Deploy pytorch/ignite docs 0dfeabe

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 33830b069a953b17e2a10846eb2cb5d4
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/contrib/engines.rst.txt

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+ignite.contrib.engines
+======================
+
+Contribution module of engines and helper tools:
+
+ignite.contrib.engines.tbptt
+
+.. currentmodule:: ignite.contrib.engines.tbptt
+
+.. autosummary::
+    :nosignatures:
+    :autolist:
+
+ignite.contrib.engines.common
+
+.. currentmodule:: ignite.contrib.engines.common
+
+.. autosummary::
+    :nosignatures:
+    :autolist:
+
+Truncated Backpropagation Throught Time
+---------------------------------------
+
+.. automodule:: ignite.contrib.engines.tbptt
+   :members:
+
+Helper methods to setup trainer/evaluator
+-----------------------------------------
+
+.. automodule:: ignite.contrib.engines.common
+   :members:

_sources/contrib/handlers.rst.txt

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+ignite.contrib.handlers
+=======================
+
+Contribution module of handlers
+
+
+Parameter scheduler [deprecated]
+--------------------------------
+
+.. deprecated:: 0.4.4
+   Use :class:`~ignite.handlers.param_scheduler.ParamScheduler` instead, will be removed in version 0.6.0.
+
+   Was moved to :ref:`param-scheduler-label`.
+
+LR finder [deprecated]
+----------------------
+
+.. deprecated:: 0.4.4
+    Use :class:`~ignite.handlers.lr_finder.FastaiLRFinder` instead, will be removed in version 0.6.0.
+
+Time profilers [deprecated]
+---------------------------
+
+.. deprecated:: 0.4.6
+    Use :class:`~ignite.handlers.time_profilers.BasicTimeProfiler` instead, will be removed in version 0.6.0.
+    Use :class:`~ignite.handlers.time_profilers.HandlersTimeProfiler` instead, will be removed in version 0.6.0.
+
+Loggers
+-------
+
+.. currentmodule:: ignite.contrib.handlers
+
+.. autosummary::
+    :nosignatures:
+    :toctree: ../generated
+    :recursive:
+
+    base_logger
+    clearml_logger
+    mlflow_logger
+    neptune_logger
+    polyaxon_logger
+    tensorboard_logger
+    tqdm_logger
+
+    visdom_logger
+    wandb_logger
+
+.. seealso::
+
+    Below are a comprehensive list of examples of various loggers.
+
+    * See `tensorboardX mnist example <https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_tensorboard_logger.py>`_
+      and `CycleGAN and EfficientNet notebooks <https://github.com/pytorch/ignite/tree/master/examples/notebooks>`_ for detailed usage.
+
+    * See `visdom mnist example <https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_visdom_logger.py>`_ for detailed usage.
+
+    * See `neptune mnist example <https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_neptune_logger.py>`_ for detailed usage.
+
+    * See `tqdm mnist example <https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_tqdm_logger.py>`_ for detailed usage.
+
+    * See `wandb mnist example <https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_wandb_logger.py>`_ for detailed usage.
+
+    * See `clearml mnist example <https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_clearml_logger.py>`_ for detailed usage.

_sources/contrib/metrics.rst.txt

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+ignite.contrib.metrics
+======================
+
+Contrib module metrics
+----------------------
+
+.. currentmodule:: ignite.contrib.metrics
+
+.. autosummary::
+    :nosignatures:
+    :toctree: ../generated
+
+    AveragePrecision
+    CohenKappa
+    GpuInfo
+    PrecisionRecallCurve
+    ROC_AUC
+    RocCurve
+
+Regression metrics
+------------------
+
+.. currentmodule:: ignite.contrib.metrics.regression
+
+.. automodule:: ignite.contrib.metrics.regression
+
+
+Module :mod:`ignite.contrib.metrics.regression` provides implementations of
+metrics useful for regression tasks. Definitions of metrics are based on `Botchkarev 2018`_, page 30 "Appendix 2. Metrics mathematical definitions".
+
+.. _`Botchkarev 2018`:
+        https://arxiv.org/abs/1809.03006
+
+Complete list of metrics:
+
+.. currentmodule:: ignite.contrib.metrics.regression
+
+.. autosummary::
+    :nosignatures:
+    :toctree: ../generated
+
+    CanberraMetric
+    FractionalAbsoluteError
+    FractionalBias
+    GeometricMeanAbsoluteError
+    GeometricMeanRelativeAbsoluteError
+    ManhattanDistance
+    MaximumAbsoluteError
+    MeanAbsoluteRelativeError
+    MeanError
+    MeanNormalizedBias
+    MedianAbsoluteError
+    MedianAbsolutePercentageError
+    MedianRelativeAbsoluteError
+    R2Score
+    WaveHedgesDistance

_sources/defaults.rst.txt

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+:orphan:
+
+.. toggle::
+
+    .. testcode:: default, 1, 2, 3, 4, 5
+
+        from collections import OrderedDict
+
+        import torch
+        from torch import nn, optim
+
+        from ignite.engine import *
+        from ignite.handlers import *
+        from ignite.metrics import *
+        from ignite.utils import *
+        from ignite.contrib.metrics.regression import *
+        from ignite.contrib.metrics import *
+
+        # create default evaluator for doctests
+
+        def eval_step(engine, batch):
+            return batch
+
+        default_evaluator = Engine(eval_step)
+
+        # create default optimizer for doctests
+
+        param_tensor = torch.zeros([1], requires_grad=True)
+        default_optimizer = torch.optim.SGD([param_tensor], lr=0.1)
+
+        # create default trainer for doctests
+        # as handlers could be attached to the trainer,
+        # each test must define his own trainer using `.. testsetup:`
+
+        def get_default_trainer():
+
+            def train_step(engine, batch):
+                return batch
+
+            return Engine(train_step)
+
+        # create default model for doctests
+
+        default_model = nn.Sequential(OrderedDict([
+            ('base', nn.Linear(4, 2)),
+            ('fc', nn.Linear(2, 1))
+        ]))
+
+        manual_seed(666)

_sources/distributed.rst.txt

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+ignite.distributed
+==================
+
+Helper module to use distributed settings for multiple backends:
+
+- backends from native torch distributed configuration: "nccl", "gloo", "mpi"
+
+- XLA on TPUs via `pytorch/xla <https://github.com/pytorch/xla>`_
+
+- using `Horovod framework <https://horovod.readthedocs.io/en/stable/>`_ as a backend
+
+
+Distributed launcher and `auto` helpers
+---------------------------------------
+
+We provide a context manager to simplify the code of distributed configuration setup for all above supported backends.
+In addition, methods like :meth:`~ignite.distributed.auto.auto_model`, :meth:`~ignite.distributed.auto.auto_optim` and
+:meth:`~ignite.distributed.auto.auto_dataloader` helps to adapt in a transparent way provided model, optimizer and data
+loaders to existing configuration:
+
+.. code-block:: python
+
+    # main.py
+
+    import ignite.distributed as idist
+
+    def training(local_rank, config, **kwargs):
+
+        print(idist.get_rank(), ": run with config:", config, "- backend=", idist.backend())
+
+        train_loader = idist.auto_dataloader(dataset, batch_size=32, num_workers=12, shuffle=True, **kwargs)
+        # batch size, num_workers and sampler are automatically adapted to existing configuration
+        # ...
+        model = resnet50()
+        model = idist.auto_model(model)
+        # model is DDP or DP or just itself according to existing configuration
+        # ...
+        optimizer = optim.SGD(model.parameters(), lr=0.01)
+        optimizer = idist.auto_optim(optimizer)
+        # optimizer is itself, except XLA configuration and overrides `step()` method.
+        # User can safely call `optimizer.step()` (behind `xm.optimizer_step(optimizier)` is performed)
+
+
+    backend = "nccl"  # torch native distributed configuration on multiple GPUs
+    # backend = "xla-tpu"  # XLA TPUs distributed configuration
+    # backend = None  # no distributed configuration
+    # 
+    # dist_configs = {'nproc_per_node': 4}  # Use specified distributed configuration if launch as python main.py
+    # dist_configs["start_method"] = "fork"  # Add start_method as "fork" if using Jupyter Notebook
+    with idist.Parallel(backend=backend, **dist_configs) as parallel:
+        parallel.run(training, config, a=1, b=2)
+
+Above code may be executed with `torch.distributed.launch`_ tool or by python and specifying distributed configuration
+in the code. For more details, please, see :class:`~ignite.distributed.launcher.Parallel`,
+:meth:`~ignite.distributed.auto.auto_model`, :meth:`~ignite.distributed.auto.auto_optim` and
+:meth:`~ignite.distributed.auto.auto_dataloader`.
+
+Complete example of CIFAR10 training can be found
+`here <https://github.com/pytorch/ignite/tree/master/examples/cifar10>`_.
+
+
+.. _torch.distributed.launch: https://pytorch.org/docs/stable/distributed.html#launch-utility
+
+
+ignite.distributed.auto
+-----------------------
+
+.. currentmodule:: ignite.distributed.auto
+
+.. autosummary::
+    :nosignatures:
+    :toctree: generated
+
+    DistributedProxySampler
+    auto_dataloader
+    auto_model
+    auto_optim
+
+.. Note ::
+
+    In distributed configuration, methods :meth:`~ignite.distributed.auto.auto_model`, :meth:`~ignite.distributed.auto.auto_optim`
+    and :meth:`~ignite.distributed.auto.auto_dataloader` will have effect only when distributed group is initialized.
+
+
+ignite.distributed.launcher
+---------------------------
+
+.. currentmodule:: ignite.distributed.launcher
+
+.. autosummary::
+    :nosignatures:
+    :toctree: generated
+
+    Parallel
+
+ignite.distributed.utils
+------------------------
+
+This module wraps common methods to fetch information about distributed configuration, initialize/finalize process
+group or spawn multiple processes.
+
+.. currentmodule:: ignite.distributed.utils
+
+.. autosummary::
+    :nosignatures:
+    :autolist:
+
+.. automodule:: ignite.distributed.utils
+    :members:
+
+    .. attribute:: has_native_dist_support
+
+        True if `torch.distributed` is available
+
+    .. attribute:: has_xla_support
+
+        True if `torch_xla` package is found