API Reference

Training Loop

Extensions Manager

training.ExtensionsManager(models, Dict[str, …)

Manages the extensions and the current status.

training.IgniteExtensionsManager(engine, …)

Manages extensions and the current status in Ignite training loop.

Extensions

training.extension.make_extension([trigger, …])

Decorator to make given function into an extension.

training.extensions.Evaluator(self, …[, …])

An extension to evaluate models on a validation set.

training.extensions.LogReport([keys, …])

An extension to output the accumulated results to a log file.

training.extensions.MicroAverage(…[, trigger])

Calculates micro-average ratio.

training.extensions.observe_lr(optimizer[, …])

Returns an extension to record the learning rate.

training.extensions.observe_value(…)

Returns an extension to continuously record a value.

training.extensions.ParameterStatistics(links)

An extension to report parameter statistics.

training.extensions.PlotReport(y_keys[, …])

An extension to output plots.

training.extensions.PrintReport([entries, …])

An extension to print the accumulated results.

training.extensions.ProgressBar([…])

An extension to print a progress bar and recent training status.

training.extensions.ProfileReport([…])

Writes the profile results to a file.

training.extensions.snapshot([savefun, …])

Returns a trainer extension to take snapshots of the trainer.

training.extensions.VariableStatisticsPlot(targets)

An extension to plot statistics for Tensors.

Triggers

training.triggers.EarlyStoppingTrigger(self)

Trigger for Early Stopping

training.triggers.IntervalTrigger(period, unit)

Trigger based on a fixed interval.

training.triggers.ManualScheduleTrigger(…)

Trigger invoked at specified point(s) of iterations or epochs.

training.triggers.BestValueTrigger(key, compare)

Trigger invoked when specific value becomes best.

training.triggers.MaxValueTrigger(key[, trigger])

Trigger invoked when specific value becomes maximum.

training.triggers.MinValueTrigger(key[, trigger])

Trigger invoked when specific value becomes minimum.

training.triggers.OnceTrigger([call_on_resume])

Trigger based on the starting point of the iteration.

training.triggers.TimeTrigger(period)

Trigger based on a fixed time interval.

Reporting

reporting.Reporter()

Object to which observed values are reported.

reporting.report(values[, observer])

Reports observed values with the current reporter object.

reporting.report_scope(observation)

Returns a report scope with the current reporter.

Logging

logging.get_logger(name)

Returns a child logger to be used by applications.

Profiler

profiler.TimeSummary.report(tag[, use_cuda])

Context manager to automatically report execution times.

Distributed Training

nn.parallel.DistributedDataParallel(module)

Module for distributed data parallelism

Check Pointing

utils.checkpoint

Lazy Modules

nn.LazyLinear(in_features, *args, **kwargs)

Linear module with lazy weight initialization.

nn.LazyConv1d(in_channels, *args, **kwargs)

Conv1d module with lazy weight initialization.

nn.LazyConv2d(in_channels, *args, **kwargs)

Conv2d module with lazy weight initialization.

nn.LazyConv3d(in_channels, *args, **kwargs)

Conv3d module with lazy weight initialization.

nn.LazyBatchNorm1d(num_features, *args, **kwargs)

BatchNorm1d module with lazy weight initialization.

nn.LazyBatchNorm2d(num_features, *args, **kwargs)

BatchNorm2d module with lazy weight initialization.

nn.LazyBatchNorm3d(num_features, *args, **kwargs)

BatchNorm3d module with lazy weight initialization.

ONNX

Export

onnx.export(model, args, f[, return_output, …])

Export model into ONNX Graph.

onnx.export_testcase(model, args, out_dir, *)

Export model and I/O tensors of the model in protobuf format.

Annotation

onnx.annotate(**attrs)

Annotation parameters to the target function.

onnx.apply_annotation(fn, *args, **attrs)

Annotation applier to the target function

onnx.scoped_anchor(**attrs)

Add anchor node to the scoped modules

onnx.export(model, args, f[, return_output, …])

Export model into ONNX Graph.

onnx.export_testcase(model, args, out_dir, *)

Export model and I/O tensors of the model in protobuf format.

Datasets

dataset.SharedDataset(sm_size[, cache_type])

Dataset that caches the load samples in shared memory

dataset.TabularDataset(*args, **kwds)

An abstract class that represents tabular dataset.

dataset.ItemNotFoundException

NumPy/CuPy Compatibility

from_ndarray(ndarray)

Creates a torch.Tensor from a numpy.ndarray or cupy.ndarray.

as_ndarray(tensor)

Creates a numpy.ndarray or cupy.ndarray from torch.Tensor.

get_xp(obj)

Returns a module of ndarray implementation (numpy or cupy) for the given obj.

as_numpy_dtype(torch_dtype)

Returns NumPy dtype for the given PyTorch dtype.

from_numpy_dtype(numpy_dtype)

Returns PyTorch dtype for the given NumPy dtype.

cuda.stream(stream)

Context-manager that selects a given stream.

cuda.use_torch_mempool_in_cupy()

Use the PyTorch memory pool in CuPy.

cuda.use_default_mempool_in_cupy()

Use the default memory pool in CuPy.
CUDA (CuPy Interoperability) pytorch_pfn_extras.training.ExtensionsManager