Lightning 1.7: Apple Silicon, Multi-GPU and more

PyTorch Lightning 1.7, developed through 492 commits from 106 contributors, expands distributed training, hardware compatibility, notebook workflows, and production-oriented tooling. Key additions include the experimental MPSAccelerator for GPU training on Apple Silicon, native PyTorch Fully Sharded Data Parallel support alongside the existing FairScale option, and Hivemind-based collaborative training across decentralized and potentially unreliable machines. The release also introduces experimental Distributed Data Parallel support in macOS and Linux Jupyter notebooks, addressing limitations of the previously available Data Parallel strategy. Other improvements include versioned and automatically reloadable “last” checkpoints, validation based on total batches across epochs, CPU and Habana device monitoring, automatic distributed sampler handling, inference-mode evaluation, warn-level deterministic operation support, and expanded LightningCLI capabilities. New extensibility features allow callback registration through Python entry points and simplified checkpoint customization, while experimental ServableModule validation and asynchronous checkpointing aim to help users prepare models for deployment and save checkpoints without interrupting training.

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