoop FileSystem as their data access layer (for example, Spark and Presto). It is a massively scalable, secure data lake functionality built on Azure Blob Storage which is designed for big data analytics and offers a hierarchical file system.

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.
With the growing popularity of both Azure ABS for storage and PyTorch models for AI deployments, it is unsurprising that many organizations are seeking to train PyTorch models using data in Azure ABS. Kaspian offers native connectors for this operation. Just register your Azure ABS datastore and link your model training job; Kaspian's autoscaling compute layer makes it easy to train and deploy PyTorch models using any data in your cloud with minimal setup or management.
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.

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