Implementing your AI strategy with PyTorch Lightning in the cloud
summary
Demonstrates PyTorch Lightning–based tooling that takes code from notebook to cloud, runs data prep jobs that extract features from S3, launches multi-node training and serves a Streamlit demo — all with persistent, snapshot environments and shared links. Uses Lightning Studio/Grid AI to PyTorch Lightning workflows, turns laptop into cloud IDE and automates orchestration via an SDK. Shows multi-node runs that scales to 32 GPUs, provisions A10G hardware quickly, and completes prototype train + deploy cycles in minutes.
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chapters (9)
- 0:00Introduction to PyTorch
- 2:14Challenges in ML platforms
- 3:38Demo: The Lightning Studio
- 5:33Data preparation pipeline
- 7:10Scaling model training
- 10:34Orchestration and automation
- 11:49Environment management
- 14:12Multi-node scaling in depth
- 17:02Deployment and conclusion
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