ignite.handlers

Complete list of generic handlers

checkpoint.Checkpoint	Checkpoint handler can be used to periodically save and load objects which have attribute state_dict/load_state_dict.
DiskSaver	Handler that saves input checkpoint on a disk.
checkpoint.ModelCheckpoint	ModelCheckpoint handler, inherits from Checkpoint, can be used to periodically save objects to disk only.
ema_handler.EMAHandler	Exponential moving average (EMA) handler can be used to compute a smoothed version of model.
early_stopping.EarlyStopping	EarlyStopping handler can be used to stop the training if no improvement after a given number of events.
lr_finder.FastaiLRFinder	Learning rate finder handler for supervised trainers.
terminate_on_nan.TerminateOnNan	TerminateOnNan handler can be used to stop the training if the process_function's output contains a NaN or infinite number or torch.tensor.
TimeLimit	TimeLimit handler can be used to control training time for computing environments where session time is limited.
time_profilers.BasicTimeProfiler	BasicTimeProfiler can be used to profile the handlers, events, data loading and data processing times.
time_profilers.HandlersTimeProfiler	HandlersTimeProfiler can be used to profile the handlers, data loading and data processing times.
timing.Timer	Timer object can be used to measure (average) time between events.
global_step_from_engine	Helper method to setup global_step_transform function using another engine.
stores.EpochOutputStore	EpochOutputStore handler to save output prediction and target history after every epoch, could be useful for e.g., visualization purposes.

checkpoint.BaseSaveHandler	Base class for save handlers
param_scheduler.ParamScheduler	An abstract class for updating an optimizer's parameter value during training.
state_param_scheduler.StateParamScheduler	An abstract class for updating an engine state parameter values during training.

Loggers

base_logger	Base logger and its helper handlers.
clearml_logger	ClearML logger and its helper handlers.
mlflow_logger	MLflow logger and its helper handlers.
neptune_logger	Neptune logger and its helper handlers.
polyaxon_logger	Polyaxon logger and its helper handlers.
tensorboard_logger	TensorBoard logger and its helper handlers.
tqdm_logger	TQDM logger.
visdom_logger	Visdom logger and its helper handlers.
wandb_logger	WandB logger and its helper handlers.
fbresearch_logger	FBResearch logger and its helper handlers.

See also

Below are a comprehensive list of examples of various loggers.

• See tensorboardX mnist example and CycleGAN and EfficientNet notebooks for detailed usage.

• See visdom mnist example for detailed usage.

• See neptune mnist example for detailed usage.

• See tqdm mnist example for detailed usage.

• See wandb mnist example for detailed usage.

• See clearml mnist example for detailed usage.

Parameter scheduler

BaseParamScheduler	An abstract class for updating an engine state or optimizer's parameter value during training.
ConcatScheduler	Concat a list of parameter schedulers.
CosineAnnealingScheduler	Anneals 'start_value' to 'end_value' over each cycle.
CyclicalScheduler	An abstract class for updating an optimizer's parameter value over a cycle of some size.
LRScheduler	A wrapper class to call torch.optim.lr_scheduler objects as ignite handlers.
LinearCyclicalScheduler	Linearly adjusts param value to 'end_value' for a half-cycle, then linearly adjusts it back to 'start_value' for a half-cycle.
ParamGroupScheduler	Scheduler helper to group multiple schedulers into one.
ParamScheduler	An abstract class for updating an optimizer's parameter value during training.
PiecewiseLinear	Piecewise linear parameter scheduler
ReduceLROnPlateauScheduler	Reduce LR when a metric stops improving.
create_lr_scheduler_with_warmup	Helper method to create a learning rate scheduler with a linear warm-up.

State Parameter scheduler

StateParamScheduler	An abstract class for updating an engine state parameter values during training.
LambdaStateScheduler	Update a parameter during training by using a user defined callable object.
PiecewiseLinearStateScheduler	Piecewise linear state parameter scheduler.
ExpStateScheduler	Update a parameter during training by using exponential function.
StepStateScheduler	Update a parameter during training by using a step function.
MultiStepStateScheduler	Update a parameter during training by using a multi step function.

More on parameter scheduling

In this section there are visual examples of various parameter schedulings that can be achieved.

Example with CosineAnnealingScheduler

import numpy as np
import matplotlib.pylab as plt
from ignite.handlers import CosineAnnealingScheduler

lr_values_1 = np.array(CosineAnnealingScheduler.simulate_values(num_events=75, param_name='lr',
                                                            start_value=1e-1, end_value=2e-2, cycle_size=20))

lr_values_2 = np.array(CosineAnnealingScheduler.simulate_values(num_events=75, param_name='lr',
                                                                start_value=1e-1, end_value=2e-2, cycle_size=20, cycle_mult=1.3))

lr_values_3 = np.array(CosineAnnealingScheduler.simulate_values(num_events=75, param_name='lr',
                                                                start_value=1e-1, end_value=2e-2,
                                                                cycle_size=20, start_value_mult=0.7))

lr_values_4 = np.array(CosineAnnealingScheduler.simulate_values(num_events=75, param_name='lr',
                                                                start_value=1e-1, end_value=2e-2,
                                                                cycle_size=20, end_value_mult=0.1))


plt.figure(figsize=(25, 5))

plt.subplot(141)
plt.title("Cosine annealing")
plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()
plt.ylim([0.0, 0.12])

plt.subplot(142)
plt.title("Cosine annealing with cycle_mult=1.3")
plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()
plt.ylim([0.0, 0.12])

plt.subplot(143)
plt.title("Cosine annealing with start_value_mult=0.7")
plt.plot(lr_values_3[:, 0], lr_values_3[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()
plt.ylim([0.0, 0.12])

plt.subplot(144)
plt.title("Cosine annealing with end_value_mult=0.1")
plt.plot(lr_values_4[:, 0], lr_values_4[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()
plt.ylim([0.0, 0.12])

Example with ignite.handlers.param_scheduler.LinearCyclicalScheduler

import numpy as np
import matplotlib.pylab as plt
from ignite.handlers import LinearCyclicalScheduler

lr_values_1 = np.array(LinearCyclicalScheduler.simulate_values(num_events=75, param_name='lr',
                                                                start_value=1e-1, end_value=2e-2, cycle_size=20))

lr_values_2 = np.array(LinearCyclicalScheduler.simulate_values(num_events=75, param_name='lr',
                                                                start_value=1e-1, end_value=2e-2, cycle_size=20, cycle_mult=1.3))

lr_values_3 = np.array(LinearCyclicalScheduler.simulate_values(num_events=75, param_name='lr',
                                                                start_value=1e-1, end_value=2e-2,
                                                                cycle_size=20, start_value_mult=0.7))

lr_values_4 = np.array(LinearCyclicalScheduler.simulate_values(num_events=75, param_name='lr',
                                                                start_value=1e-1, end_value=2e-2,
                                                                cycle_size=20, end_value_mult=0.1))


plt.figure(figsize=(25, 5))

plt.subplot(141)
plt.title("Linear cyclical scheduler")
plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()
plt.ylim([0.0, 0.12])

plt.subplot(142)
plt.title("Linear cyclical scheduler with cycle_mult=1.3")
plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()
plt.ylim([0.0, 0.12])

plt.subplot(143)
plt.title("Linear cyclical scheduler with start_value_mult=0.7")
plt.plot(lr_values_3[:, 0], lr_values_3[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()
plt.ylim([0.0, 0.12])

plt.subplot(144)
plt.title("Linear cyclical scheduler with end_value_mult=0.1")
plt.plot(lr_values_4[:, 0], lr_values_4[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()
plt.ylim([0.0, 0.12])

Example with ignite.handlers.param_scheduler.ConcatScheduler

import numpy as np
import matplotlib.pylab as plt
from ignite.handlers import LinearCyclicalScheduler, CosineAnnealingScheduler, ConcatScheduler

import torch

t1 = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([t1], lr=0.1)


scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.1, end_value=0.5, cycle_size=30)
scheduler_2 = CosineAnnealingScheduler(optimizer, "lr", start_value=0.5, end_value=0.01, cycle_size=50)
durations = [15, ]

lr_values_1 = np.array(ConcatScheduler.simulate_values(num_events=100, schedulers=[scheduler_1, scheduler_2], durations=durations))


t1 = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([t1], lr=0.1)

scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.1, end_value=0.5, cycle_size=30)
scheduler_2 = CosineAnnealingScheduler(optimizer, "momentum", start_value=0.5, end_value=0.01, cycle_size=50)
durations = [15, ]

lr_values_2 = np.array(ConcatScheduler.simulate_values(num_events=100, schedulers=[scheduler_1, scheduler_2], durations=durations,
                                                        param_names=["lr", "momentum"]))

plt.figure(figsize=(25, 5))

plt.subplot(131)
plt.title("Concat scheduler of linear + cosine annealing")
plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

plt.subplot(132)
plt.title("Concat scheduler of linear LR scheduler\n and cosine annealing on momentum")
plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

plt.subplot(133)
plt.title("Concat scheduler of linear LR scheduler\n and cosine annealing on momentum")
plt.plot(lr_values_2[:, 0], lr_values_2[:, 2], label="momentum")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

Piecewise linear scheduler

import numpy as np
import matplotlib.pylab as plt
from ignite.handlers import LinearCyclicalScheduler, ConcatScheduler

scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.0, end_value=0.6, cycle_size=50)
scheduler_2 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.6, end_value=0.0, cycle_size=150)
durations = [25, ]

lr_values = np.array(ConcatScheduler.simulate_values(num_events=100, schedulers=[scheduler_1, scheduler_2], durations=durations))


plt.title("Piecewise linear scheduler")
plt.plot(lr_values[:, 0], lr_values[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

Example with ignite.handlers.param_scheduler.LRScheduler

import numpy as np
import matplotlib.pylab as plt
from ignite.handlers import LRScheduler

import torch
from torch.optim.lr_scheduler import ExponentialLR, StepLR, CosineAnnealingLR

tensor = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([tensor], lr=0.1)

lr_scheduler_1 = StepLR(optimizer=optimizer, step_size=10, gamma=0.77)
lr_scheduler_2 = ExponentialLR(optimizer=optimizer, gamma=0.98)
lr_scheduler_3 = CosineAnnealingLR(optimizer=optimizer, T_max=10, eta_min=0.01)

lr_values_1 = np.array(LRScheduler.simulate_values(num_events=100, lr_scheduler=lr_scheduler_1))
lr_values_2 = np.array(LRScheduler.simulate_values(num_events=100, lr_scheduler=lr_scheduler_2))
lr_values_3 = np.array(LRScheduler.simulate_values(num_events=100, lr_scheduler=lr_scheduler_3))


plt.figure(figsize=(25, 5))

plt.subplot(131)
plt.title("Torch LR scheduler wrapping StepLR")
plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

plt.subplot(132)
plt.title("Torch LR scheduler wrapping ExponentialLR")
plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

plt.subplot(133)
plt.title("Torch LR scheduler wrapping CosineAnnealingLR")
plt.plot(lr_values_3[:, 0], lr_values_3[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

Concatenate with torch schedulers

import numpy as np
import matplotlib.pylab as plt
from ignite.handlers import LRScheduler, ConcatScheduler

import torch
from torch.optim.lr_scheduler import ExponentialLR, StepLR

t1 = torch.zeros([1], requires_grad=True)
optimizer = torch.optim.SGD([t1], lr=0.1)

scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.001, end_value=0.1, cycle_size=30)
lr_scheduler = ExponentialLR(optimizer=optimizer, gamma=0.7)
scheduler_2 = LRScheduler(lr_scheduler=lr_scheduler)
durations = [15, ]
lr_values_1 = np.array(ConcatScheduler.simulate_values(num_events=30, schedulers=[scheduler_1, scheduler_2], durations=durations))


scheduler_1 = LinearCyclicalScheduler(optimizer, "lr", start_value=0.001, end_value=0.1, cycle_size=30)
lr_scheduler = StepLR(optimizer=optimizer, step_size=10, gamma=0.7)
scheduler_2 = LRScheduler(lr_scheduler=lr_scheduler)
durations = [15, ]
lr_values_2 = np.array(ConcatScheduler.simulate_values(num_events=75, schedulers=[scheduler_1, scheduler_2], durations=durations))

plt.figure(figsize=(15, 5))
plt.subplot(121)
plt.title("Concat scheduler of linear + ExponentialLR")
plt.plot(lr_values_1[:, 0], lr_values_1[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

plt.subplot(122)
plt.title("Concat scheduler of linear + StepLR")
plt.plot(lr_values_2[:, 0], lr_values_2[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

Example with ignite.handlers.param_scheduler.ReduceLROnPlateauScheduler

import matplotlib.pyplot as plt
import numpy as np
from ignite.handlers import ReduceLROnPlateauScheduler

metric_values = [0.7, 0.78, 0.81, 0.82, 0.82, 0.83, 0.80, 0.81, 0.84, 0.78]
num_events = 10
init_lr = 0.1

lr_values = np.array(ReduceLROnPlateauScheduler.simulate_values(
    num_events, metric_values, init_lr,
    factor=0.5, patience=1, mode='max', threshold=0.01, threshold_mode='abs'
    )
)

plt.figure(figsize=(15, 5))
plt.suptitle("ReduceLROnPlateauScheduler")
plt.subplot(121)
plt.plot(lr_values[:, 1], label="learning rate")
plt.xticks(lr_values[:, 0])
plt.xlabel("events")
plt.ylabel("values")
plt.legend()

plt.subplot(122)
plt.plot(metric_values, label="metric")
plt.xticks(lr_values[:, 0])
plt.xlabel("events")
plt.ylabel("values")
plt.legend()