Https Github Com Facebookresearch Schedule Free

Authors: Aaron Defazio, Xingyu (Alice) Yang, Harsh Mehta, Konstantin Mishchenko, Ahmed Khaled, Ashok Cutkosky TLDR Faster training without schedules - no need to specify the stopping time/steps in advance! We provide several Schedule-Free optimizer implementations: ScheduleFreeReference versions have a simplified implementation, but which use more memory. There are also ScheduleFreeClosure versions which can be used with PyTorch's optimizer step closures. A Jax implementation is availiable as part of Optax.

An open API service indexing awesome lists of open source software. Schedule-Free Optimization in PyTorch https://github.com/facebookresearch/schedule_free Last synced: 7 months ago JSON representation # Schedule-Free Learning [](https://pepy.tech/project/schedulefree) [](https://pepy.tech/project/schedulefree) Preprint: [The Road Less Scheduled](https://arxiv.org/abs/2405.15682) This page provides an introduction to the Schedule-Free optimization approach, its core concepts, and its benefits for deep learning optimization.

Schedule-Free Learning is a novel optimization method that eliminates the need for manually crafted learning rate schedules while maintaining or exceeding their performance. For in-depth mathematical details, see Mathematical Background, and for specific optimizer implementations, refer to Core Optimizers. Schedule-Free Learning solves a fundamental challenge in deep learning: it removes the need to design and tune learning rate schedules, which typically require specifying the total number of training steps in advance. This makes training more flexible and often more effective. Schedule-Free Learning replaces traditional momentum in optimizers with a combination of interpolation and averaging techniques. The approach maintains three different parameter states (with only two needing storage at any time):

The key innovation is how these parameter states are managed and updated during the optimization process, eliminating the need for learning rate decay schedules. The Schedule-Free update equations for gradient descent are: There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

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