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Optimization (Optimizers and Schedulers)

Using optimizers and learning rate schedulers with Flash has become easier and cleaner than ever.

With the use of Registry, instantiation of an optimzer or a learning rate scheduler can done with just a string.

Setting an optimizer to a task

Each task has a built-in method available_optimizers() which will list all the optimizers registered with Flash.

>>> from flash.core.classification import ClassificationTask
>>> ClassificationTask.available_optimizers()
['adadelta', ..., 'sgd']

To train / finetune a Task of your choice, just pass on a string.

from flash.image import ImageClassifier

model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer="Adam", learning_rate=1e-4)

In order to customize specific parameters of the Optimizer, pass along a dictionary of kwargs with the string as a tuple.

from flash.image import ImageClassifier

model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer=("Adam", {"amsgrad": True}), learning_rate=1e-4)

An alternative to customizing an optimizer using a tuple is to pass it as a callable.

from functools import partial
from torch.optim import Adam
from flash.image import ImageClassifier

model = ImageClassifier(num_classes=10, backbone="resnet18", optimizer=partial(Adam, amsgrad=True), learning_rate=1e-4)

Setting a Learning Rate Scheduler

Each task has a built-in method available_lr_schedulers() which will list all the learning rate schedulers registered with Flash.

>>> from flash.core.classification import ClassificationTask
>>> ClassificationTask.available_lr_schedulers()
['lambdalr', ..., 'cosineannealingwarmrestarts']

To train / finetune a Task of your choice, just pass on a string.

from flash.image import ImageClassifier

model = ImageClassifier(
    num_classes=10, backbone="resnet18", optimizer="Adam", learning_rate=1e-4, lr_scheduler="constant_schedule"
)

Note

"constant_schedule" and a few other lr schedulers will be available only if you have installed the transformers library from Hugging Face.

In order to customize specific parameters of the LR Scheduler, pass along a dictionary of kwargs with the string as a tuple.

from flash.image import ImageClassifier

model = ImageClassifier(
    num_classes=10,
    backbone="resnet18",
    optimizer="Adam",
    learning_rate=1e-4,
    lr_scheduler=("StepLR", {"step_size": 10}),
)

An alternative to customizing the LR Scheduler using a tuple is to pass it as a callable.

from functools import partial
from torch.optim.lr_scheduler import CyclicLR
from flash.image import ImageClassifier

model = ImageClassifier(
    num_classes=10,
    backbone="resnet18",
    optimizer="Adam",
    learning_rate=1e-4,
    lr_scheduler=partial(CyclicLR, step_size_up=1500, mode="exp_range", gamma=0.5),
)

Additionally, the lr_scheduler parameter also accepts the Lightning Scheduler configuration which can be passed on using a tuple.

The Lightning Scheduler configuration is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.

lr_scheduler_config = {
    # REQUIRED: The scheduler instance
    "scheduler": lr_scheduler,
    # The unit of the scheduler's step size, could also be 'step'.
    # 'epoch' updates the scheduler on epoch end whereas 'step'
    # updates it after a optimizer update.
    "interval": "epoch",
    # How many epochs/steps should pass between calls to
    # `scheduler.step()`. 1 corresponds to updating the learning
    # rate after every epoch/step.
    "frequency": 1,
    # Metric to to monitor for schedulers like `ReduceLROnPlateau`
    "monitor": "val_loss",
    # If set to `True`, will enforce that the value specified 'monitor'
    # is available when the scheduler is updated, thus stopping
    # training if not found. If set to `False`, it will only produce a warning
    "strict": True,
    # If using the `LearningRateMonitor` callback to monitor the
    # learning rate progress, this keyword can be used to specify
    # a custom logged name
    "name": None,
}

When there are schedulers in which the .step() method is conditioned on a value, such as the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Flash requires that the Lightning Scheduler configuration contains the keyword "monitor" set to the metric name that the scheduler should be conditioned on. Below is an example for this:

from flash.image import ImageClassifier

model = ImageClassifier(
    num_classes=10,
    backbone="resnet18",
    optimizer="Adam",
    learning_rate=1e-4,
    lr_scheduler=("reducelronplateau", {"mode": "max"}, {"monitor": "val_accuracy"}),
)

Note

Do not set the "scheduler" key in the Lightning Scheduler configuration, it will overriden with an instance of the provided scheduler key.

Pre-Registering optimizers and scheduler recipes

Flash registry also provides the flexiblty of registering functions. This feature is also provided in the Optimizer and Scheduler registry.

Using the optimizers and lr_schedulers decorator pertaining to each Task, custom optimizer and LR scheduler recipes can be pre-registered.

import torch
from flash.image import ImageClassifier


@ImageClassifier.lr_schedulers
def my_flash_steplr_recipe(optimizer):
    return torch.optim.lr_scheduler.StepLR(optimizer, step_size=10)


model = ImageClassifier(backbone="resnet18", num_classes=2, optimizer="Adam", lr_scheduler="my_flash_steplr_recipe")

Provider specific requirements

Schedulers

Certain LR Schedulers provided by Hugging Face require both num_training_steps and num_warmup_steps.

In order to use them in Flash, just provide num_warmup_steps as float between 0 and 1 which indicates the fraction of the training steps that will be used as warmup steps. Flash’s Trainer will take care of computing the number of training steps and number of warmup steps based on the flags that are set in the Trainer.

from flash.image import ImageClassifier

model = ImageClassifier(
    backbone="resnet18",
    num_classes=2,
    optimizer="Adam",
    lr_scheduler=("cosine_schedule_with_warmup", {"num_warmup_steps": 0.1}),
)
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