Fixed NeptuneLogger when using DDP

CHANGELOG.md

@@ -287,8 +287,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 -
 
 
-- Avoid the deprecated `onnx.export(example_outputs=...)` in torch 1.10 ([#11116](https://github.com/PyTorchLightning/pytorch-lightning/pull/11116))
+- Fixed `NeptuneLogger` when using DDP ([#11030](https://github.com/PyTorchLightning/pytorch-lightning/pull/11030))
+
 
+- Avoid the deprecated `onnx.export(example_outputs=...)` in torch 1.10 ([#11116](https://github.com/PyTorchLightning/pytorch-lightning/pull/11116))
 
 
 - Fixed an issue when torch-scripting a `LightningModule` after training with `Trainer(sync_batchnorm=True)` ([#11078](https://github.com/PyTorchLightning/pytorch-lightning/pull/11078))

environment.yml

@@ -51,4 +51,4 @@ dependencies:
         - mlflow>=1.0.0
         - comet_ml>=3.1.12
         - wandb>=0.8.21
-        - neptune-client>=0.4.109
+        - neptune-client>=0.10.0

pytorch_lightning/loggers/neptune.py

@@ -44,7 +44,7 @@
         from neptune.new.types import File as NeptuneFile
     except ModuleNotFoundError:
         import neptune
-        from neptune.exceptions import NeptuneLegacyProjectException
+        from neptune.exceptions import NeptuneLegacyProjectException, NeptuneOfflineModeFetchException
         from neptune.run import Run
         from neptune.types import File as NeptuneFile
 else:
@@ -266,51 +266,64 @@ def __init__(
         prefix: str = "training",
         **neptune_run_kwargs,
     ):
-
         # verify if user passed proper init arguments
         self._verify_input_arguments(api_key, project, name, run, neptune_run_kwargs)
+        if neptune is None:
+            raise ModuleNotFoundError(
+                "You want to use the `Neptune` logger which is not installed yet, install it with"
+                " `pip install neptune-client`."
+            )
 
         super().__init__()
         self._log_model_checkpoints = log_model_checkpoints
         self._prefix = prefix
+        self._run_name = name
+        self._project_name = project
+        self._api_key = api_key
+        self._run_instance = run
+        self._neptune_run_kwargs = neptune_run_kwargs
+        self._run_short_id = None
 
-        self._run_instance = self._init_run_instance(api_key, project, name, run, neptune_run_kwargs)
+        if self._run_instance is not None:
+            self._retrieve_run_data()
 
-        self._run_short_id = self.run._short_id  # skipcq: PYL-W0212
+            # make sure that we've log integration version for outside `Run` instances
+            self._run_instance[_INTEGRATION_VERSION_KEY] = __version__
+
+    def _retrieve_run_data(self):
         try:
-            self.run.wait()
+            self._run_instance.wait()
+            self._run_short_id = self.run._short_id  # skipcq: PYL-W0212
             self._run_name = self._run_instance["sys/name"].fetch()
         except NeptuneOfflineModeFetchException:
             self._run_name = "offline-name"
 
-    def _init_run_instance(self, api_key, project, name, run, neptune_run_kwargs) -> Run:
-        if run is not None:
-            run_instance = run
-        else:
-            try:
-                run_instance = neptune.init(
-                    project=project,
-                    api_token=api_key,
-                    name=name,
-                    **neptune_run_kwargs,
-                )
-            except NeptuneLegacyProjectException as e:
-                raise TypeError(
-                    f"""Project {project} has not been migrated to the new structure.
-                    You can still integrate it with the Neptune logger using legacy Python API
-                    available as part of neptune-contrib package:
-                      - https://docs-legacy.neptune.ai/integrations/pytorch_lightning.html\n
-                    """
-                ) from e
-
-        # make sure that we've log integration version for both newly created and outside `Run` instances
-        run_instance[_INTEGRATION_VERSION_KEY] = __version__
-
-        # keep api_key and project, they will be required when resuming Run for pickled logger
-        self._api_key = api_key
-        self._project_name = run_instance._project_name  # skipcq: PYL-W0212
+    @property
+    def _neptune_init_args(self):
+        args = {}
+        # Backward compatibility in case of previous version retrieval
+        try:
+            args = self._neptune_run_kwargs
+        except AttributeError:
+            pass
+
+        if self._project_name is not None:
+            args["project"] = self._project_name
+
+        if self._api_key is not None:
+            args["api_token"] = self._api_key
 
-        return run_instance
+        if self._run_short_id is not None:
+            args["run"] = self._run_short_id
+
+        # Backward compatibility in case of previous version retrieval
+        try:
+            if self._run_name is not None:
+                args["name"] = self._run_name
+        except AttributeError:
+            pass
+
+        return args
 
     def _construct_path_with_prefix(self, *keys) -> str:
         """Return sequence of keys joined by `LOGGER_JOIN_CHAR`, started with `_prefix` if defined."""
@@ -379,7 +392,7 @@ def __getstate__(self):
 
     def __setstate__(self, state):
         self.__dict__ = state
-        self._run_instance = neptune.init(project=self._project_name, api_token=self._api_key, run=self._run_short_id)
+        self._run_instance = neptune.init(**self._neptune_init_args)
 
     @property
     @rank_zero_experiment
@@ -412,8 +425,23 @@ def training_step(self, batch, batch_idx):
         return self.run
 
     @property
+    @rank_zero_experiment
     def run(self) -> Run:
-        return self._run_instance
+        try:
+            if not self._run_instance:
+                self._run_instance = neptune.init(**self._neptune_init_args)
+                self._retrieve_run_data()
+                # make sure that we've log integration version for newly created
+                self._run_instance[_INTEGRATION_VERSION_KEY] = __version__
+
+            return self._run_instance
+        except NeptuneLegacyProjectException as e:
+            raise TypeError(
+                f"Project {self._project_name} has not been migrated to the new structure."
+                " You can still integrate it with the Neptune logger using legacy Python API"
+                " available as part of neptune-contrib package:"
+                " https://docs-legacy.neptune.ai/integrations/pytorch_lightning.html\n"
+            ) from e
 
     @rank_zero_only
     def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:  # skipcq: PYL-W0221
@@ -474,12 +502,12 @@ def log_metrics(self, metrics: Dict[str, Union[torch.Tensor, float]], step: Opti
         for key, val in metrics.items():
             # `step` is ignored because Neptune expects strictly increasing step values which
             # Lightning does not always guarantee.
-            self.experiment[key].log(val)
+            self.run[key].log(val)
 
     @rank_zero_only
     def finalize(self, status: str) -> None:
         if status:
-            self.experiment[self._construct_path_with_prefix("status")] = status
+            self.run[self._construct_path_with_prefix("status")] = status
 
         super().finalize(status)
 
@@ -493,12 +521,14 @@ def save_dir(self) -> Optional[str]:
         """
         return os.path.join(os.getcwd(), ".neptune")
 
+    @rank_zero_only
     def log_model_summary(self, model, max_depth=-1):
         model_str = str(ModelSummary(model=model, max_depth=max_depth))
-        self.experiment[self._construct_path_with_prefix("model/summary")] = neptune.types.File.from_content(
+        self.run[self._construct_path_with_prefix("model/summary")] = neptune.types.File.from_content(
             content=model_str, extension="txt"
         )
 
+    @rank_zero_only
     def after_save_checkpoint(self, checkpoint_callback: "ReferenceType[ModelCheckpoint]") -> None:
         """Automatically log checkpointed model. Called after model checkpoint callback saves a new checkpoint.
 
@@ -515,35 +545,33 @@ def after_save_checkpoint(self, checkpoint_callback: "ReferenceType[ModelCheckpo
         if checkpoint_callback.last_model_path:
             model_last_name = self._get_full_model_name(checkpoint_callback.last_model_path, checkpoint_callback)
             file_names.add(model_last_name)
-            self.experiment[f"{checkpoints_namespace}/{model_last_name}"].upload(checkpoint_callback.last_model_path)
+            self.run[f"{checkpoints_namespace}/{model_last_name}"].upload(checkpoint_callback.last_model_path)
 
         # save best k models
         for key in checkpoint_callback.best_k_models.keys():
             model_name = self._get_full_model_name(key, checkpoint_callback)
             file_names.add(model_name)
-            self.experiment[f"{checkpoints_namespace}/{model_name}"].upload(key)
+            self.run[f"{checkpoints_namespace}/{model_name}"].upload(key)
 
         # log best model path and checkpoint
         if checkpoint_callback.best_model_path:
-            self.experiment[
-                self._construct_path_with_prefix("model/best_model_path")
-            ] = checkpoint_callback.best_model_path
+            self.run[self._construct_path_with_prefix("model/best_model_path")] = checkpoint_callback.best_model_path
 
             model_name = self._get_full_model_name(checkpoint_callback.best_model_path, checkpoint_callback)
             file_names.add(model_name)
-            self.experiment[f"{checkpoints_namespace}/{model_name}"].upload(checkpoint_callback.best_model_path)
+            self.run[f"{checkpoints_namespace}/{model_name}"].upload(checkpoint_callback.best_model_path)
 
         # remove old models logged to experiment if they are not part of best k models at this point
-        if self.experiment.exists(checkpoints_namespace):
-            exp_structure = self.experiment.get_structure()
+        if self.run.exists(checkpoints_namespace):
+            exp_structure = self.run.get_structure()
             uploaded_model_names = self._get_full_model_names_from_exp_structure(exp_structure, checkpoints_namespace)
 
             for file_to_drop in list(uploaded_model_names - file_names):
-                del self.experiment[f"{checkpoints_namespace}/{file_to_drop}"]
+                del self.run[f"{checkpoints_namespace}/{file_to_drop}"]
 
         # log best model score
         if checkpoint_callback.best_model_score:
-            self.experiment[self._construct_path_with_prefix("model/best_model_score")] = (
+            self.run[self._construct_path_with_prefix("model/best_model_score")] = (
                 checkpoint_callback.best_model_score.cpu().detach().numpy()
             )
 
@@ -637,13 +665,11 @@ def log_artifact(self, artifact: str, destination: Optional[str] = None) -> None
         self._signal_deprecated_api_usage("log_artifact", f"logger.run['{key}].log('path_to_file')")
         self.run[key].log(destination)
 
-    @rank_zero_only
     def set_property(self, *args, **kwargs):
         self._signal_deprecated_api_usage(
             "log_artifact", f"logger.run['{self._prefix}/{self.PARAMETERS_KEY}/key'].log(value)", raise_exception=True
         )
 
-    @rank_zero_only
     def append_tags(self, *args, **kwargs):
         self._signal_deprecated_api_usage(
             "append_tags", "logger.run['sys/tags'].add(['foo', 'bar'])", raise_exception=True

tests/loggers/test_all.py

@@ -47,6 +47,8 @@ def _get_logger_args(logger_class, save_dir):
         logger_args.update(offline_mode=True)
     if "offline" in inspect.getfullargspec(logger_class).args:
         logger_args.update(offline=True)
+    if issubclass(logger_class, NeptuneLogger):
+        logger_args.update(mode="offline")
     return logger_args
 
 
@@ -330,7 +332,9 @@ def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
 
 
 @RunIf(skip_windows=True, skip_49370=True, skip_hanging_spawn=True)
-@pytest.mark.parametrize("logger_class", [CometLogger, CSVLogger, MLFlowLogger, TensorBoardLogger, TestTubeLogger])
+@pytest.mark.parametrize(
+    "logger_class", [CometLogger, CSVLogger, MLFlowLogger, NeptuneLogger, TensorBoardLogger, TestTubeLogger]
+)
 def test_logger_created_on_rank_zero_only(tmpdir, monkeypatch, logger_class):
     """Test that loggers get replaced by dummy loggers on global rank > 0."""
     _patch_comet_atexit(monkeypatch)

tests/loggers/test_neptune.py

@@ -77,7 +77,7 @@ def tmpdir_unittest_fixture(request, tmpdir):
 class TestNeptuneLogger(unittest.TestCase):
     def test_neptune_online(self, neptune):
         logger = NeptuneLogger(api_key="test", project="project")
-        created_run_mock = logger._run_instance
+        created_run_mock = logger.run
 
         self.assertEqual(logger._run_instance, created_run_mock)
         self.assertEqual(logger.name, "Run test name")
@@ -109,7 +109,7 @@ def test_neptune_pickling(self, neptune):
         pickled_logger = pickle.dumps(logger)
         unpickled = pickle.loads(pickled_logger)
 
-        neptune.init.assert_called_once_with(project="test-project", api_token=None, run="TEST-42")
+        neptune.init.assert_called_once_with(name="Test name", run=unpickleable_run._short_id)
         self.assertIsNotNone(unpickled.experiment)
 
     @patch("pytorch_lightning.loggers.neptune.Run", Run)
@@ -360,7 +360,7 @@ def test_legacy_functions(self, neptune, neptune_file_mock, warnings_mock):
         logger = NeptuneLogger(api_key="test", project="project")
 
         # test deprecated functions which will be shut down in pytorch-lightning 1.7.0
-        attr_mock = logger._run_instance.__getitem__
+        attr_mock = logger.run.__getitem__
         attr_mock.reset_mock()
         fake_image = {}