INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Helper that returns if parent/item is a directory. | def _IsDirectory(parent, item):
"""Helper that returns if parent/item is a directory."""
return tf.io.gfile.isdir(os.path.join(parent, item)) |
List all the plugins that have registered assets in logdir.
If the plugins_dir does not exist, it returns an empty list. This maintains
compatibility with old directories that have no plugins written.
Args:
logdir: A directory that was created by a TensorFlow events writer.
Returns:
a list of plugin ... | def ListPlugins(logdir):
"""List all the plugins that have registered assets in logdir.
If the plugins_dir does not exist, it returns an empty list. This maintains
compatibility with old directories that have no plugins written.
Args:
logdir: A directory that was created by a TensorFlow events writer.
... |
List all the assets that are available for given plugin in a logdir.
Args:
logdir: A directory that was created by a TensorFlow summary.FileWriter.
plugin_name: A string name of a plugin to list assets for.
Returns:
A string list of available plugin assets. If the plugin subdirectory does
not exis... | def ListAssets(logdir, plugin_name):
"""List all the assets that are available for given plugin in a logdir.
Args:
logdir: A directory that was created by a TensorFlow summary.FileWriter.
plugin_name: A string name of a plugin to list assets for.
Returns:
A string list of available plugin assets. If... |
Retrieve a particular plugin asset from a logdir.
Args:
logdir: A directory that was created by a TensorFlow summary.FileWriter.
plugin_name: The plugin we want an asset from.
asset_name: The name of the requested asset.
Returns:
string contents of the plugin asset.
Raises:
KeyError: if the... | def RetrieveAsset(logdir, plugin_name, asset_name):
"""Retrieve a particular plugin asset from a logdir.
Args:
logdir: A directory that was created by a TensorFlow summary.FileWriter.
plugin_name: The plugin we want an asset from.
asset_name: The name of the requested asset.
Returns:
string cont... |
Result of the form `(body, mime_type)`, or `ValueError`. | def distributions_impl(self, tag, run):
"""Result of the form `(body, mime_type)`, or `ValueError`."""
(histograms, mime_type) = self._histograms_plugin.histograms_impl(
tag, run, downsample_to=self.SAMPLE_SIZE)
return ([self._compress(histogram) for histogram in histograms],
mime_type) |
Given a tag and single run, return an array of compressed histograms. | def distributions_route(self, request):
"""Given a tag and single run, return an array of compressed histograms."""
tag = request.args.get('tag')
run = request.args.get('run')
try:
(body, mime_type) = self.distributions_impl(tag, run)
code = 200
except ValueError as e:
(body, mime_... |
Loads new values.
The watcher will load from one path at a time; as soon as that path stops
yielding events, it will move on to the next path. We assume that old paths
are never modified after a newer path has been written. As a result, Load()
can be called multiple times in a row without losing events... | def Load(self):
"""Loads new values.
The watcher will load from one path at a time; as soon as that path stops
yielding events, it will move on to the next path. We assume that old paths
are never modified after a newer path has been written. As a result, Load()
can be called multiple times in a ro... |
Internal implementation of Load().
The only difference between this and Load() is that the latter will throw
DirectoryDeletedError on I/O errors if it thinks that the directory has been
permanently deleted.
Yields:
All values that have not been yielded yet. | def _LoadInternal(self):
"""Internal implementation of Load().
The only difference between this and Load() is that the latter will throw
DirectoryDeletedError on I/O errors if it thinks that the directory has been
permanently deleted.
Yields:
All values that have not been yielded yet.
""... |
Sets the current path to watch for new events.
This also records the size of the old path, if any. If the size can't be
found, an error is logged.
Args:
path: The full path of the file to watch. | def _SetPath(self, path):
"""Sets the current path to watch for new events.
This also records the size of the old path, if any. If the size can't be
found, an error is logged.
Args:
path: The full path of the file to watch.
"""
old_path = self._path
if old_path and not io_wrapper.IsC... |
Gets the next path to load from.
This function also does the checking for out-of-order writes as it iterates
through the paths.
Returns:
The next path to load events from, or None if there are no more paths. | def _GetNextPath(self):
"""Gets the next path to load from.
This function also does the checking for out-of-order writes as it iterates
through the paths.
Returns:
The next path to load events from, or None if there are no more paths.
"""
paths = sorted(path
for path i... |
Returns whether the path has had an out-of-order write. | def _HasOOOWrite(self, path):
"""Returns whether the path has had an out-of-order write."""
# Check the sizes of each path before the current one.
size = tf.io.gfile.stat(path).length
old_size = self._finalized_sizes.get(path, None)
if size != old_size:
if old_size is None:
logger.erro... |
Returns a number of examples from the provided path.
Args:
path: A string path to the examples.
num_examples: The maximum number of examples to return from the path.
parse_examples: If true then parses the serialized proto from the path into
proto objects. Defaults to True.
sampling_odds: Odd... | def example_protos_from_path(path,
num_examples=10,
start_index=0,
parse_examples=True,
sampling_odds=1,
example_class=tf.train.Example):
"""Returns a number of examples fro... |
Convert `value` to a new-style value, if necessary and possible.
An "old-style" value is a value that uses any `value` field other than
the `tensor` field. A "new-style" value is a value that uses the
`tensor` field. TensorBoard continues to support old-style values on
disk; this method converts them to new-st... | def migrate_value(value):
"""Convert `value` to a new-style value, if necessary and possible.
An "old-style" value is a value that uses any `value` field other than
the `tensor` field. A "new-style" value is a value that uses the
`tensor` field. TensorBoard continues to support old-style values on
disk; this... |
Send an RPC request to the Servomatic prediction service.
Args:
examples: A list of examples that matches the model spec.
serving_bundle: A `ServingBundle` object that contains the information to
make the serving request.
Returns:
A ClassificationResponse or RegressionResponse proto. | def call_servo(examples, serving_bundle):
"""Send an RPC request to the Servomatic prediction service.
Args:
examples: A list of examples that matches the model spec.
serving_bundle: A `ServingBundle` object that contains the information to
make the serving request.
Returns:
A ClassificationRe... |
Obtains a mapping between routes and handlers. Stores the logdir.
Returns:
A mapping between routes and handlers (functions that respond to
requests). | def get_plugin_apps(self):
"""Obtains a mapping between routes and handlers. Stores the logdir.
Returns:
A mapping between routes and handlers (functions that respond to
requests).
"""
return {
'/infer': self._infer,
'/update_example': self._update_example,
'/example... |
Returns JSON of the specified examples.
Args:
request: A request that should contain 'examples_path' and 'max_examples'.
Returns:
JSON of up to max_examlpes of the examples in the path. | def _examples_from_path_handler(self, request):
"""Returns JSON of the specified examples.
Args:
request: A request that should contain 'examples_path' and 'max_examples'.
Returns:
JSON of up to max_examlpes of the examples in the path.
"""
examples_count = int(request.args.get('max_ex... |
Updates the specified example.
Args:
request: A request that should contain 'index' and 'example'.
Returns:
An empty response. | def _update_example(self, request):
"""Updates the specified example.
Args:
request: A request that should contain 'index' and 'example'.
Returns:
An empty response.
"""
if request.method != 'POST':
return http_util.Respond(request, {'error': 'invalid non-POST request'},
... |
Duplicates the specified example.
Args:
request: A request that should contain 'index'.
Returns:
An empty response. | def _duplicate_example(self, request):
"""Duplicates the specified example.
Args:
request: A request that should contain 'index'.
Returns:
An empty response.
"""
index = int(request.args.get('index'))
if index >= len(self.examples):
return http_util.Respond(request, {'error':... |
Deletes the specified example.
Args:
request: A request that should contain 'index'.
Returns:
An empty response. | def _delete_example(self, request):
"""Deletes the specified example.
Args:
request: A request that should contain 'index'.
Returns:
An empty response.
"""
index = int(request.args.get('index'))
if index >= len(self.examples):
return http_util.Respond(request, {'error': 'inva... |
Parses comma separated request arguments
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_version', 'model_signature'.
Returns:
A tuple of lists for model parameters | def _parse_request_arguments(self, request):
"""Parses comma separated request arguments
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_version', 'model_signature'.
Returns:
A tuple of lists for model parameters
"""
inference_addresses = ... |
Returns JSON for the `vz-line-chart`s for a feature.
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_type, 'model_version', 'model_signature' and 'label_vocab_path'.
Returns:
A list of JSON objects, one for each chart. | def _infer(self, request):
"""Returns JSON for the `vz-line-chart`s for a feature.
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_type, 'model_version', 'model_signature' and 'label_vocab_path'.
Returns:
A list of JSON objects, one for each chart... |
Returns a list of JSON objects for each feature in the example.
Args:
request: A request for features.
Returns:
A list with a JSON object for each feature.
Numeric features are represented as {name: observedMin: observedMax:}.
Categorical features are repesented as {name: samples:[]}. | def _eligible_features_from_example_handler(self, request):
"""Returns a list of JSON objects for each feature in the example.
Args:
request: A request for features.
Returns:
A list with a JSON object for each feature.
Numeric features are represented as {name: observedMin: observedMax:}... |
Returns JSON for the `vz-line-chart`s for a feature.
Args:
request: A request that should contain 'feature_name', 'example_index',
'inference_address', 'model_name', 'model_type', 'model_version', and
'model_signature'.
Returns:
A list of JSON objects, one for each chart. | def _infer_mutants_handler(self, request):
"""Returns JSON for the `vz-line-chart`s for a feature.
Args:
request: A request that should contain 'feature_name', 'example_index',
'inference_address', 'model_name', 'model_type', 'model_version', and
'model_signature'.
Returns:
A... |
Serves a pre-gzipped static asset from the zip file. | def _serve_asset(self, path, gzipped_asset_bytes, request):
"""Serves a pre-gzipped static asset from the zip file."""
mimetype = mimetypes.guess_type(path)[0] or 'application/octet-stream'
return http_util.Respond(
request, gzipped_asset_bytes, mimetype, content_encoding='gzip') |
Serve a JSON object containing some base properties used by the frontend.
* data_location is either a path to a directory or an address to a
database (depending on which mode TensorBoard is running in).
* window_title is the title of the TensorBoard web page. | def _serve_environment(self, request):
"""Serve a JSON object containing some base properties used by the frontend.
* data_location is either a path to a directory or an address to a
database (depending on which mode TensorBoard is running in).
* window_title is the title of the TensorBoard web page.... |
Serve a JSON array of run names, ordered by run started time.
Sort order is by started time (aka first event time) with empty times sorted
last, and then ties are broken by sorting on the run name. | def _serve_runs(self, request):
"""Serve a JSON array of run names, ordered by run started time.
Sort order is by started time (aka first event time) with empty times sorted
last, and then ties are broken by sorting on the run name.
"""
if self._db_connection_provider:
db = self._db_connectio... |
Serve a JSON array of experiments. Experiments are ordered by experiment
started time (aka first event time) with empty times sorted last, and then
ties are broken by sorting on the experiment name. | def _serve_experiments(self, request):
"""Serve a JSON array of experiments. Experiments are ordered by experiment
started time (aka first event time) with empty times sorted last, and then
ties are broken by sorting on the experiment name.
"""
results = self.list_experiments_impl()
return http_... |
Serve a JSON runs of an experiment, specified with query param
`experiment`, with their nested data, tag, populated. Runs returned are
ordered by started time (aka first event time) with empty times sorted last,
and then ties are broken by sorting on the run name. Tags are sorted by
its name, displayNam... | def _serve_experiment_runs(self, request):
"""Serve a JSON runs of an experiment, specified with query param
`experiment`, with their nested data, tag, populated. Runs returned are
ordered by started time (aka first event time) with empty times sorted last,
and then ties are broken by sorting on the run... |
Adds standard TensorBoard CLI flags to parser. | def define_flags(self, parser):
"""Adds standard TensorBoard CLI flags to parser."""
parser.add_argument(
'--logdir',
metavar='PATH',
type=str,
default='',
help='''\
Directory where TensorBoard will look to find TensorFlow event files
that it can display. TensorBoard will... |
Fixes standard TensorBoard CLI flags to parser. | def fix_flags(self, flags):
"""Fixes standard TensorBoard CLI flags to parser."""
FlagsError = base_plugin.FlagsError
if flags.version_tb:
pass
elif flags.inspect:
if flags.logdir and flags.event_file:
raise FlagsError(
'Must specify either --logdir or --event_file, but n... |
Put a message into the outgoing message stack.
Outgoing message will be stored indefinitely to support multi-users. | def put(self, message):
"""Put a message into the outgoing message stack.
Outgoing message will be stored indefinitely to support multi-users.
"""
with self._outgoing_lock:
self._outgoing.append(message)
self._outgoing_counter += 1
# Check to see if there are pending queues waiting f... |
Get message(s) from the outgoing message stack.
Blocks until an item at stack position pos becomes available.
This method is thread safe.
Args:
pos: An int specifying the top position of the message stack to access.
For example, if the stack counter is at 3 and pos == 2, then the 2nd
... | def get(self, pos):
"""Get message(s) from the outgoing message stack.
Blocks until an item at stack position pos becomes available.
This method is thread safe.
Args:
pos: An int specifying the top position of the message stack to access.
For example, if the stack counter is at 3 and p... |
Run custom scalar demo and generate event files. | def run():
"""Run custom scalar demo and generate event files."""
step = tf.compat.v1.placeholder(tf.float32, shape=[])
with tf.name_scope('loss'):
# Specify 2 different loss values, each tagged differently.
summary_lib.scalar('foo', tf.pow(0.9, step))
summary_lib.scalar('bar', tf.pow(0.85, step + 2)... |
Returns the plugin, if possible.
Args:
context: The TBContext flags.
Returns:
A InteractiveInferencePlugin instance or None if it couldn't be loaded. | def load(self, context):
"""Returns the plugin, if possible.
Args:
context: The TBContext flags.
Returns:
A InteractiveInferencePlugin instance or None if it couldn't be loaded.
"""
try:
# pylint: disable=g-import-not-at-top,unused-import
import tensorflow
except Import... |
Stores a config file used by the embedding projector.
Args:
summary_writer: The summary writer used for writing events.
config: `tf.contrib.tensorboard.plugins.projector.ProjectorConfig`
proto that holds the configuration for the projector such as paths to
checkpoint files and metadata files for ... | def visualize_embeddings(summary_writer, config):
"""Stores a config file used by the embedding projector.
Args:
summary_writer: The summary writer used for writing events.
config: `tf.contrib.tensorboard.plugins.projector.ProjectorConfig`
proto that holds the configuration for the projector such as ... |
Wraps absl.flags's define functions so tf.flags accepts old names. | def _wrap_define_function(original_function):
"""Wraps absl.flags's define functions so tf.flags accepts old names."""
def wrapper(*args, **kwargs):
"""Wrapper function that turns old keyword names to new ones."""
has_old_names = False
for old_name, new_name in _six.iteritems(_RENAMED_A... |
Returns a (run,tag) tuple storing the evaluations of the specified metric.
Args:
session_name: str.
metric_name: MetricName protobuffer.
Returns: (run, tag) tuple. | def run_tag_from_session_and_metric(session_name, metric_name):
"""Returns a (run,tag) tuple storing the evaluations of the specified metric.
Args:
session_name: str.
metric_name: MetricName protobuffer.
Returns: (run, tag) tuple.
"""
assert isinstance(session_name, six.string_types)
assert isinsta... |
Returns the last evaluations of the given metric at the given session.
Args:
multiplexer: The EventMultiplexer instance allowing access to
the exported summary data.
session_name: String. The session name for which to get the metric
evaluations.
metric_name: api_pb2.MetricName proto. The ... | def last_metric_eval(multiplexer, session_name, metric_name):
"""Returns the last evaluations of the given metric at the given session.
Args:
multiplexer: The EventMultiplexer instance allowing access to
the exported summary data.
session_name: String. The session name for which to get the metric
... |
Return {runName: {tagName: {displayName: ..., description: ...}}}. | def index_impl(self):
"""Return {runName: {tagName: {displayName: ..., description: ...}}}."""
if self._db_connection_provider:
# Read tags from the database.
db = self._db_connection_provider()
cursor = db.execute('''
SELECT
Tags.tag_name,
Tags.display_name,
... |
Result of the form `(body, mime_type)`. | def scalars_impl(self, tag, run, experiment, output_format):
"""Result of the form `(body, mime_type)`."""
if self._db_connection_provider:
db = self._db_connection_provider()
# We select for steps greater than -1 because the writer inserts
# placeholder rows en masse. The check for step filte... |
Obtains value for scalar event given blob and dtype enum.
Args:
scalar_data_blob: The blob obtained from the database.
dtype_enum: The enum representing the dtype.
Returns:
The scalar value. | def _get_value(self, scalar_data_blob, dtype_enum):
"""Obtains value for scalar event given blob and dtype enum.
Args:
scalar_data_blob: The blob obtained from the database.
dtype_enum: The enum representing the dtype.
Returns:
The scalar value.
"""
tensorflow_dtype = tf.DType(dt... |
Given a tag and single run, return array of ScalarEvents. | def scalars_route(self, request):
"""Given a tag and single run, return array of ScalarEvents."""
# TODO: return HTTP status code for malformed requests
tag = request.args.get('tag')
run = request.args.get('run')
experiment = request.args.get('experiment')
output_format = request.args.get('forma... |
Add a run to the multiplexer.
If the name is not specified, it is the same as the path.
If a run by that name exists, and we are already watching the right path,
do nothing. If we are watching a different path, replace the event
accumulator.
If `Reload` has been called, it will `Reload` the n... | def AddRun(self, path, name=None):
"""Add a run to the multiplexer.
If the name is not specified, it is the same as the path.
If a run by that name exists, and we are already watching the right path,
do nothing. If we are watching a different path, replace the event
accumulator.
If `Reloa... |
Load runs from a directory; recursively walks subdirectories.
If path doesn't exist, no-op. This ensures that it is safe to call
`AddRunsFromDirectory` multiple times, even before the directory is made.
If path is a directory, load event files in the directory (if any exist) and
recursively call A... | def AddRunsFromDirectory(self, path, name=None):
"""Load runs from a directory; recursively walks subdirectories.
If path doesn't exist, no-op. This ensures that it is safe to call
`AddRunsFromDirectory` multiple times, even before the directory is made.
If path is a directory, load event files in t... |
Call `Reload` on every `EventAccumulator`. | def Reload(self):
"""Call `Reload` on every `EventAccumulator`."""
logger.info('Beginning EventMultiplexer.Reload()')
self._reload_called = True
# Build a list so we're safe even if the list of accumulators is modified
# even while we're reloading.
with self._accumulators_mutex:
items = li... |
Get index of runs and assets for a given plugin.
Args:
plugin_name: Name of the plugin we are checking for.
Returns:
A dictionary that maps from run_name to a list of plugin
assets for that run. | def PluginAssets(self, plugin_name):
"""Get index of runs and assets for a given plugin.
Args:
plugin_name: Name of the plugin we are checking for.
Returns:
A dictionary that maps from run_name to a list of plugin
assets for that run.
"""
with self._accumulators_mutex:
# ... |
Return the contents for a specific plugin asset from a run.
Args:
run: The string name of the run.
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 not avail... | def RetrievePluginAsset(self, run, plugin_name, asset_name):
"""Return the contents for a specific plugin asset from a run.
Args:
run: The string name of the run.
plugin_name: The string name of a plugin.
asset_name: The string name of an asset.
Returns:
The string contents of the ... |
Retrieve the scalar events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found, or the tag is not available for
the given run.
R... | def Scalars(self, run, tag):
"""Retrieve the scalar events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found, or the tag is not availab... |
Get the session.run() metadata associated with a TensorFlow run and tag.
Args:
run: A string name of a TensorFlow run.
tag: A string name of the tag associated with a particular session.run().
Raises:
KeyError: If the run is not found, or the tag is not available for the
given run.
... | def RunMetadata(self, run, tag):
"""Get the session.run() metadata associated with a TensorFlow run and tag.
Args:
run: A string name of a TensorFlow run.
tag: A string name of the tag associated with a particular session.run().
Raises:
KeyError: If the run is not found, or the tag is no... |
Retrieve the audio events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found, or the tag is not available for
the given run.
Re... | def Audio(self, run, tag):
"""Retrieve the audio events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found, or the tag is not available ... |
Retrieve the tensor events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found, or the tag is not available for
the given run.
R... | def Tensors(self, run, tag):
"""Retrieve the tensor events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found, or the tag is not availab... |
Returns a 2-layer dictionary of the form {run: {tag: content}}.
The `content` referred above is the content field of the PluginData proto
for the specified plugin within a Summary.Value proto.
Args:
plugin_name: The name of the plugin for which to fetch content.
Returns:
A dictionary of t... | def PluginRunToTagToContent(self, plugin_name):
"""Returns a 2-layer dictionary of the form {run: {tag: content}}.
The `content` referred above is the content field of the PluginData proto
for the specified plugin within a Summary.Value proto.
Args:
plugin_name: The name of the plugin for which ... |
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... | 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... |
Return all the run names in the `EventMultiplexer`.
Returns:
```
{runName: { scalarValues: [tagA, tagB, tagC],
graph: true, meta_graph: true}}
``` | 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... |
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... | 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 ... |
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.... | 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`.
... |
Initializes flags and calls main(). | 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(),
... |
Create a `summary_pb2.SummaryMetadata` proto for image plugin data.
Returns:
A `summary_pb2.SummaryMetadata` protobuf object. | 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... |
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. ... | 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... |
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` ... | 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 ... |
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... | 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... |
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... | 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... |
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 ... | def streaming_op(name,
labels,
predictions,
num_thresholds=None,
weights=None,
metrics_collections=None,
updates_collections=None,
display_name=None,
description=None):
"""Computes a... |
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... | 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.
... |
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: ... | 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... |
Executes the request.
Returns:
An array of tuples representing the metric evaluations--each of the form
(<wall time in secs>, <training step>, <metric value>). | def run(self):
"""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... |
This plugin is active iff any run has at least one histograms tag. | def is_active(self):
"""This plugin is active iff any run has at least one histograms 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
... |
Result of the form `(body, mime_type)`, or `ValueError`.
At most `downsample_to` events will be returned. If this value is
`None`, then no downsampling will be performed. | def histograms_impl(self, tag, run, downsample_to=None):
"""Result of the form `(body, mime_type)`, or `ValueError`.
At most `downsample_to` events will be returned. If this value is
`None`, then no downsampling will be performed.
"""
if self._db_connection_provider:
# Serve data from the dat... |
Obtains values for histogram data given blob and dtype enum.
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.
Returns:
The histogram values as a list served to the frontend... | def _get_values(self, data_blob, dtype_enum, shape_string):
"""Obtains values for histogram data given blob and dtype enum.
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.
R... |
Given a tag and single run, return array of histogram values. | 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(
tag, run, downsample_to=self.SAMPLE_SIZE)
code = 200
except ValueE... |
Initialize the graph and session, if this has not yet been done. | 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()
... |
Tries to get the scalars plugin.
Returns:
The scalars plugin. Or None if it is not yet registered. | def _get_scalars_plugin(self):
"""Tries to get the scalars plugin.
Returns:
The scalars plugin. Or None if it is not yet registered.
"""
if scalars_metadata.PLUGIN_NAME in self._plugin_name_to_instance:
# The plugin is registered.
return self._plugin_name_to_instance[scalars_metadata.... |
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. | def is_active(self):
"""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.
"""
if not self._multiplexer:
return False
scalars_plugin_instance = s... |
Provides a response for downloading scalars data for a data series.
Args:
run: The run.
tag: The specific tag.
response_format: A string. One of the values of the OutputFormat enum of
the scalar plugin.
Raises:
ValueError: If the scalars plugin is not registered.
Returns:
... | def download_data_impl(self, run, tag, response_format):
"""Provides a response for downloading scalars data for a data series.
Args:
run: The run.
tag: The specific tag.
response_format: A string. One of the values of the OutputFormat enum of
the scalar plugin.
Raises:
Val... |
Given a tag regex and single run, return ScalarEvents.
This route takes 2 GET params:
run: A run string to find tags for.
tag: A string that is a regex used to find matching tags.
The response is a JSON object:
{
// Whether the regular expression is valid. Also false if empty.
regexVali... | def scalars_route(self, request):
"""Given a tag regex and single run, return ScalarEvents.
This route takes 2 GET params:
run: A run string to find tags for.
tag: A string that is a regex used to find matching tags.
The response is a JSON object:
{
// Whether the regular expression is va... |
Given a tag regex and single run, return ScalarEvents.
Args:
run: A run string.
tag_regex_string: A regular expression that captures portions of tags.
Raises:
ValueError: if the scalars plugin is not registered.
Returns:
A dictionary that is the JSON-able response. | def scalars_impl(self, run, tag_regex_string):
"""Given a tag regex and single run, return ScalarEvents.
Args:
run: A run string.
tag_regex_string: A regular expression that captures portions of tags.
Raises:
ValueError: if the scalars plugin is not registered.
Returns:
A dict... |
r"""Fetches the custom layout specified by the config file in the logdir.
If more than 1 run contains a layout, this method merges the layouts by
merging charts within individual categories. If 2 categories with the same
name are found, the charts within are merged. The merging is based on the
order of... | def layout_route(self, request):
r"""Fetches the custom layout specified by the config file in the logdir.
If more than 1 run contains a layout, this method merges the layouts by
merging charts within individual categories. If 2 categories with the same
name are found, the charts within are merged. The... |
Given an iterable of string contents, make a table row.
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.
Example: make_table_row(['one', 'two', 'three... | def make_table_row(contents, tag='td'):
"""Given an iterable of string contents, make a table row.
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.
... |
Given a numpy ndarray of strings, concatenate them into a html table.
Args:
contents: A np.ndarray of strings. May be 1d or 2d. In the 1d case, the
table is laid out vertically (i.e. row-major).
headers: A np.ndarray or list of string header names for the table.
Returns:
A string containing all ... | def make_table(contents, headers=None):
"""Given a numpy ndarray of strings, concatenate them into a html table.
Args:
contents: A np.ndarray of strings. May be 1d or 2d. In the 1d case, the
table is laid out vertically (i.e. row-major).
headers: A np.ndarray or list of string header names for the ta... |
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:
ValueError: If the argument is not a ... | 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... |
Take a numpy.ndarray containing strings, and convert it into html.
If the ndarray contains a single scalar string, that string is converted to
html via our sanitized markdown parser. If it contains an array of strings,
the strings are individually converted to html and then composed into a table
using make_tab... | def text_array_to_html(text_arr):
"""Take a numpy.ndarray containing strings, and convert it into html.
If the ndarray contains a single scalar string, that string is converted to
html via our sanitized markdown parser. If it contains an array of strings,
the strings are individually converted to html and then... |
Convert a TensorEvent into a JSON-compatible response. | 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 {
'wall_time': event.wall_time,
'step': event.step,
'text': html,
} |
Determines whether this plugin is active.
This plugin is only active if TensorBoard sampled any text summaries.
Returns:
Whether this plugin is active. | 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 ... |
Attempts to launch a thread to compute index_impl().
This may not launch a new thread if one is already running to compute
index_impl(); in that case, this function is a no-op. | 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
index_impl(); in that case, this function is a no-op.
"""
# Try to acquire the lock for computing index_impl(), without blocking.
... |
Computes index_impl() asynchronously on a separate thread. | 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
self._index_impl_lock.release()
elapsed = t... |
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:
A `summary_pb2.SummaryMetadata` protob... | def create_summary_metadata(display_name, description, num_thresholds):
"""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... |
Parse summary metadata to a Python object.
Arguments:
content: The `content` field of a `SummaryMetadata` proto
corresponding to the pr_curves plugin.
Returns:
A `PrCurvesPlugin` protobuf object. | def parse_plugin_metadata(content):
"""Parse summary metadata to a Python object.
Arguments:
content: The `content` field of a `SummaryMetadata` proto
corresponding to the pr_curves plugin.
Returns:
A `PrCurvesPlugin` protobuf object.
"""
if not isinstance(content, bytes):
raise TypeError(... |
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. | def get_field_to_observations_map(generator, query_for_tag=''):
"""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 m... |
Returns a dictionary of tags that a user could query over.
Args:
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
the event files. If the dict does not have any observations of the type,
maps to an e... | def get_unique_tags(field_to_obs):
"""Returns a dictionary of tags that a user could query over.
Args:
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
the event files. If the dict does not have any ob... |
Prints a shallow dict to console.
Args:
d: Dict to print.
show_missing: Whether to show keys with empty values. | 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()):
if (not v) and show_missing:
# No instances of the key, so print missing symbol.
print('{} -'.forma... |
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. | 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.
"""
def compressed_steps(steps):
return {'num_steps': len(... |
Returns elements that break the monotonically non-decreasing trend.
This is used to find instances of global step values that are "out-of-order",
which may trigger TensorBoard event discarding logic.
Args:
list_of_numbers: A list of numbers.
Returns:
A list of tuples in which each tuple are two eleme... | def get_out_of_order(list_of_numbers):
"""Returns elements that break the monotonically non-decreasing trend.
This is used to find instances of global step values that are "out-of-order",
which may trigger TensorBoard event discarding logic.
Args:
list_of_numbers: A list of numbers.
Returns:
A list... |
Returns a list of event generators for subdirectories with event files.
The number of generators returned should equal the number of directories
within logdir that contain event files. If only logdir contains event files,
returns a list of length one.
Args:
logdir: A log directory that contains event file... | def generators_from_logdir(logdir):
"""Returns a list of event generators for subdirectories with event files.
The number of generators returned should equal the number of directories
within logdir that contain event files. If only logdir contains event files,
returns a list of length one.
Args:
logdir:... |
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... | 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 ... |
Main function for inspector that prints out a digest of event files.
Args:
logdir: A log directory that contains event files.
event_file: Or, a particular event file path.
tag: An optional tag name to query for.
Raises:
ValueError: If neither logdir and event_file are given, or both are given. | def inspect(logdir='', event_file='', tag=''):
"""Main function for inspector that prints out a digest of event files.
Args:
logdir: A log directory that contains event files.
event_file: Or, a particular event file path.
tag: An optional tag name to query for.
Raises:
ValueError: If neither log... |
Adds DebuggerPlugin CLI flags to parser. | def define_flags(self, parser):
"""Adds DebuggerPlugin CLI flags to parser."""
group = parser.add_argument_group('debugger plugin')
group.add_argument(
'--debugger_data_server_grpc_port',
metavar='PORT',
type=int,
default=-1,
help='''\
The port at which the non-intera... |
Returns the debugger plugin, if possible.
Args:
context: The TBContext flags including `add_arguments`.
Returns:
A DebuggerPlugin instance or None if it couldn't be loaded. | def load(self, context):
"""Returns the debugger plugin, if possible.
Args:
context: The TBContext flags including `add_arguments`.
Returns:
A DebuggerPlugin instance or None if it couldn't be loaded.
"""
if not (context.flags.debugger_data_server_grpc_port > 0 or
context.f... |
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
PLUGIN_DATA_VERSION.
Args:
hparams_plugin_data_pb: the HParamsPlug... | def create_summary_metadata(hparams_plugin_data_pb):
"""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
PLUGIN_DATA_VER... |
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... | def _parse_plugin_data_as(content, data_oneof_field):
"""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.conten... |
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. | 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... |
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