Github Facebookresearch Schedule Free Schedule Free Optimization In

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.

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: 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) There was an error while loading. Please reload this page. Reference implementations in the schedule-free learning framework provide simplified, clear versions of the optimizers that prioritize readability and theoretical alignment over memory efficiency. They serve as educational resources, tools for research experimentation, and reference points for understanding the core algorithmic concepts.

Unlike the primary implementations covered in the Core Optimizers section, these reference implementations explicitly store and manage multiple parameter states, making the algorithm flow more transparent at the cost of higher memory usage. The framework provides the following reference implementations: All reference implementations maintain multiple parameter states that serve different purposes in the schedule-free optimization process. SGDScheduleFreeReference provides a simplified implementation of Schedule-Free SGD that explicitly manages all parameter states for clarity. pip install schedulefree Copy PIP instructions 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. There was an error while loading. Please reload this page.

I've gotten a lot of use out of prodigy over the past few months, and I'd love if I could take advantage of schedule-free optimization alongside it. I see that there is a reference example in the repo, but it uses closure. The problem is that the training loop I am using is not set up for closure and I am not very smart, and I don't understand nearly enough about the math here to create... Would it be possible to provide one like what has been provided for AdamW and SGD?

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