mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-10-04 13:00:36 +08:00
JakubandWilliam Falcon
8dc8a8bfd3
* added neptune integration * added tests for NeptuneLogger, added neptune to docs * updated link to neptune support * fixed docstrings, fixed try/except in tests, changed append_tags input * fixed docstrings line lenght * bumped epoch nr in model restore tests * added tags support for single strings * fixed passing neptune token to backend * fixed project name in offline mode * added save_top_k=-1 to checkpoint callback * reformated initialization of neptune in online mode * bumped epoch nr to 4 in test_load_model_from_checkpoint * bumped epoch nr to 5 Co-authored-by: William Falcon <waf2107@columbia.edu>
243 lines
10 KiB
Python
243 lines
10 KiB
Python
"""
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Log using `neptune <https://www.neptune.ml>`_
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Neptune logger can be used in the online mode or offline (silent) mode.
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To log experiment data in online mode, NeptuneLogger requries an API key:
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.. code-block:: python
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from pytorch_lightning.logging import NeptuneLogger
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# arguments made to NeptuneLogger are passed on to the neptune.experiments.Experiment class
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neptune_logger = NeptuneLogger(
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api_key=os.environ["NEPTUNE_API_TOKEN"],
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project_name="USER_NAME/PROJECT_NAME",
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experiment_name="default", # Optional,
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params={"max_epochs": 10}, # Optional,
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tags=["pytorch-lightning","mlp"] # Optional,
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)
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trainer = Trainer(max_epochs=10, logger=neptune_logger)
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Use the logger anywhere in you LightningModule as follows:
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.. code-block:: python
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def train_step(...):
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# example
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self.logger.experiment.log_metric("acc_train", acc_train) # log metrics
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self.logger.experiment.log_image("worse_predictions", prediction_image) # log images
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self.logger.experiment.log_artifact("model_checkpoint.pt", prediction_image) # log model checkpoint
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self.logger.experiment.whatever_neptune_supports(...)
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def any_lightning_module_function_or_hook(...):
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self.logger.experiment.log_metric("acc_train", acc_train) # log metrics
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self.logger.experiment.log_image("worse_predictions", prediction_image) # log images
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self.logger.experiment.log_artifact("model_checkpoint.pt", prediction_image) # log model checkpoint
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self.logger.experiment.whatever_neptune_supports(...)
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"""
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from logging import getLogger
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try:
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import neptune
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except ImportError:
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raise ImportError('Missing neptune package. Run `pip install neptune-client`')
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from torch import is_tensor
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# from .base import LightningLoggerBase, rank_zero_only
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from pytorch_lightning.logging.base import LightningLoggerBase, rank_zero_only
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logger = getLogger(__name__)
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class NeptuneLogger(LightningLoggerBase):
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def __init__(self, api_key=None, project_name=None, offline_mode=False,
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experiment_name=None, upload_source_files=None,
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params=None, properties=None, tags=None, **kwargs):
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"""Initialize a neptune.ml logger.
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Requires either an API Key (online mode) or a local directory path (offline mode)
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:param str|None api_key: Required in online mode. Neputne API token, found on https://neptune.ml.
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Read how to get your API key https://docs.neptune.ml/python-api/tutorials/get-started.html#copy-api-token.
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:param str project_name: Required in online mode. Qualified name of a project in a form of
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"namespace/project_name" for example "tom/minst-classification".
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If None, the value of NEPTUNE_PROJECT environment variable will be taken.
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You need to create the project in https://neptune.ml first.
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:param bool offline_mode: Optional default False. If offline_mode=True no logs will be send to neptune.
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Usually used for debug purposes.
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:param str|None experiment_name: Optional. Editable name of the experiment.
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Name is displayed in the experiment’s Details (Metadata section) and in experiments view as a column.
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:param list|None upload_source_files: Optional. List of source files to be uploaded.
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Must be list of str or single str. Uploaded sources are displayed in the experiment’s Source code tab.
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If None is passed, Python file from which experiment was created will be uploaded.
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Pass empty list ([]) to upload no files. Unix style pathname pattern expansion is supported.
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For example, you can pass '*.py' to upload all python source files from the current directory.
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For recursion lookup use '**/*.py' (for Python 3.5 and later). For more information see glob library.
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:param dict|None params: Optional. Parameters of the experiment. After experiment creation params are read-only.
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Parameters are displayed in the experiment’s Parameters section and each key-value pair can be
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viewed in experiments view as a column.
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:param dict|None properties: Optional default is {}. Properties of the experiment.
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They are editable after experiment is created. Properties are displayed in the experiment’s Details and
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each key-value pair can be viewed in experiments view as a column.
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:param list|None tags: Optional default []. Must be list of str. Tags of the experiment.
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They are editable after experiment is created (see: append_tag() and remove_tag()).
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Tags are displayed in the experiment’s Details and can be viewed in experiments view as a column.
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"""
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super().__init__()
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self.api_key = api_key
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self.project_name = project_name
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self.offline_mode = offline_mode
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self.experiment_name = experiment_name
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self.upload_source_files = upload_source_files
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self.params = params
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self.properties = properties
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self.tags = tags
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self._experiment = None
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self._kwargs = kwargs
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if offline_mode:
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self.mode = "offline"
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neptune.init(project_qualified_name='dry-run/project',
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backend=neptune.OfflineBackend())
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else:
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self.mode = "online"
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neptune.init(api_token=self.api_key,
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project_qualified_name=self.project_name)
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logger.info(f"NeptuneLogger was initialized in {self.mode} mode")
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@property
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def experiment(self):
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if self._experiment is not None:
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return self._experiment
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else:
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self._experiment = neptune.create_experiment(name=self.experiment_name,
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params=self.params,
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properties=self.properties,
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tags=self.tags,
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upload_source_files=self.upload_source_files,
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**self._kwargs)
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return self._experiment
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@rank_zero_only
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def log_hyperparams(self, params):
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for key, val in vars(params).items():
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self.experiment.set_property(f"param__{key}", val)
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@rank_zero_only
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def log_metrics(self, metrics, step=None):
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"""Log metrics (numeric values) in Neptune experiments
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:param float metric: Dictionary with metric names as keys and measured quanties as values
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:param int|None step: Step number at which the metrics should be recorded, must be strictly increasing
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"""
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for key, val in metrics.items():
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if is_tensor(val):
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val = val.cpu().detach()
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if step is None:
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self.experiment.log_metric(key, val)
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else:
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self.experiment.log_metric(key, x=step, y=val)
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@rank_zero_only
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def finalize(self, status):
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self.experiment.stop()
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@property
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def name(self):
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if self.mode == "offline":
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return "offline-name"
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else:
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return self.experiment.name
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@property
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def version(self):
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if self.mode == "offline":
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return "offline-id-1234"
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else:
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return self.experiment.id
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@rank_zero_only
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def log_metric(self, metric_name, metric_value, step=None):
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"""Log metrics (numeric values) in Neptune experiments
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:param str metric_name: The name of log, i.e. mse, loss, accuracy.
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:param str metric_value: The value of the log (data-point).
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:param int|None step: Step number at which the metrics should be recorded, must be strictly increasing
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"""
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if step is None:
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self.experiment.log_metric(metric_name, metric_value)
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else:
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self.experiment.log_metric(metric_name, x=step, y=metric_value)
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@rank_zero_only
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def log_text(self, log_name, text, step=None):
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"""Log text data in Neptune experiment
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:param str log_name: The name of log, i.e. mse, my_text_data, timing_info.
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:param str text: The value of the log (data-point).
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:param int|None step: Step number at which the metrics should be recorded, must be strictly increasing
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"""
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if step is None:
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self.experiment.log_metric(log_name, text)
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else:
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self.experiment.log_metric(log_name, x=step, y=text)
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@rank_zero_only
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def log_image(self, log_name, image, step=None):
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"""Log image data in Neptune experiment
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:param str log_name: The name of log, i.e. bboxes, visualisations, sample_images.
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:param str|PIL.Image|matplotlib.figure.Figure image: The value of the log (data-point).
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Can be one of the following types: PIL image, matplotlib.figure.Figure, path to image file (str)
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:param int|None step: Step number at which the metrics should be recorded, must be strictly increasing
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"""
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if step is None:
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self.experiment.log_image(log_name, image)
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else:
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self.experiment.log_image(log_name, x=step, y=image)
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@rank_zero_only
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def log_artifact(self, artifact, destination=None):
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"""Save an artifact (file) in Neptune experiment storage.
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:param str artifact: A path to the file in local filesystem.
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:param str|None destination: Optional default None.
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A destination path. If None is passed, an artifact file name will be used.
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"""
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self.experiment.log_artifact(artifact, destination)
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@rank_zero_only
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def set_property(self, key, value):
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"""Set key-value pair as Neptune experiment property.
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:param str key: Property key.
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:param obj value: New value of a property.
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"""
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self.experiment.set_property(key, value)
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@rank_zero_only
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def append_tags(self, tags):
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"""appends tags to neptune experiment
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:param str|tuple|list(str) tags: Tags to add to the current experiment.
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If str is passed, singe tag is added.
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If multiple - comma separated - str are passed, all of them are added as tags.
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If list of str is passed, all elements of the list are added as tags.
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"""
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if not isinstance(tags, (list, set, tuple)):
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tags = [tags] # make it as an iterable is if it is not yet
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self.experiment.append_tags(*tags)
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