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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.
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Live book now enables elastic scaling and GPU workloads through Flame integration, allowing code execution across distributed nodes
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Flame provides distributed garbage collection and code synchronization between parent/child nodes with zero dependencies, using standard Erlang/Elixir libraries
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Complex ML/data science workflows can run seamlessly across multiple machines without changing application code - the same code works locally and distributed
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Live Book + Flame eliminates need for complex infrastructure setup - users can instantly provision GPU instances and distributed computing resources
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Code notebooks can now interact with production infrastructure, databases, and services while maintaining collaborative features
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Large datasets can be processed across multiple nodes transparently using Explorer and distributed data frames
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ML model training, video processing, and other CPU/GPU intensive tasks can scale elastically without managing infrastructure
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System handles code synchronization, file transfers, and process coordination automatically across distributed nodes
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All functionality is built on standard Erlang/Elixir features - no proprietary services or complex deployment required
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Comparable functionality from commercial services/platforms often requires millions in funding and multiple proprietary service integrations