INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Grab all the values in task_instance that are found in task_cls. | def common_params(task_instance, task_cls):
"""
Grab all the values in task_instance that are found in task_cls.
"""
if not isinstance(task_cls, task.Register):
raise TypeError("task_cls must be an uninstantiated Task")
task_instance_param_names = dict(task_instance.get_params()).keys()
... |
Lets a task call methods on subtask ( s ). | def delegates(task_that_delegates):
""" Lets a task call methods on subtask(s).
The way this works is that the subtask is run as a part of the task, but
the task itself doesn't have to care about the requirements of the subtasks.
The subtask doesn't exist from the scheduler's point of view, and
its... |
Return a previous Task of the same family. | def previous(task):
"""
Return a previous Task of the same family.
By default checks if this task family only has one non-global parameter and if
it is a DateParameter, DateHourParameter or DateIntervalParameter in which case
it returns with the time decremented by 1 (hour, day or interval)
"""... |
Given that we want one of the hadoop cli clients ( unlike snakebite ) this one will return the right one. | def create_hadoopcli_client():
"""
Given that we want one of the hadoop cli clients (unlike snakebite),
this one will return the right one.
"""
version = hdfs_config.get_configured_hadoop_version()
if version == "cdh4":
return HdfsClient()
elif version == "cdh3":
return HdfsC... |
Use hadoop fs - stat to check file existence. | def exists(self, path):
"""
Use ``hadoop fs -stat`` to check file existence.
"""
cmd = load_hadoop_cmd() + ['fs', '-stat', path]
logger.debug('Running file existence check: %s', subprocess.list2cmdline(cmd))
p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subpro... |
No explicit - p switch this version of Hadoop always creates parent directories. | def mkdir(self, path, parents=True, raise_if_exists=False):
"""
No explicit -p switch, this version of Hadoop always creates parent directories.
"""
try:
self.call_check(load_hadoop_cmd() + ['fs', '-mkdir', path])
except hdfs_error.HDFSCliError as ex:
if "... |
Runs the hive from the command line passing in the given args and returning stdout. | def run_hive(args, check_return_code=True):
"""
Runs the `hive` from the command line, passing in the given args, and
returning stdout.
With the apache release of Hive, so of the table existence checks
(which are done using DESCRIBE do not exit with a return code of 0
so we need an option to ig... |
Runs the contents of the given script in hive and returns stdout. | def run_hive_script(script):
"""
Runs the contents of the given script in hive and returns stdout.
"""
if not os.path.isfile(script):
raise RuntimeError("Hive script: {0} does not exist.".format(script))
return run_hive(['-f', script]) |
Returns a dict of key = value settings to be passed along to the hive command line via -- hiveconf. By default sets mapred. job. name to task_id and if not None sets: | def hiveconfs(self):
"""
Returns a dict of key=value settings to be passed along
to the hive command line via --hiveconf. By default, sets
mapred.job.name to task_id and if not None, sets:
* mapred.reduce.tasks (n_reduce_tasks)
* mapred.fairscheduler.pool (pool) or mapre... |
Called before job is started. | def prepare_outputs(self, job):
"""
Called before job is started.
If output is a `FileSystemTarget`, create parent directories so the hive command won't fail
"""
outputs = flatten(job.output())
for o in outputs:
if isinstance(o, FileSystemTarget):
... |
Returns the path to this table in HDFS. | def path(self):
"""
Returns the path to this table in HDFS.
"""
location = self.client.table_location(self.table, self.database)
if not location:
raise Exception("Couldn't find location for table: {0}".format(str(self)))
return location |
Mark this update as complete. | def touch(self):
"""
Mark this update as complete.
We index the parameters `update_id` and `date`.
"""
marker_key = self.marker_key()
self.redis_client.hset(marker_key, 'update_id', self.update_id)
self.redis_client.hset(marker_key, 'date', datetime.datetime.now(... |
Meant to be used as a context manager. | def global_instance(cls, cmdline_args, allow_override=False):
"""
Meant to be used as a context manager.
"""
orig_value = cls._instance
assert (orig_value is None) or allow_override
new_value = None
try:
new_value = CmdlineParser(cmdline_args)
... |
Get the local task arguments as a dictionary. The return value is in the form dict ( my_param = my_value... ) | def _get_task_kwargs(self):
"""
Get the local task arguments as a dictionary. The return value is in
the form ``dict(my_param='my_value', ...)``
"""
res = {}
for (param_name, param_obj) in self._get_task_cls().get_params():
attr = getattr(self.known_args, para... |
Check if the user passed -- help [ - all ] if so print a message and exit. | def _possibly_exit_with_help(parser, known_args):
"""
Check if the user passed --help[-all], if so, print a message and exit.
"""
if known_args.core_help or known_args.core_help_all:
parser.print_help()
sys.exit() |
Compute path given current file and relative path. | def relpath(self, current_file, rel_path):
"""
Compute path given current file and relative path.
"""
script_dir = os.path.dirname(os.path.abspath(current_file))
rel_path = os.path.abspath(os.path.join(script_dir, rel_path))
return rel_path |
Returns an array of args to pass to the job. | def args(self):
"""
Returns an array of args to pass to the job.
"""
arglist = []
for k, v in six.iteritems(self.requires_hadoop()):
arglist.append('--' + k)
arglist.extend([t.output().path for t in flatten(v)])
arglist.extend(['--output', self.out... |
Adds an event to the event file. | def add_event(self, event):
"""Adds an event to the event file.
Args:
event: An `Event` protocol buffer.
"""
if not isinstance(event, event_pb2.Event):
raise TypeError("Expected an event_pb2.Event proto, "
" but got %s" % type(event))
... |
Enqueue the given bytes to be written asychronously | def write(self, bytestring):
'''Enqueue the given bytes to be written asychronously'''
with self._lock:
if self._closed:
raise IOError('Writer is closed')
self._byte_queue.put(bytestring) |
Write all the enqueued bytestring before this flush call to disk. Block until all the above bytestring are written. | def flush(self):
'''Write all the enqueued bytestring before this flush call to disk.
Block until all the above bytestring are written.
'''
with self._lock:
if self._closed:
raise IOError('Writer is closed')
self._byte_queue.join()
self... |
Closes the underlying writer flushing any pending writes first. | def close(self):
'''Closes the underlying writer, flushing any pending writes first.'''
if not self._closed:
with self._lock:
if not self._closed:
self._closed = True
self._worker.stop()
self._writer.flush()
... |
Extract device name from a tf. Event proto carrying tensor value. | def _extract_device_name_from_event(event):
"""Extract device name from a tf.Event proto carrying tensor value."""
plugin_data_content = json.loads(
tf.compat.as_str(event.summary.value[0].metadata.plugin_data.content))
return plugin_data_content['device'] |
Create a dict () as the outgoing data in the tensor data comm route. | def _comm_tensor_data(device_name,
node_name,
maybe_base_expanded_node_name,
output_slot,
debug_op,
tensor_value,
wall_time):
"""Create a dict() as the outgoing data in the tensor data c... |
Add a GraphDef. | def add_graph(self, run_key, device_name, graph_def, debug=False):
"""Add a GraphDef.
Args:
run_key: A key for the run, containing information about the feeds,
fetches, and targets.
device_name: The name of the device that the `GraphDef` is for.
graph_def: An instance of the `GraphDef... |
Get the runtime GraphDef protos associated with a run key. | def get_graphs(self, run_key, debug=False):
"""Get the runtime GraphDef protos associated with a run key.
Args:
run_key: A Session.run kay.
debug: Whether the debugger-decoratedgraph is to be retrieved.
Returns:
A `dict` mapping device name to `GraphDef` protos.
"""
graph_dict = ... |
Get the runtime GraphDef proto associated with a run key and a device. | def get_graph(self, run_key, device_name, debug=False):
"""Get the runtime GraphDef proto associated with a run key and a device.
Args:
run_key: A Session.run kay.
device_name: Name of the device in question.
debug: Whether the debugger-decoratedgraph is to be retrieved.
Returns:
A... |
Obtain possibly base - expanded node name. | def get_maybe_base_expanded_node_name(self, node_name, run_key, device_name):
"""Obtain possibly base-expanded node name.
Base-expansion is the transformation of a node name which happens to be the
name scope of other nodes in the same graph. For example, if two nodes,
called 'a/b' and 'a/b/read' in a ... |
Implementation of the core metadata - carrying Event proto callback. | def on_core_metadata_event(self, event):
"""Implementation of the core metadata-carrying Event proto callback.
Args:
event: An Event proto that contains core metadata about the debugged
Session::Run() in its log_message.message field, as a JSON string.
See the doc string of debug_data.Deb... |
Implementation of the GraphDef - carrying Event proto callback. | def on_graph_def(self, graph_def, device_name, wall_time):
"""Implementation of the GraphDef-carrying Event proto callback.
Args:
graph_def: A GraphDef proto. N.B.: The GraphDef is from
the core runtime of a debugged Session::Run() call, after graph
partition. Therefore it may differ from... |
Records the summary values based on an updated message from the debugger. | def on_value_event(self, event):
"""Records the summary values based on an updated message from the debugger.
Logs an error message if writing the event to disk fails.
Args:
event: The Event proto to be processed.
"""
if not event.summary.value:
logger.info('The summary of the event la... |
Add a DebuggedSourceFile proto. | def add_debugged_source_file(self, debugged_source_file):
"""Add a DebuggedSourceFile proto."""
# TODO(cais): Should the key include a host name, for certain distributed
# cases?
key = debugged_source_file.file_path
self._source_file_host[key] = debugged_source_file.host
self._source_file_last... |
Get the traceback of an op in the latest version of the TF graph. | def get_op_traceback(self, op_name):
"""Get the traceback of an op in the latest version of the TF graph.
Args:
op_name: Name of the op.
Returns:
Creation traceback of the op, in the form of a list of 2-tuples:
(file_path, lineno)
Raises:
ValueError: If the op with the given... |
Get the lists of ops created at lines of a specified source file. | def get_file_tracebacks(self, file_path):
"""Get the lists of ops created at lines of a specified source file.
Args:
file_path: Path to the source file.
Returns:
A dict mapping line number to a list of 2-tuples,
`(op_name, stack_position)`
`op_name` is the name of the name of the... |
Query tensor store for a given debugged tensor value. | def query_tensor_store(self,
watch_key,
time_indices=None,
slicing=None,
mapping=None):
"""Query tensor store for a given debugged tensor value.
Args:
watch_key: The watch key of the debugged tensor being ... |
Construct a werkzeug Response. | def Respond(request,
content,
content_type,
code=200,
expires=0,
content_encoding=None,
encoding='utf-8'):
"""Construct a werkzeug Response.
Responses are transmitted to the browser with compression if: a) the browser
supports it; b) it's sa... |
Finds the longest parent - path of path in path_set. | def _find_longest_parent_path(path_set, path):
"""Finds the longest "parent-path" of 'path' in 'path_set'.
This function takes and returns "path-like" strings which are strings
made of strings separated by os.sep. No file access is performed here, so
these strings need not correspond to actual files in some fi... |
Returns the type of the google. protobuf. Value message as an api. DataType. | def _protobuf_value_type(value):
"""Returns the type of the google.protobuf.Value message as an api.DataType.
Returns None if the type of 'value' is not one of the types supported in
api_pb2.DataType.
Args:
value: google.protobuf.Value message.
"""
if value.HasField("number_value"):
return api_pb2... |
Returns a string representation of given google. protobuf. Value message. | def _protobuf_value_to_string(value):
"""Returns a string representation of given google.protobuf.Value message.
Args:
value: google.protobuf.Value message. Assumed to be of type 'number',
'string' or 'bool'.
"""
value_in_json = json_format.MessageToJson(value)
if value.HasField("string_value"):
... |
Finds the experiment associcated with the metadata. EXPERIMENT_TAG tag. | def _find_experiment_tag(self):
"""Finds the experiment associcated with the metadata.EXPERIMENT_TAG tag.
Caches the experiment if it was found.
Returns:
The experiment or None if no such experiment is found.
"""
with self._experiment_from_tag_lock:
if self._experiment_from_tag is None... |
Computes a minimal Experiment protocol buffer by scanning the runs. | def _compute_experiment_from_runs(self):
"""Computes a minimal Experiment protocol buffer by scanning the runs."""
hparam_infos = self._compute_hparam_infos()
if not hparam_infos:
return None
metric_infos = self._compute_metric_infos()
return api_pb2.Experiment(hparam_infos=hparam_infos,
... |
Computes a list of api_pb2. HParamInfo from the current run tag info. | def _compute_hparam_infos(self):
"""Computes a list of api_pb2.HParamInfo from the current run, tag info.
Finds all the SessionStartInfo messages and collects the hparams values
appearing in each one. For each hparam attempts to deduce a type that fits
all its values. Finally, sets the 'domain' of the ... |
Builds an HParamInfo message from the hparam name and list of values. | def _compute_hparam_info_from_values(self, name, values):
"""Builds an HParamInfo message from the hparam name and list of values.
Args:
name: string. The hparam name.
values: list of google.protobuf.Value messages. The list of values for the
hparam.
Returns:
An api_pb2.HParamInf... |
Computes the list of metric names from all the scalar ( run tag ) pairs. | def _compute_metric_names(self):
"""Computes the list of metric names from all the scalar (run, tag) pairs.
The return value is a list of (tag, group) pairs representing the metric
names. The list is sorted in Python tuple-order (lexicographical).
For example, if the scalar (run, tag) pairs are:
(... |
Handles the request specified on construction. | def run(self):
"""Handles the request specified on construction.
Returns:
An Experiment object.
"""
experiment = self._context.experiment()
if experiment is None:
raise error.HParamsError(
"Can't find an HParams-plugin experiment data in"
" the log directory. Note t... |
Creates a summary that defines a hyperparameter - tuning experiment. | def experiment_pb(
hparam_infos,
metric_infos,
user='',
description='',
time_created_secs=None):
"""Creates a summary that defines a hyperparameter-tuning experiment.
Args:
hparam_infos: Array of api_pb2.HParamInfo messages. Describes the
hyperparameters used in the experiment.
... |
Constructs a SessionStartInfo protobuffer. | def session_start_pb(hparams,
model_uri='',
monitor_url='',
group_name='',
start_time_secs=None):
"""Constructs a SessionStartInfo protobuffer.
Creates a summary that contains a training session metadata information.
One such sum... |
Constructs a SessionEndInfo protobuffer. | def session_end_pb(status, end_time_secs=None):
"""Constructs a SessionEndInfo protobuffer.
Creates a summary that contains status information for a completed
training session. Should be exported after the training session is completed.
One such summary per training session should be created. Each should have
... |
Returns a summary holding the given HParamsPluginData message. | def _summary(tag, hparams_plugin_data):
"""Returns a summary holding the given HParamsPluginData message.
Helper function.
Args:
tag: string. The tag to use.
hparams_plugin_data: The HParamsPluginData message to use.
"""
summary = tf.compat.v1.Summary()
summary.value.add(
tag=tag,
meta... |
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. | 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. | 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. | 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. | 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 (). | 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. | 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. | 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. | 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... |
Send an RPC request to the Servomatic prediction service. | 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... |
Convert value to a new - style value if necessary and possible. | 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... |
Obtains a mapping between routes and handlers. Stores the logdir. | 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. | 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. | 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. | 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. | 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 | 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. | 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. | 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. | 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. | 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. | 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 displayName and lastly inserted ... | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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 }}. | 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 ... |
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