snie2012: Automatically determine the length of finite iterator of unknown length seems very neat! Thanks for adding it:)
diff --git a/docs/source/faq.rst b/docs/source/faq.rst
index bf146c8a..b228d7b6 100644
--- a/docs/source/faq.rst
+++ b/docs/source/faq.rst
@@ -66,7 +66,7 @@ flexibility to the user to allow for this:
         engine.fire_event(BackpropEvents.BACKWARD_COMPLETED)
         optimizer.step()
         engine.fire_event(BackpropEvents.OPTIM_STEP_COMPLETED)
-        
+
         return loss.item()
 
     trainer = Engine(update)
@@ -83,7 +83,7 @@ More detailed implementation can be found in `TBPTT Trainer <_modules/ignite/con
 Gradients accumulation
 ----------------------
 
-A best practice to use if we need to increase effectively the batchsize on limited GPU resources. There several ways to
+A best practice to use if we need to increase effectively the batch size on limited GPU resources. There several ways to
 do this, the most simple is the following:
 
 .. code-block:: python
@@ -110,5 +110,241 @@ Based on `this blog article <https://medium.com/huggingface/training-larger-batc
 `this code <https://gist.github.com/thomwolf/ac7a7da6b1888c2eeac8ac8b9b05d3d3#file-gradient_accumulation-py>`_.
 
 
+Working with iterators
+----------------------
+
+If data provider for training or validation is an iterator (infinite or finite with known or unknown size), here are
+basic examples of how to setup trainer or evaluator.
+
+
+Infinite iterator for training
+``````````````````````````````
+
+Let's use an infinite data iterator as training dataflow
+
+.. code-block:: python
+
+    import torch
+    from ignite.engine import Engine, Events
+
+    torch.manual_seed(12)
+
+    def infinite_iterator(batch_size):
+        while True:
+            batch = torch.rand(batch_size, 3, 32, 32)
+            yield batch
+
+    def train_step(trainer, batch):
+        # ...
+        s = trainer.state
+        print(
+            "{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch.norm())
+        )
+
+    trainer = Engine(train_step)
+    # We need to specify epoch_length to define the epoch
+    trainer.run(infinite_iterator(4), epoch_length=5, max_epochs=3)
+
+In this case we will obtain the following output:
+
+.. code-block:: text
+
+    1/3 : 1 - 63.862
+    1/3 : 2 - 64.042
+    1/3 : 3 - 63.936
+    1/3 : 4 - 64.141
+    1/3 : 5 - 64.767
+    2/3 : 6 - 63.791
+    2/3 : 7 - 64.565
+    2/3 : 8 - 63.602
+    2/3 : 9 - 63.995
+    2/3 : 10 - 63.943
+    3/3 : 11 - 63.831
+    3/3 : 12 - 64.276
+    3/3 : 13 - 64.148
+    3/3 : 14 - 63.920
+    3/3 : 15 - 64.226
+
+If we do not specify `epoch_length`, we can stop the training explicitly by calling :meth:`~ignite.engine.Engine.terminate`
+In this case, there will be only a single epoch defined.
+
+.. code-block:: python
+
+    import torch
+    from ignite.engine import Engine, Events
+
+    torch.manual_seed(12)
+
+    def infinite_iterator(batch_size):
+        while True:
+            batch = torch.rand(batch_size, 3, 32, 32)
+            yield batch
+
+    def train_step(trainer, batch):
+        # ...
+        s = trainer.state
+        print(
+            "{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch.norm())
+        )
+
+    trainer = Engine(train_step)
+
+    @trainer.on(Events.ITERATION_COMPLETED(once=15))
+    def stop_training():
+        trainer.terminate()
+
+    trainer.run(infinite_iterator(4))
+
+We obtain the following output:
+
+.. code-block:: text
+
+    1/1 : 1 - 63.862
+    1/1 : 2 - 64.042
+    1/1 : 3 - 63.936
+    1/1 : 4 - 64.141
+    1/1 : 5 - 64.767
+    1/1 : 6 - 63.791
+    1/1 : 7 - 64.565
+    1/1 : 8 - 63.602
+    1/1 : 9 - 63.995
+    1/1 : 10 - 63.943
+    1/1 : 11 - 63.831
+    1/1 : 12 - 64.276
+    1/1 : 13 - 64.148
+    1/1 : 14 - 63.920
+    1/1 : 15 - 64.226
+
+
+Same code can be used for validating models.
+
+
+Finite iterator with unknown length
+```````````````````````````````````
+
+Let's use a finite data iterator but with unknown length (for user). In case of training, we would like to perform
+several passes over the dataflow and thus we need to restart the data iterator when it is exhausted.
+In the code, we do not specify `epoch_length` which will be automatically determined.
+
+.. code-block:: python
+
+    import torch
+    from ignite.engine import Engine, Events
+
+    torch.manual_seed(12)
+
+    def finite_unk_size_data_iter():
+        for i in range(11):
+            yield i
+
+    def train_step(trainer, batch):
+        # ...
+        s = trainer.state
+        print(
+            "{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch)
+        )
+
+    trainer = Engine(train_step)
+
+    @trainer.on(Events.DATALOADER_STOP_ITERATION)
+    def restart_iter():
+        trainer.state.dataloader = finite_unk_size_data_iter()
+
+    data_iter = finite_unk_size_data_iter()
+    trainer.run(data_iter, max_epochs=5)
+
+
+In case of validation, the code is simply
+
+.. code-block:: python
+
+    import torch
+    from ignite.engine import Engine, Events
+
+    torch.manual_seed(12)
+
+    def finite_unk_size_data_iter():
+        for i in range(11):
+            yield i
+
+    def val_step(evaluator, batch):
+        # ...
+        s = evaluator.state
+        print(
+            "{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch)
+        )
+
+    evaluator = Engine(val_step)
+
+    data_iter = finite_unk_size_data_iter()
+    evaluator.run(data_iter)
+
+
+Finite iterator with known length
+`````````````````````````````````
+
+Let's use a finite data iterator with known size for training or validation.
+If we need to restart the data iterator, we can do this either as in case of
+unknown size by attaching the restart handler on `@trainer.on(Events.DATALOADER_STOP_ITERATION)`,
+but here we will do this explicitly on iteration:
+
+.. code-block:: python
+
+    import torch
+    from ignite.engine import Engine, Events
+
+    torch.manual_seed(12)
+
+    size = 11
+
+    def finite_size_data_iter(size):
+        for i in range(size):
+            yield i
+
+    def train_step(trainer, batch):
+        # ...
+        s = trainer.state
+        print(
+            "{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch)
+        )
+
+    trainer = Engine(train_step)
+
+    @trainer.on(Events.ITERATION_COMPLETED(every=size))
+    def restart_iter():
+        trainer.state.dataloader = finite_size_data_iter(size)
+
+    data_iter = finite_size_data_iter(size)
+    trainer.run(data_iter, max_epochs=5)
+
+
+In case of validation, the code is simply
+
+.. code-block:: python
+
+    import torch
+    from ignite.engine import Engine, Events
+
+    torch.manual_seed(12)
+
+    size = 11
+
+    def finite_size_data_iter(size):
+        for i in range(size):
+            yield i
+
+    def val_step(evaluator, batch):
+        # ...
+        s = evaluator.state
+        print(
+            "{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch)
+        )
+
+    evaluator = Engine(val_step)
+
+    data_iter = finite_size_data_iter(size)
+    evaluator.run(data_iter)
+
+
 Other answers can be found on the github among the issues labeled by
 `question <https://github.com/pytorch/ignite/issues?utf8=%E2%9C%93&q=is%3Aissue+label%3Aquestion+>`_.
diff --git a/ignite/engine/engine.py b/ignite/engine/engine.py
index 34c13972..825cccd8 100644
--- a/ignite/engine/engine.py
+++ b/ignite/engine/engine.py
@@ -554,7 +554,8 @@ class Engine:
                 If a new state should be created (first run or run again from ended engine), it's default value is 1.
                 This argument should be `None` if run is resuming from a state.
             epoch_length (int, optional): Number of iterations to count as one epoch. By default, it can be set as
-                `len(data)`. If `data` is an iterator and `epoch_length` is not set, an error is raised.
+                `len(data)`. If `data` is an iterator and `epoch_length` is not set, then it will be automatically
+                determined as the iteration on which data iterator raises `StopIteration`.
                 This argument should be `None` if run is resuming from a state.
             seed (int, optional): Deprecated argument. Please, use `torch.manual_seed` or
                 :meth:`~ignite.utils.manual_seed`.
@@ -590,8 +591,7 @@ class Engine:
                     epoch_length = len(data)
                     if epoch_length < 1:
                         raise ValueError("Input data has zero size. Please provide non-empty data")
-                else:
-                    raise ValueError("Argument `epoch_length` should be defined if `data` is an iterator")
+
             self.state = State(iteration=0, epoch=0, max_epochs=max_epochs, epoch_length=epoch_length)
             self.logger.info("Engine run starting with max_epochs={}.".format(max_epochs))
         else:
@@ -694,6 +694,7 @@ class Engine:
                     iter_counter += 1
                     should_exit = False
                 except StopIteration:
+
                     if self._dataloader_len is None:
                         if iter_counter > 0:
                             self._dataloader_len = iter_counter
@@ -701,6 +702,12 @@ class Engine:
                             # this can happen when data is finite iterator and epoch_length is equal to its size
                             self._dataloader_len = self.state.iteration
 
+                    # Define self.state.epoch_length if it is not yet set
+                    if self.state.epoch_length is None:
+                        # Define epoch length and stop the epoch
+                        self.state.epoch_length = iter_counter
+                        break
+
                     # Should exit while loop if we can not iterate
                     if should_exit:
                         if not self._is_done(self.state):
@@ -734,7 +741,7 @@ class Engine:
                     self._dataloader_iter = iter(self.state.dataloader)
                     break
 
-                if iter_counter == self.state.epoch_length:
+                if self.state.epoch_length is not None and iter_counter == self.state.epoch_length:
                     break
 
         except Exception as e:

Make epoch_length optional
## 🚀 Feature

Following discussions and the message: https://github.com/pytorch/ignite/issues/853#issuecomment-606732951 it could be useful to make `epoch_length` optional for finite iterators.


diff --git a/tests/ignite/engine/__init__.py b/tests/ignite/engine/__init__.py
index e40827db..18111896 100644
--- a/tests/ignite/engine/__init__.py
+++ b/tests/ignite/engine/__init__.py
@@ -10,7 +10,8 @@ class BatchChecker:
     def check(self, batch):
         self.true_batch = self.data[self.counter % len(self.data)]
         self.counter += 1
-        return (self.true_batch == batch).all()
+        res = self.true_batch == batch
+        return res.all() if not isinstance(res, bool) else res
 
 
 class IterationCounter:
diff --git a/tests/ignite/engine/test_engine.py b/tests/ignite/engine/test_engine.py
index 4c370ed5..cda3a1c7 100644
--- a/tests/ignite/engine/test_engine.py
+++ b/tests/ignite/engine/test_engine.py
@@ -10,7 +10,7 @@ from ignite.engine import Engine, Events, State
 from ignite.engine.deterministic import keep_random_state
 from ignite.metrics import Average
 
-from tests.ignite.engine import IterationCounter, EpochCounter
+from tests.ignite.engine import IterationCounter, EpochCounter, BatchChecker
 
 
 def test_terminate():
@@ -341,9 +341,6 @@ def test_run_asserts():
     with pytest.raises(ValueError, match=r"Input data has zero size. Please provide non-empty data"):
         engine.run([])
 
-    with pytest.raises(ValueError, match=r"Argument `epoch_length` should be defined if `data` is an iterator"):
-        engine.run(iter([0, 1, 2, 3]))
-
     with pytest.warns(UserWarning, match="Argument seed is deprecated"):
         engine.run([0, 1, 2, 3, 4], seed=1234)
 
@@ -638,3 +635,235 @@ def test_engine_with_dataloader_no_auto_batching():
     engine.run(data_loader, epoch_length=10, max_epochs=5)
 
     assert counter[0] == 50
+
+
+def test_run_once_finite_iterator_no_epoch_length():
+    # FR: https://github.com/pytorch/ignite/issues/871
+
+    unknown_size = 11
+
+    def finite_unk_size_data_iter():
+        for i in range(unknown_size):
+            yield i
+
+    bc = BatchChecker(data=list(range(unknown_size)))
+
+    engine = Engine(lambda e, b: bc.check(b))
+
+    completed_handler = MagicMock()
+    engine.add_event_handler(Events.COMPLETED, completed_handler)
+
+    data_iter = finite_unk_size_data_iter()
+    engine.run(data_iter)
+
+    assert engine.state.epoch == 1
+    assert engine.state.iteration == unknown_size
+    assert completed_handler.call_count == 1
+
+
+def test_run_finite_iterator_no_epoch_length():
+    # FR: https://github.com/pytorch/ignite/issues/871
+    unknown_size = 11
+
+    def finite_unk_size_data_iter():
+        for i in range(unknown_size):
+            yield i
+
+    bc = BatchChecker(data=list(range(unknown_size)))
+
+    engine = Engine(lambda e, b: bc.check(b))
+
+    @engine.on(Events.DATALOADER_STOP_ITERATION)
+    def restart_iter():
+        engine.state.dataloader = finite_unk_size_data_iter()
+
+    data_iter = finite_unk_size_data_iter()
+    engine.run(data_iter, max_epochs=5)
+
+    assert engine.state.epoch == 5
+    assert engine.state.iteration == unknown_size * 5
+
+
+def test_run_finite_iterator_no_epoch_length_2():
+    # FR: https://github.com/pytorch/ignite/issues/871
+    known_size = 11
+
+    def finite_size_data_iter(size):
+        for i in range(size):
+            yield i
+
+    bc = BatchChecker(data=list(range(known_size)))
+
+    engine = Engine(lambda e, b: bc.check(b))
+
+    @engine.on(Events.ITERATION_COMPLETED(every=known_size))
+    def restart_iter():
+        engine.state.dataloader = finite_size_data_iter(known_size)
+
+    data_iter = finite_size_data_iter(known_size)
+    engine.run(data_iter, max_epochs=5)
+
+    assert engine.state.epoch == 5
+    assert engine.state.iteration == known_size * 5
+
+
+def test_faq_inf_iterator_with_epoch_length():
+    # Code snippet from FAQ
+
+    import torch
+
+    torch.manual_seed(12)
+
+    def infinite_iterator(batch_size):
+        while True:
+            batch = torch.rand(batch_size, 3, 32, 32)
+            yield batch
+
+    def train_step(trainer, batch):
+        # ...
+        s = trainer.state
+        print("{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch.norm()))
+
+    trainer = Engine(train_step)
+    # We need to specify epoch_length to define the epoch
+    trainer.run(infinite_iterator(4), epoch_length=5, max_epochs=3)
+
+    assert trainer.state.epoch == 3
+    assert trainer.state.iteration == 3 * 5
+
+
+def test_faq_inf_iterator_no_epoch_length():
+    # Code snippet from FAQ
+
+    import torch
+
+    torch.manual_seed(12)
+
+    def infinite_iterator(batch_size):
+        while True:
+            batch = torch.rand(batch_size, 3, 32, 32)
+            yield batch
+
+    def train_step(trainer, batch):
+        # ...
+        s = trainer.state
+        print("{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch.norm()))
+
+    trainer = Engine(train_step)
+
+    @trainer.on(Events.ITERATION_COMPLETED(once=15))
+    def stop_training():
+        trainer.terminate()
+
+    trainer.run(infinite_iterator(4))
+
+    assert trainer.state.epoch == 1
+    assert trainer.state.iteration == 15
+
+
+def test_faq_fin_iterator_unknw_size():
+    # Code snippet from FAQ
+
+    import torch
+
+    torch.manual_seed(12)
+
+    def finite_unk_size_data_iter():
+        for i in range(11):
+            yield i
+
+    def train_step(trainer, batch):
+        # ...
+        s = trainer.state
+        print("{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch))
+
+    trainer = Engine(train_step)
+
+    @trainer.on(Events.DATALOADER_STOP_ITERATION)
+    def restart_iter():
+        trainer.state.dataloader = finite_unk_size_data_iter()
+
+    data_iter = finite_unk_size_data_iter()
+    trainer.run(data_iter, max_epochs=5)
+
+    assert trainer.state.epoch == 5
+    assert trainer.state.iteration == 5 * 11
+
+    # # # # #
+
+    import torch
+
+    torch.manual_seed(12)
+
+    def finite_unk_size_data_iter():
+        for i in range(11):
+            yield i
+
+    def val_step(evaluator, batch):
+        # ...
+        s = evaluator.state
+        print("{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch))
+
+    evaluator = Engine(val_step)
+
+    data_iter = finite_unk_size_data_iter()
+    evaluator.run(data_iter)
+
+    assert evaluator.state.epoch == 1
+    assert evaluator.state.iteration == 1 * 11
+
+
+def test_faq_fin_iterator():
+    # Code snippet from FAQ
+
+    import torch
+
+    torch.manual_seed(12)
+
+    size = 11
+
+    def finite_size_data_iter(size):
+        for i in range(size):
+            yield i
+
+    def train_step(trainer, batch):
+        # ...
+        s = trainer.state
+        print("{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch))
+
+    trainer = Engine(train_step)
+
+    @trainer.on(Events.ITERATION_COMPLETED(every=size))
+    def restart_iter():
+        trainer.state.dataloader = finite_size_data_iter(size)
+
+    data_iter = finite_size_data_iter(size)
+    trainer.run(data_iter, max_epochs=5)
+
+    assert trainer.state.epoch == 5
+    assert trainer.state.iteration == 5 * size
+
+    # # # # #
+
+    import torch
+
+    torch.manual_seed(12)
+
+    size = 11
+
+    def finite_size_data_iter(size):
+        for i in range(size):
+            yield i
+
+    def val_step(evaluator, batch):
+        # ...
+        s = evaluator.state
+        print("{}/{} : {} - {:.3f}".format(s.epoch, s.max_epochs, s.iteration, batch))
+
+    evaluator = Engine(val_step)
+
+    data_iter = finite_size_data_iter(size)
+    evaluator.run(data_iter)
+
+    assert evaluator.state.epoch == 1
+    assert evaluator.state.iteration == size

pip install -e .
parse_log_pytest
requirements.txt
pytest
apt-get update apt-get install -y gcc pip install torch torchvision -f https://download.pytorch.org/whl/cpu/torch_stable.html -U
3.7
requirements-dev.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
absl-py==2.1.0
alembic==1.12.1
arrow==1.2.3
attrs==24.2.0
boto3==1.33.13
botocore==1.33.13
bravado==11.1.0
bravado-core==6.1.1
cached-property==1.5.2
cachetools==5.5.2
certifi @ file:///croot/certifi_1671487769961/work/certifi
charset-normalizer==3.4.1
click==7.1.2
cloudpickle==2.2.1
codecov==2.1.13
coverage==7.2.7
cycler==0.11.0
databricks-cli==0.18.0
dill==0.3.7
docker==6.1.3
entrypoints==0.4
exceptiongroup==1.2.2
execnet==2.0.2
Flask==1.1.4
fonttools==4.38.0
fqdn==1.5.1
future==1.0.0
gitdb==4.0.12
GitPython==3.1.44
google-auth==2.38.0
google-auth-oauthlib==0.4.6
greenlet==3.1.1
grpcio==1.62.3
gunicorn==20.1.0
gym==0.26.2
gym-notices==0.0.8
hestia==0.6.0
idna==3.10
importlib-metadata==5.2.0
importlib-resources==5.12.0
iniconfig==2.0.0
isoduration==20.11.0
itsdangerous==1.1.0
Jinja2==2.10.3
jmespath==1.0.1
joblib==1.3.2
jsonpatch==1.33
jsonpointer==3.0.0
jsonref==1.1.0
jsonschema==4.17.3
kiwisolver==1.4.5
Mako==1.2.4
Markdown==3.4.4
MarkupSafe==2.1.5
marshmallow==3.0.0rc5
matplotlib==3.5.3
mlflow==1.30.1
monotonic==1.6
msgpack==1.0.5
neptune-client==1.11.1
networkx==2.6.3
numpy==1.21.6
oauthlib==3.2.2
packaging==21.3
pandas==1.3.5
Pillow==9.5.0
pkgutil_resolve_name==1.3.10
pluggy==1.2.0
polyaxon-client==0.6.1
polyaxon-schemas==0.6.1
polystores==0.2.5
prometheus-client==0.17.1
prometheus_flask_exporter==0.23.2
protobuf==3.20.3
psutil==7.0.0
pyasn1==0.5.1
pyasn1-modules==0.3.0
PyJWT==2.8.0
pynvml==11.5.3
pyparsing==3.1.4
pyrsistent==0.19.3
pytest==7.4.4
pytest-cov==4.1.0
pytest-xdist==3.5.0
python-dateutil==2.9.0.post0
-e git+https://github.com/pytorch/ignite.git@67eb7b85f50a16db92d08a1c781d92209f1f0d1e#egg=pytorch_ignite
pytz==2022.7.1
PyYAML==6.0.1
querystring-parser==1.2.4
requests==2.31.0
requests-oauthlib==2.0.0
requests-toolbelt==1.0.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rhea==0.5.5
rsa==4.9
s3transfer==0.8.2
scikit-learn==1.0.2
scipy==1.7.3
simplejson==3.20.1
six==1.17.0
smmap==5.0.2
SQLAlchemy==1.4.54
sqlparse==0.4.4
swagger-spec-validator==3.0.3
tabulate==0.9.0
tensorboard==2.11.2
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorboardX==2.6.2.2
threadpoolctl==3.1.0
tomli==2.0.1
torch==1.13.1+cpu
torchvision==0.14.1+cpu
tornado==6.2
tqdm==4.67.1
typing_extensions==4.7.1
uri-template==1.3.0
urllib3==1.26.20
visdom==0.2.4
webcolors==1.13
websocket-client==1.6.1
Werkzeug==1.0.1
zipp==3.15.0

name: ignite
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2022.12.7=py37h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - pip=22.3.1=py37h06a4308_0
  - python=3.7.16=h7a1cb2a_0
  - readline=8.2=h5eee18b_0
  - setuptools=65.6.3=py37h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - wheel=0.38.4=py37h06a4308_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - absl-py==2.1.0
      - alembic==1.12.1
      - arrow==1.2.3
      - attrs==24.2.0
      - boto3==1.33.13
      - botocore==1.33.13
      - bravado==11.1.0
      - bravado-core==6.1.1
      - cached-property==1.5.2
      - cachetools==5.5.2
      - charset-normalizer==3.4.1
      - click==7.1.2
      - cloudpickle==2.2.1
      - codecov==2.1.13
      - coverage==7.2.7
      - cycler==0.11.0
      - databricks-cli==0.18.0
      - dill==0.3.7
      - docker==6.1.3
      - entrypoints==0.4
      - exceptiongroup==1.2.2
      - execnet==2.0.2
      - flask==1.1.4
      - fonttools==4.38.0
      - fqdn==1.5.1
      - future==1.0.0
      - gitdb==4.0.12
      - gitpython==3.1.44
      - google-auth==2.38.0
      - google-auth-oauthlib==0.4.6
      - greenlet==3.1.1
      - grpcio==1.62.3
      - gunicorn==20.1.0
      - gym==0.26.2
      - gym-notices==0.0.8
      - hestia==0.6.0
      - idna==3.10
      - importlib-metadata==5.2.0
      - importlib-resources==5.12.0
      - iniconfig==2.0.0
      - isoduration==20.11.0
      - itsdangerous==1.1.0
      - jinja2==2.10.3
      - jmespath==1.0.1
      - joblib==1.3.2
      - jsonpatch==1.33
      - jsonpointer==3.0.0
      - jsonref==1.1.0
      - jsonschema==4.17.3
      - kiwisolver==1.4.5
      - mako==1.2.4
      - markdown==3.4.4
      - markupsafe==2.1.5
      - marshmallow==3.0.0rc5
      - matplotlib==3.5.3
      - mlflow==1.30.1
      - monotonic==1.6
      - msgpack==1.0.5
      - neptune-client==1.11.1
      - networkx==2.6.3
      - numpy==1.21.6
      - oauthlib==3.2.2
      - packaging==21.3
      - pandas==1.3.5
      - pillow==9.5.0
      - pkgutil-resolve-name==1.3.10
      - pluggy==1.2.0
      - polyaxon-client==0.6.1
      - polyaxon-schemas==0.6.1
      - polystores==0.2.5
      - prometheus-client==0.17.1
      - prometheus-flask-exporter==0.23.2
      - protobuf==3.20.3
      - psutil==7.0.0
      - pyasn1==0.5.1
      - pyasn1-modules==0.3.0
      - pyjwt==2.8.0
      - pynvml==11.5.3
      - pyparsing==3.1.4
      - pyrsistent==0.19.3
      - pytest==7.4.4
      - pytest-cov==4.1.0
      - pytest-xdist==3.5.0
      - python-dateutil==2.9.0.post0
      - pytorch-ignite==0.4.0
      - pytz==2022.7.1
      - pyyaml==6.0.1
      - querystring-parser==1.2.4
      - requests==2.31.0
      - requests-oauthlib==2.0.0
      - requests-toolbelt==1.0.0
      - rfc3339-validator==0.1.4
      - rfc3986-validator==0.1.1
      - rhea==0.5.5
      - rsa==4.9
      - s3transfer==0.8.2
      - scikit-learn==1.0.2
      - scipy==1.7.3
      - simplejson==3.20.1
      - six==1.17.0
      - smmap==5.0.2
      - sqlalchemy==1.4.54
      - sqlparse==0.4.4
      - swagger-spec-validator==3.0.3
      - tabulate==0.9.0
      - tensorboard==2.11.2
      - tensorboard-data-server==0.6.1
      - tensorboard-plugin-wit==1.8.1
      - tensorboardx==2.6.2.2
      - threadpoolctl==3.1.0
      - tomli==2.0.1
      - torch==1.13.1+cpu
      - torchvision==0.14.1+cpu
      - tornado==6.2
      - tqdm==4.67.1
      - typing-extensions==4.7.1
      - uri-template==1.3.0
      - urllib3==1.26.20
      - visdom==0.2.4
      - webcolors==1.13
      - websocket-client==1.6.1
      - werkzeug==1.0.1
      - zipp==3.15.0
prefix: /opt/conda/envs/ignite

tests/ignite/engine/test_engine.py::test_run_once_finite_iterator_no_epoch_length tests/ignite/engine/test_engine.py::test_run_finite_iterator_no_epoch_length tests/ignite/engine/test_engine.py::test_run_finite_iterator_no_epoch_length_2 tests/ignite/engine/test_engine.py::test_faq_inf_iterator_no_epoch_length tests/ignite/engine/test_engine.py::test_faq_fin_iterator_unknw_size tests/ignite/engine/test_engine.py::test_faq_fin_iterator
tests/ignite/engine/test_create_supervised.py::test_create_supervised_trainer tests/ignite/engine/test_create_supervised.py::test_create_supervised_trainer_with_cpu tests/ignite/engine/test_create_supervised.py::test_create_supervised_trainer_traced_with_cpu tests/ignite/engine/test_create_supervised.py::test_create_supervised_evaluator tests/ignite/engine/test_create_supervised.py::test_create_supervised_evaluator_on_cpu tests/ignite/engine/test_create_supervised.py::test_create_supervised_evaluator_traced_on_cpu tests/ignite/engine/test_create_supervised.py::test_create_supervised_evaluator_with_metrics tests/ignite/engine/test_engine.py::test_terminate tests/ignite/engine/test_engine.py::test_invalid_process_raises_with_invalid_signature tests/ignite/engine/test_engine.py::test_current_epoch_counter_increases_every_epoch tests/ignite/engine/test_engine.py::test_current_iteration_counter_increases_every_iteration tests/ignite/engine/test_engine.py::test_stopping_criterion_is_max_epochs tests/ignite/engine/test_engine.py::test_terminate_at_end_of_epoch_stops_run tests/ignite/engine/test_engine.py::test_terminate_at_start_of_epoch_stops_run_after_completing_iteration tests/ignite/engine/test_engine.py::test_terminate_stops_run_mid_epoch tests/ignite/engine/test_engine.py::test_terminate_epoch_stops_mid_epoch tests/ignite/engine/test_engine.py::test_iteration_events_are_fired tests/ignite/engine/test_engine.py::test_last_event_name tests/ignite/engine/test_engine.py::test_reset_should_terminate tests/ignite/engine/test_engine.py::test_batch_values tests/ignite/engine/test_engine.py::test_state_repr tests/ignite/engine/test_engine.py::test_alter_batch tests/ignite/engine/test_engine.py::test__is_done tests/ignite/engine/test_engine.py::test__setup_engine tests/ignite/engine/test_engine.py::test_run_asserts tests/ignite/engine/test_engine.py::test_state_get_event_attrib_value tests/ignite/engine/test_engine.py::test_time_stored_in_state tests/ignite/engine/test_engine.py::test_run_check_triggered_events_list tests/ignite/engine/test_engine.py::test_run_check_triggered_events_on_iterator tests/ignite/engine/test_engine.py::test_distrib_cpu tests/ignite/engine/test_engine.py::test_engine_with_iterable_dataloader tests/ignite/engine/test_engine.py::test_engine_random_state tests/ignite/engine/test_engine.py::test_altered_random_state tests/ignite/engine/test_engine.py::test_engine_with_dataloader_no_auto_batching tests/ignite/engine/test_engine.py::test_faq_inf_iterator_with_epoch_length
ignite/engine/engine.py docs/source/faq.rst
ignite/engine/engine.py docs/source/faq.rst