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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | EventMultiplexer.SummaryMetadata | Return the summary metadata for the given tag on the given run.
Args:
run: A string name of the run for which summary metadata is to be
retrieved.
tag: A string name of the tag whose summary metadata is to be
retrieved.
Raises:
KeyError: If the run is not found, or the tag is... | tensorboard/backend/event_processing/plugin_event_multiplexer.py | def SummaryMetadata(self, run, tag):
"""Return the summary metadata for the given tag on the given run.
Args:
run: A string name of the run for which summary metadata is to be
retrieved.
tag: A string name of the tag whose summary metadata is to be
retrieved.
Raises:
KeyE... | def SummaryMetadata(self, run, tag):
"""Return the summary metadata for the given tag on the given run.
Args:
run: A string name of the run for which summary metadata is to be
retrieved.
tag: A string name of the tag whose summary metadata is to be
retrieved.
Raises:
KeyE... | [
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train | EventMultiplexer.Runs | Return all the run names in the `EventMultiplexer`.
Returns:
```
{runName: { scalarValues: [tagA, tagB, tagC],
graph: true, meta_graph: true}}
``` | tensorboard/backend/event_processing/plugin_event_multiplexer.py | def Runs(self):
"""Return all the run names in the `EventMultiplexer`.
Returns:
```
{runName: { scalarValues: [tagA, tagB, tagC],
graph: true, meta_graph: true}}
```
"""
with self._accumulators_mutex:
# To avoid nested locks, we construct a copy of the run-accumula... | def Runs(self):
"""Return all the run names in the `EventMultiplexer`.
Returns:
```
{runName: { scalarValues: [tagA, tagB, tagC],
graph: true, meta_graph: true}}
```
"""
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# To avoid nested locks, we construct a copy of the run-accumula... | [
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train | text | Write a text summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
data: A UTF-8 string tensor value.
step: Explicit `int64`-castable monotonic step value for this summary. If
omitted, this defaults to `tf... | tensorboard/plugins/text/summary_v2.py | def text(name, data, step=None, description=None):
"""Write a text summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
data: A UTF-8 string tensor value.
step: Explicit `int64`-castable monotonic step value ... | def text(name, data, step=None, description=None):
"""Write a text summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
data: A UTF-8 string tensor value.
step: Explicit `int64`-castable monotonic step value ... | [
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train | text_pb | Create a text tf.Summary protobuf.
Arguments:
tag: String tag for the summary.
data: A Python bytestring (of type bytes), a Unicode string, or a numpy data
array of those types.
description: Optional long-form description for this summary, as a `str`.
Markdown is supported. Defaults to empty.... | tensorboard/plugins/text/summary_v2.py | def text_pb(tag, data, description=None):
"""Create a text tf.Summary protobuf.
Arguments:
tag: String tag for the summary.
data: A Python bytestring (of type bytes), a Unicode string, or a numpy data
array of those types.
description: Optional long-form description for this summary, as a `str`.
... | def text_pb(tag, data, description=None):
"""Create a text tf.Summary protobuf.
Arguments:
tag: String tag for the summary.
data: A Python bytestring (of type bytes), a Unicode string, or a numpy data
array of those types.
description: Optional long-form description for this summary, as a `str`.
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train | run_main | Initializes flags and calls main(). | tensorboard/main.py | def run_main():
"""Initializes flags and calls main()."""
program.setup_environment()
if getattr(tf, '__version__', 'stub') == 'stub':
print("TensorFlow installation not found - running with reduced feature set.",
file=sys.stderr)
tensorboard = program.TensorBoard(default.get_plugins(),
... | def run_main():
"""Initializes flags and calls main()."""
program.setup_environment()
if getattr(tf, '__version__', 'stub') == 'stub':
print("TensorFlow installation not found - running with reduced feature set.",
file=sys.stderr)
tensorboard = program.TensorBoard(default.get_plugins(),
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","... | 8e5f497b48e40f2a774f85416b8a35ac0693c35e |
train | create_summary_metadata | Create a `summary_pb2.SummaryMetadata` proto for image plugin data.
Returns:
A `summary_pb2.SummaryMetadata` protobuf object. | tensorboard/plugins/image/metadata.py | def create_summary_metadata(display_name, description):
"""Create a `summary_pb2.SummaryMetadata` proto for image plugin data.
Returns:
A `summary_pb2.SummaryMetadata` protobuf object.
"""
content = plugin_data_pb2.ImagePluginData(version=PROTO_VERSION)
metadata = summary_pb2.SummaryMetadata(
displ... | def create_summary_metadata(display_name, description):
"""Create a `summary_pb2.SummaryMetadata` proto for image plugin data.
Returns:
A `summary_pb2.SummaryMetadata` protobuf object.
"""
content = plugin_data_pb2.ImagePluginData(version=PROTO_VERSION)
metadata = summary_pb2.SummaryMetadata(
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train | op | Create a legacy audio summary op for use in a TensorFlow graph.
Arguments:
name: A unique name for the generated summary node.
audio: A `Tensor` representing audio data with shape `[k, t, c]`,
where `k` is the number of audio clips, `t` is the number of
frames, and `c` is the number of channels. ... | tensorboard/plugins/audio/summary.py | def op(name,
audio,
sample_rate,
labels=None,
max_outputs=3,
encoding=None,
display_name=None,
description=None,
collections=None):
"""Create a legacy audio summary op for use in a TensorFlow graph.
Arguments:
name: A unique name for the generated summary... | def op(name,
audio,
sample_rate,
labels=None,
max_outputs=3,
encoding=None,
display_name=None,
description=None,
collections=None):
"""Create a legacy audio summary op for use in a TensorFlow graph.
Arguments:
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train | pb | Create a legacy audio summary protobuf.
This behaves as if you were to create an `op` with the same arguments
(wrapped with constant tensors where appropriate) and then execute
that summary op in a TensorFlow session.
Arguments:
name: A unique name for the generated summary node.
audio: An `np.array` ... | tensorboard/plugins/audio/summary.py | def pb(name,
audio,
sample_rate,
labels=None,
max_outputs=3,
encoding=None,
display_name=None,
description=None):
"""Create a legacy audio summary protobuf.
This behaves as if you were to create an `op` with the same arguments
(wrapped with constant tensors where ... | def pb(name,
audio,
sample_rate,
labels=None,
max_outputs=3,
encoding=None,
display_name=None,
description=None):
"""Create a legacy audio summary protobuf.
This behaves as if you were to create an `op` with the same arguments
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train | op | Create a PR curve summary op for a single binary classifier.
Computes true/false positive/negative values for the given `predictions`
against the ground truth `labels`, against a list of evenly distributed
threshold values in `[0, 1]` of length `num_thresholds`.
Each number in `predictions`, a float in `[0, 1... | tensorboard/plugins/pr_curve/summary.py | def op(
name,
labels,
predictions,
num_thresholds=None,
weights=None,
display_name=None,
description=None,
collections=None):
"""Create a PR curve summary op for a single binary classifier.
Computes true/false positive/negative values for the given `predictions`
against the ground... | def op(
name,
labels,
predictions,
num_thresholds=None,
weights=None,
display_name=None,
description=None,
collections=None):
"""Create a PR curve summary op for a single binary classifier.
Computes true/false positive/negative values for the given `predictions`
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] | tensorflow/tensorboard | python | https://github.com/tensorflow/tensorboard/blob/8e5f497b48e40f2a774f85416b8a35ac0693c35e/tensorboard/plugins/pr_curve/summary.py#L37-L172 | [
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train | pb | Create a PR curves summary protobuf.
Arguments:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
labels: The ground truth values. A bool numpy array.
predictions: A float32 numpy array whose values are in the range `[0, 1]`.
Dimensions must match those... | tensorboard/plugins/pr_curve/summary.py | def pb(name,
labels,
predictions,
num_thresholds=None,
weights=None,
display_name=None,
description=None):
"""Create a PR curves summary protobuf.
Arguments:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
labels: The g... | def pb(name,
labels,
predictions,
num_thresholds=None,
weights=None,
display_name=None,
description=None):
"""Create a PR curves summary protobuf.
Arguments:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
labels: The g... | [
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train | streaming_op | Computes a precision-recall curve summary across batches of data.
This function is similar to op() above, but can be used to compute the PR
curve across multiple batches of labels and predictions, in the same style
as the metrics found in tf.metrics.
This function creates multiple local variables for storing ... | tensorboard/plugins/pr_curve/summary.py | def streaming_op(name,
labels,
predictions,
num_thresholds=None,
weights=None,
metrics_collections=None,
updates_collections=None,
display_name=None,
description=None):
"""Computes a... | def streaming_op(name,
labels,
predictions,
num_thresholds=None,
weights=None,
metrics_collections=None,
updates_collections=None,
display_name=None,
description=None):
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train | raw_data_op | Create an op that collects data for visualizing PR curves.
Unlike the op above, this one avoids computing precision, recall, and the
intermediate counts. Instead, it accepts those tensors as arguments and
relies on the caller to ensure that the calculations are correct (and the
counts yield the provided precis... | tensorboard/plugins/pr_curve/summary.py | def raw_data_op(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None,
collections=None):
"""Create an op that collects data for visualizing PR curves.
... | def raw_data_op(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None,
collections=None):
"""Create an op that collects data for visualizing PR curves.
... | [
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train | raw_data_pb | Create a PR curves summary protobuf from raw data values.
Args:
name: A tag attached to the summary. Used by TensorBoard for organization.
true_positive_counts: A rank-1 numpy array of true positive counts. Must
contain `num_thresholds` elements and be castable to float32.
false_positive_counts: ... | tensorboard/plugins/pr_curve/summary.py | def raw_data_pb(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None):
"""Create a PR curves summary protobuf from raw data values.
Args:
name: A tag a... | def raw_data_pb(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None):
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train | _create_tensor_summary | A private helper method for generating a tensor summary.
We use a helper method instead of having `op` directly call `raw_data_op`
to prevent the scope of `raw_data_op` from being embedded within `op`.
Arguments are the same as for raw_data_op.
Returns:
A tensor summary that collects data for PR curves. | tensorboard/plugins/pr_curve/summary.py | def _create_tensor_summary(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None,
collections=None):
"""A private helper method for generating a tensor sum... | def _create_tensor_summary(
name,
true_positive_counts,
false_positive_counts,
true_negative_counts,
false_negative_counts,
precision,
recall,
num_thresholds=None,
display_name=None,
description=None,
collections=None):
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train | Handler.run | Executes the request.
Returns:
An array of tuples representing the metric evaluations--each of the form
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"""Executes the request.
Returns:
An array of tuples representing the metric evaluations--each of the form
(<wall time in secs>, <training step>, <metric value>).
"""
run, tag = metrics.run_tag_from_session_and_metric(
self._request.session_name, self._request.metri... | def run(self):
"""Executes the request.
Returns:
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run, tag = metrics.run_tag_from_session_and_metric(
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train | HistogramsPlugin.is_active | This plugin is active iff any run has at least one histograms tag. | tensorboard/plugins/histogram/histograms_plugin.py | def is_active(self):
"""This plugin is active iff any run has at least one histograms tag."""
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train | HistogramsPlugin.histograms_impl | Result of the form `(body, mime_type)`, or `ValueError`.
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train | HistogramsPlugin._get_values | Obtains values for histogram data given blob and dtype enum.
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dtype_enum: The enum representing the dtype.
shape_string: A comma-separated string of numbers denoting shape.
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Args:
data_blob: The blob obtained from the database.
dtype_enum: The enum representing the dtype.
shape_string: A comma-separated string of numbers denoting shape.
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data_blob: The blob obtained from the database.
dtype_enum: The enum representing the dtype.
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train | HistogramsPlugin.histograms_route | Given a tag and single run, return array of histogram values. | tensorboard/plugins/histogram/histograms_plugin.py | def histograms_route(self, request):
"""Given a tag and single run, return array of histogram values."""
tag = request.args.get('tag')
run = request.args.get('run')
try:
(body, mime_type) = self.histograms_impl(
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code = 200
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train | PersistentOpEvaluator._lazily_initialize | Initialize the graph and session, if this has not yet been done. | tensorboard/util/op_evaluator.py | def _lazily_initialize(self):
"""Initialize the graph and session, if this has not yet been done."""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
with self._initialization_lock:
if self._session:
return
graph = tf.Graph()
... | def _lazily_initialize(self):
"""Initialize the graph and session, if this has not yet been done."""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
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if self._session:
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train | CustomScalarsPlugin._get_scalars_plugin | Tries to get the scalars plugin.
Returns:
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"""Tries to get the scalars plugin.
Returns:
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"""
if scalars_metadata.PLUGIN_NAME in self._plugin_name_to_instance:
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train | CustomScalarsPlugin.is_active | This plugin is active if 2 conditions hold.
1. The scalars plugin is registered and active.
2. There is a custom layout for the dashboard.
Returns: A boolean. Whether the plugin is active. | tensorboard/plugins/custom_scalar/custom_scalars_plugin.py | def is_active(self):
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2. There is a custom layout for the dashboard.
Returns: A boolean. Whether the plugin is active.
"""
if not self._multiplexer:
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train | CustomScalarsPlugin.download_data_impl | Provides a response for downloading scalars data for a data series.
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run: The run.
tag: The specific tag.
response_format: A string. One of the values of the OutputFormat enum of
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Raises:
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Returns:
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Args:
run: The run.
tag: The specific tag.
response_format: A string. One of the values of the OutputFormat enum of
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Raises:
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run: The run.
tag: The specific tag.
response_format: A string. One of the values of the OutputFormat enum of
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train | CustomScalarsPlugin.scalars_route | Given a tag regex and single run, return ScalarEvents.
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run: A run string to find tags for.
tag: A string that is a regex used to find matching tags.
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run: A run string to find tags for.
tag: A string that is a regex used to find matching tags.
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tag: A string that is a regex used to find matching tags.
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tag_regex_string: A regular expression that captures portions of tags.
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run: A run string.
tag_regex_string: A regular expression that captures portions of tags.
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train | CustomScalarsPlugin.layout_route | r"""Fetches the custom layout specified by the config file in the logdir.
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train | make_table_row | Given an iterable of string contents, make a table row.
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tag: The tag to place contents in. Defaults to 'td', you might want 'th'.
Returns:
A string containing the content strings, organized into a table row.
Example: make_table_row(['one', 'two', 'three... | tensorboard/plugins/text/text_plugin.py | def make_table_row(contents, tag='td'):
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Args:
contents: An iterable yielding strings.
tag: The tag to place contents in. Defaults to 'td', you might want 'th'.
Returns:
A string containing the content strings, organized into a table row.
... | def make_table_row(contents, tag='td'):
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contents: An iterable yielding strings.
tag: The tag to place contents in. Defaults to 'td', you might want 'th'.
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train | make_table | Given a numpy ndarray of strings, concatenate them into a html table.
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contents: A np.ndarray of strings. May be 1d or 2d. In the 1d case, the
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headers: A np.ndarray or list of string header names for the table.
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contents: A np.ndarray of strings. May be 1d or 2d. In the 1d case, the
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train | reduce_to_2d | Given a np.npdarray with nDims > 2, reduce it to 2d.
It does this by selecting the zeroth coordinate for every dimension greater
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Args:
arr: a numpy ndarray of dimension at least 2.
Returns:
A two-dimensional subarray from the input array.
Raises:
ValueError: If the argument is not a ... | tensorboard/plugins/text/text_plugin.py | def reduce_to_2d(arr):
"""Given a np.npdarray with nDims > 2, reduce it to 2d.
It does this by selecting the zeroth coordinate for every dimension greater
than two.
Args:
arr: a numpy ndarray of dimension at least 2.
Returns:
A two-dimensional subarray from the input array.
Raises:
ValueErro... | def reduce_to_2d(arr):
"""Given a np.npdarray with nDims > 2, reduce it to 2d.
It does this by selecting the zeroth coordinate for every dimension greater
than two.
Args:
arr: a numpy ndarray of dimension at least 2.
Returns:
A two-dimensional subarray from the input array.
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train | text_array_to_html | Take a numpy.ndarray containing strings, and convert it into html.
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train | process_string_tensor_event | Convert a TensorEvent into a JSON-compatible response. | tensorboard/plugins/text/text_plugin.py | def process_string_tensor_event(event):
"""Convert a TensorEvent into a JSON-compatible response."""
string_arr = tensor_util.make_ndarray(event.tensor_proto)
html = text_array_to_html(string_arr)
return {
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'text': html,
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"""Convert a TensorEvent into a JSON-compatible response."""
string_arr = tensor_util.make_ndarray(event.tensor_proto)
html = text_array_to_html(string_arr)
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train | TextPlugin.is_active | Determines whether this plugin is active.
This plugin is only active if TensorBoard sampled any text summaries.
Returns:
Whether this plugin is active. | tensorboard/plugins/text/text_plugin.py | def is_active(self):
"""Determines whether this plugin is active.
This plugin is only active if TensorBoard sampled any text summaries.
Returns:
Whether this plugin is active.
"""
if not self._multiplexer:
return False
if self._index_cached is not None:
# If we already have ... | def is_active(self):
"""Determines whether this plugin is active.
This plugin is only active if TensorBoard sampled any text summaries.
Returns:
Whether this plugin is active.
"""
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train | TextPlugin._maybe_launch_index_impl_thread | Attempts to launch a thread to compute index_impl().
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index_impl(); in that case, this function is a no-op. | tensorboard/plugins/text/text_plugin.py | def _maybe_launch_index_impl_thread(self):
"""Attempts to launch a thread to compute index_impl().
This may not launch a new thread if one is already running to compute
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"""Attempts to launch a thread to compute index_impl().
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train | TextPlugin._async_index_impl | Computes index_impl() asynchronously on a separate thread. | tensorboard/plugins/text/text_plugin.py | def _async_index_impl(self):
"""Computes index_impl() asynchronously on a separate thread."""
start = time.time()
logger.info('TextPlugin computing index_impl() in a new thread')
self._index_cached = self.index_impl()
self._index_impl_thread = None
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elapsed = t... | def _async_index_impl(self):
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train | create_summary_metadata | Create a `summary_pb2.SummaryMetadata` proto for pr_curves plugin data.
Arguments:
display_name: The display name used in TensorBoard.
description: The description to show in TensorBoard.
num_thresholds: The number of thresholds to use for PR curves.
Returns:
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"""Create a `summary_pb2.SummaryMetadata` proto for pr_curves plugin data.
Arguments:
display_name: The display name used in TensorBoard.
description: The description to show in TensorBoard.
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"""Create a `summary_pb2.SummaryMetadata` proto for pr_curves plugin data.
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display_name: The display name used in TensorBoard.
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train | parse_plugin_metadata | Parse summary metadata to a Python object.
Arguments:
content: The `content` field of a `SummaryMetadata` proto
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Returns:
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"""Parse summary metadata to a Python object.
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content: The `content` field of a `SummaryMetadata` proto
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Returns:
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"""Parse summary metadata to a Python object.
Arguments:
content: The `content` field of a `SummaryMetadata` proto
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Returns:
A `PrCurvesPlugin` protobuf object.
"""
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train | get_field_to_observations_map | Return a field to `Observations` dict for the event generator.
Args:
generator: A generator over event protos.
query_for_tag: A string that if specified, only create observations for
events with this tag name.
Returns:
A dict mapping keys in `TRACKED_FIELDS` to an `Observation` list. | tensorboard/backend/event_processing/event_file_inspector.py | def get_field_to_observations_map(generator, query_for_tag=''):
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Args:
generator: A generator over event protos.
query_for_tag: A string that if specified, only create observations for
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Returns:
A dict m... | def get_field_to_observations_map(generator, query_for_tag=''):
"""Return a field to `Observations` dict for the event generator.
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generator: A generator over event protos.
query_for_tag: A string that if specified, only create observations for
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train | get_unique_tags | Returns a dictionary of tags that a user could query over.
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field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict that maps keys in `TAG_FIELDS` to a list of string tags present in
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field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict that maps keys in `TAG_FIELDS` to a list of string tags present in
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field_to_obs: Dict that maps string field to `Observation` list.
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train | print_dict | Prints a shallow dict to console.
Args:
d: Dict to print.
show_missing: Whether to show keys with empty values. | tensorboard/backend/event_processing/event_file_inspector.py | def print_dict(d, show_missing=True):
"""Prints a shallow dict to console.
Args:
d: Dict to print.
show_missing: Whether to show keys with empty values.
"""
for k, v in sorted(d.items()):
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print('{} -'.forma... | def print_dict(d, show_missing=True):
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Args:
d: Dict to print.
show_missing: Whether to show keys with empty values.
"""
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train | get_dict_to_print | Transform the field-to-obs mapping into a printable dictionary.
Args:
field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict with the keys and values to print to console. | tensorboard/backend/event_processing/event_file_inspector.py | def get_dict_to_print(field_to_obs):
"""Transform the field-to-obs mapping into a printable dictionary.
Args:
field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict with the keys and values to print to console.
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field_to_obs: Dict that maps string field to `Observation` list.
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A dict with the keys and values to print to console.
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train | get_out_of_order | Returns elements that break the monotonically non-decreasing trend.
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Args:
list_of_numbers: A list of numbers.
Returns:
A list of tuples in which each tuple are two eleme... | tensorboard/backend/event_processing/event_file_inspector.py | def get_out_of_order(list_of_numbers):
"""Returns elements that break the monotonically non-decreasing trend.
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which may trigger TensorBoard event discarding logic.
Args:
list_of_numbers: A list of numbers.
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A list... | def get_out_of_order(list_of_numbers):
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train | generators_from_logdir | Returns a list of event generators for subdirectories with event files.
The number of generators returned should equal the number of directories
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returns a list of length one.
Args:
logdir: A log directory that contains event file... | tensorboard/backend/event_processing/event_file_inspector.py | def generators_from_logdir(logdir):
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train | get_inspection_units | Returns a list of InspectionUnit objects given either logdir or event_file.
If logdir is given, the number of InspectionUnits should equal the
number of directories or subdirectories that contain event files.
If event_file is given, the number of InspectionUnits should be 1.
Args:
logdir: A log directory... | tensorboard/backend/event_processing/event_file_inspector.py | def get_inspection_units(logdir='', event_file='', tag=''):
"""Returns a list of InspectionUnit objects given either logdir or event_file.
If logdir is given, the number of InspectionUnits should equal the
number of directories or subdirectories that contain event files.
If event_file is given, the number of ... | def get_inspection_units(logdir='', event_file='', tag=''):
"""Returns a list of InspectionUnit objects given either logdir or event_file.
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train | inspect | Main function for inspector that prints out a digest of event files.
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event_file: Or, a particular event file path.
tag: An optional tag name to query for.
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train | DebuggerPluginLoader.define_flags | Adds DebuggerPlugin CLI flags to parser. | tensorboard/plugins/debugger/debugger_plugin_loader.py | def define_flags(self, parser):
"""Adds DebuggerPlugin CLI flags to parser."""
group = parser.add_argument_group('debugger plugin')
group.add_argument(
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"""Adds DebuggerPlugin CLI flags to parser."""
group = parser.add_argument_group('debugger plugin')
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train | DebuggerPluginLoader.load | Returns the debugger plugin, if possible.
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context: The TBContext flags including `add_arguments`.
Returns:
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train | create_summary_metadata | Returns a summary metadata for the HParams plugin.
Returns a summary_pb2.SummaryMetadata holding a copy of the given
HParamsPluginData message in its plugin_data.content field.
Sets the version field of the hparams_plugin_data_pb copy to
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Args:
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train | _parse_plugin_data_as | Returns a data oneof's field from plugin_data.content.
Raises HParamsError if the content doesn't have 'data_oneof_field' set or
this file is incompatible with the version of the metadata stored.
Args:
content: The SummaryMetadata.plugin_data.content to use.
data_oneof_field: string. The name of the dat... | tensorboard/plugins/hparams/metadata.py | def _parse_plugin_data_as(content, data_oneof_field):
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Raises HParamsError if the content doesn't have 'data_oneof_field' set or
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Args:
content: The SummaryMetadata.plugin_data.conten... | def _parse_plugin_data_as(content, data_oneof_field):
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train | EventsWriterManager.write_event | Writes an event proto to disk.
This method is threadsafe with respect to invocations of itself.
Args:
event: The event proto.
Raises:
IOError: If writing the event proto to disk fails. | tensorboard/plugins/debugger/events_writer_manager.py | def write_event(self, event):
"""Writes an event proto to disk.
This method is threadsafe with respect to invocations of itself.
Args:
event: The event proto.
Raises:
IOError: If writing the event proto to disk fails.
"""
self._lock.acquire()
try:
self._events_writer.Wri... | def write_event(self, event):
"""Writes an event proto to disk.
This method is threadsafe with respect to invocations of itself.
Args:
event: The event proto.
Raises:
IOError: If writing the event proto to disk fails.
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self._lock.acquire()
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train | EventsWriterManager.dispose | Disposes of this events writer manager, making it no longer usable.
Call this method when this object is done being used in order to clean up
resources and handlers. This method should ever only be called once. | tensorboard/plugins/debugger/events_writer_manager.py | def dispose(self):
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"""
self._lock.acquire()
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self._lock.acquire()
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train | EventsWriterManager._create_events_writer | Creates a new events writer.
Args:
directory: The directory in which to write files containing events.
Returns:
A new events writer, which corresponds to a new events file. | tensorboard/plugins/debugger/events_writer_manager.py | def _create_events_writer(self, directory):
"""Creates a new events writer.
Args:
directory: The directory in which to write files containing events.
Returns:
A new events writer, which corresponds to a new events file.
"""
total_size = 0
events_files = self._fetch_events_files_on_... | def _create_events_writer(self, directory):
"""Creates a new events writer.
Args:
directory: The directory in which to write files containing events.
Returns:
A new events writer, which corresponds to a new events file.
"""
total_size = 0
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train | EventsWriterManager._fetch_events_files_on_disk | Obtains the names of debugger-related events files within the directory.
Returns:
The names of the debugger-related events files written to disk. The names
are sorted in increasing events file index. | tensorboard/plugins/debugger/events_writer_manager.py | def _fetch_events_files_on_disk(self):
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Returns:
The names of the debugger-related events files written to disk. The names
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"""
all_files = tf.io.gfile.listdir(self._events_... | def _fetch_events_files_on_disk(self):
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The names of the debugger-related events files written to disk. The names
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train | reexport_tf_summary | Re-export all symbols from the original tf.summary.
This function finds the original tf.summary V2 API and re-exports all the
symbols from it within this module as well, so that when this module is
patched into the TF API namespace as the new tf.summary, the effect is an
overlay that just adds TensorBoard-prov... | tensorboard/summary/_tf/summary/__init__.py | def reexport_tf_summary():
"""Re-export all symbols from the original tf.summary.
This function finds the original tf.summary V2 API and re-exports all the
symbols from it within this module as well, so that when this module is
patched into the TF API namespace as the new tf.summary, the effect is an
overlay... | def reexport_tf_summary():
"""Re-export all symbols from the original tf.summary.
This function finds the original tf.summary V2 API and re-exports all the
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train | bench | Encode `image` to PNG on `thread_count` threads in parallel.
Returns:
A `float` representing number of seconds that it takes all threads
to finish encoding `image`. | tensorboard/encode_png_benchmark.py | def bench(image, thread_count):
"""Encode `image` to PNG on `thread_count` threads in parallel.
Returns:
A `float` representing number of seconds that it takes all threads
to finish encoding `image`.
"""
threads = [threading.Thread(target=lambda: encoder.encode_png(image))
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A `float` representing number of seconds that it takes all threads
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train | _image_of_size | Generate a square RGB test image of the given side length. | tensorboard/encode_png_benchmark.py | def _image_of_size(image_size):
"""Generate a square RGB test image of the given side length."""
return np.random.uniform(0, 256, [image_size, image_size, 3]).astype(np.uint8) | def _image_of_size(image_size):
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train | _format_line | Format a line of a table.
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headers: A list of strings that are used as the table headers.
fields: A list of the same length as `headers` where `fields[i]` is
the entry for `headers[i]` in this row. Elements can be of
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Returns:
... | tensorboard/encode_png_benchmark.py | def _format_line(headers, fields):
"""Format a line of a table.
Arguments:
headers: A list of strings that are used as the table headers.
fields: A list of the same length as `headers` where `fields[i]` is
the entry for `headers[i]` in this row. Elements can be of
arbitrary types. Pass `headers... | def _format_line(headers, fields):
"""Format a line of a table.
Arguments:
headers: A list of strings that are used as the table headers.
fields: A list of the same length as `headers` where `fields[i]` is
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train | DebugGraphWrapper.get_gated_grpc_tensors | Extract all nodes with gated-gRPC debug ops attached.
Uses cached values if available.
This method is thread-safe.
Args:
graph_def: A tf.GraphDef proto.
matching_debug_op: Return tensors and nodes with only matching the
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... | tensorboard/plugins/debugger/debug_graphs_helper.py | def get_gated_grpc_tensors(self, matching_debug_op=None):
"""Extract all nodes with gated-gRPC debug ops attached.
Uses cached values if available.
This method is thread-safe.
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graph_def: A tf.GraphDef proto.
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train | DbImportMultiplexer.Reload | Load events from every detected run. | tensorboard/backend/event_processing/db_import_multiplexer.py | def Reload(self):
"""Load events from every detected run."""
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train | _RunLoader.load_batches | Returns a batched event iterator over the run directory event files. | tensorboard/backend/event_processing/db_import_multiplexer.py | def load_batches(self):
"""Returns a batched event iterator over the run directory event files."""
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event_bytes = 0
start = time.time()
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train | _SqliteWriterEventSink._process_event | Processes a single tf.Event and records it in tagged_data. | tensorboard/backend/event_processing/db_import_multiplexer.py | def _process_event(self, event, tagged_data):
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train | _buckets | Create a TensorFlow op to group data into histogram buckets.
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bucket_count: Optional positive `int` or scalar `int32` `Tensor`.
Returns:
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a triple `[left_edge, ri... | tensorboard/plugins/histogram/summary.py | def _buckets(data, bucket_count=None):
"""Create a TensorFlow op to group data into histogram buckets.
Arguments:
data: A `Tensor` of any shape. Must be castable to `float64`.
bucket_count: Optional positive `int` or scalar `int32` `Tensor`.
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A `Tensor` of shape `[k, 3]` and type `float64`. T... | def _buckets(data, bucket_count=None):
"""Create a TensorFlow op to group data into histogram buckets.
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data: A `Tensor` of any shape. Must be castable to `float64`.
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train | op | Create a legacy histogram summary op.
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data: A `Tensor` of any shape. Must be castable to `float64`.
bucket_count: Optional positive `int`. The output will have this
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description=None,
collections=None):
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Arguments:
name: A unique name for the generated summary node.
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display_name=None,
description=None,
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"""Create a legacy histogram summary op.
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name: A unique name for the generated summary node.
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train | pb | Create a legacy histogram summary protobuf.
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train | _WatchStore.add | Add a tensor the watch store. | tensorboard/plugins/debugger/tensor_store.py | def add(self, value):
"""Add a tensor the watch store."""
if self._disposed:
raise ValueError(
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self._data.append(value)
if hasattr(value, 'nbytes'):
self._in_mem_bytes += value.nbytes
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train | _WatchStore.num_in_memory | Get number of values in memory. | tensorboard/plugins/debugger/tensor_store.py | def num_in_memory(self):
"""Get number of values in memory."""
n = len(self._data) - 1
while n >= 0:
if isinstance(self._data[n], _TensorValueDiscarded):
break
n -= 1
return len(self._data) - 1 - n | def num_in_memory(self):
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train | _WatchStore.num_discarded | Get the number of values discarded due to exceeding both limits. | tensorboard/plugins/debugger/tensor_store.py | def num_discarded(self):
"""Get the number of values discarded due to exceeding both limits."""
if not self._data:
return 0
n = 0
while n < len(self._data):
if not isinstance(self._data[n], _TensorValueDiscarded):
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"""Get the number of values discarded due to exceeding both limits."""
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train | _WatchStore.query | Query the values at given time indices.
Args:
time_indices: 0-based time indices to query, as a `list` of `int`.
Returns:
Values as a list of `numpy.ndarray` (for time indices in memory) or
`None` (for time indices discarded). | tensorboard/plugins/debugger/tensor_store.py | def query(self, time_indices):
"""Query the values at given time indices.
Args:
time_indices: 0-based time indices to query, as a `list` of `int`.
Returns:
Values as a list of `numpy.ndarray` (for time indices in memory) or
`None` (for time indices discarded).
"""
if self._dispos... | def query(self, time_indices):
"""Query the values at given time indices.
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time_indices: 0-based time indices to query, as a `list` of `int`.
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train | TensorStore.add | Add a tensor value.
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watch_key: A string representing the debugger tensor watch, e.g.,
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tensor_value: The value of the tensor as a numpy.ndarray. | tensorboard/plugins/debugger/tensor_store.py | def add(self, watch_key, tensor_value):
"""Add a tensor value.
Args:
watch_key: A string representing the debugger tensor watch, e.g.,
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tensor_value: The value of the tensor as a numpy.ndarray.
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watch_key: A string representing the debugger tensor watch, e.g.,
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watch_key: The watch key to query.
time_indices: A numpy-style slicing string for time indices. E.g.,
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slicing: A numpy-style slicing string for individual time steps.
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watch_key: The watch key to query.
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watch_key: The watch key to query.
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train | DebuggerPlugin.listen | Start listening on the given gRPC port.
This method of an instance of DebuggerPlugin can be invoked at most once.
This method is not thread safe.
Args:
grpc_port: port number to listen at.
Raises:
ValueError: If this instance is already listening at a gRPC port. | tensorboard/plugins/debugger/debugger_plugin.py | def listen(self, grpc_port):
"""Start listening on the given gRPC port.
This method of an instance of DebuggerPlugin can be invoked at most once.
This method is not thread safe.
Args:
grpc_port: port number to listen at.
Raises:
ValueError: If this instance is already listening at a g... | def listen(self, grpc_port):
"""Start listening on the given gRPC port.
This method of an instance of DebuggerPlugin can be invoked at most once.
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Args:
grpc_port: port number to listen at.
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train | DebuggerPlugin.is_active | Determines whether this plugin is active.
This plugin is active if any health pills information is present for any
run.
Returns:
A boolean. Whether this plugin is active. | tensorboard/plugins/debugger/debugger_plugin.py | def is_active(self):
"""Determines whether this plugin is active.
This plugin is active if any health pills information is present for any
run.
Returns:
A boolean. Whether this plugin is active.
"""
return bool(
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This plugin is active if any health pills information is present for any
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Returns:
A boolean. Whether this plugin is active.
"""
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train | DebuggerPlugin._serve_health_pills_handler | A (wrapped) werkzeug handler for serving health pills.
Accepts POST requests and responds with health pills. The request accepts
several POST parameters:
node_names: (required string) A JSON-ified list of node names for which
the client would like to request health pills.
run: (optional ... | tensorboard/plugins/debugger/debugger_plugin.py | def _serve_health_pills_handler(self, request):
"""A (wrapped) werkzeug handler for serving health pills.
Accepts POST requests and responds with health pills. The request accepts
several POST parameters:
node_names: (required string) A JSON-ified list of node names for which
the client wo... | def _serve_health_pills_handler(self, request):
"""A (wrapped) werkzeug handler for serving health pills.
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train | DebuggerPlugin._obtain_sampled_health_pills | Obtains the health pills for a run sampled by the event multiplexer.
This is much faster than the alternative path of reading health pills from
disk.
Args:
run: The run to fetch health pills for.
node_names: A list of node names for which to retrieve health pills.
Returns:
A diction... | tensorboard/plugins/debugger/debugger_plugin.py | def _obtain_sampled_health_pills(self, run, node_names):
"""Obtains the health pills for a run sampled by the event multiplexer.
This is much faster than the alternative path of reading health pills from
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Args:
run: The run to fetch health pills for.
node_names: A list of node names f... | def _obtain_sampled_health_pills(self, run, node_names):
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This is much faster than the alternative path of reading health pills from
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train | DebuggerPlugin._tensor_proto_to_health_pill | Converts an event_accumulator.TensorEvent to a HealthPillEvent.
Args:
tensor_event: The event_accumulator.TensorEvent to convert.
node_name: The name of the node (without the output slot).
device: The device.
output_slot: The integer output slot this health pill is relevant to.
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output_slot):
"""Converts an event_accumulator.TensorEvent to a HealthPillEvent.
Args:
tensor_event: The event_accumulator.TensorEvent to convert.
node_name: The name of the node (without the ... | def _tensor_proto_to_health_pill(self, tensor_event, node_name, device,
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train | DebuggerPlugin._obtain_health_pills_at_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 complete this call for large graphs for big step values (in the
thousands).
... | tensorboard/plugins/debugger/debugger_plugin.py | 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... | def _obtain_health_pills_at_step(self, events_directory, node_names, step):
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train | DebuggerPlugin._process_health_pill_event | 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.
mapping: The mapping from node name to HealthPillEvents.
This object may be destructively modified.
target_s... | tensorboard/plugins/debugger/debugger_plugin.py | 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.
... | 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.
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train | DebuggerPlugin._process_health_pill_value | Creates a HealthPillEvent containing various properties of a health pill.
Args:
wall_time: The wall time in seconds.
step: The session run step of the event.
device_name: The name of the node's device.
output_slot: The numeric output slot.
node_name: The name of the node (without the ... | tensorboard/plugins/debugger/debugger_plugin.py | def _process_health_pill_value(self,
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device_name,
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node_name,
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train | DebuggerPlugin._serve_numerics_alert_report_handler | 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:
{
'device_name': string,
'tensor_name': string,
'first_t... | tensorboard/plugins/debugger/debugger_plugin.py | 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... | 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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train | _info_to_string | 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 correct type.
Returns:
A... | tensorboard/manager.py | 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... | 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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info: A valid `TensorBoardInfo` object.
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train | _info_from_string | Parse a `TensorBoardInfo` object from its string representation.
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Returns:
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Args:
info_string: A string representation of a `TensorBoardInfo`, as
produced by a previous call to `_info_to_string`.
Returns:
A `TensorBoardInfo` value.
Raises:
ValueError: If the prov... | def _info_from_string(info_string):
"""Parse a `TensorBoardInfo` object from its string representation.
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info_string: A string representation of a `TensorBoardInfo`, as
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train | cache_key | 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 TensorBoard was launched
and relative to which paths like `--... | tensorboard/manager.py | def cache_key(working_directory, arguments, configure_kwargs):
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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... | 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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train | _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 it does not exist. | tensorboard/manager.py | def _get_info_dir():
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The directory returned by this function is "owned" by this module. If
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train | write_info_file | 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: If any field on `info` is not of the corre... | tensorboard/manager.py | 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... | 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.
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train | 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. | tensorboard/manager.py | def remove_info_file():
"""Remove the current process's TensorBoardInfo file, if it exists.
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"""
try:
os.unlink(_get_info_file_path())
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if e.errno == errno.ENOENT:
# The user may have wiped their temporary... | def remove_info_file():
"""Remove the current process's TensorBoardInfo file, if it exists.
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train | process_raw_trace | Processes raw trace data and returns the UI data. | tensorboard/plugins/profile/profile_plugin.py | def process_raw_trace(raw_trace):
"""Processes raw trace data and returns the UI data."""
trace = trace_events_pb2.Trace()
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return ''.join(trace_events_json.TraceEventsJsonStream(trace)) | def process_raw_trace(raw_trace):
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Returns:
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Returns:
Whether any run has profile data.
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"""Whether this plugin is active and has any profile data to show.
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train | ProfilePlugin.host_impl | Returns available hosts for the run and tool in the log directory.
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train | ProfilePlugin.data_impl | Retrieves and processes the tool data for a run and a host.
Args:
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Returns:
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request: XMLHttpRequest
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train | run | Run a temperature simulation.
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The object has some intrinsic `heat_coefficient`, which indicates
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thermal ... | tensorboard/plugins/scalar/scalars_demo.py | def run(logdir, run_name,
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"""Run a temperature simulation.
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sitting at rest in a large room at temperature `ambient_temperature`.
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train | run_all | Run simulations on a reasonable set of parameters.
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logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins | tensorboard/plugins/scalar/scalars_demo.py | def run_all(logdir, verbose=False):
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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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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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train | Cleanse | Makes Python object appropriate for JSON serialization.
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- Turns byte strings into unicode strings.
- Turns sets into sorted lists.
- Turns tuples into lists.
Args:
obj: Python data structure.
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... | tensorboard/backend/json_util.py | def Cleanse(obj, encoding='utf-8'):
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- Turns byte strings into unicode strings.
- Turns sets into sorted lists.
- Turns tuples into lists.
Args:
obj: Python data structure.
encodi... | def Cleanse(obj, encoding='utf-8'):
"""Makes Python object appropriate for JSON serialization.
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- Turns tuples into lists.
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train | op | 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 will automatically organize 1D and 2D tensors into tables.
If a tensor with more than 2 dimensions is prov... | tensorboard/plugins/text/summary.py | def op(name,
data,
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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 ... | 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 ... | [
"Create",
"a",
"legacy",
"text",
"summary",
"op",
"."
] | tensorflow/tensorboard | python | https://github.com/tensorflow/tensorboard/blob/8e5f497b48e40f2a774f85416b8a35ac0693c35e/tensorboard/plugins/text/summary.py#L30-L76 | [
"def",
"op",
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"name",
",",
"data",
",",
"display_name",
"=",
"None",
",",
"description",
"=",
"None",
",",
"collections",
"=",
"None",
")",
":",
"# TODO(nickfelt): remove on-demand imports once dep situation is fixed.",
"import",
"tensorflow",
".",
"compat",
"."... | 8e5f497b48e40f2a774f85416b8a35ac0693c35e |
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