diff --git a/.travis.yml b/.travis.yml
index c744c8e1..f9d05c1d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -78,7 +78,7 @@ jobs:
       install:
         # Minimal install : ignite and dependencies just to build the docs
         - pip install -r docs/requirements.txt
-        - pip install http://download.pytorch.org/whl/cpu/torch-0.4.0-cp35-cp35m-linux_x86_64.whl
+        - pip install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp35-cp35m-linux_x86_64.whl
         # `pip install .` vs `python setup.py install` : 1st works better to produce _module/ignite with source links
         - pip install .
       script:
diff --git a/examples/mnist/mnist_with_visdom.py b/examples/mnist/mnist_with_visdom.py
index 78ccdb43..9758333e 100644
--- a/examples/mnist/mnist_with_visdom.py
+++ b/examples/mnist/mnist_with_visdom.py
@@ -54,8 +54,9 @@ def create_plot_window(vis, xlabel, ylabel, title):
 
 def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_interval):
     vis = visdom.Visdom()
-    if not vis.check_connection():
-        raise RuntimeError("Visdom server not running. Please run python -m visdom.server")
+
+    # if not vis.check_connection():
+    #     raise RuntimeError("Visdom server not running. Please run python -m visdom.server")
 
     train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size)
     model = Net()
diff --git a/ignite/_utils.py b/ignite/_utils.py
index 60cd14ec..7daad7a7 100644
--- a/ignite/_utils.py
+++ b/ignite/_utils.py
@@ -11,10 +11,10 @@ def _to_hours_mins_secs(time_taken):
     return hours, mins, secs
 
 
-def convert_tensor(input_, device=None):
+def convert_tensor(input_, device=None, non_blocking=False):
     """Move tensors to relevant device."""
     def _func(tensor):
-        return tensor.to(device=device) if device else tensor
+        return tensor.to(device=device, non_blocking=non_blocking) if device else tensor
 
     return apply_to_tensor(input_, _func)
 
diff --git a/ignite/engine/__init__.py b/ignite/engine/__init__.py
index a943a8dc..5333c5e0 100644
--- a/ignite/engine/__init__.py
+++ b/ignite/engine/__init__.py
@@ -4,12 +4,13 @@ from ignite.engine.engine import Engine, State, Events
 from ignite._utils import convert_tensor
 
 
-def _prepare_batch(batch, device=None):
+def _prepare_batch(batch, device=None, non_blocking=False):
     x, y = batch
-    return convert_tensor(x, device=device), convert_tensor(y, device=device)
+    return (convert_tensor(x, device=device, non_blocking=non_blocking),
+            convert_tensor(y, device=device, non_blocking=non_blocking))
 
 
-def create_supervised_trainer(model, optimizer, loss_fn, device=None):
+def create_supervised_trainer(model, optimizer, loss_fn, device=None, non_blocking=False):
     """
     Factory function for creating a trainer for supervised models
 
@@ -19,6 +20,8 @@ def create_supervised_trainer(model, optimizer, loss_fn, device=None):
         loss_fn (torch.nn loss function): the loss function to use
         device (str, optional): device type specification (default: None).
             Applies to both model and batches.
+        non_blocking (bool, optional): if True and this copy is between CPU and GPU, the copy may occur asynchronously
+            with respect to the host. For other cases, this argument has no effect.
 
     Returns:
         Engine: a trainer engine with supervised update function
@@ -29,7 +32,7 @@ def create_supervised_trainer(model, optimizer, loss_fn, device=None):
     def _update(engine, batch):
         model.train()
         optimizer.zero_grad()
-        x, y = _prepare_batch(batch, device=device)
+        x, y = _prepare_batch(batch, device=device, non_blocking=non_blocking)
         y_pred = model(x)
         loss = loss_fn(y_pred, y)
         loss.backward()
diff --git a/ignite/handlers/checkpoint.py b/ignite/handlers/checkpoint.py
index 946d22fa..4e68a4e1 100644
--- a/ignite/handlers/checkpoint.py
+++ b/ignite/handlers/checkpoint.py
@@ -42,6 +42,8 @@ class ModelCheckpoint(object):
             in the directory 'dirname'
         create_dir (bool, optional):
             If True, will create directory 'dirname' if it doesnt exist.
+        save_as_state_dict (bool, optional):
+            If True, will save only the `state_dict` of the objects specified, otherwise the whole object will be saved.
 
     Notes:
           This handler expects two arguments: an `Engine` object and a `dict`

Update the "Concepts" part in the documentation
Recently we've added a possibility to handle custom events, so we need to keep "Concepts" documentation updated.

There is also a bad docstring rendering of parameters on methods
- [add_event_handler](https://pytorch.org/ignite/engine.html#ignite.engine.Engine.add_event_handler)
- [fire_event](https://pytorch.org/ignite/engine.html#ignite.engine.Engine.fire_event)
- [on](https://pytorch.org/ignite/engine.html#ignite.engine.Engine.fire_event)
- [register_events](https://pytorch.org/ignite/engine.html#ignite.engine.Engine.register_events)
diff --git a/tests/ignite/test__utils.py b/tests/ignite/test__utils.py
index 3a1c8eab..6f5b7f67 100644
--- a/tests/ignite/test__utils.py
+++ b/tests/ignite/test__utils.py
@@ -8,6 +8,14 @@ def test_convert_tensor():
     tensor = convert_tensor(x)
     assert torch.is_tensor(tensor)
 
+    x = torch.Tensor([0.0])
+    tensor = convert_tensor(x, device='cpu', non_blocking=True)
+    assert torch.is_tensor(tensor)
+
+    x = torch.Tensor([0.0])
+    tensor = convert_tensor(x, device='cpu', non_blocking=False)
+    assert torch.is_tensor(tensor)
+
     x = (torch.Tensor([0.0]), torch.Tensor([0.0]))
     list_ = convert_tensor(x)
     assert isinstance(list_, list)

pip install -e .
parse_log_pytest
pytest
numpy mock pytest codecov pytest-cov tqdm scikit-learn visdom torchvision tensorboardX gym
apt-get update apt-get install -y gcc
3.6
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
attrs @ file:///opt/conda/conda-bld/attrs_1642510447205/work
certifi==2021.5.30
charset-normalizer==2.0.12
cloudpickle==2.2.1
codecov==2.1.13
coverage==6.2
dataclasses==0.8
decorator==4.4.2
gym==0.26.2
gym-notices==0.0.8
idna==3.10
importlib-metadata @ file:///tmp/build/80754af9/importlib-metadata_1631916693255/work
importlib-resources==5.4.0
iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work
joblib==1.1.1
jsonpatch==1.32
jsonpointer==2.3
mock==5.2.0
more-itertools @ file:///tmp/build/80754af9/more-itertools_1637733554872/work
networkx==2.5.1
numpy==1.19.5
packaging @ file:///tmp/build/80754af9/packaging_1637314298585/work
Pillow==8.4.0
pluggy @ file:///tmp/build/80754af9/pluggy_1615976315926/work
protobuf==4.21.0
py @ file:///opt/conda/conda-bld/py_1644396412707/work
pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work
pytest==6.2.4
pytest-cov==4.0.0
-e git+https://github.com/pytorch/ignite.git@18ea860826e86b87e310baa28657a7224a933b20#egg=pytorch_ignite
requests==2.27.1
scikit-learn==0.24.2
scipy==1.5.4
six==1.17.0
tensorboardX==2.6.2.2
threadpoolctl==3.1.0
toml @ file:///tmp/build/80754af9/toml_1616166611790/work
tomli==1.2.3
torch==1.10.1
torchvision==0.11.2
tornado==6.1
tqdm==4.64.1
typing_extensions @ file:///opt/conda/conda-bld/typing_extensions_1647553014482/work
urllib3==1.26.20
visdom==0.2.4
websocket-client==1.3.1
zipp @ file:///tmp/build/80754af9/zipp_1633618647012/work

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
  - attrs=21.4.0=pyhd3eb1b0_0
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2021.5.30=py36h06a4308_0
  - importlib-metadata=4.8.1=py36h06a4308_0
  - importlib_metadata=4.8.1=hd3eb1b0_0
  - iniconfig=1.1.1=pyhd3eb1b0_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.3=he6710b0_2
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - more-itertools=8.12.0=pyhd3eb1b0_0
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - packaging=21.3=pyhd3eb1b0_0
  - pip=21.2.2=py36h06a4308_0
  - pluggy=0.13.1=py36h06a4308_0
  - py=1.11.0=pyhd3eb1b0_0
  - pyparsing=3.0.4=pyhd3eb1b0_0
  - pytest=6.2.4=py36h06a4308_2
  - python=3.6.13=h12debd9_1
  - readline=8.2=h5eee18b_0
  - setuptools=58.0.4=py36h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - toml=0.10.2=pyhd3eb1b0_0
  - typing_extensions=4.1.1=pyh06a4308_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - xz=5.6.4=h5eee18b_1
  - zipp=3.6.0=pyhd3eb1b0_0
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - charset-normalizer==2.0.12
      - cloudpickle==2.2.1
      - codecov==2.1.13
      - coverage==6.2
      - dataclasses==0.8
      - decorator==4.4.2
      - gym==0.26.2
      - gym-notices==0.0.8
      - idna==3.10
      - importlib-resources==5.4.0
      - joblib==1.1.1
      - jsonpatch==1.32
      - jsonpointer==2.3
      - mock==5.2.0
      - networkx==2.5.1
      - numpy==1.19.5
      - pillow==8.4.0
      - protobuf==4.21.0
      - pytest-cov==4.0.0
      - requests==2.27.1
      - scikit-learn==0.24.2
      - scipy==1.5.4
      - six==1.17.0
      - tensorboardx==2.6.2.2
      - threadpoolctl==3.1.0
      - tomli==1.2.3
      - torch==1.10.1
      - torchvision==0.11.2
      - tornado==6.1
      - tqdm==4.64.1
      - urllib3==1.26.20
      - visdom==0.2.4
      - websocket-client==1.3.1
prefix: /opt/conda/envs/ignite

tests/ignite/test__utils.py::test_convert_tensor
tests/ignite/test__utils.py::test_to_onehot
ignite/handlers/checkpoint.py ignite/_utils.py ignite/engine/__init__.py examples/mnist/mnist_with_visdom.py .travis.yml
ignite/handlers/checkpoint.py ignite/_utils.py ignite/engine/__init__.py examples/mnist/mnist_with_visdom.py .travis.yml