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Source code for lightning.pytorch.loggers.neptune

# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
"""
Neptune Logger
--------------
"""
__all__ = [
    "NeptuneLogger",
]

import contextlib
import logging
import os
from argparse import Namespace
from typing import Any, Dict, Generator, List, Optional, Set, Union

from lightning_utilities.core.imports import RequirementCache
from torch import Tensor

import lightning.pytorch as pl
from lightning.fabric.utilities.logger import _add_prefix, _convert_params, _sanitize_callable_params
from lightning.pytorch.callbacks import Checkpoint
from lightning.pytorch.loggers.logger import Logger, rank_zero_experiment
from lightning.pytorch.utilities.model_summary import ModelSummary
from lightning.pytorch.utilities.rank_zero import rank_zero_only

_NEPTUNE_AVAILABLE = RequirementCache("neptune-client")
if _NEPTUNE_AVAILABLE:
    from neptune import new as neptune
    from neptune.new.run import Run
    from neptune.new.types import File
else:
    # needed for test mocks, and function signatures
    neptune, Run, File = None, None, None

log = logging.getLogger(__name__)

_INTEGRATION_VERSION_KEY = "source_code/integrations/pytorch-lightning"


[docs]class NeptuneLogger(Logger): r"""Log using `Neptune <https://neptune.ai>`_. Install it with pip: .. code-block:: bash pip install neptune-client or conda: .. code-block:: bash conda install -c conda-forge neptune-client **Quickstart** Pass NeptuneLogger instance to the Trainer to log metadata with Neptune: .. code-block:: python from lightning.pytorch import Trainer from lightning.pytorch.loggers import NeptuneLogger neptune_logger = NeptuneLogger( api_key="ANONYMOUS", # replace with your own project="common/pytorch-lightning-integration", # format "<WORKSPACE/PROJECT>" tags=["training", "resnet"], # optional ) trainer = Trainer(max_epochs=10, logger=neptune_logger) **How to use NeptuneLogger?** Use the logger anywhere in your :class:`~lightning.pytorch.core.module.LightningModule` as follows: .. code-block:: python from neptune.new.types import File from lightning.pytorch import LightningModule class LitModel(LightningModule): def training_step(self, batch, batch_idx): # log metrics acc = ... self.log("train/loss", loss) def any_lightning_module_function_or_hook(self): # log images img = ... self.logger.experiment["train/misclassified_images"].log(File.as_image(img)) # generic recipe metadata = ... self.logger.experiment["your/metadata/structure"].log(metadata) Note that syntax: ``self.logger.experiment["your/metadata/structure"].log(metadata)`` is specific to Neptune and it extends logger capabilities. Specifically, it allows you to log various types of metadata like scores, files, images, interactive visuals, CSVs, etc. Refer to the `Neptune docs <https://docs.neptune.ai/you-should-know/logging-metadata#essential-logging-methods>`_ for more detailed explanations. You can also use regular logger methods ``log_metrics()``, and ``log_hyperparams()`` with NeptuneLogger as these are also supported. **Log after fitting or testing is finished** You can log objects after the fitting or testing methods are finished: .. code-block:: python neptune_logger = NeptuneLogger(project="common/pytorch-lightning-integration") trainer = pl.Trainer(logger=neptune_logger) model = ... datamodule = ... trainer.fit(model, datamodule=datamodule) trainer.test(model, datamodule=datamodule) # Log objects after `fit` or `test` methods # model summary neptune_logger.log_model_summary(model=model, max_depth=-1) # generic recipe metadata = ... neptune_logger.experiment["your/metadata/structure"].log(metadata) **Log model checkpoints** If you have :class:`~lightning.pytorch.callbacks.ModelCheckpoint` configured, Neptune logger automatically logs model checkpoints. Model weights will be uploaded to the: "model/checkpoints" namespace in the Neptune Run. You can disable this option: .. code-block:: python neptune_logger = NeptuneLogger(project="common/pytorch-lightning-integration", log_model_checkpoints=False) **Pass additional parameters to the Neptune run** You can also pass ``neptune_run_kwargs`` to specify the run in the greater detail, like ``tags`` or ``description``: .. testcode:: :skipif: not _NEPTUNE_AVAILABLE from lightning.pytorch import Trainer from lightning.pytorch.loggers import NeptuneLogger neptune_logger = NeptuneLogger( project="common/pytorch-lightning-integration", name="lightning-run", description="mlp quick run with pytorch-lightning", tags=["mlp", "quick-run"], ) trainer = Trainer(max_epochs=3, logger=neptune_logger) Check `run documentation <https://docs.neptune.ai/essentials/api-reference/run>`_ for more info about additional run parameters. **Details about Neptune run structure** Runs can be viewed as nested dictionary-like structures that you can define in your code. Thanks to this you can easily organize your metadata in a way that is most convenient for you. The hierarchical structure that you apply to your metadata will be reflected later in the UI. You can organize this way any type of metadata - images, parameters, metrics, model checkpoint, CSV files, etc. See Also: - Read about `what object you can log to Neptune <https://docs.neptune.ai/you-should-know/what-can-you-log-and-display>`_. - Check `example run <https://app.neptune.ai/o/common/org/pytorch-lightning-integration/e/PTL-1/all>`_ with multiple types of metadata logged. - For more detailed info check `user guide <https://docs.neptune.ai/integrations-and-supported-tools/model-training/pytorch-lightning>`_. Args: api_key: Optional. Neptune API token, found on https://neptune.ai upon registration. Read: `how to find and set Neptune API token <https://docs.neptune.ai/administration/security-and-privacy/ how-to-find-and-set-neptune-api-token>`_. It is recommended to keep it in the `NEPTUNE_API_TOKEN` environment variable and then you can drop ``api_key=None``. project: Optional. Name of a project in a form of "my_workspace/my_project" for example "tom/mask-rcnn". If ``None``, the value of `NEPTUNE_PROJECT` environment variable will be taken. You need to create the project in https://neptune.ai first. name: Optional. Editable name of the run. Run name appears in the "all metadata/sys" section in Neptune UI. run: Optional. Default is ``None``. The Neptune ``Run`` object. If specified, this `Run`` will be used for logging, instead of a new Run. When run object is passed you can't specify other neptune properties. log_model_checkpoints: Optional. Default is ``True``. Log model checkpoint to Neptune. Works only if ``ModelCheckpoint`` is passed to the ``Trainer``. prefix: Optional. Default is ``"training"``. Root namespace for all metadata logging. \**neptune_run_kwargs: Additional arguments like ``tags``, ``description``, ``capture_stdout``, etc. used when run is created. Raises: ModuleNotFoundError: If required Neptune package is not installed. ValueError: If argument passed to the logger's constructor is incorrect. """ LOGGER_JOIN_CHAR = "/" PARAMETERS_KEY = "hyperparams" ARTIFACTS_KEY = "artifacts" def __init__( self, *, # force users to call `NeptuneLogger` initializer with `kwargs` api_key: Optional[str] = None, project: Optional[str] = None, name: Optional[str] = None, run: Optional["Run"] = None, log_model_checkpoints: Optional[bool] = True, prefix: str = "training", **neptune_run_kwargs: Any, ): if not _NEPTUNE_AVAILABLE: raise ModuleNotFoundError(str(_NEPTUNE_AVAILABLE)) # verify if user passed proper init arguments self._verify_input_arguments(api_key, project, name, run, neptune_run_kwargs) 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: Optional[str] = None if self._run_instance is not None: self._retrieve_run_data() # make sure that we've log integration version for outside `Run` instances self._run_instance[_INTEGRATION_VERSION_KEY] = pl.__version__ def _retrieve_run_data(self) -> None: assert self._run_instance is not None self._run_instance.wait() if self._run_instance.exists("sys/id"): self._run_short_id = self._run_instance["sys/id"].fetch() self._run_name = self._run_instance["sys/name"].fetch() else: self._run_short_id = "OFFLINE" self._run_name = "offline-name" @property def _neptune_init_args(self) -> Dict: args: Dict = {} # Backward compatibility in case of previous version retrieval with contextlib.suppress(AttributeError): args = self._neptune_run_kwargs 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 if self._run_short_id is not None: args["run"] = self._run_short_id # Backward compatibility in case of previous version retrieval with contextlib.suppress(AttributeError): if self._run_name is not None: args["name"] = self._run_name return args def _construct_path_with_prefix(self, *keys: str) -> str: """Return sequence of keys joined by `LOGGER_JOIN_CHAR`, started with `_prefix` if defined.""" if self._prefix: return self.LOGGER_JOIN_CHAR.join([self._prefix, *keys]) return self.LOGGER_JOIN_CHAR.join(keys) @staticmethod def _verify_input_arguments( api_key: Optional[str], project: Optional[str], name: Optional[str], run: Optional["Run"], neptune_run_kwargs: dict, ) -> None: # check if user passed the client `Run` object if run is not None and not isinstance(run, Run): raise ValueError("Run parameter expected to be of type `neptune.new.Run`.") # check if user passed redundant neptune.init_run arguments when passed run any_neptune_init_arg_passed = any(arg is not None for arg in [api_key, project, name]) or neptune_run_kwargs if run is not None and any_neptune_init_arg_passed: raise ValueError( "When an already initialized run object is provided" " you can't provide other neptune.init_run() parameters.\n" ) def __getstate__(self) -> Dict[str, Any]: state = self.__dict__.copy() # Run instance can't be pickled state["_run_instance"] = None return state def __setstate__(self, state: Dict[str, Any]) -> None: self.__dict__ = state self._run_instance = neptune.init_run(**self._neptune_init_args) @property @rank_zero_experiment def experiment(self) -> Run: r""" Actual Neptune run object. Allows you to use neptune logging features in your :class:`~lightning.pytorch.core.module.LightningModule`. Example:: class LitModel(LightningModule): def training_step(self, batch, batch_idx): # log metrics acc = ... self.logger.experiment["train/acc"].log(acc) # log images img = ... self.logger.experiment["train/misclassified_images"].log(File.as_image(img)) Note that syntax: ``self.logger.experiment["your/metadata/structure"].log(metadata)`` is specific to Neptune and it extends logger capabilities. Specifically, it allows you to log various types of metadata like scores, files, images, interactive visuals, CSVs, etc. Refer to the `Neptune docs <https://docs.neptune.ai/you-should-know/logging-metadata#essential-logging-methods>`_ for more detailed explanations. You can also use regular logger methods ``log_metrics()``, and ``log_hyperparams()`` with NeptuneLogger as these are also supported. """ return self.run @property @rank_zero_experiment def run(self) -> Run: if not self._run_instance: self._run_instance = neptune.init_run(**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] = pl.__version__ return self._run_instance
[docs] @rank_zero_only def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None: # skipcq: PYL-W0221 r"""Log hyper-parameters to the run. Hyperparams will be logged under the "<prefix>/hyperparams" namespace. Note: You can also log parameters by directly using the logger instance: ``neptune_logger.experiment["model/hyper-parameters"] = params_dict``. In this way you can keep hierarchical structure of the parameters. Args: params: `dict`. Python dictionary structure with parameters. Example:: from lightning.pytorch.loggers import NeptuneLogger PARAMS = { "batch_size": 64, "lr": 0.07, "decay_factor": 0.97 } neptune_logger = NeptuneLogger( api_key="ANONYMOUS", project="common/pytorch-lightning-integration" ) neptune_logger.log_hyperparams(PARAMS) """ params = _convert_params(params) params = _sanitize_callable_params(params) parameters_key = self.PARAMETERS_KEY parameters_key = self._construct_path_with_prefix(parameters_key) self.run[parameters_key] = params
[docs] @rank_zero_only def log_metrics(self, metrics: Dict[str, Union[Tensor, float]], step: Optional[int] = None) -> None: """Log metrics (numeric values) in Neptune runs. Args: metrics: Dictionary with metric names as keys and measured quantities as values. step: Step number at which the metrics should be recorded, currently ignored. """ if rank_zero_only.rank != 0: raise ValueError("run tried to log from global_rank != 0") metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR) for key, val in metrics.items(): # `step` is ignored because Neptune expects strictly increasing step values which # Lightning does not always guarantee. self.run[key].log(val)
[docs] @rank_zero_only def finalize(self, status: str) -> None: if not self._run_instance: # When using multiprocessing, finalize() should be a no-op on the main process, as no experiment has been # initialized there return if status: self.run[self._construct_path_with_prefix("status")] = status super().finalize(status)
@property def save_dir(self) -> Optional[str]: """Gets the save directory of the experiment which in this case is ``None`` because Neptune does not save locally. Returns: the root directory where experiment logs get saved """ return os.path.join(os.getcwd(), ".neptune") @rank_zero_only def log_model_summary(self, model: "pl.LightningModule", max_depth: int = -1) -> None: model_str = str(ModelSummary(model=model, max_depth=max_depth)) self.run[self._construct_path_with_prefix("model/summary")] = neptune.types.File.from_content( content=model_str, extension="txt" )
[docs] @rank_zero_only def after_save_checkpoint(self, checkpoint_callback: Checkpoint) -> None: """Automatically log checkpointed model. Called after model checkpoint callback saves a new checkpoint. Args: checkpoint_callback: the model checkpoint callback instance """ if not self._log_model_checkpoints: return file_names = set() checkpoints_namespace = self._construct_path_with_prefix("model/checkpoints") # save last model if hasattr(checkpoint_callback, "last_model_path") and 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) with open(checkpoint_callback.last_model_path, "rb") as fp: self.run[f"{checkpoints_namespace}/{model_last_name}"] = File.from_stream(fp) # save best k models if hasattr(checkpoint_callback, "best_k_models"): for key in checkpoint_callback.best_k_models: model_name = self._get_full_model_name(key, checkpoint_callback) file_names.add(model_name) self.run[f"{checkpoints_namespace}/{model_name}"].upload(key) # log best model path and checkpoint if hasattr(checkpoint_callback, "best_model_path") and 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) with open(checkpoint_callback.best_model_path, "rb") as fp: self.run[f"{checkpoints_namespace}/{model_name}"] = File.from_stream(fp) # remove old models logged to experiment if they are not part of best k models at this point 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.run[f"{checkpoints_namespace}/{file_to_drop}"] # log best model score if hasattr(checkpoint_callback, "best_model_score") and checkpoint_callback.best_model_score: self.run[self._construct_path_with_prefix("model/best_model_score")] = ( checkpoint_callback.best_model_score.cpu().detach().numpy() )
@staticmethod def _get_full_model_name(model_path: str, checkpoint_callback: Checkpoint) -> str: """Returns model name which is string `model_path` appended to `checkpoint_callback.dirpath`.""" if hasattr(checkpoint_callback, "dirpath"): expected_model_path = f"{checkpoint_callback.dirpath}{os.path.sep}" if not model_path.startswith(expected_model_path): raise ValueError(f"{model_path} was expected to start with {expected_model_path}.") # Remove extension from filepath filepath, _ = os.path.splitext(model_path[len(expected_model_path) :]) return filepath return model_path @classmethod def _get_full_model_names_from_exp_structure(cls, exp_structure: Dict[str, Any], namespace: str) -> Set[str]: """Returns all paths to properties which were already logged in `namespace`""" structure_keys: List[str] = namespace.split(cls.LOGGER_JOIN_CHAR) for key in structure_keys: exp_structure = exp_structure[key] uploaded_models_dict = exp_structure return set(cls._dict_paths(uploaded_models_dict)) @classmethod def _dict_paths(cls, d: Dict[str, Any], path_in_build: Optional[str] = None) -> Generator: for k, v in d.items(): path = f"{path_in_build}/{k}" if path_in_build is not None else k if not isinstance(v, dict): yield path else: yield from cls._dict_paths(v, path) @property def name(self) -> Optional[str]: """Return the experiment name or 'offline-name' when exp is run in offline mode.""" return self._run_name @property def version(self) -> Optional[str]: """Return the experiment version. It's Neptune Run's short_id """ return self._run_short_id

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