https://github.com/ancestor-mithril/bs-scheduler

A Batch Size Scheduler library compatible with PyTorch DataLoaders.
https://github.com/ancestor-mithril/bs-scheduler

batch-size dataloaders pytorch scheduler

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A Batch Size Scheduler library compatible with PyTorch DataLoaders.

• Host: GitHub
• URL: https://github.com/ancestor-mithril/bs-scheduler
• Owner: ancestor-mithril
• License: bsd-3-clause
• Created: 2023-09-18T12:27:55.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2024-09-16T09:31:23.000Z (11 days ago)
• Last Synced: 2024-09-18T10:26:58.540Z (8 days ago)
• Topics: batch-size, dataloaders, pytorch, scheduler
• Language: Python
• Homepage:
• Size: 1.92 MB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# bs-scheduler

A Batch Size Scheduler library compatible with PyTorch DataLoaders.

*** 

## Documentation

* [API Reference](https://ancestor-mithril.github.io/bs-scheduler).

* [Examples](https://ancestor-mithril.github.io/bs-scheduler/tutorials).

***

## Why use a Batch Size Scheduler?

* Using a big batch size has several advantages:

  * Better hardware utilization.

  * Enhanced parallelism.

  * Faster training.

* However, using a big batch size from the start may lead to a generalization gap.

* Therefore, the solution is to gradually increase the batch size, similar to a learning rate decay policy.

* See [Don't Decay the Learning Rate, Increase the Batch Size](https://arxiv.org/abs/1711.00489).

## Available Schedulers

### Batch Size Schedulers

1. `LambdaBS` - sets the batch size to the base batch size times a given lambda.

2. `MultiplicativeBS` - sets the batch size to the current batch size times a given lambda.

3. `StepBS` - multiplies the batch size with a given factor at a given number of steps.

4. `MultiStepBS` - multiplies the batch size with a given factor each time a milestone is reached.

5. `ConstantBS` - multiplies the batch size by a given factor once and decreases it again to its base value after a

   given number of steps.

6. `LinearBS` - increases the batch size by a linearly changing multiplicative factor for a given number of steps.

7. `ExponentialBS` - increases the batch size by a given $\gamma$ each step.

8. `PolynomialBS` - increases the batch size using a polynomial function in a given number of steps.

9. `CosineAnnealingBS` - increases the batch size to a maximum batch size and decreases it again following a cyclic

   cosine curve.

10. `IncreaseBSOnPlateau` - increases the batch size each time a given metric has stopped improving for a given number

    of steps.

11. `CyclicBS` - cycles the batch size between two boundaries with a constant frequency, while also scaling the

    distance between boundaries.

12. `CosineAnnealingBSWithWarmRestarts` - increases the batch size to a maximum batch size following a cosine curve,

    then restarts while also scaling the number of iterations until the next restart.

13. `OneCycleBS` - decreases the batch size to a minimum batch size then increases it to a given maximum batch size,

    following a linear or cosine annealing strategy.

14. `SequentialBS` - calls a list of schedulers sequentially given a list of milestone points which reflect which

    scheduler should be called when.

15. `ChainedBSScheduler` - chains a list of batch size schedulers and calls them together each step.

## Installation

Please install [PyTorch](https://github.com/pytorch/pytorch) first before installing this repository.

```

pip install bs-scheduler

```

## Licensing

The library is licensed under the [BSD-3-Clause license](LICENSE).

## Citation

To be added...