Livebook in the cloud: GPUs and clustered workflows in seconds

Learn how Livebook + Flame enables instant GPU access and distributed computing for ML/data science workflows, with zero infrastructure setup and native Elixir scaling.

Key takeaways
  • Live book now enables elastic scaling and GPU workloads through Flame integration, allowing code execution across distributed nodes

  • Flame provides distributed garbage collection and code synchronization between parent/child nodes with zero dependencies, using standard Erlang/Elixir libraries

  • Complex ML/data science workflows can run seamlessly across multiple machines without changing application code - the same code works locally and distributed

  • Live Book + Flame eliminates need for complex infrastructure setup - users can instantly provision GPU instances and distributed computing resources

  • Code notebooks can now interact with production infrastructure, databases, and services while maintaining collaborative features

  • Large datasets can be processed across multiple nodes transparently using Explorer and distributed data frames

  • ML model training, video processing, and other CPU/GPU intensive tasks can scale elastically without managing infrastructure

  • System handles code synchronization, file transfers, and process coordination automatically across distributed nodes

  • All functionality is built on standard Erlang/Elixir features - no proprietary services or complex deployment required

  • Comparable functionality from commercial services/platforms often requires millions in funding and multiple proprietary service integrations