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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | iter_coords | Returns all the coordinate tuples from a geometry or feature. | folium/utilities.py | def iter_coords(obj):
"""
Returns all the coordinate tuples from a geometry or feature.
"""
if isinstance(obj, (tuple, list)):
coords = obj
elif 'features' in obj:
coords = [geom['geometry']['coordinates'] for geom in obj['features']]
elif 'geometry' in obj:
coords = obj... | def iter_coords(obj):
"""
Returns all the coordinate tuples from a geometry or feature.
"""
if isinstance(obj, (tuple, list)):
coords = obj
elif 'features' in obj:
coords = [geom['geometry']['coordinates'] for geom in obj['features']]
elif 'geometry' in obj:
coords = obj... | [
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train | _locations_mirror | Mirrors the points in a list-of-list-of-...-of-list-of-points.
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>>> _locations_mirror([[[1, 2], [3, 4]], [5, 6], [7, 8]])
[[[2, 1], [4, 3]], [6, 5], [8, 7]] | folium/utilities.py | def _locations_mirror(x):
"""
Mirrors the points in a list-of-list-of-...-of-list-of-points.
For example:
>>> _locations_mirror([[[1, 2], [3, 4]], [5, 6], [7, 8]])
[[[2, 1], [4, 3]], [6, 5], [8, 7]]
"""
if hasattr(x, '__iter__'):
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"""
Mirrors the points in a list-of-list-of-...-of-list-of-points.
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>>> _locations_mirror([[[1, 2], [3, 4]], [5, 6], [7, 8]])
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train | get_bounds | Computes the bounds of the object in the form
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"""
Computes the bounds of the object in the form
[[lat_min, lon_min], [lat_max, lon_max]]
"""
bounds = [[None, None], [None, None]]
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"""
Computes the bounds of the object in the form
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bounds = [[None, None], [None, None]]
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train | camelize | Convert a python_style_variable_name to lowerCamelCase.
Examples
--------
>>> camelize('variable_name')
'variableName'
>>> camelize('variableName')
'variableName' | folium/utilities.py | def camelize(key):
"""Convert a python_style_variable_name to lowerCamelCase.
Examples
--------
>>> camelize('variable_name')
'variableName'
>>> camelize('variableName')
'variableName'
"""
return ''.join(x.capitalize() if i > 0 else x
for i, x in enumerate(key.spl... | def camelize(key):
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Examples
--------
>>> camelize('variable_name')
'variableName'
>>> camelize('variableName')
'variableName'
"""
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train | normalize | Return the input string without non-functional spaces or newlines. | folium/utilities.py | def normalize(rendered):
"""Return the input string without non-functional spaces or newlines."""
out = ''.join([line.strip()
for line in rendered.splitlines()
if line.strip()])
out = out.replace(', ', ',')
return out | def normalize(rendered):
"""Return the input string without non-functional spaces or newlines."""
out = ''.join([line.strip()
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if line.strip()])
out = out.replace(', ', ',')
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train | _tmp_html | Yields the path of a temporary HTML file containing data. | folium/utilities.py | def _tmp_html(data):
"""Yields the path of a temporary HTML file containing data."""
filepath = ''
try:
fid, filepath = tempfile.mkstemp(suffix='.html', prefix='folium_')
os.write(fid, data.encode('utf8'))
os.close(fid)
yield filepath
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if os.path.isfile(fi... | def _tmp_html(data):
"""Yields the path of a temporary HTML file containing data."""
filepath = ''
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os.write(fid, data.encode('utf8'))
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train | deep_copy | Return a recursive deep-copy of item where each copy has a new ID. | folium/utilities.py | def deep_copy(item_original):
"""Return a recursive deep-copy of item where each copy has a new ID."""
item = copy.copy(item_original)
item._id = uuid.uuid4().hex
if hasattr(item, '_children') and len(item._children) > 0:
children_new = collections.OrderedDict()
for subitem_original in i... | def deep_copy(item_original):
"""Return a recursive deep-copy of item where each copy has a new ID."""
item = copy.copy(item_original)
item._id = uuid.uuid4().hex
if hasattr(item, '_children') and len(item._children) > 0:
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train | get_obj_in_upper_tree | Return the first object in the parent tree of class `cls`. | folium/utilities.py | def get_obj_in_upper_tree(element, cls):
"""Return the first object in the parent tree of class `cls`."""
if not hasattr(element, '_parent'):
raise ValueError('The top of the tree was reached without finding a {}'
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"""Return the first object in the parent tree of class `cls`."""
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train | parse_options | Return a dict with lower-camelcase keys and non-None values.. | folium/utilities.py | def parse_options(**kwargs):
"""Return a dict with lower-camelcase keys and non-None values.."""
return {camelize(key): value
for key, value in kwargs.items()
if value is not None} | def parse_options(**kwargs):
"""Return a dict with lower-camelcase keys and non-None values.."""
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train | LayerControl.render | Renders the HTML representation of the element. | folium/map.py | def render(self, **kwargs):
"""Renders the HTML representation of the element."""
for item in self._parent._children.values():
if not isinstance(item, Layer) or not item.control:
continue
key = item.layer_name
if not item.overlay:
self.... | def render(self, **kwargs):
"""Renders the HTML representation of the element."""
for item in self._parent._children.values():
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continue
key = item.layer_name
if not item.overlay:
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train | Popup.render | Renders the HTML representation of the element. | folium/map.py | def render(self, **kwargs):
"""Renders the HTML representation of the element."""
for name, child in self._children.items():
child.render(**kwargs)
figure = self.get_root()
assert isinstance(figure, Figure), ('You cannot render this Element '
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assert isinstance(figure, Figure), ('You cannot render this Element '
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train | Tooltip.parse_options | Validate the provided kwargs and return options as json string. | folium/map.py | def parse_options(self, kwargs):
"""Validate the provided kwargs and return options as json string."""
kwargs = {camelize(key): value for key, value in kwargs.items()}
for key in kwargs.keys():
assert key in self.valid_options, (
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train | Map._repr_html_ | Displays the HTML Map in a Jupyter notebook. | folium/folium.py | def _repr_html_(self, **kwargs):
"""Displays the HTML Map in a Jupyter notebook."""
if self._parent is None:
self.add_to(Figure())
out = self._parent._repr_html_(**kwargs)
self._parent = None
else:
out = self._parent._repr_html_(**kwargs)
r... | def _repr_html_(self, **kwargs):
"""Displays the HTML Map in a Jupyter notebook."""
if self._parent is None:
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out = self._parent._repr_html_(**kwargs)
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train | Map._to_png | Export the HTML to byte representation of a PNG image.
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Examples
--------
>>> m._to_png()
>>> m._to_png(time=10) # Wait 10 s... | folium/folium.py | def _to_png(self, delay=3):
"""Export the HTML to byte representation of a PNG image.
Uses selenium to render the HTML and record a PNG. You may need to
adjust the `delay` time keyword argument if maps render without data or tiles.
Examples
--------
>>> m._to_png()
... | def _to_png(self, delay=3):
"""Export the HTML to byte representation of a PNG image.
Uses selenium to render the HTML and record a PNG. You may need to
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Examples
--------
>>> m._to_png()
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train | Map.render | Renders the HTML representation of the element. | folium/folium.py | def render(self, **kwargs):
"""Renders the HTML representation of the element."""
figure = self.get_root()
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train | Map.fit_bounds | Fit the map to contain a bounding box with the
maximum zoom level possible.
Parameters
----------
bounds: list of (latitude, longitude) points
Bounding box specified as two points [southwest, northeast]
padding_top_left: (x, y) point, default None
Padding... | folium/folium.py | def fit_bounds(self, bounds, padding_top_left=None,
padding_bottom_right=None, padding=None, max_zoom=None):
"""Fit the map to contain a bounding box with the
maximum zoom level possible.
Parameters
----------
bounds: list of (latitude, longitude) points
... | def fit_bounds(self, bounds, padding_top_left=None,
padding_bottom_right=None, padding=None, max_zoom=None):
"""Fit the map to contain a bounding box with the
maximum zoom level possible.
Parameters
----------
bounds: list of (latitude, longitude) points
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train | Map.choropleth | Call the Choropleth class with the same arguments.
This method may be deleted after a year from now (Nov 2018). | folium/folium.py | def choropleth(self, *args, **kwargs):
"""Call the Choropleth class with the same arguments.
This method may be deleted after a year from now (Nov 2018).
"""
warnings.warn(
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train | TimestampedGeoJson._get_self_bounds | Computes the bounds of the object itself (not including it's children)
in the form [[lat_min, lon_min], [lat_max, lon_max]]. | folium/plugins/timestamped_geo_json.py | def _get_self_bounds(self):
"""
Computes the bounds of the object itself (not including it's children)
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"""
if not self.embed:
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data = json... | def _get_self_bounds(self):
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train | DualMap.add_child | Add object `child` to the first map and store it for the second. | folium/plugins/dual_map.py | def add_child(self, child, name=None, index=None):
"""Add object `child` to the first map and store it for the second."""
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train | start | Create a new multiprocess.Manager (or return existing one).
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:authkey: string authorization key
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train | ProcessXMLAnnotation | Process a single XML file containing a bounding box. | examples/imagenet/inception/data/process_bounding_boxes.py | def ProcessXMLAnnotation(xml_file):
"""Process a single XML file containing a bounding box."""
# pylint: disable=broad-except
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root = tree.getroot()
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# pylint: disable=broad-except
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# pylint: enable=broad-except
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train | Dataset.data_files | Returns a python list of all (sharded) data subset files.
Returns:
python list of all (sharded) data set files.
Raises:
ValueError: if there are not data_files matching the subset. | examples/imagenet/inception/dataset.py | def data_files(self):
"""Returns a python list of all (sharded) data subset files.
Returns:
python list of all (sharded) data set files.
Raises:
ValueError: if there are not data_files matching the subset.
"""
tf_record_pattern = os.path.join(FLAGS.data_dir, '%s-*' % self.subset)
da... | def data_files(self):
"""Returns a python list of all (sharded) data subset files.
Returns:
python list of all (sharded) data set files.
Raises:
ValueError: if there are not data_files matching the subset.
"""
tf_record_pattern = os.path.join(FLAGS.data_dir, '%s-*' % self.subset)
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train | _activation_summary | Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Creates a summary that measures the sparsity of activations.
Args:
x: Tensor
Returns:
nothing | examples/cifar10/cifar10.py | def _activation_summary(x):
"""Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Creates a summary that measures the sparsity of activations.
Args:
x: Tensor
Returns:
nothing
"""
# Remove 'tower_[0-9]/' from the name in case this is a multi-G... | def _activation_summary(x):
"""Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Creates a summary that measures the sparsity of activations.
Args:
x: Tensor
Returns:
nothing
"""
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train | _variable_on_cpu | Helper to create a Variable stored on CPU memory.
Args:
name: name of the variable
shape: list of ints
initializer: initializer for Variable
Returns:
Variable Tensor | examples/cifar10/cifar10.py | def _variable_on_cpu(name, shape, initializer):
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Args:
name: name of the variable
shape: list of ints
initializer: initializer for Variable
Returns:
Variable Tensor
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"""Helper to create a Variable stored on CPU memory.
Args:
name: name of the variable
shape: list of ints
initializer: initializer for Variable
Returns:
Variable Tensor
"""
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train | _variable_with_weight_decay | Helper to create an initialized Variable with weight decay.
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A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
stddev: standard deviation of a truncated Gaussian
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"""Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
... | def _variable_with_weight_decay(name, shape, stddev, wd):
"""Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
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train | distorted_inputs | Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir | examples/cifar10/cifar10.py | def distorted_inputs():
"""Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not FLAGS.data_dir:
rais... | def distorted_inputs():
"""Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not FLAGS.data_dir:
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train | inference | Build the CIFAR-10 model.
Args:
images: Images returned from distorted_inputs() or inputs().
Returns:
Logits. | examples/cifar10/cifar10.py | def inference(images):
"""Build the CIFAR-10 model.
Args:
images: Images returned from distorted_inputs() or inputs().
Returns:
Logits.
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# We instantiate all variables using tf.get_variable() instead of
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# If we onl... | def inference(images):
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images: Images returned from distorted_inputs() or inputs().
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train | loss | Add L2Loss to all the trainable variables.
Add summary for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
of shape [batch_size]
Returns:
Loss tensor of type float. | examples/cifar10/cifar10.py | def loss(logits, labels):
"""Add L2Loss to all the trainable variables.
Add summary for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
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Returns:
Loss tensor of type float.
"""
# Calcula... | def loss(logits, labels):
"""Add L2Loss to all the trainable variables.
Add summary for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
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Returns:
Loss tensor of type float.
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train | _add_loss_summaries | Add summaries for losses in CIFAR-10 model.
Generates moving average for all losses and associated summaries for
visualizing the performance of the network.
Args:
total_loss: Total loss from loss().
Returns:
loss_averages_op: op for generating moving averages of losses. | examples/cifar10/cifar10.py | def _add_loss_summaries(total_loss):
"""Add summaries for losses in CIFAR-10 model.
Generates moving average for all losses and associated summaries for
visualizing the performance of the network.
Args:
total_loss: Total loss from loss().
Returns:
loss_averages_op: op for generating moving averages ... | def _add_loss_summaries(total_loss):
"""Add summaries for losses in CIFAR-10 model.
Generates moving average for all losses and associated summaries for
visualizing the performance of the network.
Args:
total_loss: Total loss from loss().
Returns:
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train | train | Train CIFAR-10 model.
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Args:
total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
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Args:
total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
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Return... | def train(total_loss, global_step):
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total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
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train | add_variable | Adds a variable to the MODEL_VARIABLES collection.
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Args:
var: a variable.
restore: whether the variable should be added to the
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"""Adds a variable to the MODEL_VARIABLES collection.
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Args:
var: a variable.
restore: whether the variable should be added to the
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"""
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restore: whether the variable should be added to the
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train | get_variables | Gets the list of variables, filtered by scope and/or suffix.
Args:
scope: an optional scope for filtering the variables to return.
suffix: an optional suffix for filtering the variables to return.
Returns:
a copied list of variables with scope and suffix. | examples/imagenet/inception/slim/variables.py | def get_variables(scope=None, suffix=None):
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scope: an optional scope for filtering the variables to return.
suffix: an optional suffix for filtering the variables to return.
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scope: an optional scope for filtering the variables to return.
suffix: an optional suffix for filtering the variables to return.
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train | get_unique_variable | Gets the variable uniquely identified by that name.
Args:
name: a name that uniquely identifies the variable.
Returns:
a tensorflow variable.
Raises:
ValueError: if no variable uniquely identified by the name exists. | examples/imagenet/inception/slim/variables.py | def get_unique_variable(name):
"""Gets the variable uniquely identified by that name.
Args:
name: a name that uniquely identifies the variable.
Returns:
a tensorflow variable.
Raises:
ValueError: if no variable uniquely identified by the name exists.
"""
candidates = tf.get_collection(tf.Grap... | def get_unique_variable(name):
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Args:
name: a name that uniquely identifies the variable.
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a tensorflow variable.
Raises:
ValueError: if no variable uniquely identified by the name exists.
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train | variable_device | Fix the variable device to colocate its ops. | examples/imagenet/inception/slim/variables.py | def variable_device(device, name):
"""Fix the variable device to colocate its ops."""
if callable(device):
var_name = tf.get_variable_scope().name + '/' + name
var_def = tf.NodeDef(name=var_name, op='Variable')
device = device(var_def)
if device is None:
device = ''
return device | def variable_device(device, name):
"""Fix the variable device to colocate its ops."""
if callable(device):
var_name = tf.get_variable_scope().name + '/' + name
var_def = tf.NodeDef(name=var_name, op='Variable')
device = device(var_def)
if device is None:
device = ''
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train | global_step | Returns the global step variable.
Args:
device: Optional device to place the variable. It can be an string or a
function that is called to get the device for the variable.
Returns:
the tensor representing the global step variable. | examples/imagenet/inception/slim/variables.py | def global_step(device=''):
"""Returns the global step variable.
Args:
device: Optional device to place the variable. It can be an string or a
function that is called to get the device for the variable.
Returns:
the tensor representing the global step variable.
"""
global_step_ref = tf.get_col... | def global_step(device=''):
"""Returns the global step variable.
Args:
device: Optional device to place the variable. It can be an string or a
function that is called to get the device for the variable.
Returns:
the tensor representing the global step variable.
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train | variable | Gets an existing variable with these parameters or creates a new one.
It also add itself to a group with its name.
Args:
name: the name of the new or existing variable.
shape: shape of the new or existing variable.
dtype: type of the new or existing variable (defaults to `DT_FLOAT`).
initializer... | examples/imagenet/inception/slim/variables.py | def variable(name, shape=None, dtype=tf.float32, initializer=None,
regularizer=None, trainable=True, collections=None, device='',
restore=True):
"""Gets an existing variable with these parameters or creates a new one.
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Args:
name: the n... | def variable(name, shape=None, dtype=tf.float32, initializer=None,
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restore=True):
"""Gets an existing variable with these parameters or creates a new one.
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train | _eval_once | Runs Eval once.
Args:
saver: Saver.
summary_writer: Summary writer.
top_1_op: Top 1 op.
top_5_op: Top 5 op.
summary_op: Summary op. | examples/imagenet/inception/inception_eval.py | def _eval_once(saver, summary_writer, top_1_op, top_5_op, summary_op):
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Args:
saver: Saver.
summary_writer: Summary writer.
top_1_op: Top 1 op.
top_5_op: Top 5 op.
summary_op: Summary op.
"""
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpo... | def _eval_once(saver, summary_writer, top_1_op, top_5_op, summary_op):
"""Runs Eval once.
Args:
saver: Saver.
summary_writer: Summary writer.
top_1_op: Top 1 op.
top_5_op: Top 5 op.
summary_op: Summary op.
"""
with tf.Session() as sess:
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train | evaluate | Evaluate model on Dataset for a number of steps. | examples/imagenet/inception/inception_eval.py | def evaluate(dataset):
"""Evaluate model on Dataset for a number of steps."""
with tf.Graph().as_default():
# Get images and labels from the dataset.
images, labels = image_processing.inputs(dataset)
# Number of classes in the Dataset label set plus 1.
# Label 0 is reserved for an (unused) backgrou... | def evaluate(dataset):
"""Evaluate model on Dataset for a number of steps."""
with tf.Graph().as_default():
# Get images and labels from the dataset.
images, labels = image_processing.inputs(dataset)
# Number of classes in the Dataset label set plus 1.
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train | _run_model | mapPartitions function to run single-node inferencing from a checkpoint/saved_model, using the model's input/output mappings.
Args:
:iterator: input RDD partition iterator.
:args: arguments for TFModel, in argparse format
:tf_args: arguments for TensorFlow inferencing code, in argparse or ARGV format.
... | tensorflowonspark/pipeline.py | def _run_model(iterator, args, tf_args):
"""mapPartitions function to run single-node inferencing from a checkpoint/saved_model, using the model's input/output mappings.
Args:
:iterator: input RDD partition iterator.
:args: arguments for TFModel, in argparse format
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:iterator: input RDD partition iterator.
:args: arguments for TFModel, in argparse format
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train | single_node_env | Sets up environment for a single-node TF session.
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"""Sets up environment for a single-node TF session.
Args:
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"""
# setup ARGV for the TF process
if isinstance(args, list):
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sys.argv = args.argv
# setup ENV for Had... | def single_node_env(args):
"""Sets up environment for a single-node TF session.
Args:
:args: command line arguments as either argparse args or argv list
"""
# setup ARGV for the TF process
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train | get_meta_graph_def | Utility function to read a meta_graph_def from disk.
From `saved_model_cli.py <https://github.com/tensorflow/tensorflow/blob/8e0e8d41a3a8f2d4a6100c2ea1dc9d6c6c4ad382/tensorflow/python/tools/saved_model_cli.py#L186>`_
Args:
:saved_model_dir: path to saved_model.
:tag_set: list of string tags identifying th... | tensorflowonspark/pipeline.py | def get_meta_graph_def(saved_model_dir, tag_set):
"""Utility function to read a meta_graph_def from disk.
From `saved_model_cli.py <https://github.com/tensorflow/tensorflow/blob/8e0e8d41a3a8f2d4a6100c2ea1dc9d6c6c4ad382/tensorflow/python/tools/saved_model_cli.py#L186>`_
Args:
:saved_model_dir: path to saved_... | def get_meta_graph_def(saved_model_dir, tag_set):
"""Utility function to read a meta_graph_def from disk.
From `saved_model_cli.py <https://github.com/tensorflow/tensorflow/blob/8e0e8d41a3a8f2d4a6100c2ea1dc9d6c6c4ad382/tensorflow/python/tools/saved_model_cli.py#L186>`_
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train | yield_batch | Generator that yields batches of a DataFrame iterator.
Args:
:iterable: Spark partition iterator.
:batch_size: number of items to retrieve per invocation.
:num_tensors: number of tensors (columns) expected in each item.
Returns:
An array of ``num_tensors`` arrays, each of length `batch_size` | tensorflowonspark/pipeline.py | def yield_batch(iterable, batch_size, num_tensors=1):
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Args:
:iterable: Spark partition iterator.
:batch_size: number of items to retrieve per invocation.
:num_tensors: number of tensors (columns) expected in each item.
Returns:
An array o... | def yield_batch(iterable, batch_size, num_tensors=1):
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train | TFEstimator._fit | Trains a TensorFlow model and returns a TFModel instance with the same args/params pointing to a checkpoint or saved_model on disk.
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:dataset: A Spark DataFrame with columns that will be mapped to TensorFlow tensors.
Returns:
A TFModel representing the trained model, backed on disk by a Tenso... | tensorflowonspark/pipeline.py | def _fit(self, dataset):
"""Trains a TensorFlow model and returns a TFModel instance with the same args/params pointing to a checkpoint or saved_model on disk.
Args:
:dataset: A Spark DataFrame with columns that will be mapped to TensorFlow tensors.
Returns:
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train | TFModel._transform | Transforms the input DataFrame by applying the _run_model() mapPartitions function.
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train | run | Starts the TensorFlowOnSpark cluster and Runs the TensorFlow "main" function on the Spark executors
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train | TFCluster.shutdown | Stops the distributed TensorFlow cluster.
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"""Stops the distributed TensorFlow cluster.
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train | _int64_feature | Wrapper for inserting int64 features into Example proto. | examples/imagenet/inception/data/build_imagenet_data.py | def _int64_feature(value):
"""Wrapper for inserting int64 features into Example proto."""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value)) | def _int64_feature(value):
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train | _float_feature | Wrapper for inserting float features into Example proto. | examples/imagenet/inception/data/build_imagenet_data.py | def _float_feature(value):
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train | _convert_to_example | Build an Example proto for an example.
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filename: string, path to an image file, e.g., '/path/to/example.JPG'
image_buffer: string, JPEG encoding of RGB image
label: integer, identifier for the ground truth for the network
synset: string, unique WordNet ID specifying the label, e.g., 'n02323233'... | examples/imagenet/inception/data/build_imagenet_data.py | def _convert_to_example(filename, image_buffer, label, synset, human, bbox,
height, width):
"""Build an Example proto for an example.
Args:
filename: string, path to an image file, e.g., '/path/to/example.JPG'
image_buffer: string, JPEG encoding of RGB image
label: integer, iden... | def _convert_to_example(filename, image_buffer, label, synset, human, bbox,
height, width):
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filename: string, path to an image file, e.g., '/path/to/example.JPG'
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train | _process_image | Process a single image file.
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coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
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filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
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image_buffer: string, JPEG encoding of RGB image.
height: integer, imag... | def _process_image(filename, coder):
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filename: string, path to an image file e.g., '/path/to/example.JPG'.
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train | _find_human_readable_labels | Build a list of human-readable labels.
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synsets: list of strings; each string is a unique WordNet ID.
synset_to_human: dict of synset to human labels, e.g.,
'n02119022' --> 'red fox, Vulpes vulpes'
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List of human-readable strings corresponding to each synset. | examples/imagenet/inception/data/build_imagenet_data.py | def _find_human_readable_labels(synsets, synset_to_human):
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Args:
synsets: list of strings; each string is a unique WordNet ID.
synset_to_human: dict of synset to human labels, e.g.,
'n02119022' --> 'red fox, Vulpes vulpes'
Returns:
List of human-readab... | def _find_human_readable_labels(synsets, synset_to_human):
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synsets: list of strings; each string is a unique WordNet ID.
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train | _find_image_bounding_boxes | Find the bounding boxes for a given image file.
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filenames: list of strings; each string is a path to an image file.
image_to_bboxes: dictionary mapping image file names to a list of
bounding boxes. This list contains 0+ bounding boxes.
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List of bounding boxes for each image. Note th... | examples/imagenet/inception/data/build_imagenet_data.py | def _find_image_bounding_boxes(filenames, image_to_bboxes):
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Args:
filenames: list of strings; each string is a path to an image file.
image_to_bboxes: dictionary mapping image file names to a list of
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filenames: list of strings; each string is a path to an image file.
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train | _process_dataset | Process a complete data set and save it as a TFRecord.
Args:
name: string, unique identifier specifying the data set.
directory: string, root path to the data set.
num_shards: integer number of shards for this data set.
synset_to_human: dict of synset to human labels, e.g.,
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"""Process a complete data set and save it as a TFRecord.
Args:
name: string, unique identifier specifying the data set.
directory: string, root path to the data set.
num_shards: integer number of ... | def _process_dataset(name, directory, num_shards, synset_to_human,
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"""Process a complete data set and save it as a TFRecord.
Args:
name: string, unique identifier specifying the data set.
directory: string, root path to the data set.
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train | _build_synset_lookup | Build lookup for synset to human-readable label.
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imagenet_metadata_file: string, path to file containing mapping from
synset to human-readable label.
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n02119247 black fox
n02119359 silver fox
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"""Build lookup for synset to human-readable label.
Args:
imagenet_metadata_file: string, path to file containing mapping from
synset to human-readable label.
Assumes each line of the file looks like:
n02119247 black fox
n021193... | def _build_synset_lookup(imagenet_metadata_file):
"""Build lookup for synset to human-readable label.
Args:
imagenet_metadata_file: string, path to file containing mapping from
synset to human-readable label.
Assumes each line of the file looks like:
n02119247 black fox
n021193... | [
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train | _build_bounding_box_lookup | Build a lookup from image file to bounding boxes.
Args:
bounding_box_file: string, path to file with bounding boxes annotations.
Assumes each line of the file looks like:
n00007846_64193.JPEG,0.0060,0.2620,0.7545,0.9940
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"""Build a lookup from image file to bounding boxes.
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bounding_box_file: string, path to file with bounding boxes annotations.
Assumes each line of the file looks like:
n00007846_64193.JPEG,0.0060,0.2620,0.7545,0.9940
where each lin... | def _build_bounding_box_lookup(bounding_box_file):
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Args:
bounding_box_file: string, path to file with bounding boxes annotations.
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train | cnn_model_fn | Model function for CNN. | examples/mnist/estimator/mnist_estimator.py | def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# MNIST images are 28x28 pixels, and have one color channel
input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
# Convolutional Layer #1
# Computes 3... | def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# MNIST images are 28x28 pixels, and have one color channel
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train | read_cifar10 | Reads and parses examples from CIFAR10 data files.
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train | _generate_image_and_label_batch | Construct a queued batch of images and labels.
Args:
image: 3-D Tensor of [height, width, 3] of type.float32.
label: 1-D Tensor of type.int32
min_queue_examples: int32, minimum number of samples to retain
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batch_size: Number of images per batch.... | examples/cifar10/cifar10_input.py | def _generate_image_and_label_batch(image, label, min_queue_examples,
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"""Construct a queued batch of images and labels.
Args:
image: 3-D Tensor of [height, width, 3] of type.float32.
label: 1-D Tensor of type.int32
min_queue_examples: int32, min... | def _generate_image_and_label_batch(image, label, min_queue_examples,
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image: 3-D Tensor of [height, width, 3] of type.float32.
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train | distorted_inputs | Construct distorted input for CIFAR training using the Reader ops.
Args:
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size. | examples/cifar10/cifar10_input.py | def distorted_inputs(data_dir, batch_size):
"""Construct distorted input for CIFAR training using the Reader ops.
Args:
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
label... | def distorted_inputs(data_dir, batch_size):
"""Construct distorted input for CIFAR training using the Reader ops.
Args:
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
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train | inputs | Construct input for CIFAR evaluation using the Reader ops.
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eval_data: bool, indicating if one should use the train or eval data set.
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
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"""Construct input for CIFAR evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Image... | def inputs(eval_data, data_dir, batch_size):
"""Construct input for CIFAR evaluation using the Reader ops.
Args:
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train | saveAsTFRecords | Save a Spark DataFrame as TFRecords.
This will convert the DataFrame rows to TFRecords prior to saving.
Args:
:df: Spark DataFrame
:output_dir: Path to save TFRecords | tensorflowonspark/dfutil.py | def saveAsTFRecords(df, output_dir):
"""Save a Spark DataFrame as TFRecords.
This will convert the DataFrame rows to TFRecords prior to saving.
Args:
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:output_dir: Path to save TFRecords
"""
tf_rdd = df.rdd.mapPartitions(toTFExample(df.dtypes))
tf_rdd.saveAsNewAPIHadoopFile(out... | def saveAsTFRecords(df, output_dir):
"""Save a Spark DataFrame as TFRecords.
This will convert the DataFrame rows to TFRecords prior to saving.
Args:
:df: Spark DataFrame
:output_dir: Path to save TFRecords
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train | loadTFRecords | Load TFRecords from disk into a Spark DataFrame.
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Note: TensorFlow represents both strings and binary types as tf.train.BytesList, an... | def loadTFRecords(sc, input_dir, binary_features=[]):
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train | toTFExample | mapPartition function to convert a Spark RDD of Row into an RDD of serialized tf.train.Example bytestring.
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train | infer_schema | Given a tf.train.Example, infer the Spark DataFrame schema (StructFields).
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train | fromTFExample | mapPartition function to convert an RDD of serialized tf.train.Example bytestring into an RDD of Row.
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train | build_estimator | Build an estimator appropriate for the given model type. | examples/wide_deep/census_main.py | def build_estimator(model_dir, model_type, model_column_fn, inter_op, intra_op, ctx):
"""Build an estimator appropriate for the given model type."""
wide_columns, deep_columns = model_column_fn()
hidden_units = [100, 75, 50, 25]
# Create a tf.estimator.RunConfig to ensure the model is run on CPU, which
# tra... | def build_estimator(model_dir, model_type, model_column_fn, inter_op, intra_op, ctx):
"""Build an estimator appropriate for the given model type."""
wide_columns, deep_columns = model_column_fn()
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train | run_census | Construct all necessary functions and call run_loop.
Args:
flags_obj: Object containing user specified flags. | examples/wide_deep/census_main.py | def run_census(flags_obj, ctx):
"""Construct all necessary functions and call run_loop.
Args:
flags_obj: Object containing user specified flags.
"""
train_file = os.path.join(flags_obj.data_dir, census_dataset.TRAINING_FILE)
test_file = os.path.join(flags_obj.data_dir, census_dataset.EVAL_FILE)
# Trai... | def run_census(flags_obj, ctx):
"""Construct all necessary functions and call run_loop.
Args:
flags_obj: Object containing user specified flags.
"""
train_file = os.path.join(flags_obj.data_dir, census_dataset.TRAINING_FILE)
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train | inception_v3 | Latest Inception from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vision"
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
Zbigniew Wojna
Args:
inputs: a tensor of size [batch_size, height, width, channels].
dropout_keep_prob: dropout... | examples/imagenet/inception/slim/inception_model.py | def inception_v3(inputs,
dropout_keep_prob=0.8,
num_classes=1000,
is_training=True,
restore_logits=True,
scope=''):
"""Latest Inception from http://arxiv.org/abs/1512.00567.
"Rethinking the Inception Architecture for Computer Vi... | def inception_v3(inputs,
dropout_keep_prob=0.8,
num_classes=1000,
is_training=True,
restore_logits=True,
scope=''):
"""Latest Inception from http://arxiv.org/abs/1512.00567.
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train | inception_v3_parameters | Yields the scope with the default parameters for inception_v3.
Args:
weight_decay: the weight decay for weights variables.
stddev: standard deviation of the truncated guassian weight distribution.
batch_norm_decay: decay for the moving average of batch_norm momentums.
batch_norm_epsilon: small float ... | examples/imagenet/inception/slim/inception_model.py | def inception_v3_parameters(weight_decay=0.00004, stddev=0.1,
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"""Yields the scope with the default parameters for inception_v3.
Args:
weight_decay: the weight decay for weights variables.
stddev: standard deviation of the trunc... | def inception_v3_parameters(weight_decay=0.00004, stddev=0.1,
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train | single_node_env | Setup environment variables for Hadoop compatibility and GPU allocation | tensorflowonspark/util.py | def single_node_env(num_gpus=1):
"""Setup environment variables for Hadoop compatibility and GPU allocation"""
import tensorflow as tf
# ensure expanded CLASSPATH w/o glob characters (required for Spark 2.1 + JNI)
if 'HADOOP_PREFIX' in os.environ and 'TFOS_CLASSPATH_UPDATED' not in os.environ:
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"""Setup environment variables for Hadoop compatibility and GPU allocation"""
import tensorflow as tf
# ensure expanded CLASSPATH w/o glob characters (required for Spark 2.1 + JNI)
if 'HADOOP_PREFIX' in os.environ and 'TFOS_CLASSPATH_UPDATED' not in os.environ:
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train | get_ip_address | Simple utility to get host IP address. | tensorflowonspark/util.py | def get_ip_address():
"""Simple utility to get host IP address."""
try:
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s.connect(("8.8.8.8", 80))
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ip_address = socket... | def get_ip_address():
"""Simple utility to get host IP address."""
try:
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train | find_in_path | Find a file in a given path string. | tensorflowonspark/util.py | def find_in_path(path, file):
"""Find a file in a given path string."""
for p in path.split(os.pathsep):
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"""Find a file in a given path string."""
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train | l1_regularizer | Define a L1 regularizer.
Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function. | examples/imagenet/inception/slim/losses.py | def l1_regularizer(weight=1.0, scope=None):
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Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function.
"""
def regularizer(tensor):
with tf.name_scope(scope, 'L1Regularizer', [tensor]):
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Args:
weight: scale the loss by this factor.
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Returns:
a regularizer function.
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train | l2_regularizer | Define a L2 regularizer.
Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function. | examples/imagenet/inception/slim/losses.py | def l2_regularizer(weight=1.0, scope=None):
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Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function.
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def regularizer(tensor):
with tf.name_scope(scope, 'L2Regularizer', [tensor]):
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Args:
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function.
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train | l1_l2_regularizer | Define a L1L2 regularizer.
Args:
weight_l1: scale the L1 loss by this factor.
weight_l2: scale the L2 loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function. | examples/imagenet/inception/slim/losses.py | def l1_l2_regularizer(weight_l1=1.0, weight_l2=1.0, scope=None):
"""Define a L1L2 regularizer.
Args:
weight_l1: scale the L1 loss by this factor.
weight_l2: scale the L2 loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function.
"""
def regularizer(tensor):
... | def l1_l2_regularizer(weight_l1=1.0, weight_l2=1.0, scope=None):
"""Define a L1L2 regularizer.
Args:
weight_l1: scale the L1 loss by this factor.
weight_l2: scale the L2 loss by this factor.
scope: Optional scope for name_scope.
Returns:
a regularizer function.
"""
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train | l1_loss | Define a L1Loss, useful for regularize, i.e. lasso.
Args:
tensor: tensor to regularize.
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
the L1 loss op. | examples/imagenet/inception/slim/losses.py | def l1_loss(tensor, weight=1.0, scope=None):
"""Define a L1Loss, useful for regularize, i.e. lasso.
Args:
tensor: tensor to regularize.
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
the L1 loss op.
"""
with tf.name_scope(scope, 'L1Loss', [tensor]):
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"""Define a L1Loss, useful for regularize, i.e. lasso.
Args:
tensor: tensor to regularize.
weight: scale the loss by this factor.
scope: Optional scope for name_scope.
Returns:
the L1 loss op.
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with tf.name_scope(scope, 'L1Loss', [tensor]):
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train | l2_loss | Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
tensor: tensor to regularize.
weight: an optional weight to modulate the loss.
scope: Optional scope for name_scope.
Returns:
the L2 loss op. | examples/imagenet/inception/slim/losses.py | def l2_loss(tensor, weight=1.0, scope=None):
"""Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
tensor: tensor to regularize.
weight: an optional weight to modulate the loss.
scope: Optional scope for name_scope.
Returns:
the L2 loss op.
"""
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"""Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
tensor: tensor to regularize.
weight: an optional weight to modulate the loss.
scope: Optional scope for name_scope.
Returns:
the L2 loss op.
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train | cross_entropy_loss | Define a Cross Entropy loss using softmax_cross_entropy_with_logits.
It can scale the loss by weight factor, and smooth the labels.
Args:
logits: [batch_size, num_classes] logits outputs of the network .
one_hot_labels: [batch_size, num_classes] target one_hot_encoded labels.
label_smoothing: if great... | examples/imagenet/inception/slim/losses.py | def cross_entropy_loss(logits, one_hot_labels, label_smoothing=0,
weight=1.0, scope=None):
"""Define a Cross Entropy loss using softmax_cross_entropy_with_logits.
It can scale the loss by weight factor, and smooth the labels.
Args:
logits: [batch_size, num_classes] logits outputs of t... | def cross_entropy_loss(logits, one_hot_labels, label_smoothing=0,
weight=1.0, scope=None):
"""Define a Cross Entropy loss using softmax_cross_entropy_with_logits.
It can scale the loss by weight factor, and smooth the labels.
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train | arg_scope | Stores the default arguments for the given set of list_ops.
For usage, please see examples at top of the file.
Args:
list_ops_or_scope: List or tuple of operations to set argument scope for or
a dictionary containg the current scope. When list_ops_or_scope is a dict,
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"""Stores the default arguments for the given set of list_ops.
For usage, please see examples at top of the file.
Args:
list_ops_or_scope: List or tuple of operations to set argument scope for or
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train | add_arg_scope | Decorates a function with args so it can be used within an arg_scope.
Args:
func: function to decorate.
Returns:
A tuple with the decorated function func_with_args(). | examples/imagenet/inception/slim/scopes.py | def add_arg_scope(func):
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func: function to decorate.
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Args:
func: function to decorate.
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A tuple with the decorated function func_with_args().
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train | _get_manager | Returns this executor's "singleton" instance of the multiprocessing.Manager, reconnecting per python-worker if needed.
Args:
:cluster_info: cluster node reservations
:host: host IP address
:executor_id: unique id per executor (created during initial call to run())
Returns:
TFManager instance for t... | tensorflowonspark/TFSparkNode.py | def _get_manager(cluster_info, host, executor_id):
"""Returns this executor's "singleton" instance of the multiprocessing.Manager, reconnecting per python-worker if needed.
Args:
:cluster_info: cluster node reservations
:host: host IP address
:executor_id: unique id per executor (created during initial... | def _get_manager(cluster_info, host, executor_id):
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Args:
:cluster_info: cluster node reservations
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train | run | Wraps the user-provided TensorFlow main function in a Spark mapPartitions function.
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Args:
:fn: TensorFlow "main" function provided by the user.
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train | inference | Feeds Spark partitions into the shared multiprocessing.Queue and returns inference results.
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train | shutdown | Stops all TensorFlow nodes by feeding ``None`` into the multiprocessing.Queues.
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:cluster_info: node reservation information for the cluster (e.g. host, executor_id, pid, ports, etc).
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train | TFNodeContext.start_cluster_server | Convenience function to access ``TFNode.start_cluster_server`` directly from this object instance. | tensorflowonspark/TFSparkNode.py | def start_cluster_server(self, num_gpus=1, rdma=False):
"""Convenience function to access ``TFNode.start_cluster_server`` directly from this object instance."""
return TFNode.start_cluster_server(self, num_gpus, rdma) | def start_cluster_server(self, num_gpus=1, rdma=False):
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train | TFNodeContext.export_saved_model | Convenience function to access ``TFNode.export_saved_model`` directly from this object instance. | tensorflowonspark/TFSparkNode.py | def export_saved_model(self, sess, export_dir, tag_set, signatures):
"""Convenience function to access ``TFNode.export_saved_model`` directly from this object instance."""
TFNode.export_saved_model(sess, export_dir, tag_set, signatures) | def export_saved_model(self, sess, export_dir, tag_set, signatures):
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train | TFNodeContext.get_data_feed | Convenience function to access ``TFNode.DataFeed`` directly from this object instance. | tensorflowonspark/TFSparkNode.py | def get_data_feed(self, train_mode=True, qname_in='input', qname_out='output', input_mapping=None):
"""Convenience function to access ``TFNode.DataFeed`` directly from this object instance."""
return TFNode.DataFeed(self.mgr, train_mode, qname_in, qname_out, input_mapping) | def get_data_feed(self, train_mode=True, qname_in='input', qname_out='output', input_mapping=None):
"""Convenience function to access ``TFNode.DataFeed`` directly from this object instance."""
return TFNode.DataFeed(self.mgr, train_mode, qname_in, qname_out, input_mapping) | [
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train | _download_and_clean_file | Downloads data from url, and makes changes to match the CSV format. | examples/wide_deep/census_dataset.py | def _download_and_clean_file(filename, url):
"""Downloads data from url, and makes changes to match the CSV format."""
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... | def _download_and_clean_file(filename, url):
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train | download | Download census data if it is not already present. | examples/wide_deep/census_dataset.py | def download(data_dir):
"""Download census data if it is not already present."""
tf.gfile.MakeDirs(data_dir)
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train | build_model_columns | Builds a set of wide and deep feature columns. | examples/wide_deep/census_dataset.py | def build_model_columns():
"""Builds a set of wide and deep feature columns."""
# Continuous variable columns
age = tf.feature_column.numeric_column('age')
education_num = tf.feature_column.numeric_column('education_num')
capital_gain = tf.feature_column.numeric_column('capital_gain')
capital_loss = tf.feat... | def build_model_columns():
"""Builds a set of wide and deep feature columns."""
# Continuous variable columns
age = tf.feature_column.numeric_column('age')
education_num = tf.feature_column.numeric_column('education_num')
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train | input_fn | Generate an input function for the Estimator. | examples/wide_deep/census_dataset.py | def input_fn(data_file, num_epochs, shuffle, batch_size):
"""Generate an input function for the Estimator."""
assert tf.gfile.Exists(data_file), (
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def parse_csv(value):... | def input_fn(data_file, num_epochs, shuffle, batch_size):
"""Generate an input function for the Estimator."""
assert tf.gfile.Exists(data_file), (
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def parse_csv(value):... | [
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train | MessageSocket.receive | Receive a message on ``sock``. | tensorflowonspark/reservation.py | def receive(self, sock):
"""Receive a message on ``sock``."""
msg = None
data = b''
recv_done = False
recv_len = -1
while not recv_done:
buf = sock.recv(BUFSIZE)
if buf is None or len(buf) == 0:
raise Exception("socket closed")
if recv_len == -1:
recv_len = stru... | def receive(self, sock):
"""Receive a message on ``sock``."""
msg = None
data = b''
recv_done = False
recv_len = -1
while not recv_done:
buf = sock.recv(BUFSIZE)
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recv_len = stru... | [
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train | MessageSocket.send | Send ``msg`` to destination ``sock``. | tensorflowonspark/reservation.py | def send(self, sock, msg):
"""Send ``msg`` to destination ``sock``."""
data = pickle.dumps(msg)
buf = struct.pack('>I', len(data)) + data
sock.sendall(buf) | def send(self, sock, msg):
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data = pickle.dumps(msg)
buf = struct.pack('>I', len(data)) + data
sock.sendall(buf) | [
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train | Server.await_reservations | Block until all reservations are received. | tensorflowonspark/reservation.py | def await_reservations(self, sc, status={}, timeout=600):
"""Block until all reservations are received."""
timespent = 0
while not self.reservations.done():
logging.info("waiting for {0} reservations".format(self.reservations.remaining()))
# check status flags for any errors
if 'error' in ... | def await_reservations(self, sc, status={}, timeout=600):
"""Block until all reservations are received."""
timespent = 0
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train | Server.start | Start listener in a background thread
Returns:
address of the Server as a tuple of (host, port) | tensorflowonspark/reservation.py | def start(self):
"""Start listener in a background thread
Returns:
address of the Server as a tuple of (host, port)
"""
server_sock = self.start_listening_socket()
# hostname may not be resolvable but IP address probably will be
host = self.get_server_ip()
port = server_sock.getsockn... | def start(self):
"""Start listener in a background thread
Returns:
address of the Server as a tuple of (host, port)
"""
server_sock = self.start_listening_socket()
# hostname may not be resolvable but IP address probably will be
host = self.get_server_ip()
port = server_sock.getsockn... | [
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] | yahoo/TensorFlowOnSpark | python | https://github.com/yahoo/TensorFlowOnSpark/blob/5e4b6c185ab722fd0104ede0377e1149ea8d6f7c/tensorflowonspark/reservation.py#L146-L186 | [
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train | Client._request | Helper function to wrap msg w/ msg_type. | tensorflowonspark/reservation.py | def _request(self, msg_type, msg_data=None):
"""Helper function to wrap msg w/ msg_type."""
msg = {}
msg['type'] = msg_type
if msg_data:
msg['data'] = msg_data
done = False
tries = 0
while not done and tries < MAX_RETRIES:
try:
MessageSocket.send(self, self.sock, msg)
... | def _request(self, msg_type, msg_data=None):
"""Helper function to wrap msg w/ msg_type."""
msg = {}
msg['type'] = msg_type
if msg_data:
msg['data'] = msg_data
done = False
tries = 0
while not done and tries < MAX_RETRIES:
try:
MessageSocket.send(self, self.sock, msg)
... | [
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] | yahoo/TensorFlowOnSpark | python | https://github.com/yahoo/TensorFlowOnSpark/blob/5e4b6c185ab722fd0104ede0377e1149ea8d6f7c/tensorflowonspark/reservation.py#L220-L245 | [
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