Stable release of automatic mixed precision (AMP). New Beta features include a TensorPipe backend for RPC, memory profiler, and several improvements to distributed training for both RPC and DDP.

Stable release of automatic mixed precision (AMP). New Beta features include a TensorPipe backend for RPC, memory profiler, and several improvements to distributed training for both RPC and DDP. pytorch / pytorch Public Notifications You must be signed in to change notification settings Fork 30.5k Star 103k Stable release of automatic mixed precision (AMP). New Beta features include a TensorPipe backend for RPC, memory profiler, and several improvements to distributed training for both RPC and DDP.

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• pytorch / pytorch Public Notifications You must be signed in to change notification settings Fork 30.5k Star 103k Stable release of automatic mixed precision (AMP).
• New Beta features include a TensorPipe backend for RPC, memory profiler, and several improvements to distributed training for both RPC and DDP.
• zou3519 released this 28 Jul 17:13 v1.6.0 b31f58d This commit was signed with the committer’s verified signature .
• seemethere Eli Uriegas GPG key ID: 24C48C7F709F9CEF Expired Verified Learn about vigilant mode .
• PyTorch 1.6.0 Release Notes Highlights Backwards Incompatible Changes Deprecations New Features Improvements Bug Fixes Performance Documentation Highlights The PyTorch 1.6 release includes a number of new APIs, tools for performance improvement and profiling, as well as major updates to both distributed data parallel (DDP) and remote procedure call (RPC) based distributed training.
• A few of the highlights include: Automatic mixed precision (AMP) training is now natively supported and a stable feature - thanks to NVIDIA’s contributions; Native TensorPipe support now added for tensor-aware, point-to-point communication primitives built specifically for machine learning; New profiling tools providing tensor-level memory consumption information; and Numerous improvements and new features for both distributed data parallel (DDP) training and the remote procedural call (RPC) packages.