In these comprehensive case studies, we demonstrate how to seamlessly integrate vineyard’s capabilities with existing data-intensive tasks. By incorporating vineyard into complex workflows involving multiple computing engines, users can experience significant improvements in both performance and ease of use.
Effortlessly share Python objects between processes using vineyard’s intuitive and efficient approach.
Utilize vineyard as an elegant alternative to
multiprocessing.shared_memory in Python.
Discover how vineyard enhances distributed machine learning training workflows by seamlessly integrating with various computing engines for improved efficiency and elegance.
Vineyard serves as the
DataSet backend for Kedro pipelines, enabling
efficient data sharing between tasks without intrusive code modification, even
when the pipeline is deployed to Kubernetes.
Vineyard supports sharing GPU memory in zero-copy manner, enabling efficient data sharing between GPU-accelerated tasks.