erip: Hopefully I understood the issue correctly - the tests seem to be OK. If these tests look representative, I think they can (and should) be refactored to generalize the original frequency tests w/ the full engine setup.
vfdev-5: Yes, you are right, we can refactor original and new tests into a single code. 

And there is a docstring update.


erip: This looks good now. 😄 Which docstring should be updated? Just adding another example in the `Frequency` docstring?
vfdev-5: > Just adding another example in the Frequency docstring?

Yes, another example with the explanation of what it does :)
vfdev-5: Elijah, I was also wondering how's going integrations of training examples of fairseq with ignite you were working on previously ? 
erip: It's going slowly, unfortunately. Fairseq has been working on moving from a framework to a library and it's made development outside of adding new architecture slow. I have a handful of pending PRs that would hopefully move the needle a bit, but there hasn't been much movement.
vfdev-5: I see, thanks for the info. Yes, maybe also they do not have a point to use such libs like ignite (and introduce it as a dependency)...

Are you at PyTorch slack where we could discuss in more fluent way ?
diff --git a/ignite/metrics/frequency.py b/ignite/metrics/frequency.py
index 3a127995..7ae1e0ce 100644
--- a/ignite/metrics/frequency.py
+++ b/ignite/metrics/frequency.py
@@ -19,8 +19,21 @@ class Frequency(Metric):
             wps_metric.attach(trainer, name='wps')
             # Logging with TQDM
             ProgressBar(persist=True).attach(trainer, metric_names=['wps'])
-            # Progress bar will looks like
+            # Progress bar will look like
             # Epoch [2/10]: [12/24]  50%|█████      , wps=400 [00:17<1:23]
+
+
+        To compute examples processed per second every 50th iteration:
+
+        .. code-block:: python
+
+            # Compute number of tokens processed
+            wps_metric = Frequency(output_transform=lambda x: x['ntokens'])
+            wps_metric.attach(trainer, name='wps', event_name=Events.ITERATION_COMPLETED(every=50))
+            # Logging with TQDM
+            ProgressBar(persist=True).attach(trainer, metric_names=['wps'])
+            # Progress bar will look like
+            # Epoch [2/10]: [50/100]  50%|█████      , wps=400 [00:17<00:35]
     """
 
     def __init__(self, output_transform=lambda x: x, device=None):
@@ -59,5 +72,5 @@ class Frequency(Metric):
 
     def attach(self, engine, name, event_name=Events.ITERATION_COMPLETED):
         engine.add_event_handler(Events.EPOCH_STARTED, self.started)
-        engine.add_event_handler(event_name, self.iteration_completed)
+        engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed)
         engine.add_event_handler(event_name, self.completed, name)

Improve Frequency
## 🚀 Feature

If we would like to log datapoints/second every 100 iterations, we most probably do like this 
```python
wps_metric = Frequency(output_transformer=lambda x: x['ntokens'])
wps_metric.attach(trainer, name='wps', event_name=Events.ITERATION_COMPLETED(every=100))
```
however, seems like this wont take into account all other iterations while computing the total number of tokens.
```python
class Frequency(Metric):
    ....
    def attach(self, engine, name, event_name=Events.ITERATION_COMPLETED):
        engine.add_event_handler(Events.EPOCH_STARTED, self.started)
        engine.add_event_handler(event_name, self.iteration_completed)
        engine.add_event_handler(event_name, self.completed, name)
```
IMO, should be 
```python
class Frequency(Metric):
    ....
    def attach(self, engine, name, event_name=Events.ITERATION_COMPLETED):
        engine.add_event_handler(Events.EPOCH_STARTED, self.started)
        engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed)
        engine.add_event_handler(event_name, self.completed, name)
```

cc @erip 

diff --git a/tests/ignite/metrics/test_frequency.py b/tests/ignite/metrics/test_frequency.py
index 369ad13f..03f01a1b 100644
--- a/tests/ignite/metrics/test_frequency.py
+++ b/tests/ignite/metrics/test_frequency.py
@@ -21,7 +21,7 @@ def test_nondistributed_average():
     assert average_lower_bound < average < average_upper_bound
 
 
-def _test_frequency_with_engine(device, workers):
+def _test_frequency_with_engine(device, workers, every=1):
 
     artificial_time = 0.1 / workers  # seconds
     total_tokens = 1200 // workers
@@ -35,9 +35,10 @@ def _test_frequency_with_engine(device, workers):
 
     engine = Engine(update_fn)
     wps_metric = Frequency(output_transform=lambda x: x["ntokens"], device=device)
-    wps_metric.attach(engine, "wps")
+    event = Events.ITERATION_COMPLETED(every=every)
+    wps_metric.attach(engine, "wps", event_name=event)
 
-    @engine.on(Events.ITERATION_COMPLETED)
+    @engine.on(event)
     def assert_wps(e):
         wps = e.state.metrics["wps"]
         assert estimated_wps * 0.80 < wps < estimated_wps, "{}: {} < {} < {}".format(
@@ -57,3 +58,18 @@ def test_frequency_with_engine():
 def test_frequency_with_engine_distributed(distributed_context_single_node_gloo):
     device = "cpu"
     _test_frequency_with_engine(device, workers=dist.get_world_size())
+
+
+def test_frequency_with_engine_with_every():
+    device = "cpu"
+    _test_frequency_with_engine(device, workers=1, every=1)
+    _test_frequency_with_engine(device, workers=1, every=2)
+    _test_frequency_with_engine(device, workers=1, every=10)
+
+
+@pytest.mark.distributed
+def test_frequency_with_engine_distributed_with_every(distributed_context_single_node_gloo):
+    device = "cpu"
+    _test_frequency_with_engine(device, workers=dist.get_world_size(), every=1)
+    _test_frequency_with_engine(device, workers=dist.get_world_size(), every=2)
+    _test_frequency_with_engine(device, workers=dist.get_world_size(), every=10)

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@ad4b8a5d94166cfe23d91666a82bc86df81caba5#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/metrics/test_frequency.py::test_frequency_with_engine_with_every tests/ignite/metrics/test_frequency.py::test_frequency_with_engine_distributed_with_every
tests/ignite/metrics/test_frequency.py::test_nondistributed_average tests/ignite/metrics/test_frequency.py::test_frequency_with_engine tests/ignite/metrics/test_frequency.py::test_frequency_with_engine_distributed
ignite/metrics/frequency.py
ignite/metrics/frequency.py