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tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin.py | DebuggerPlugin._obtain_health_pills_at_step | def _obtain_health_pills_at_step(self, events_directory, node_names, step):
"""Reads disk to obtain the health pills for a run at a specific step.
This could be much slower than the alternative path of just returning all
health pills sampled by the event multiplexer. It could take tens of minutes
to co... | python | def _obtain_health_pills_at_step(self, events_directory, node_names, step):
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tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin.py | DebuggerPlugin._process_health_pill_event | def _process_health_pill_event(self, node_name_set, mapping, target_step,
file_path):
"""Creates health pills out of data in an event.
Creates health pills out of the event and adds them to the mapping.
Args:
node_name_set: A set of node names that are relevant.
... | python | def _process_health_pill_event(self, node_name_set, mapping, target_step,
file_path):
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Creates health pills out of the event and adds them to the mapping.
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node_name_set: A set of node names that are relevant.
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tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin.py | DebuggerPlugin._process_health_pill_value | def _process_health_pill_value(self,
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tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_plugin.py | DebuggerPlugin._serve_numerics_alert_report_handler | def _serve_numerics_alert_report_handler(self, request):
"""A (wrapped) werkzeug handler for serving numerics alert report.
Accepts GET requests and responds with an array of JSON-ified
NumericsAlertReportRow.
Each JSON-ified NumericsAlertReportRow object has the following format:
{
'devic... | python | def _serve_numerics_alert_report_handler(self, request):
"""A (wrapped) werkzeug handler for serving numerics alert report.
Accepts GET requests and responds with an array of JSON-ified
NumericsAlertReportRow.
Each JSON-ified NumericsAlertReportRow object has the following format:
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tensorflow/tensorboard | tensorboard/manager.py | _info_to_string | def _info_to_string(info):
"""Convert a `TensorBoardInfo` to string form to be stored on disk.
The format returned by this function is opaque and should only be
interpreted by `_info_from_string`.
Args:
info: A valid `TensorBoardInfo` object.
Raises:
ValueError: If any field on `info` is not of the... | python | def _info_to_string(info):
"""Convert a `TensorBoardInfo` to string form to be stored on disk.
The format returned by this function is opaque and should only be
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Args:
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tensorflow/tensorboard | tensorboard/manager.py | _info_from_string | def _info_from_string(info_string):
"""Parse a `TensorBoardInfo` object from its string representation.
Args:
info_string: A string representation of a `TensorBoardInfo`, as
produced by a previous call to `_info_to_string`.
Returns:
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"""Parse a `TensorBoardInfo` object from its string representation.
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tensorflow/tensorboard | tensorboard/manager.py | cache_key | def cache_key(working_directory, arguments, configure_kwargs):
"""Compute a `TensorBoardInfo.cache_key` field.
The format returned by this function is opaque. Clients may only
inspect it by comparing it for equality with other results from this
function.
Args:
working_directory: The directory from which... | python | def cache_key(working_directory, arguments, configure_kwargs):
"""Compute a `TensorBoardInfo.cache_key` field.
The format returned by this function is opaque. Clients may only
inspect it by comparing it for equality with other results from this
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tensorflow/tensorboard | tensorboard/manager.py | _get_info_dir | def _get_info_dir():
"""Get path to directory in which to store info files.
The directory returned by this function is "owned" by this module. If
the contents of the directory are modified other than via the public
functions of this module, subsequent behavior is undefined.
The directory will be created if ... | python | def _get_info_dir():
"""Get path to directory in which to store info files.
The directory returned by this function is "owned" by this module. If
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tensorflow/tensorboard | tensorboard/manager.py | write_info_file | def write_info_file(tensorboard_info):
"""Write TensorBoardInfo to the current process's info file.
This should be called by `main` once the server is ready. When the
server shuts down, `remove_info_file` should be called.
Args:
tensorboard_info: A valid `TensorBoardInfo` object.
Raises:
ValueError... | python | def write_info_file(tensorboard_info):
"""Write TensorBoardInfo to the current process's info file.
This should be called by `main` once the server is ready. When the
server shuts down, `remove_info_file` should be called.
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tensorboard_info: A valid `TensorBoardInfo` object.
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tensorflow/tensorboard | tensorboard/manager.py | remove_info_file | def remove_info_file():
"""Remove the current process's TensorBoardInfo file, if it exists.
If the file does not exist, no action is taken and no error is raised.
"""
try:
os.unlink(_get_info_file_path())
except OSError as e:
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# The user may have wiped their temporary... | python | def remove_info_file():
"""Remove the current process's TensorBoardInfo file, if it exists.
If the file does not exist, no action is taken and no error is raised.
"""
try:
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tensorflow/tensorboard | tensorboard/manager.py | get_all | def get_all():
"""Return TensorBoardInfo values for running TensorBoard processes.
This function may not provide a perfect snapshot of the set of running
processes. Its result set may be incomplete if the user has cleaned
their /tmp/ directory while TensorBoard processes are running. It may
contain extraneou... | python | def get_all():
"""Return TensorBoardInfo values for running TensorBoard processes.
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tensorflow/tensorboard | tensorboard/manager.py | start | def start(arguments, timeout=datetime.timedelta(seconds=60)):
"""Start a new TensorBoard instance, or reuse a compatible one.
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tensorflow/tensorboard | tensorboard/manager.py | _find_matching_instance | def _find_matching_instance(cache_key):
"""Find a running TensorBoard instance compatible with the cache key.
Returns:
A `TensorBoardInfo` object, or `None` if none matches the cache key.
"""
infos = get_all()
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"""Find a running TensorBoard instance compatible with the cache key.
Returns:
A `TensorBoardInfo` object, or `None` if none matches the cache key.
"""
infos = get_all()
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tensorflow/tensorboard | tensorboard/manager.py | _maybe_read_file | def _maybe_read_file(filename):
"""Read the given file, if it exists.
Args:
filename: A path to a file.
Returns:
A string containing the file contents, or `None` if the file does
not exist.
"""
try:
with open(filename) as infile:
return infile.read()
except IOError as e:
if e.err... | python | def _maybe_read_file(filename):
"""Read the given file, if it exists.
Args:
filename: A path to a file.
Returns:
A string containing the file contents, or `None` if the file does
not exist.
"""
try:
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tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | process_raw_trace | def process_raw_trace(raw_trace):
"""Processes raw trace data and returns the UI data."""
trace = trace_events_pb2.Trace()
trace.ParseFromString(raw_trace)
return ''.join(trace_events_json.TraceEventsJsonStream(trace)) | python | def process_raw_trace(raw_trace):
"""Processes raw trace data and returns the UI data."""
trace = trace_events_pb2.Trace()
trace.ParseFromString(raw_trace)
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tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin.is_active | def is_active(self):
"""Whether this plugin is active and has any profile data to show.
Detecting profile data is expensive, so this process runs asynchronously
and the value reported by this method is the cached value and may be stale.
Returns:
Whether any run has profile data.
"""
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tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin._run_dir | def _run_dir(self, run):
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tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin.generate_run_to_tools | def generate_run_to_tools(self):
"""Generator for pairs of "run name" and a list of tools for that run.
The "run name" here is a "frontend run name" - see _run_dir() for the
definition of a "frontend run name" and how it maps to a directory of
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tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin.host_impl | def host_impl(self, run, tool):
"""Returns available hosts for the run and tool in the log directory.
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single run (identified by the directory name), and files in the run
directory contains data for different tools and hosts. The fi... | python | def host_impl(self, run, tool):
"""Returns available hosts for the run and tool in the log directory.
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tensorflow/tensorboard | tensorboard/plugins/profile/profile_plugin.py | ProfilePlugin.data_impl | def data_impl(self, request):
"""Retrieves and processes the tool data for a run and a host.
Args:
request: XMLHttpRequest
Returns:
A string that can be served to the frontend tool or None if tool,
run or host is invalid.
"""
run = request.args.get('run')
tool = request.arg... | python | def data_impl(self, request):
"""Retrieves and processes the tool data for a run and a host.
Args:
request: XMLHttpRequest
Returns:
A string that can be served to the frontend tool or None if tool,
run or host is invalid.
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tensorflow/tensorboard | tensorboard/plugins/scalar/scalars_demo.py | run | def run(logdir, run_name,
initial_temperature, ambient_temperature, heat_coefficient):
"""Run a temperature simulation.
This will simulate an object at temperature `initial_temperature`
sitting at rest in a large room at temperature `ambient_temperature`.
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initial_temperature, ambient_temperature, heat_coefficient):
"""Run a temperature simulation.
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tensorflow/tensorboard | tensorboard/plugins/scalar/scalars_demo.py | run_all | def run_all(logdir, verbose=False):
"""Run simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
"""
for initial_temperature in [270.0, 310.0, 350.0]:
for final_temperature in [... | python | def run_all(logdir, verbose=False):
"""Run simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
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tensorflow/tensorboard | tensorboard/backend/json_util.py | Cleanse | def Cleanse(obj, encoding='utf-8'):
"""Makes Python object appropriate for JSON serialization.
- Replaces instances of Infinity/-Infinity/NaN with strings.
- Turns byte strings into unicode strings.
- Turns sets into sorted lists.
- Turns tuples into lists.
Args:
obj: Python data structure.
encodi... | python | def Cleanse(obj, encoding='utf-8'):
"""Makes Python object appropriate for JSON serialization.
- Replaces instances of Infinity/-Infinity/NaN with strings.
- Turns byte strings into unicode strings.
- Turns sets into sorted lists.
- Turns tuples into lists.
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tensorflow/tensorboard | tensorboard/plugins/text/summary.py | op | def op(name,
data,
display_name=None,
description=None,
collections=None):
"""Create a legacy text summary op.
Text data summarized via this plugin will be visible in the Text Dashboard
in TensorBoard. The standard TensorBoard Text Dashboard will render markdown
in the strings, and ... | python | def op(name,
data,
display_name=None,
description=None,
collections=None):
"""Create a legacy text summary op.
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tensorflow/tensorboard | tensorboard/plugins/text/summary.py | pb | def pb(name, data, display_name=None, description=None):
"""Create a legacy text summary protobuf.
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name: A name for the generated node. Will also serve as a series name in
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data: A Python bytestring (of type bytes), or Unicode string. Or a numpy
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | _GetPurgeMessage | def _GetPurgeMessage(most_recent_step, most_recent_wall_time, event_step,
event_wall_time, num_expired_scalars, num_expired_histos,
num_expired_comp_histos, num_expired_images,
num_expired_audio):
"""Return the string message associated with TensorBoard p... | python | def _GetPurgeMessage(most_recent_step, most_recent_wall_time, event_step,
event_wall_time, num_expired_scalars, num_expired_histos,
num_expired_comp_histos, num_expired_images,
num_expired_audio):
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | _GeneratorFromPath | def _GeneratorFromPath(path):
"""Create an event generator for file or directory at given path string."""
if not path:
raise ValueError('path must be a valid string')
if io_wrapper.IsTensorFlowEventsFile(path):
return event_file_loader.EventFileLoader(path)
else:
return directory_watcher.DirectoryWa... | python | def _GeneratorFromPath(path):
"""Create an event generator for file or directory at given path string."""
if not path:
raise ValueError('path must be a valid string')
if io_wrapper.IsTensorFlowEventsFile(path):
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | _ParseFileVersion | def _ParseFileVersion(file_version):
"""Convert the string file_version in event.proto into a float.
Args:
file_version: String file_version from event.proto
Returns:
Version number as a float.
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... | python | def _ParseFileVersion(file_version):
"""Convert the string file_version in event.proto into a float.
Args:
file_version: String file_version from event.proto
Returns:
Version number as a float.
"""
tokens = file_version.split('brain.Event:')
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.Reload | def Reload(self):
"""Loads all events added since the last call to `Reload`.
If `Reload` was never called, loads all events in the file.
Returns:
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"""
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"""Loads all events added since the last call to `Reload`.
If `Reload` was never called, loads all events in the file.
Returns:
The `EventAccumulator`.
"""
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.RetrievePluginAsset | def RetrievePluginAsset(self, plugin_name, asset_name):
"""Return the contents of a given plugin asset.
Args:
plugin_name: The string name of a plugin.
asset_name: The string name of an asset.
Returns:
The string contents of the plugin asset.
Raises:
KeyError: If the asset is ... | python | def RetrievePluginAsset(self, plugin_name, asset_name):
"""Return the contents of a given plugin asset.
Args:
plugin_name: The string name of a plugin.
asset_name: The string name of an asset.
Returns:
The string contents of the plugin asset.
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.FirstEventTimestamp | def FirstEventTimestamp(self):
"""Returns the timestamp in seconds of the first event.
If the first event has been loaded (either by this method or by `Reload`,
this returns immediately. Otherwise, it will load in the first event. Note
that this means that calling `Reload` will cause this to block unti... | python | def FirstEventTimestamp(self):
"""Returns the timestamp in seconds of the first event.
If the first event has been loaded (either by this method or by `Reload`,
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.PluginTagToContent | def PluginTagToContent(self, plugin_name):
"""Returns a dict mapping tags to content specific to that plugin.
Args:
plugin_name: The name of the plugin for which to fetch plugin-specific
content.
Raises:
KeyError: if the plugin name is not found.
Returns:
A dict mapping tags... | python | def PluginTagToContent(self, plugin_name):
"""Returns a dict mapping tags to content specific to that plugin.
Args:
plugin_name: The name of the plugin for which to fetch plugin-specific
content.
Raises:
KeyError: if the plugin name is not found.
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.Tags | def Tags(self):
"""Return all tags found in the value stream.
Returns:
A `{tagType: ['list', 'of', 'tags']}` dictionary.
"""
return {
IMAGES: self.images.Keys(),
AUDIO: self.audios.Keys(),
HISTOGRAMS: self.histograms.Keys(),
SCALARS: self.scalars.Keys(),
CO... | python | def Tags(self):
"""Return all tags found in the value stream.
Returns:
A `{tagType: ['list', 'of', 'tags']}` dictionary.
"""
return {
IMAGES: self.images.Keys(),
AUDIO: self.audios.Keys(),
HISTOGRAMS: self.histograms.Keys(),
SCALARS: self.scalars.Keys(),
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.Graph | def Graph(self):
"""Return the graph definition, if there is one.
If the graph is stored directly, return that. If no graph is stored
directly but a metagraph is stored containing a graph, return that.
Raises:
ValueError: If there is no graph for this run.
Returns:
The `graph_def` pr... | python | def Graph(self):
"""Return the graph definition, if there is one.
If the graph is stored directly, return that. If no graph is stored
directly but a metagraph is stored containing a graph, return that.
Raises:
ValueError: If there is no graph for this run.
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.MetaGraph | def MetaGraph(self):
"""Return the metagraph definition, if there is one.
Raises:
ValueError: If there is no metagraph for this run.
Returns:
The `meta_graph_def` proto.
"""
if self._meta_graph is None:
raise ValueError('There is no metagraph in this EventAccumulator')
meta_g... | python | def MetaGraph(self):
"""Return the metagraph definition, if there is one.
Raises:
ValueError: If there is no metagraph for this run.
Returns:
The `meta_graph_def` proto.
"""
if self._meta_graph is None:
raise ValueError('There is no metagraph in this EventAccumulator')
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator.RunMetadata | def RunMetadata(self, tag):
"""Given a tag, return the associated session.run() metadata.
Args:
tag: A string tag associated with the event.
Raises:
ValueError: If the tag is not found.
Returns:
The metadata in form of `RunMetadata` proto.
"""
if tag not in self._tagged_meta... | python | def RunMetadata(self, tag):
"""Given a tag, return the associated session.run() metadata.
Args:
tag: A string tag associated with the event.
Raises:
ValueError: If the tag is not found.
Returns:
The metadata in form of `RunMetadata` proto.
"""
if tag not in self._tagged_meta... | [
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._MaybePurgeOrphanedData | def _MaybePurgeOrphanedData(self, event):
"""Maybe purge orphaned data due to a TensorFlow crash.
When TensorFlow crashes at step T+O and restarts at step T, any events
written after step T are now "orphaned" and will be at best misleading if
they are included in TensorBoard.
This logic attempts t... | python | def _MaybePurgeOrphanedData(self, event):
"""Maybe purge orphaned data due to a TensorFlow crash.
When TensorFlow crashes at step T+O and restarts at step T, any events
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._CheckForRestartAndMaybePurge | def _CheckForRestartAndMaybePurge(self, event):
"""Check and discard expired events using SessionLog.START.
Check for a SessionLog.START event and purge all previously seen events
with larger steps, because they are out of date. Because of supervisor
threading, it is possible that this logic will cause... | python | def _CheckForRestartAndMaybePurge(self, event):
"""Check and discard expired events using SessionLog.START.
Check for a SessionLog.START event and purge all previously seen events
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._CheckForOutOfOrderStepAndMaybePurge | def _CheckForOutOfOrderStepAndMaybePurge(self, event):
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Check if the event is out of order relative to the global most recent step.
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event: The event... | python | def _CheckForOutOfOrderStepAndMaybePurge(self, event):
"""Check for out-of-order event.step and discard expired events for tags.
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._ProcessHistogram | def _ProcessHistogram(self, tag, wall_time, step, histo):
"""Processes a proto histogram by adding it to accumulated state."""
histo = self._ConvertHistogramProtoToTuple(histo)
histo_ev = HistogramEvent(wall_time, step, histo)
self.histograms.AddItem(tag, histo_ev)
self.compressed_histograms.AddItem... | python | def _ProcessHistogram(self, tag, wall_time, step, histo):
"""Processes a proto histogram by adding it to accumulated state."""
histo = self._ConvertHistogramProtoToTuple(histo)
histo_ev = HistogramEvent(wall_time, step, histo)
self.histograms.AddItem(tag, histo_ev)
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._CompressHistogram | def _CompressHistogram(self, histo_ev):
"""Callback for _ProcessHistogram."""
return CompressedHistogramEvent(
histo_ev.wall_time,
histo_ev.step,
compressor.compress_histogram_proto(
histo_ev.histogram_value, self._compression_bps)) | python | def _CompressHistogram(self, histo_ev):
"""Callback for _ProcessHistogram."""
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._ProcessImage | def _ProcessImage(self, tag, wall_time, step, image):
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event = ImageEvent(wall_time=wall_time,
step=step,
encoded_image_string=image.encoded_image_string,
width=image.width,
... | python | def _ProcessImage(self, tag, wall_time, step, image):
"""Processes an image by adding it to accumulated state."""
event = ImageEvent(wall_time=wall_time,
step=step,
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width=image.width,
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._ProcessAudio | def _ProcessAudio(self, tag, wall_time, step, audio):
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step=step,
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._ProcessScalar | def _ProcessScalar(self, tag, wall_time, step, scalar):
"""Processes a simple value by adding it to accumulated state."""
sv = ScalarEvent(wall_time=wall_time, step=step, value=scalar)
self.scalars.AddItem(tag, sv) | python | def _ProcessScalar(self, tag, wall_time, step, scalar):
"""Processes a simple value by adding it to accumulated state."""
sv = ScalarEvent(wall_time=wall_time, step=step, value=scalar)
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_accumulator.py | EventAccumulator._Purge | def _Purge(self, event, by_tags):
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_loader.py | RawEventFileLoader.Load | def Load(self):
"""Loads all new events from disk as raw serialized proto bytestrings.
Calling Load multiple times in a row will not 'drop' events as long as the
return value is not iterated over.
Yields:
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"""
logger.... | python | def Load(self):
"""Loads all new events from disk as raw serialized proto bytestrings.
Calling Load multiple times in a row will not 'drop' events as long as the
return value is not iterated over.
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tensorflow/tensorboard | tensorboard/backend/event_processing/event_file_loader.py | EventFileLoader.Load | def Load(self):
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tensorflow/tensorboard | tensorboard/plugins/beholder/im_util.py | scale_sections | def scale_sections(sections, scaling_scope):
'''
input: unscaled sections.
returns: sections scaled to [0, 255]
'''
new_sections = []
if scaling_scope == 'layer':
for section in sections:
new_sections.append(scale_image_for_display(section))
elif scaling_scope == 'network':
global_min, glo... | python | def scale_sections(sections, scaling_scope):
'''
input: unscaled sections.
returns: sections scaled to [0, 255]
'''
new_sections = []
if scaling_scope == 'layer':
for section in sections:
new_sections.append(scale_image_for_display(section))
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tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_server_lib.py | DebuggerDataStreamHandler.on_value_event | def on_value_event(self, event):
"""Records the summary values based on an updated message from the debugger.
Logs an error message if writing the event to disk fails.
Args:
event: The Event proto to be processed.
"""
if not event.summary.value:
logger.warn("The summary of the event la... | python | def on_value_event(self, event):
"""Records the summary values based on an updated message from the debugger.
Logs an error message if writing the event to disk fails.
Args:
event: The Event proto to be processed.
"""
if not event.summary.value:
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tensorflow/tensorboard | tensorboard/plugins/debugger/debugger_server_lib.py | DebuggerDataStreamHandler._parse_session_run_index | def _parse_session_run_index(self, event):
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Args:
event: The event with metadata that contains the session_run_index.
Returns:
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event: The event with metadata that contains the session_run_index.
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tensorflow/tensorboard | tensorboard/plugins/distribution/compressor.py | compress_histogram_proto | def compress_histogram_proto(histo, bps=NORMAL_HISTOGRAM_BPS):
"""Creates fixed size histogram by adding compression to accumulated state.
This routine transforms a histogram at a particular step by interpolating its
variable number of buckets to represent their cumulative weight at a constant
number of compre... | python | def compress_histogram_proto(histo, bps=NORMAL_HISTOGRAM_BPS):
"""Creates fixed size histogram by adding compression to accumulated state.
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tensorflow/tensorboard | tensorboard/plugins/distribution/compressor.py | compress_histogram | def compress_histogram(buckets, bps=NORMAL_HISTOGRAM_BPS):
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tensorflow/tensorboard | tensorboard/plugins/distribution/compressor.py | _lerp | def _lerp(x, x0, x1, y0, y1):
"""Affinely map from [x0, x1] onto [y0, y1]."""
return y0 + (x - x0) * float(y1 - y0) / (x1 - x0) | python | def _lerp(x, x0, x1, y0, y1):
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tensorflow/tensorboard | tensorboard/plugins/image/images_plugin.py | ImagesPlugin.is_active | def is_active(self):
"""The images plugin is active iff any run has at least one relevant tag."""
if self._db_connection_provider:
# The plugin is active if one relevant tag can be found in the database.
db = self._db_connection_provider()
cursor = db.execute(
'''
SELECT 1
... | python | def is_active(self):
"""The images plugin is active iff any run has at least one relevant tag."""
if self._db_connection_provider:
# The plugin is active if one relevant tag can be found in the database.
db = self._db_connection_provider()
cursor = db.execute(
'''
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tensorflow/tensorboard | tensorboard/plugins/image/images_plugin.py | ImagesPlugin._serve_image_metadata | def _serve_image_metadata(self, request):
"""Given a tag and list of runs, serve a list of metadata for images.
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to the images. The frontend should treat these URLs as opaque and should not
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"""Given a tag and list of runs, serve a list of metadata for images.
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tensorflow/tensorboard | tensorboard/plugins/image/images_plugin.py | ImagesPlugin._image_response_for_run | def _image_response_for_run(self, run, tag, sample):
"""Builds a JSON-serializable object with information about images.
Args:
run: The name of the run.
tag: The name of the tag the images all belong to.
sample: The zero-indexed sample of the image for which to retrieve
information. F... | python | def _image_response_for_run(self, run, tag, sample):
"""Builds a JSON-serializable object with information about images.
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run: The name of the run.
tag: The name of the tag the images all belong to.
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tensorflow/tensorboard | tensorboard/plugins/image/images_plugin.py | ImagesPlugin._get_individual_image | def _get_individual_image(self, run, tag, index, sample):
"""
Returns the actual image bytes for a given image.
Args:
run: The name of the run the image belongs to.
tag: The name of the tag the images belongs to.
index: The index of the image in the current reservoir.
sample: The ze... | python | def _get_individual_image(self, run, tag, index, sample):
"""
Returns the actual image bytes for a given image.
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run: The name of the run the image belongs to.
tag: The name of the tag the images belongs to.
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tensorflow/tensorboard | tensorboard/plugins/image/images_plugin.py | ImagesPlugin._serve_individual_image | def _serve_individual_image(self, request):
"""Serves an individual image."""
run = request.args.get('run')
tag = request.args.get('tag')
index = int(request.args.get('index'))
sample = int(request.args.get('sample', 0))
data = self._get_individual_image(run, tag, index, sample)
image_type =... | python | def _serve_individual_image(self, request):
"""Serves an individual image."""
run = request.args.get('run')
tag = request.args.get('tag')
index = int(request.args.get('index'))
sample = int(request.args.get('sample', 0))
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tensorflow/tensorboard | tensorboard/plugins/pr_curve/pr_curve_demo.py | run_all | def run_all(logdir, steps, thresholds, verbose=False):
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logdir: The directory into which to store all the runs' data.
steps: The number of steps to run for.
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tensorflow/tensorboard | tensorboard/plugins/image/summary_v2.py | image | def image(name,
data,
step=None,
max_outputs=3,
description=None):
"""Write an image summary.
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name: A name for this summary. The summary tag used for TensorBoard will
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data,
step=None,
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description=None):
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/witwidget/notebook/visualization.py | WitConfigBuilder.set_examples | def set_examples(self, examples):
"""Sets the examples to be displayed in WIT.
Args:
examples: List of example protos.
Returns:
self, in order to enabled method chaining.
"""
self.store('examples', examples)
if len(examples) > 0:
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... | python | def set_examples(self, examples):
"""Sets the examples to be displayed in WIT.
Args:
examples: List of example protos.
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/witwidget/notebook/visualization.py | WitConfigBuilder.set_estimator_and_feature_spec | def set_estimator_and_feature_spec(self, estimator, feature_spec):
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/witwidget/notebook/visualization.py | WitConfigBuilder.set_compare_estimator_and_feature_spec | def set_compare_estimator_and_feature_spec(self, estimator, feature_spec):
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tensorflow/tensorboard | tensorboard/plugins/interactive_inference/witwidget/notebook/visualization.py | WitConfigBuilder.set_custom_predict_fn | def set_custom_predict_fn(self, predict_fn):
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tensorflow/tensorboard | tensorboard/plugins/beholder/visualizer.py | Visualizer._reshape_conv_array | def _reshape_conv_array(self, array, section_height, image_width):
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tensorflow/tensorboard | tensorboard/plugins/beholder/visualizer.py | Visualizer._reshape_irregular_array | def _reshape_irregular_array(self, array, section_height, image_width):
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section_area = section_height * image_width
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tensorflow/tensorboard | tensorboard/plugins/beholder/visualizer.py | Visualizer._arrays_to_sections | def _arrays_to_sections(self, arrays):
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tensorflow/tensorboard | tensorboard/plugins/beholder/visualizer.py | Visualizer._maybe_clear_deque | def _maybe_clear_deque(self):
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tensorflow/tensorboard | tensorboard/lazy.py | lazy_load | def lazy_load(name):
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"""Decorator to define a function that lazily loads the module 'name'.
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tensorflow/tensorboard | tensorboard/lazy.py | _memoize | def _memoize(f):
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tensorflow/tensorboard | tensorboard/compat/__init__.py | tf | def tf():
"""Provide the root module of a TF-like API for use within TensorBoard.
By default this is equivalent to `import tensorflow as tf`, but it can be used
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"""Provide the root module of a TF-like API for use within TensorBoard.
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tensorflow/tensorboard | tensorboard/compat/__init__.py | tf2 | def tf2():
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tensorflow/tensorboard | tensorboard/compat/__init__.py | _pywrap_tensorflow | def _pywrap_tensorflow():
"""Provide pywrap_tensorflow access in TensorBoard.
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"""Provide pywrap_tensorflow access in TensorBoard.
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_minimal_demo.py | create_experiment_summary | def create_experiment_summary():
"""Returns a summary proto buffer holding this experiment."""
# Convert TEMPERATURE_LIST to google.protobuf.ListValue
temperature_list = struct_pb2.ListValue()
temperature_list.extend(TEMPERATURE_LIST)
materials = struct_pb2.ListValue()
materials.extend(HEAT_COEFFICIENTS.ke... | python | def create_experiment_summary():
"""Returns a summary proto buffer holding this experiment."""
# Convert TEMPERATURE_LIST to google.protobuf.ListValue
temperature_list = struct_pb2.ListValue()
temperature_list.extend(TEMPERATURE_LIST)
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_minimal_demo.py | run | def run(logdir, session_id, hparams, group_name):
"""Runs a temperature simulation.
This will simulate an object at temperature `initial_temperature`
sitting at rest in a large room at temperature `ambient_temperature`.
The object has some intrinsic `heat_coefficient`, which indicates
how much thermal conduc... | python | def run(logdir, session_id, hparams, group_name):
"""Runs a temperature simulation.
This will simulate an object at temperature `initial_temperature`
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tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_minimal_demo.py | run_all | def run_all(logdir, verbose=False):
"""Run simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins.
"""
writer = tf.summary.FileWriter(logdir)
writer.add_summary(create_experiment_s... | python | def run_all(logdir, verbose=False):
"""Run simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins.
"""
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | get_filesystem | def get_filesystem(filename):
"""Return the registered filesystem for the given file."""
filename = compat.as_str_any(filename)
prefix = ""
index = filename.find("://")
if index >= 0:
prefix = filename[:index]
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rais... | python | def get_filesystem(filename):
"""Return the registered filesystem for the given file."""
filename = compat.as_str_any(filename)
prefix = ""
index = filename.find("://")
if index >= 0:
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | walk | def walk(top, topdown=True, onerror=None):
"""Recursive directory tree generator for directories.
Args:
top: string, a Directory name
topdown: bool, Traverse pre order if True, post order if False.
onerror: optional handler for errors. Should be a function, it will be
called with the ... | python | def walk(top, topdown=True, onerror=None):
"""Recursive directory tree generator for directories.
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top: string, a Directory name
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | LocalFileSystem.read | def read(self, filename, binary_mode=False, size=None, offset=None):
"""Reads contents of a file to a string.
Args:
filename: string, a path
binary_mode: bool, read as binary if True, otherwise text
size: int, number of bytes or characters to read, otherwise
... | python | def read(self, filename, binary_mode=False, size=None, offset=None):
"""Reads contents of a file to a string.
Args:
filename: string, a path
binary_mode: bool, read as binary if True, otherwise text
size: int, number of bytes or characters to read, otherwise
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | LocalFileSystem.glob | def glob(self, filename):
"""Returns a list of files that match the given pattern(s)."""
if isinstance(filename, six.string_types):
return [
# Convert the filenames to string from bytes.
compat.as_str_any(matching_filename)
for matching_filenam... | python | def glob(self, filename):
"""Returns a list of files that match the given pattern(s)."""
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | LocalFileSystem.listdir | def listdir(self, dirname):
"""Returns a list of entries contained within a directory."""
if not self.isdir(dirname):
raise errors.NotFoundError(None, None, "Could not find directory")
entries = os.listdir(compat.as_str_any(dirname))
entries = [compat.as_str_any(item) for it... | python | def listdir(self, dirname):
"""Returns a list of entries contained within a directory."""
if not self.isdir(dirname):
raise errors.NotFoundError(None, None, "Could not find directory")
entries = os.listdir(compat.as_str_any(dirname))
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | LocalFileSystem.stat | def stat(self, filename):
"""Returns file statistics for a given path."""
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try:
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except OSError:
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"""Returns file statistics for a given path."""
# NOTE: Size of the file is given by .st_size as returned from
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | S3FileSystem.bucket_and_path | def bucket_and_path(self, url):
"""Split an S3-prefixed URL into bucket and path."""
url = compat.as_str_any(url)
if url.startswith("s3://"):
url = url[len("s3://"):]
idx = url.index("/")
bucket = url[:idx]
path = url[(idx + 1):]
return bucket, path | python | def bucket_and_path(self, url):
"""Split an S3-prefixed URL into bucket and path."""
url = compat.as_str_any(url)
if url.startswith("s3://"):
url = url[len("s3://"):]
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path = url[(idx + 1):]
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] | 8e5f497b48e40f2a774f85416b8a35ac0693c35e | https://github.com/tensorflow/tensorboard/blob/8e5f497b48e40f2a774f85416b8a35ac0693c35e/tensorboard/compat/tensorflow_stub/io/gfile.py#L161-L169 | train |
tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | S3FileSystem.exists | def exists(self, filename):
"""Determines whether a path exists or not."""
client = boto3.client("s3")
bucket, path = self.bucket_and_path(filename)
r = client.list_objects(Bucket=bucket, Prefix=path, Delimiter="/")
if r.get("Contents") or r.get("CommonPrefixes"):
ret... | python | def exists(self, filename):
"""Determines whether a path exists or not."""
client = boto3.client("s3")
bucket, path = self.bucket_and_path(filename)
r = client.list_objects(Bucket=bucket, Prefix=path, Delimiter="/")
if r.get("Contents") or r.get("CommonPrefixes"):
ret... | [
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | S3FileSystem.read | def read(self, filename, binary_mode=False, size=None, offset=None):
"""Reads contents of a file to a string.
Args:
filename: string, a path
binary_mode: bool, read as binary if True, otherwise text
size: int, number of bytes or characters to read, otherwise
... | python | def read(self, filename, binary_mode=False, size=None, offset=None):
"""Reads contents of a file to a string.
Args:
filename: string, a path
binary_mode: bool, read as binary if True, otherwise text
size: int, number of bytes or characters to read, otherwise
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | S3FileSystem.glob | def glob(self, filename):
"""Returns a list of files that match the given pattern(s)."""
# Only support prefix with * at the end and no ? in the string
star_i = filename.find('*')
quest_i = filename.find('?')
if quest_i >= 0:
raise NotImplementedError(
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"""Returns a list of files that match the given pattern(s)."""
# Only support prefix with * at the end and no ? in the string
star_i = filename.find('*')
quest_i = filename.find('?')
if quest_i >= 0:
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | S3FileSystem.isdir | def isdir(self, dirname):
"""Returns whether the path is a directory or not."""
client = boto3.client("s3")
bucket, path = self.bucket_and_path(dirname)
if not path.endswith("/"):
path += "/" # This will now only retrieve subdir content
r = client.list_objects(Bucket... | python | def isdir(self, dirname):
"""Returns whether the path is a directory or not."""
client = boto3.client("s3")
bucket, path = self.bucket_and_path(dirname)
if not path.endswith("/"):
path += "/" # This will now only retrieve subdir content
r = client.list_objects(Bucket... | [
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | S3FileSystem.listdir | def listdir(self, dirname):
"""Returns a list of entries contained within a directory."""
client = boto3.client("s3")
bucket, path = self.bucket_and_path(dirname)
p = client.get_paginator("list_objects")
if not path.endswith("/"):
path += "/" # This will now only ret... | python | def listdir(self, dirname):
"""Returns a list of entries contained within a directory."""
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tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/io/gfile.py | S3FileSystem.stat | def stat(self, filename):
"""Returns file statistics for a given path."""
# NOTE: Size of the file is given by ContentLength from S3,
# but we convert to .length
client = boto3.client("s3")
bucket, path = self.bucket_and_path(filename)
try:
obj = client.head_o... | python | def stat(self, filename):
"""Returns file statistics for a given path."""
# NOTE: Size of the file is given by ContentLength from S3,
# but we convert to .length
client = boto3.client("s3")
bucket, path = self.bucket_and_path(filename)
try:
obj = client.head_o... | [
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tensorflow/tensorboard | tensorboard/notebook.py | _get_context | def _get_context():
"""Determine the most specific context that we're in.
Returns:
_CONTEXT_COLAB: If in Colab with an IPython notebook context.
_CONTEXT_IPYTHON: If not in Colab, but we are in an IPython notebook
context (e.g., from running `jupyter notebook` at the command
line).
_CONTEXT... | python | def _get_context():
"""Determine the most specific context that we're in.
Returns:
_CONTEXT_COLAB: If in Colab with an IPython notebook context.
_CONTEXT_IPYTHON: If not in Colab, but we are in an IPython notebook
context (e.g., from running `jupyter notebook` at the command
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tensorflow/tensorboard | tensorboard/notebook.py | start | def start(args_string):
"""Launch and display a TensorBoard instance as if at the command line.
Args:
args_string: Command-line arguments to TensorBoard, to be
interpreted by `shlex.split`: e.g., "--logdir ./logs --port 0".
Shell metacharacters are not supported: e.g., "--logdir 2>&1" will
po... | python | def start(args_string):
"""Launch and display a TensorBoard instance as if at the command line.
Args:
args_string: Command-line arguments to TensorBoard, to be
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tensorflow/tensorboard | tensorboard/notebook.py | _time_delta_from_info | def _time_delta_from_info(info):
"""Format the elapsed time for the given TensorBoardInfo.
Args:
info: A TensorBoardInfo value.
Returns:
A human-readable string describing the time since the server
described by `info` started: e.g., "2 days, 0:48:58".
"""
delta_seconds = int(time.time()) - info.... | python | def _time_delta_from_info(info):
"""Format the elapsed time for the given TensorBoardInfo.
Args:
info: A TensorBoardInfo value.
Returns:
A human-readable string describing the time since the server
described by `info` started: e.g., "2 days, 0:48:58".
"""
delta_seconds = int(time.time()) - info.... | [
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tensorflow/tensorboard | tensorboard/notebook.py | display | def display(port=None, height=None):
"""Display a TensorBoard instance already running on this machine.
Args:
port: The port on which the TensorBoard server is listening, as an
`int`, or `None` to automatically select the most recently
launched TensorBoard.
height: The height of the frame into ... | python | def display(port=None, height=None):
"""Display a TensorBoard instance already running on this machine.
Args:
port: The port on which the TensorBoard server is listening, as an
`int`, or `None` to automatically select the most recently
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tensorflow/tensorboard | tensorboard/notebook.py | _display | def _display(port=None, height=None, print_message=False, display_handle=None):
"""Internal version of `display`.
Args:
port: As with `display`.
height: As with `display`.
print_message: True to print which TensorBoard instance was selected
for display (if applicable), or False otherwise.
dis... | python | def _display(port=None, height=None, print_message=False, display_handle=None):
"""Internal version of `display`.
Args:
port: As with `display`.
height: As with `display`.
print_message: True to print which TensorBoard instance was selected
for display (if applicable), or False otherwise.
dis... | [
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tensorflow/tensorboard | tensorboard/notebook.py | _display_colab | def _display_colab(port, height, display_handle):
"""Display a TensorBoard instance in a Colab output frame.
The Colab VM is not directly exposed to the network, so the Colab
runtime provides a service worker tunnel to proxy requests from the
end user's browser through to servers running on the Colab VM: the
... | python | def _display_colab(port, height, display_handle):
"""Display a TensorBoard instance in a Colab output frame.
The Colab VM is not directly exposed to the network, so the Colab
runtime provides a service worker tunnel to proxy requests from the
end user's browser through to servers running on the Colab VM: the
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tensorflow/tensorboard | tensorboard/notebook.py | list | def list():
"""Print a listing of known running TensorBoard instances.
TensorBoard instances that were killed uncleanly (e.g., with SIGKILL
or SIGQUIT) may appear in this list even if they are no longer
running. Conversely, this list may be missing some entries if your
operating system's temporary directory ... | python | def list():
"""Print a listing of known running TensorBoard instances.
TensorBoard instances that were killed uncleanly (e.g., with SIGKILL
or SIGQUIT) may appear in this list even if they are no longer
running. Conversely, this list may be missing some entries if your
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tensorflow/tensorboard | tensorboard/backend/event_processing/io_wrapper.py | IsTensorFlowEventsFile | def IsTensorFlowEventsFile(path):
"""Check the path name to see if it is probably a TF Events file.
Args:
path: A file path to check if it is an event file.
Raises:
ValueError: If the path is an empty string.
Returns:
If path is formatted like a TensorFlowEventsFile.
"""
if not path:
rais... | python | def IsTensorFlowEventsFile(path):
"""Check the path name to see if it is probably a TF Events file.
Args:
path: A file path to check if it is an event file.
Raises:
ValueError: If the path is an empty string.
Returns:
If path is formatted like a TensorFlowEventsFile.
"""
if not path:
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