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Many European PyTorch teams run their GPU workloads on hyperscalers like Azure, AWS, or GCP—often without realizing that this places their data and models under US jurisdiction.
This lightning talk shows how PyTorch compute nodes can be migrated to European cloud providers while keeping the full ML environment intact. Through a live demo, we migrate a GPU-enabled PyTorch VM—including CUDA drivers and Jupyter notebooks—from Azure to European infrastructure, without retraining models or rebuilding environments.
The focus is on practical challenges: GPU compatibility, reproducibility, and data movement across clouds.
The migration is demonstrated using DigitalNomadSky, an open-source Python platform for cross-cloud VM migration, but the lessons apply broadly to PyTorch teams aiming to reduce jurisdictional risk and vendor lock-in.
Key takeaways Why PyTorch workloads on hyperscalers raise sovereignty concerns for EU teams What actually breaks (and what doesn’t) when migrating GPU-based ML nodes How to regain control over ML infrastructure without rewriting your stack
I am a software architect and lead developer of the open-source project DigitalNomadSky. I have extensive experience with Microsoft Azure from working at Microsoft and supporting large-scale cloud migrations. My work focuses on supporting datascience and ML-teams with cloud infrastructure... Read More →
