Weekly Patch Release v1.3.4 [full merge, no squash]

.pre-commit-config.yaml

@@ -19,17 +19,28 @@ repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v2.3.0
     hooks:
-      - id: trailing-whitespace
       - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: check-docstring-first
+      - id: check-executables-have-shebangs
+      - id: check-toml
 
   - repo: https://github.com/PyCQA/isort
     rev: 5.7.0
     hooks:
       - id: isort
-        args: [--settings-path, ./pyproject.toml]
+        name: Format imports
 
   - repo: https://github.com/pre-commit/mirrors-yapf
     rev: v0.30.0
     hooks:
       - id: yapf
-        args: [--parallel, --in-place]
+        name: Format code
+        language: python
+
+  - repo: https://github.com/PyCQA/flake8
+    rev: 3.9.2
+    hooks:
+      - id: flake8
+        name: Check PEP8

CHANGELOG.md

@@ -5,6 +5,13 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
+## [1.3.4] - 2021-06-01
+
+### Fixed
+
+- Fixed info message when max training time reached ([#7780](https://github.com/PyTorchLightning/pytorch-lightning/pull/7780))
+- Fixed missing `__len__` method to `IndexBatchSamplerWrapper` ([#7681](https://github.com/PyTorchLightning/pytorch-lightning/pull/7681))
+
 ## [1.3.3] - 2021-05-27
 
 ### Changed

pytorch_lightning/__about__.py

@@ -1,7 +1,7 @@
 import time
 
 _this_year = time.strftime("%Y")
-__version__ = '1.3.3'
+__version__ = '1.3.4'
 __author__ = 'William Falcon et al.'
 __author_email__ = '[email protected]'
 __license__ = 'Apache-2.0'

pytorch_lightning/callbacks/timer.py

@@ -170,4 +170,5 @@ def _check_time_remaining(self, trainer: 'pl.Trainer') -> None:
         should_stop = trainer.accelerator.broadcast(should_stop)
         trainer.should_stop = trainer.should_stop or should_stop
         if should_stop and self._verbose:
-            rank_zero_info(f"Time limit reached. Elapsed time is {self.time_elapsed}. Signaling Trainer to stop.")
+            elapsed = timedelta(seconds=int(self.time_elapsed(RunningStage.TRAINING)))
+            rank_zero_info(f"Time limit reached. Elapsed time is {elapsed}. Signaling Trainer to stop.")

pytorch_lightning/overrides/distributed.py

@@ -132,6 +132,9 @@ def __iter__(self) -> Iterator[List[int]]:
             self.batch_indices = batch
             yield batch
 
+    def __len__(self) -> int:
+        return len(self._sampler)
+
     @property
     def drop_last(self) -> bool:
         return self._sampler.drop_last

tests/callbacks/test_timer.py

@@ -95,7 +95,7 @@ def test_timer_time_remaining(time_mock):
     assert round(timer.time_elapsed()) == 3
 
 
-def test_timer_stops_training(tmpdir):
+def test_timer_stops_training(tmpdir, caplog):
     """ Test that the timer stops training before reaching max_epochs """
     model = BoringModel()
     duration = timedelta(milliseconds=100)
@@ -106,9 +106,12 @@ def test_timer_stops_training(tmpdir):
         max_epochs=1000,
         callbacks=[timer],
     )
-    trainer.fit(model)
+    with caplog.at_level(logging.INFO):
+        trainer.fit(model)
     assert trainer.global_step > 1
     assert trainer.current_epoch < 999
+    assert "Time limit reached." in caplog.text
+    assert "Signaling Trainer to stop." in caplog.text
 
 
 @pytest.mark.parametrize("interval", ["step", "epoch"])

tests/overrides/test_distributed.py

@@ -11,11 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from collections.abc import Iterable
+
 import pytest
 from torch.utils.data import BatchSampler, SequentialSampler
 
 from pytorch_lightning import seed_everything
 from pytorch_lightning.overrides.distributed import IndexBatchSamplerWrapper, UnrepeatedDistributedSampler
+from pytorch_lightning.utilities.data import has_len
 
 
 @pytest.mark.parametrize("shuffle", [False, True])
@@ -54,3 +57,13 @@ def test_index_batch_sampler(tmpdir):
 
     for batch in index_batch_sampler:
         assert index_batch_sampler.batch_indices == batch
+
+
+def test_index_batch_sampler_methods():
+    dataset = range(15)
+    sampler = SequentialSampler(dataset)
+    batch_sampler = BatchSampler(sampler, 3, False)
+    index_batch_sampler = IndexBatchSamplerWrapper(batch_sampler)
+
+    assert isinstance(index_batch_sampler, Iterable)
+    assert has_len(index_batch_sampler)