# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # 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. # ============================================================================== """Experimental framework for generic TensorBoard data providers.""" from typing import Collection, Sequence, Tuple, Union import abc import dataclasses import enum import numpy as np class DataProvider(metaclass=abc.ABCMeta): """Interface for reading TensorBoard scalar, tensor, and blob data. These APIs are under development and subject to change. For instance, providers may be asked to implement more filtering mechanisms, such as downsampling strategies or domain restriction by step or wall time. The data provider interface specifies three *data classes*: scalars, tensors, and blob sequences. All data is stored in *time series* for one of these data classes. A time series is identified by run name and tag name (each a non-empty text string), as well as an experiment ID and plugin name (see below). Points in a time series are uniquely indexed by *step*, an arbitrary non-negative integer. Each point in a time series also has an associated wall time, plus its actual value, which is drawn from the corresponding data class. Each point in a scalar time series contains a single scalar value, as a 64-bit floating point number. Scalars are "privileged" rather than being subsumed under tensors because there are useful operations on scalars that don't make sense in the general tensor case: e.g., "list all scalar time series with tag name `accuracy` whose exponentially weighted moving average is at least 0.999". Each point in a tensor time series contains a tensor of arbitrary dtype (including byte strings and text strings) and shape (including rank-0 tensors, a.k.a. scalars). Each tensor is expected to be "reasonably small" to accommodate common database cell size limits. For instance, a histogram with a bounded number of buckets (say, 30) occupies about 500 bytes, and a PR curve with a bounded number of thresholds (say, 201) occupies about 5000 bytes. These are both well within typical database tolerances (Google Cloud Spanner: 10 MiB; MySQL: 64 KiB), and would be appropriate to store as tensors. By contrast, image, audio, or model graph data may easily be multiple megabytes in size, and so should be stored as blobs instead. The tensors at each step in a time series need not have the same dtype or shape. Each point in a blob sequence time series contains an ordered sequence of zero or more blobs, which are arbitrary data with no tensor structure. These might represent PNG-encoded image data, protobuf wire encodings of TensorFlow graphs, or PLY-format 3D mesh data, for some examples. This data class provides blob *sequences* rather than just blobs because it's common to want to take multiple homogeneous samples of a given time series: say, "show me the bounding box classifications for 3 random inputs from this batch". A single blob can of course be represented as a blob sequence that always has exactly one element. When reading time series, *downsampling* refers to selecting a subset of the points in each time series. Downsampling only occurs across the step axis (rather than, e.g., the blobs in a single blob sequence datum), and occurs individually within each time series. When downsampling, the latest datum should always be included in the sample, so that clients have a view of metrics that is maximally up to date. Implementations may choose to force the first (oldest) datum to be included in each sample as well, but this is not required; clients should not make assumptions either way. The remainder of the points in the sample should be selected uniformly at random from available points. Downsampling should be deterministic within a time series. It is also useful for the downsampling behavior to depend only on the set of step values within a time series, such that two "parallel" time series with data at exactly the same steps also retain the same steps after downsampling. Every time series belongs to a specific experiment and is owned by a specific plugin. (Thus, the "primary key" for a time series has four components: experiment, plugin, run, tag.) The experiment ID is an arbitrary URL-safe non-empty text string, whose interpretation is at the discretion of the data provider. As a special case, the empty string as an experiment ID denotes that no experiment was given. Data providers may or may not fully support an empty experiment ID. The plugin name should correspond to the `plugin_data.plugin_name` field of the `SummaryMetadata` proto passed to `tf.summary.write`. Additionally, the data provider interface specifies one *hyperparameter* class, which is metadata about the parameters used to generate the data for one or more runs within one or more experiments. Each hyperparameter has a value type -- one of string, bool, and float. Each one also has a domain, which describes the set of known values for that hyperparameter across the given set of experiments. There is a corresponding *hyperparameter value* class, which describes an actual value of a hyperparameter that was logged during experiment execution. Each run within an experiment may specify its own value for a hyperparameter. Runs that were logically executed together with the same set of hyperparameter values form a hyperparameter `session`. Sessions that include the same hyperparameter values can be grouped together in a hyperparameter `session group`. Often a session group will contain only a single session. However, in some scenarios, the same hyperparameters will be used to execute multiple jobs with the idea to aggregate the metrics across those jobs and analyze non-deterministic factors. In that case, a session group will contain multiple sessions. The result will group runs by hyperparameter session group and provide one set of hyperparameter values for each group. All methods on this class take a `RequestContext` parameter as the first positional argument. This argument is temporarily optional to facilitate migration, but will be required in the future. Unless otherwise noted, any methods on this class may raise errors defined in `tensorboard.errors`, like `tensorboard.errors.NotFoundError`. If not implemented, optional methods may return `None`. """ def experiment_metadata(self, ctx=None, *, experiment_id): """Retrieve metadata of a given experiment. The metadata may include fields such as name and description of the experiment, as well as a timestamp for the experiment. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of the experiment in question. Returns: An `ExperimentMetadata` object containing metadata about the experiment. """ return ExperimentMetadata() def list_plugins(self, ctx=None, *, experiment_id): """List all plugins that own data in a given experiment. This should be the set of all plugin names `p` such that calling `list_scalars`, `list_tensors`, or `list_blob_sequences` for the given `experiment_id` and plugin name `p` gives a non-empty result. This operation is optional, but may later become required. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of enclosing experiment. Returns: A collection of strings representing plugin names, or `None` if this operation is not supported by this data provider. """ return None @abc.abstractmethod def list_runs(self, ctx=None, *, experiment_id): """List all runs within an experiment. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of enclosing experiment. Returns: A collection of `Run` values. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ pass @abc.abstractmethod def list_scalars( self, ctx=None, *, experiment_id, plugin_name, run_tag_filter=None ): """List metadata about scalar time series. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of enclosing experiment. plugin_name: String name of the TensorBoard plugin that created the data to be queried. Required. run_tag_filter: Optional `RunTagFilter` value. If omitted, all runs and tags will be included. The result will only contain keys for run-tag combinations that actually exist, which may not include all entries in the `run_tag_filter`. Returns: A nested map `d` such that `d[run][tag]` is a `ScalarTimeSeries` value. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ pass @abc.abstractmethod def read_scalars( self, ctx=None, *, experiment_id, plugin_name, downsample=None, run_tag_filter=None, ): """Read values from scalar time series. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of enclosing experiment. plugin_name: String name of the TensorBoard plugin that created the data to be queried. Required. downsample: Integer number of steps to which to downsample the results (e.g., `1000`). The most recent datum (last scalar) should always be included. See `DataProvider` class docstring for details about this parameter. Required. run_tag_filter: Optional `RunTagFilter` value. If provided, a time series will only be included in the result if its run and tag both pass this filter. If `None`, all time series will be included. The result will only contain keys for run-tag combinations that actually exist, which may not include all entries in the `run_tag_filter`. Returns: A nested map `d` such that `d[run][tag]` is a list of `ScalarDatum` values sorted by step. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ pass @abc.abstractmethod def read_last_scalars( self, ctx=None, *, experiment_id, plugin_name, run_tag_filter=None, ): """Read the most recent values from scalar time series. The most recent scalar value for each tag under each run is retrieved from the latest event (at the latest timestamp). Note that this is different from the sorting used in `read_scalars`, which is by step. This was an accidental misalignment that would need considerable effort to change across our implementations, so we're leaving it as is for now. In most cases this should not matter, but if the same log dir is used for multiple runs, this might not match the last data point returned by the `read_scalars`. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of enclosing experiment. plugin_name: String name of the TensorBoard plugin that created the data to be queried. Required. run_tag_filter: Optional `RunTagFilter` value. If provided, a datum series will only be included in the result if its run and tag both pass this filter. If `None`, all time series will be included. The result will only contain keys for run-tag combinations that actually exist, which may not include all entries in the `run_tag_filter`. Returns: A nested map `d` such that `d[run][tag]` is a `ScalarDatum` representing the latest scalar in the time series. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ pass def list_tensors( self, ctx=None, *, experiment_id, plugin_name, run_tag_filter=None ): """List metadata about tensor time series. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of enclosing experiment. plugin_name: String name of the TensorBoard plugin that created the data to be queried. Required. run_tag_filter: Optional `RunTagFilter` value. If omitted, all runs and tags will be included. The result will only contain keys for run-tag combinations that actually exist, which may not include all entries in the `run_tag_filter`. Returns: A nested map `d` such that `d[run][tag]` is a `TensorTimeSeries` value. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ pass def read_tensors( self, ctx=None, *, experiment_id, plugin_name, downsample=None, run_tag_filter=None, ): """Read values from tensor time series. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of enclosing experiment. plugin_name: String name of the TensorBoard plugin that created the data to be queried. Required. downsample: Integer number of steps to which to downsample the results (e.g., `1000`). See `DataProvider` class docstring for details about this parameter. Required. run_tag_filter: Optional `RunTagFilter` value. If provided, a time series will only be included in the result if its run and tag both pass this filter. If `None`, all time series will be included. The result will only contain keys for run-tag combinations that actually exist, which may not include all entries in the `run_tag_filter`. Returns: A nested map `d` such that `d[run][tag]` is a list of `TensorDatum` values sorted by step. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ pass def list_blob_sequences( self, ctx=None, *, experiment_id, plugin_name, run_tag_filter=None ): """List metadata about blob sequence time series. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of enclosing experiment. plugin_name: String name of the TensorBoard plugin that created the data to be queried. Required. run_tag_filter: Optional `RunTagFilter` value. If omitted, all runs and tags will be included. The result will only contain keys for run-tag combinations that actually exist, which may not include all entries in the `run_tag_filter`. Returns: A nested map `d` such that `d[run][tag]` is a `BlobSequenceTimeSeries` value. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ pass def read_blob_sequences( self, ctx=None, *, experiment_id, plugin_name, downsample=None, run_tag_filter=None, ): """Read values from blob sequence time series. Args: ctx: A TensorBoard `RequestContext` value. experiment_id: ID of enclosing experiment. plugin_name: String name of the TensorBoard plugin that created the data to be queried. Required. downsample: Integer number of steps to which to downsample the results (e.g., `1000`). See `DataProvider` class docstring for details about this parameter. Required. run_tag_filter: Optional `RunTagFilter` value. If provided, a time series will only be included in the result if its run and tag both pass this filter. If `None`, all time series will be included. The result will only contain keys for run-tag combinations that actually exist, which may not include all entries in the `run_tag_filter`. Returns: A nested map `d` such that `d[run][tag]` is a list of `BlobSequenceDatum` values sorted by step. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ pass def read_blob(self, ctx=None, *, blob_key): """Read data for a single blob. Args: ctx: A TensorBoard `RequestContext` value. blob_key: A key identifying the desired blob, as provided by `read_blob_sequences(...)`. Returns: Raw binary data as `bytes`. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ pass def list_hyperparameters(self, ctx=None, *, experiment_ids, limit=None): """List hyperparameters metadata. Args: ctx: A TensorBoard `RequestContext` value. experiment_ids: A Collection[string] of IDs of the enclosing experiments. limit: Optional number of hyperparameter metadata to include in the result. If unset or zero, all metadata will be included. Returns: A ListHyperparametersResult describing the hyperparameter-related metadata for the experiments. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ return ListHyperparametersResult(hyperparameters=[], session_groups=[]) def read_hyperparameters( self, ctx=None, *, experiment_ids, filters=None, sort=None, hparams_to_include=None, ): """Read hyperparameter values. Args: ctx: A TensorBoard `RequestContext` value. experiment_ids: A Collection[string] of IDs of the enclosing experiments. filters: A Collection[HyperparameterFilter] that constrain the returned session groups based on hyperparameter value. sort: A Sequence[HyperparameterSort] that specify how the results should be sorted. hparams_to_include: An optional Collection[str] of the full names of hyperparameters to include in the results. This collection will be augmented to include all the hyperparameters specified in `filters` and `sort`. If None, all hyperparameters will be returned. Returns: A Sequence[HyperparameterSessionGroup] describing the groups and their hyperparameter values. Raises: tensorboard.errors.PublicError: See `DataProvider` class docstring. """ return [] class ExperimentMetadata: """Metadata about an experiment. All fields have default values: i.e., they will always be present on the object, but may be omitted in a constructor call. Attributes: data_location: A human-readable description of the data source, such as a path to a directory on disk. experiment_name: A user-facing name for the experiment (as a `str`). experiment_description: A user-facing description for the experiment (as a `str`). creation_time: A timestamp for the creation of the experiment, as `float` seconds since the epoch. """ def __init__( self, *, data_location="", experiment_name="", experiment_description="", creation_time=0, ): self._data_location = data_location self._experiment_name = experiment_name self._experiment_description = experiment_description self._creation_time = creation_time @property def data_location(self): return self._data_location @property def experiment_name(self): return self._experiment_name @property def experiment_description(self): return self._experiment_description @property def creation_time(self): return self._creation_time def _as_tuple(self): """Helper for `__eq__` and `__hash__`.""" return ( self._data_location, self._experiment_name, self._experiment_description, self._creation_time, ) def __eq__(self, other): if not isinstance(other, ExperimentMetadata): return False return self._as_tuple() == other._as_tuple() def __hash__(self): return hash(self._as_tuple()) def __repr__(self): return "ExperimentMetadata(%s)" % ", ".join( ( "data_location=%r" % (self.data_location,), "experiment_name=%r" % (self._experiment_name,), "experiment_description=%r" % (self._experiment_description,), "creation_time=%r" % (self._creation_time,), ) ) class Run: """Metadata about a run. Attributes: run_id: A unique opaque string identifier for this run. run_name: A user-facing name for this run (as a `str`). start_time: The wall time of the earliest recorded event in this run, as `float` seconds since epoch, or `None` if this run has no recorded events. """ __slots__ = ("_run_id", "_run_name", "_start_time") def __init__(self, run_id, run_name, start_time): self._run_id = run_id self._run_name = run_name self._start_time = start_time @property def run_id(self): return self._run_id @property def run_name(self): return self._run_name @property def start_time(self): return self._start_time def __eq__(self, other): if not isinstance(other, Run): return False if self._run_id != other._run_id: return False if self._run_name != other._run_name: return False if self._start_time != other._start_time: return False return True def __hash__(self): return hash((self._run_id, self._run_name, self._start_time)) def __repr__(self): return "Run(%s)" % ", ".join( ( "run_id=%r" % (self._run_id,), "run_name=%r" % (self._run_name,), "start_time=%r" % (self._start_time,), ) ) class HyperparameterDomainType(enum.Enum): """Describes how to represent the set of known values for a hyperparameter.""" # A range of numeric values. Normally represented as Tuple[float, float]. INTERVAL = "interval" # A finite set of numeric values. Normally represented as Collection[float]. DISCRETE_FLOAT = "discrete_float" # A finite set of string values. Normally represented as Collection[string]. DISCRETE_STRING = "discrete_string" # A finite set of bool values. Normally represented as Collection[bool]. DISCRETE_BOOL = "discrete_bool" @dataclasses.dataclass(frozen=True) class Hyperparameter: """Metadata about a hyperparameter. Attributes: hyperparameter_name: A string identifier for the hyperparameter that should be unique in any result set of Hyperparameter objects. hyperparameter_display_name: A displayable name for the hyperparameter. Unlike hyperparameter_name, there is no uniqueness constraint. domain_type: A HyperparameterDomainType describing how we represent the set of known values in the `domain` attribute. domain: A representation of the set of known values for the hyperparameter. If domain_type is INTERVAL, a Tuple[float, float] describing the range of numeric values. If domain_type is DISCRETE_FLOAT, a Collection[float] describing the finite set of numeric values. If domain_type is DISCRETE_STRING, a Collection[string] describing the finite set of string values. If domain_type is DISCRETE_BOOL, a Collection[bool] describing the finite set of bool values. differs: Describes whether there are two or more known values for the hyperparameter for the set of experiments specified in the list_hyperparameters() request. Hyperparameters for which this is true are made more prominent or easier to discover in the UI. """ hyperparameter_name: str hyperparameter_display_name: str domain_type: Union[HyperparameterDomainType, None] = None domain: Union[ Tuple[float, float], Collection[float], Collection[str], Collection[bool], None, ] = None differs: bool = False @dataclasses.dataclass(frozen=True) class HyperparameterValue: """A hyperparameter value. Attributes: hyperparameter_name: A string identifier for the hyperparameters. It corresponds to the hyperparameter_name field in the Hyperparameter class. domain_type: A HyperparameterDomainType describing how we represent the set of known values in the `domain` attribute. value: The value of the hyperparameter. If domain_type is INTERVAL or DISCRETE_FLOAT, value is a float. If domain_type is DISCRETE_STRING, value is a str. If domain_type is DISCRETE_BOOL, value is a bool. If domain_type is unknown (None), value is None. """ hyperparameter_name: str domain_type: Union[HyperparameterDomainType, None] = None value: Union[float, str, bool, None] = None @dataclasses.dataclass(frozen=True) class HyperparameterSessionRun: """A single run in a HyperparameterSessionGroup. Attributes: experiment_id: The id of the experiment to which the run belongs. run: The name of the run. """ experiment_id: str run: str @dataclasses.dataclass(frozen=True) class HyperparameterSessionGroup: """A group of sessions logically executed together with the same hparam values. A `session` generally represents a particular execution of a job with a given set of hyperparameter values. A session may contain multiple related runs executed together to train and/or validate a model. Often a `session group` will contain only a single session. However, in some scenarios, the same hyperparameters will be used to execute multiple jobs with the idea to aggregate the metrics across those jobs and analyze non-deterministic factors. In that case, a session group will contain multiple sessions. Attributes: root: A descriptor of the common ancestor of all sessions in this group. In the case where the group contains all runs in the experiment, this would just be a HyperparameterSessionRun with the experiment_id property set to the experiment's id but run property set to empty. In the case where the group contains a subset of runs in the experiment, this would be a HyperparameterSessionRun with the experiment_id property set and the run property set to the largest common prefix for runs. The root might correspond to a session within the group but it is not necessary. sessions: A sequence of all sessions in this group. hyperparameter_values: A collection of all hyperparameter values in this group. """ root: HyperparameterSessionRun sessions: Sequence[HyperparameterSessionRun] hyperparameter_values: Collection[HyperparameterValue] class HyperparameterFilterType(enum.Enum): """Describes how to represent filter values.""" # A regular expression string. Normally represented as str. REGEX = "regex" # A range of numeric values. Normally represented as Tuple[float, float]. INTERVAL = "interval" # A finite set of values. Normally represented as Collection[float|str|bool]. DISCRETE = "discrete" @dataclasses.dataclass(frozen=True) class HyperparameterFilter: """A constraint based on hyperparameter value. Attributes: hyperparameter_name: A string identifier for the hyperparameter to use for the filter. It corresponds to the hyperparameter_name field in the Hyperparameter class. filter_type: A HyperparameterFilterType describing how we represent the filter values in the 'filter' attribute. filter: A representation of the set of the filter values. If filter_type is REGEX, a str containing the regular expression. If filter_type is INTERVAL, a Tuple[float, float] describing the min and max values of the filter interval. If filter_type is DISCRETE a Collection[float|str|bool] describing the finite set of filter values. """ hyperparameter_name: str filter_type: HyperparameterFilterType filter: Union[ str, Tuple[float, float], Collection[Union[float, str, bool]], ] class HyperparameterSortDirection(enum.Enum): """Describes which direction to sort a value.""" # Sort values ascending. ASCENDING = "ascending" # Sort values descending. DESCENDING = "descending" @dataclasses.dataclass(frozen=True) class HyperparameterSort: """A sort criterium based on hyperparameter value. Attributes: hyperparameter_name: A string identifier for the hyperparameter to use for the sort. It corresponds to the hyperparameter_name field in the Hyperparameter class. sort_direction: The direction to sort. """ hyperparameter_name: str sort_direction: HyperparameterSortDirection @dataclasses.dataclass(frozen=True) class ListHyperparametersResult: """The result from calling list_hyperparameters(). Attributes: hyperparameters: The hyperparameteres belonging to the experiments in the request. session_groups: The session groups present in the experiments in the request. """ hyperparameters: Collection[Hyperparameter] session_groups: Collection[HyperparameterSessionGroup] class _TimeSeries: """Metadata about time series data for a particular run and tag. Superclass of `ScalarTimeSeries`, `TensorTimeSeries`, and `BlobSequenceTimeSeries`. """ __slots__ = ( "_max_step", "_max_wall_time", "_plugin_content", "_description", "_display_name", "_last_value", ) def __init__( self, *, max_step, max_wall_time, plugin_content, description, display_name, last_value=None, ): self._max_step = max_step self._max_wall_time = max_wall_time self._plugin_content = plugin_content self._description = description self._display_name = display_name self._last_value = last_value @property def max_step(self): return self._max_step @property def max_wall_time(self): return self._max_wall_time @property def plugin_content(self): return self._plugin_content @property def description(self): return self._description @property def display_name(self): return self._display_name @property def last_value(self): return self._last_value class ScalarTimeSeries(_TimeSeries): """Metadata about a scalar time series for a particular run and tag. Attributes: max_step: The largest step value of any datum in this scalar time series; a nonnegative integer. max_wall_time: The largest wall time of any datum in this time series, as `float` seconds since epoch. plugin_content: A bytestring of arbitrary plugin-specific metadata for this time series, as provided to `tf.summary.write` in the `plugin_data.content` field of the `metadata` argument. description: An optional long-form Markdown description, as a `str` that is empty if no description was specified. display_name: An optional long-form Markdown description, as a `str` that is empty if no description was specified. Deprecated; may be removed soon. last_value: An optional value for the latest scalar in the time series, corresponding to the scalar at `max_step`. Note that this field might NOT be populated by all data provider implementations. """ def __eq__(self, other): if not isinstance(other, ScalarTimeSeries): return False if self._max_step != other._max_step: return False if self._max_wall_time != other._max_wall_time: return False if self._plugin_content != other._plugin_content: return False if self._description != other._description: return False if self._display_name != other._display_name: return False if self._last_value != other._last_value: return False return True def __hash__(self): return hash( ( self._max_step, self._max_wall_time, self._plugin_content, self._description, self._display_name, self._last_value, ) ) def __repr__(self): return "ScalarTimeSeries(%s)" % ", ".join( ( "max_step=%r" % (self._max_step,), "max_wall_time=%r" % (self._max_wall_time,), "plugin_content=%r" % (self._plugin_content,), "description=%r" % (self._description,), "display_name=%r" % (self._display_name,), "last_value=%r" % (self._last_value,), ) ) class ScalarDatum: """A single datum in a scalar time series for a run and tag. Attributes: step: The global step at which this datum occurred; an integer. This is a unique key among data of this time series. wall_time: The real-world time at which this datum occurred, as `float` seconds since epoch. value: The scalar value for this datum; a `float`. """ __slots__ = ("_step", "_wall_time", "_value") def __init__(self, step, wall_time, value): self._step = step self._wall_time = wall_time self._value = value @property def step(self): return self._step @property def wall_time(self): return self._wall_time @property def value(self): return self._value def __eq__(self, other): if not isinstance(other, ScalarDatum): return False if self._step != other._step: return False if self._wall_time != other._wall_time: return False if self._value != other._value: return False return True def __hash__(self): return hash((self._step, self._wall_time, self._value)) def __repr__(self): return "ScalarDatum(%s)" % ", ".join( ( "step=%r" % (self._step,), "wall_time=%r" % (self._wall_time,), "value=%r" % (self._value,), ) ) class TensorTimeSeries(_TimeSeries): """Metadata about a tensor time series for a particular run and tag. Attributes: max_step: The largest step value of any datum in this tensor time series; a nonnegative integer. max_wall_time: The largest wall time of any datum in this time series, as `float` seconds since epoch. plugin_content: A bytestring of arbitrary plugin-specific metadata for this time series, as provided to `tf.summary.write` in the `plugin_data.content` field of the `metadata` argument. description: An optional long-form Markdown description, as a `str` that is empty if no description was specified. display_name: An optional long-form Markdown description, as a `str` that is empty if no description was specified. Deprecated; may be removed soon. """ def __eq__(self, other): if not isinstance(other, TensorTimeSeries): return False if self._max_step != other._max_step: return False if self._max_wall_time != other._max_wall_time: return False if self._plugin_content != other._plugin_content: return False if self._description != other._description: return False if self._display_name != other._display_name: return False return True def __hash__(self): return hash( ( self._max_step, self._max_wall_time, self._plugin_content, self._description, self._display_name, ) ) def __repr__(self): return "TensorTimeSeries(%s)" % ", ".join( ( "max_step=%r" % (self._max_step,), "max_wall_time=%r" % (self._max_wall_time,), "plugin_content=%r" % (self._plugin_content,), "description=%r" % (self._description,), "display_name=%r" % (self._display_name,), ) ) class TensorDatum: """A single datum in a tensor time series for a run and tag. Attributes: step: The global step at which this datum occurred; an integer. This is a unique key among data of this time series. wall_time: The real-world time at which this datum occurred, as `float` seconds since epoch. numpy: The `numpy.ndarray` value with the tensor contents of this datum. """ __slots__ = ("_step", "_wall_time", "_numpy") def __init__(self, step, wall_time, numpy): self._step = step self._wall_time = wall_time self._numpy = numpy @property def step(self): return self._step @property def wall_time(self): return self._wall_time @property def numpy(self): return self._numpy def __eq__(self, other): if not isinstance(other, TensorDatum): return False if self._step != other._step: return False if self._wall_time != other._wall_time: return False if not np.array_equal(self._numpy, other._numpy): return False return True # Unhashable type: numpy arrays are mutable. __hash__ = None def __repr__(self): return "TensorDatum(%s)" % ", ".join( ( "step=%r" % (self._step,), "wall_time=%r" % (self._wall_time,), "numpy=%r" % (self._numpy,), ) ) class BlobSequenceTimeSeries(_TimeSeries): """Metadata about a blob sequence time series for a particular run and tag. Attributes: max_step: The largest step value of any datum in this scalar time series; a nonnegative integer. max_wall_time: The largest wall time of any datum in this time series, as `float` seconds since epoch. max_length: The largest length (number of blobs) of any datum in this scalar time series, or `None` if this time series is empty. plugin_content: A bytestring of arbitrary plugin-specific metadata for this time series, as provided to `tf.summary.write` in the `plugin_data.content` field of the `metadata` argument. description: An optional long-form Markdown description, as a `str` that is empty if no description was specified. display_name: An optional long-form Markdown description, as a `str` that is empty if no description was specified. Deprecated; may be removed soon. """ __slots__ = ("_max_length",) def __init__( self, *, max_step, max_wall_time, max_length, plugin_content, description, display_name, ): super().__init__( max_step=max_step, max_wall_time=max_wall_time, plugin_content=plugin_content, description=description, display_name=display_name, ) self._max_length = max_length @property def max_length(self): return self._max_length def __eq__(self, other): if not isinstance(other, BlobSequenceTimeSeries): return False if self._max_step != other._max_step: return False if self._max_wall_time != other._max_wall_time: return False if self._max_length != other._max_length: return False if self._plugin_content != other._plugin_content: return False if self._description != other._description: return False if self._display_name != other._display_name: return False return True def __hash__(self): return hash( ( self._max_step, self._max_wall_time, self._max_length, self._plugin_content, self._description, self._display_name, ) ) def __repr__(self): return "BlobSequenceTimeSeries(%s)" % ", ".join( ( "max_step=%r" % (self._max_step,), "max_wall_time=%r" % (self._max_wall_time,), "max_length=%r" % (self._max_length,), "plugin_content=%r" % (self._plugin_content,), "description=%r" % (self._description,), "display_name=%r" % (self._display_name,), ) ) class BlobReference: """A reference to a blob. Attributes: blob_key: A string containing a key uniquely identifying a blob, which may be dereferenced via `provider.read_blob(blob_key)`. These keys must be constructed such that they can be included directly in a URL, with no further encoding. Concretely, this means that they consist exclusively of "unreserved characters" per RFC 3986, namely [a-zA-Z0-9._~-]. These keys are case-sensitive; it may be wise for implementations to normalize case to reduce confusion. The empty string is not a valid key. Blob keys must not contain information that should be kept secret. Privacy-sensitive applications should use random keys (e.g. UUIDs), or encrypt keys containing secret fields. url: (optional) A string containing a URL from which the blob data may be fetched directly, bypassing the data provider. URLs may be a vector for data leaks (e.g. via browser history, web proxies, etc.), so these URLs should not expose secret information. """ __slots__ = ("_url", "_blob_key") def __init__(self, blob_key, url=None): self._blob_key = blob_key self._url = url @property def blob_key(self): """Provide a key uniquely identifying a blob. Callers should consider these keys to be opaque-- i.e., to have no intrinsic meaning. Some data providers may use random IDs; but others may encode information into the key, in which case callers must make no attempt to decode it. """ return self._blob_key @property def url(self): """Provide the direct-access URL for this blob, if available. Note that this method is *not* expected to construct a URL to the data-loading endpoint provided by TensorBoard. If this method returns None, then the caller should proceed to use `blob_key()` to build the URL, as needed. """ return self._url def __eq__(self, other): if not isinstance(other, BlobReference): return False if self._blob_key != other._blob_key: return False if self._url != other._url: return False return True def __hash__(self): return hash((self._blob_key, self._url)) def __repr__(self): return "BlobReference(%s)" % ", ".join( ("blob_key=%r" % (self._blob_key,), "url=%r" % (self._url,)) ) class BlobSequenceDatum: """A single datum in a blob sequence time series for a run and tag. Attributes: step: The global step at which this datum occurred; an integer. This is a unique key among data of this time series. wall_time: The real-world time at which this datum occurred, as `float` seconds since epoch. values: A tuple of `BlobReference` objects, providing access to elements of this sequence. """ __slots__ = ("_step", "_wall_time", "_values") def __init__(self, step, wall_time, values): self._step = step self._wall_time = wall_time self._values = values @property def step(self): return self._step @property def wall_time(self): return self._wall_time @property def values(self): return self._values def __eq__(self, other): if not isinstance(other, BlobSequenceDatum): return False if self._step != other._step: return False if self._wall_time != other._wall_time: return False if self._values != other._values: return False return True def __hash__(self): return hash((self._step, self._wall_time, self._values)) def __repr__(self): return "BlobSequenceDatum(%s)" % ", ".join( ( "step=%r" % (self._step,), "wall_time=%r" % (self._wall_time,), "values=%r" % (self._values,), ) ) class RunTagFilter: """Filters data by run and tag names.""" def __init__(self, runs=None, tags=None): """Construct a `RunTagFilter`. A time series passes this filter if both its run *and* its tag are included in the corresponding whitelists. Order and multiplicity are ignored; `runs` and `tags` are treated as sets. Args: runs: Collection of run names, as strings, or `None` to admit all runs. tags: Collection of tag names, as strings, or `None` to admit all tags. """ self._runs = self._parse_optional_string_set("runs", runs) self._tags = self._parse_optional_string_set("tags", tags) def _parse_optional_string_set(self, name, value): if value is None: return None if isinstance(value, str): # Prevent confusion: strings _are_ iterable, but as # sequences of characters, so this likely signals an error. raise TypeError( "%s: expected `None` or collection of strings; got %r: %r" % (name, type(value), value) ) value = frozenset(value) for item in value: if not isinstance(item, str): raise TypeError( "%s: expected `None` or collection of strings; " "got item of type %r: %r" % (name, type(item), item) ) return value @property def runs(self): return self._runs @property def tags(self): return self._tags def __repr__(self): return "RunTagFilter(%s)" % ", ".join( ( "runs=%r" % (self._runs,), "tags=%r" % (self._tags,), ) )