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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvND._pad_input | def _pad_input(self, inputs):
"""Pad input in case the desired padding type requires it.
VALID and SAME padding types are directly supported by tensorflow
convolution ops, so don't require us to pad input ourselves, at least
in cases where the same method is used for all dimensions.
Other padding ... | python | def _pad_input(self, inputs):
"""Pad input in case the desired padding type requires it.
VALID and SAME padding types are directly supported by tensorflow
convolution ops, so don't require us to pad input ourselves, at least
in cases where the same method is used for all dimensions.
Other padding ... | [
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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvND._apply_conv | def _apply_conv(self, inputs, w):
"""Apply a convolution operation on `inputs` using variable `w`.
Args:
inputs: A Tensor of shape `data_format` and of type `tf.float16`,
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w: A weight matrix of the same type as `inputs`.
Returns:
outputs: The resul... | python | def _apply_conv(self, inputs, w):
"""Apply a convolution operation on `inputs` using variable `w`.
Args:
inputs: A Tensor of shape `data_format` and of type `tf.float16`,
`tf.bfloat16` or `tf.float32`.
w: A weight matrix of the same type as `inputs`.
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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvND._apply_mask | def _apply_mask(self):
"""Applies the passed-in mask to the convolution matrix.
Returns:
w: A copy of the convolution matrix that has had the mask applied.
Raises:
base.IncompatibleShapeError: If the mask shape has more dimensions than
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base.IncompatibleShapeE... | python | def _apply_mask(self):
"""Applies the passed-in mask to the convolution matrix.
Returns:
w: A copy of the convolution matrix that has had the mask applied.
Raises:
base.IncompatibleShapeError: If the mask shape has more dimensions than
the weight matrix.
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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvND.output_channels | def output_channels(self):
"""Returns the number of output channels."""
if callable(self._output_channels):
self._output_channels = self._output_channels()
# Channel must be integer.
self._output_channels = int(self._output_channels)
return self._output_channels | python | def output_channels(self):
"""Returns the number of output channels."""
if callable(self._output_channels):
self._output_channels = self._output_channels()
# Channel must be integer.
self._output_channels = int(self._output_channels)
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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvND.padding | def padding(self):
"""Returns the padding algorithm used, if this is the same for all dims.
Use `.paddings` if you want a tuple with the padding algorithm used for each
dimension.
Returns:
The padding algorithm used, if this is the same for all dimensions.
Raises:
ValueError: If diffe... | python | def padding(self):
"""Returns the padding algorithm used, if this is the same for all dims.
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The padding algorithm used, if this is the same for all dimensions.
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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvND.clone | def clone(self, name=None):
"""Returns a cloned `_ConvND` module.
Args:
name: Optional string assigning name of cloned module. The default name
is constructed by appending "_clone" to `self.module_name`.
Returns:
A copy of the current class.
"""
if name is None:
name = se... | python | def clone(self, name=None):
"""Returns a cloned `_ConvND` module.
Args:
name: Optional string assigning name of cloned module. The default name
is constructed by appending "_clone" to `self.module_name`.
Returns:
A copy of the current class.
"""
if name is None:
name = se... | [
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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvNDTranspose._build | def _build(self, inputs):
"""Connects the _ConvNDTranspose module into the graph.
If this is not the first time the module has been connected to the graph,
the input Tensor provided here must have the same final N dimensions, in
order for the existing variables to be the correct size for the
multip... | python | def _build(self, inputs):
"""Connects the _ConvNDTranspose module into the graph.
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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvNDTranspose._infer_all_output_dims | def _infer_all_output_dims(self, inputs):
"""Calculate the output shape for `inputs` after a deconvolution.
Args:
inputs: A Tensor of shape `data_format` and of type `tf.float16`,
`tf.bfloat16` or `tf.float32`.
Returns:
output_shape: A tensor of shape (`batch_size`, `conv_output_shap... | python | def _infer_all_output_dims(self, inputs):
"""Calculate the output shape for `inputs` after a deconvolution.
Args:
inputs: A Tensor of shape `data_format` and of type `tf.float16`,
`tf.bfloat16` or `tf.float32`.
Returns:
output_shape: A tensor of shape (`batch_size`, `conv_output_shap... | [
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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvNDTranspose._recover_shape_information | def _recover_shape_information(self, inputs, outputs):
"""Recover output tensor shape value to enable shape inference.
The batch size of `inputs` isn't preserved by the convolution op. Calculate
what the proper output shape will be for `outputs`.
Args:
inputs: A Tensor of shape `data_format` and... | python | def _recover_shape_information(self, inputs, outputs):
"""Recover output tensor shape value to enable shape inference.
The batch size of `inputs` isn't preserved by the convolution op. Calculate
what the proper output shape will be for `outputs`.
Args:
inputs: A Tensor of shape `data_format` and... | [
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deepmind/sonnet | sonnet/python/modules/conv.py | _ConvNDTranspose.output_shape | def output_shape(self):
"""Returns the output shape."""
if self._output_shape is None:
self._ensure_is_connected()
if callable(self._output_shape):
self._output_shape = tuple(self._output_shape())
return self._output_shape | python | def output_shape(self):
"""Returns the output shape."""
if self._output_shape is None:
self._ensure_is_connected()
if callable(self._output_shape):
self._output_shape = tuple(self._output_shape())
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deepmind/sonnet | sonnet/python/modules/conv.py | Conv1DTranspose.transpose | def transpose(self, name=None):
"""Returns matching `Conv1D` module.
Args:
name: Optional string assigning name of transpose module. The default name
is constructed by appending "_transpose" to `self.name`.
Returns:
`Conv1D` module.
"""
if name is None:
name = self.module... | python | def transpose(self, name=None):
"""Returns matching `Conv1D` module.
Args:
name: Optional string assigning name of transpose module. The default name
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Returns:
`Conv1D` module.
"""
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deepmind/sonnet | sonnet/python/modules/conv.py | Conv2D.transpose | def transpose(self, name=None):
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Args:
name: Optional string assigning name of transpose module. The default name
is constructed by appending "_transpose" to `self.name`.
Returns:
`Conv2DTranspose` module.
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base.NotSupported... | python | def transpose(self, name=None):
"""Returns matching `Conv2DTranspose` module.
Args:
name: Optional string assigning name of transpose module. The default name
is constructed by appending "_transpose" to `self.name`.
Returns:
`Conv2DTranspose` module.
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Args:
name: Optional string assigning name of transpose module. The default name
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deepmind/sonnet | sonnet/python/modules/conv.py | InPlaneConv2D._construct_w | def _construct_w(self, inputs):
"""Construct the convolution weight matrix.
Figures out the shape of the weight matrix, initialize it, and return it.
Args:
inputs: A Tensor of shape `data_format` and of type `tf.float16`,
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Returns:
w: A weight matri... | python | def _construct_w(self, inputs):
"""Construct the convolution weight matrix.
Figures out the shape of the weight matrix, initialize it, and return it.
Args:
inputs: A Tensor of shape `data_format` and of type `tf.float16`,
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deepmind/sonnet | sonnet/python/modules/conv.py | InPlaneConv2D._apply_conv | def _apply_conv(self, inputs, w):
"""Apply a depthwise_conv2d operation on `inputs` using variable `w`.
Args:
inputs: A Tensor of shape `data_format` and of type `tf.float16`,
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w: A weight matrix of the same type as `inputs`.
Returns:
outputs: The ... | python | def _apply_conv(self, inputs, w):
"""Apply a depthwise_conv2d operation on `inputs` using variable `w`.
Args:
inputs: A Tensor of shape `data_format` and of type `tf.float16`,
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deepmind/sonnet | sonnet/python/modules/conv.py | SeparableConv2D._construct_w | def _construct_w(self, inputs):
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Args:
inputs: A 4D Tensor of shape:
[batch_size, input_height, input_width, input_channels]
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deepmind/sonnet | sonnet/python/modules/sequential.py | Sequential._build | def _build(self, *args):
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layer.
Returns:
The output value of the last layer.
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net = args
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deepmind/sonnet | sonnet/python/modules/sequential.py | Sequential.get_variables | def get_variables(self, *args, **kwargs):
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deepmind/sonnet | sonnet/python/custom_getters/override_args.py | override_args | def override_args(**kwargs):
"""Creates a custom getter that applies specified named arguments.
Args:
**kwargs: Overriding arguments for the custom getter to use in preference
the named arguments it's called with.
Returns:
Custom getter.
"""
override_kwargs = kwargs
def custom_getter(gette... | python | def override_args(**kwargs):
"""Creates a custom getter that applies specified named arguments.
Args:
**kwargs: Overriding arguments for the custom getter to use in preference
the named arguments it's called with.
Returns:
Custom getter.
"""
override_kwargs = kwargs
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deepmind/sonnet | sonnet/util/migrate_checkpoint.py | _build_migrated_variables | def _build_migrated_variables(checkpoint_reader, name_value_fn):
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Args:
checkpoint_reader: A `tf.train.NewCheckPointReader` of the checkpoint to
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name_value_fn: Function taking two arguments, `name` and `value`, which
re... | python | def _build_migrated_variables(checkpoint_reader, name_value_fn):
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checkpoint_reader: A `tf.train.NewCheckPointReader` of the checkpoint to
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deepmind/sonnet | sonnet/python/modules/base_info.py | _to_proto_sparse_tensor | def _to_proto_sparse_tensor(sparse_tensor, nested_proto,
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"""Serializes a `tf.SparseTensor` into `nested_proto`.
Args:
sparse_tensor: An instance of `tf.SparseTensor`.
nested_proto: A `module_pb2.NestedData` instance to be filled from
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deepmind/sonnet | sonnet/python/modules/base_info.py | _from_proto_sparse_tensor | def _from_proto_sparse_tensor(sparse_tensor_proto, process_leafs):
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sparse_tensor_proto: A proto representing a `tf.SparseTensor`.
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deepmind/sonnet | sonnet/python/modules/base_info.py | _nested_to_proto | def _nested_to_proto(nested_value, nested_proto, process_leafs,
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deepmind/sonnet | sonnet/python/modules/base_info.py | _module_info_to_proto | def _module_info_to_proto(module_info, export_scope=None):
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module_info: An instance of `ModuleInfo`.
export_scope: Optional `string`. Name scope to remove.
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An instance of `module_pb2.SonnetModule`.
"""
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module_info: An instance of `ModuleInfo`.
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deepmind/sonnet | sonnet/python/modules/base_info.py | _module_info_from_proto | def _module_info_from_proto(module_info_def, import_scope=None):
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module_info_def: An instance of `module_pb2.SonnetModule`.
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module_info_def: An instance of `module_pb2.SonnetModule`.
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deepmind/sonnet | sonnet/python/modules/base_info.py | _module_info_from_proto_safe | def _module_info_from_proto_safe(module_info_def, import_scope=None):
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Args:
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import_scope: Optional `string`. Name scope to use.
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deepmind/sonnet | sonnet/examples/rnn_shakespeare.py | _configure_saver | def _configure_saver(checkpoint_dir, checkpoint_interval):
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saver = tf.train.Saver()
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save_steps=checkpoint_interval,
saver=saver) | python | def _configure_saver(checkpoint_dir, checkpoint_interval):
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deepmind/sonnet | sonnet/examples/rnn_shakespeare.py | build_graph | def build_graph(lstm_depth=3, batch_size=32, num_embedding=32, num_hidden=128,
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deepmind/sonnet | sonnet/examples/rnn_shakespeare.py | train | def train(num_training_iterations, report_interval,
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"""Trains a deep LSTM model on the Tiny Shakespeare dataset."""
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deepmind/sonnet | sonnet/examples/rnn_shakespeare.py | TextModel._build | def _build(self, one_hot_input_sequence):
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one_hot_input_sequence: A Tensor with the input sequence encoded as a
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initial_logits: Starting logits to sample from.
initial_state: Starting state for the RNN core.
sequence_length: Number of characters to sample.
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initial_logits: Starting logits to sample from.
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deepmind/sonnet | sonnet/python/modules/nets/vqvae.py | VectorQuantizer._build | def _build(self, inputs, is_training):
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inputs: Tensor, final dimension must be equal to embedding_dim. All other
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deepmind/sonnet | sonnet/python/modules/pondering_rnn.py | _nested_add | def _nested_add(nested_a, nested_b):
"""Add two arbitrarily nested `Tensors`."""
return nest.map(lambda a, b: a + b, nested_a, nested_b) | python | def _nested_add(nested_a, nested_b):
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deepmind/sonnet | sonnet/python/modules/pondering_rnn.py | _nested_unary_mul | def _nested_unary_mul(nested_a, p):
"""Multiply `Tensors` in arbitrarily nested `Tensor` `nested_a` with `p`."""
def mul_with_broadcast(tensor):
ndims = tensor.shape.ndims
if ndims != 2:
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return p_reshaped * tensor
else:
return p * te... | python | def _nested_unary_mul(nested_a, p):
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deepmind/sonnet | sonnet/python/modules/pondering_rnn.py | ACTCore._cond | def _cond(self, unused_x, unused_cumul_out, unused_prev_state,
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unused_remainder):
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deepmind/sonnet | sonnet/python/modules/pondering_rnn.py | ACTCore._body | def _body(self, x, cumul_out, prev_state, cumul_state,
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deepmind/sonnet | sonnet/python/modules/pondering_rnn.py | ACTCore._build | def _build(self, x, prev_state):
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deepmind/sonnet | sonnet/python/modules/embed.py | _embedding_dim | def _embedding_dim(vocab_size):
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Args:
vocab_size: Size of the input vocabulary.
Returns:
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Raises:
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"""Calculate a reasonable embedding size for a vocabulary.
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vocab_size: Size of the input vocabulary.
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deepmind/sonnet | sonnet/python/modules/spatial_transformer.py | _create_affine_features | def _create_affine_features(output_shape, source_shape):
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deepmind/sonnet | sonnet/python/modules/spatial_transformer.py | AffineGridWarper._create_features | def _create_features(self, constraints):
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affine_warp_constraints = constraints
if not isinstance(affine_warp_constraints, AffineWarpConstraints):
affine_warp_constraints = AffineWarpConstraints(affine_warp_constraints)
mask ... | python | def _create_features(self, constraints):
"""Creates all the matrices needed to compute the output warped grids."""
affine_warp_constraints = constraints
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deepmind/sonnet | sonnet/python/modules/spatial_transformer.py | AffineGridWarper._build | def _build(self, inputs):
"""Assembles the module network and adds it to the graph.
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inputs: Tensor containing a batch of transformation parameters.
Returns:
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deepmind/sonnet | sonnet/python/modules/spatial_transformer.py | AffineWarpConstraints._calc_mask | def _calc_mask(self):
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mask = []
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mask.append(tuple(x is None for x in row))
return tuple(mask) | python | def _calc_mask(self):
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mask = []
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deepmind/sonnet | sonnet/python/modules/spatial_transformer.py | AffineWarpConstraints._combine | def _combine(self, x, y):
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raise ValueError('Incompatible set of constraints provided.')
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deepmind/sonnet | sonnet/python/modules/spatial_transformer.py | AffineWarpConstraints.combine_with | def combine_with(self, additional_constraints):
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"""Combines two sets of constraints into a coherent single set."""
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deepmind/sonnet | sonnet/python/ops/initializers.py | _Restore._partition_spec | def _partition_spec(self, shape, partition_info):
"""Build magic (and sparsely documented) shapes_and_slices spec string."""
if partition_info is None:
return '' # Empty string indicates a non-partitioned tensor.
ssi = tf.Variable.SaveSliceInfo(
full_name=self._var_name,
full_shape=pa... | python | def _partition_spec(self, shape, partition_info):
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ansible/molecule | molecule/provisioner/ansible_playbook.py | AnsiblePlaybook.bake | def bake(self):
"""
Bake an ``ansible-playbook`` command so it's ready to execute and
returns ``None``.
:return: None
"""
# Pass a directory as inventory to let Ansible merge the multiple
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... | python | def bake(self):
"""
Bake an ``ansible-playbook`` command so it's ready to execute and
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ansible/molecule | molecule/command/login.py | login | def login(ctx, host, scenario_name): # pragma: no cover
""" Log in to one instance. """
args = ctx.obj.get('args')
subcommand = base._get_subcommand(__name__)
command_args = {
'subcommand': subcommand,
'host': host,
}
s = scenarios.Scenarios(
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""" Log in to one instance. """
args = ctx.obj.get('args')
subcommand = base._get_subcommand(__name__)
command_args = {
'subcommand': subcommand,
'host': host,
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ansible/molecule | molecule/command/login.py | Login.execute | def execute(self):
"""
Execute the actions necessary to perform a `molecule login` and
returns None.
:return: None
"""
c = self._config
if ((not c.state.created) and c.driver.managed):
msg = 'Instances not created. Please create instances first.'
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"""
Execute the actions necessary to perform a `molecule login` and
returns None.
:return: None
"""
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if ((not c.state.created) and c.driver.managed):
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ansible/molecule | molecule/command/base.py | execute_cmdline_scenarios | def execute_cmdline_scenarios(scenario_name, args, command_args):
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ansible/molecule | molecule/command/base.py | execute_scenario | def execute_scenario(scenario):
"""
Execute each command in the given scenario's configured sequence.
:param scenario: The scenario to execute.
:returns: None
"""
for action in scenario.sequence:
execute_subcommand(scenario.config, action)
# pruning only if a 'destroy' step was i... | python | def execute_scenario(scenario):
"""
Execute each command in the given scenario's configured sequence.
:param scenario: The scenario to execute.
:returns: None
"""
for action in scenario.sequence:
execute_subcommand(scenario.config, action)
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ansible/molecule | molecule/command/base.py | get_configs | def get_configs(args, command_args, ansible_args=()):
"""
Glob the current directory for Molecule config files, instantiate config
objects, and returns a list.
:param args: A dict of options, arguments and commands from the CLI.
:param command_args: A dict of options passed to the subcommand from
... | python | def get_configs(args, command_args, ansible_args=()):
"""
Glob the current directory for Molecule config files, instantiate config
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ansible/molecule | molecule/command/base.py | _verify_configs | def _verify_configs(configs):
"""
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:param configs: A list containing absolute paths to Molecule config files.
:return: None
"""
if configs:
scenario_names = [c.scenario.name for c in configs]
for scenario_name, n in collections... | python | def _verify_configs(configs):
"""
Verify a Molecule config was found and returns None.
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ansible/molecule | molecule/dependency/gilt.py | Gilt.bake | def bake(self):
"""
Bake a ``gilt`` command so it's ready to execute and returns None.
:return: None
"""
self._sh_command = getattr(sh, self.command)
self._sh_command = self._sh_command.bake(
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"""
Bake a ``gilt`` command so it's ready to execute and returns None.
:return: None
"""
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self._sh_command = self._sh_command.bake(
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ansible/molecule | molecule/command/cleanup.py | Cleanup.execute | def execute(self):
"""
Execute the actions necessary to cleanup the instances and returns
None.
:return: None
"""
self.print_info()
if not self._config.provisioner.playbooks.cleanup:
msg = 'Skipping, cleanup playbook not configured.'
LOG.... | python | def execute(self):
"""
Execute the actions necessary to cleanup the instances and returns
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:return: None
"""
self.print_info()
if not self._config.provisioner.playbooks.cleanup:
msg = 'Skipping, cleanup playbook not configured.'
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ansible/molecule | molecule/provisioner/lint/ansible_lint.py | AnsibleLintMixin.bake | def bake(self):
"""
Bake an `ansible-lint` command so it's ready to execute and returns
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:return: None
"""
options = self.options
default_exclude_list = options.pop('default_exclude')
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"""
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ansible/molecule | molecule/util.py | print_environment_vars | def print_environment_vars(env):
"""
Print ``Ansible`` and ``Molecule`` environment variables and returns None.
:param env: A dict containing the shell's environment as collected by
``os.environ``.
:return: None
"""
ansible_env = {k: v for (k, v) in env.items() if 'ANSIBLE_' in k}
print... | python | def print_environment_vars(env):
"""
Print ``Ansible`` and ``Molecule`` environment variables and returns None.
:param env: A dict containing the shell's environment as collected by
``os.environ``.
:return: None
"""
ansible_env = {k: v for (k, v) in env.items() if 'ANSIBLE_' in k}
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ansible/molecule | molecule/util.py | run_command | def run_command(cmd, debug=False):
"""
Execute the given command and returns None.
:param cmd: A ``sh.Command`` object to execute.
:param debug: An optional bool to toggle debug output.
:return: ``sh`` object
"""
if debug:
# WARN(retr0h): Uses an internal ``sh`` data structure to di... | python | def run_command(cmd, debug=False):
"""
Execute the given command and returns None.
:param cmd: A ``sh.Command`` object to execute.
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:return: ``sh`` object
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ansible/molecule | molecule/util.py | write_file | def write_file(filename, content):
"""
Writes a file with the given filename and content and returns None.
:param filename: A string containing the target filename.
:param content: A string containing the data to be written.
:return: None
"""
with open_file(filename, 'w') as f:
f.wr... | python | def write_file(filename, content):
"""
Writes a file with the given filename and content and returns None.
:param filename: A string containing the target filename.
:param content: A string containing the data to be written.
:return: None
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ansible/molecule | molecule/util.py | file_prepender | def file_prepender(filename):
"""
Prepend an informational header on files managed by Molecule and returns
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:param filename: A string containing the target filename.
:return: None
"""
with open_file(filename, 'r+') as f:
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f.seek(0, 0)
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"""
Prepend an informational header on files managed by Molecule and returns
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ansible/molecule | molecule/util.py | safe_dump | def safe_dump(data):
"""
Dump the provided data to a YAML document and returns a string.
:param data: A string containing an absolute path to the file to parse.
:return: str
"""
# TODO(retr0h): Do we need to encode?
# yaml.dump(data) produces the document as a str object in both python
... | python | def safe_dump(data):
"""
Dump the provided data to a YAML document and returns a string.
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ansible/molecule | molecule/util.py | safe_load | def safe_load(string):
"""
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:return: dict
"""
try:
return yaml.safe_load(string) or {}
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ansible/molecule | molecule/util.py | merge_dicts | def merge_dicts(a, b):
"""
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- c: 2
d:
e: "aaa"
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dict b
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b:
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b:
- c: 0
- c: 2
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e: "aaa"
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ansible/molecule | molecule/model/schema_v2.py | Validator._validate_unique | def _validate_unique(self, unique, field, value):
"""Ensure value uniqueness.
The rule's arguments are validated against this schema:
{'type': 'boolean'}
"""
if unique:
root_key = self.schema_path[0]
data = (doc[field] for doc in self.root_document[root_k... | python | def _validate_unique(self, unique, field, value):
"""Ensure value uniqueness.
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ansible/molecule | molecule/model/schema_v2.py | Validator._validate_disallowed | def _validate_disallowed(self, disallowed, field, value):
""" Readonly but with a custom error.
The rule's arguments are validated against this schema:
{'type': 'boolean'}
"""
if disallowed:
msg = 'disallowed user provided config option'
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ansible/molecule | molecule/model/schema_v2.py | Validator._validate_molecule_env_var | def _validate_molecule_env_var(self, molecule_env_var, field, value):
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The rule's arguments are validated against this schema:
{'type': 'boolean'}
"""
# TODO(retr0h): This needs to be better handled.
pattern = r'^[{$]+MOLECULE[_a-z0-9... | python | def _validate_molecule_env_var(self, molecule_env_var, field, value):
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ansible/molecule | molecule/command/idempotence.py | Idempotence.execute | def execute(self):
"""
Execute the actions necessary to perform a `molecule idempotence` and
returns None.
:return: None
"""
self.print_info()
if not self._config.state.converged:
msg = 'Instances not converged. Please converge instances first.'
... | python | def execute(self):
"""
Execute the actions necessary to perform a `molecule idempotence` and
returns None.
:return: None
"""
self.print_info()
if not self._config.state.converged:
msg = 'Instances not converged. Please converge instances first.'
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ansible/molecule | molecule/command/idempotence.py | Idempotence._is_idempotent | def _is_idempotent(self, output):
"""
Parses the output of the provisioning for changed and returns a bool.
:param output: A string containing the output of the ansible run.
:return: bool
"""
# Remove blank lines to make regex matches easier
output = re.sub(r'\n... | python | def _is_idempotent(self, output):
"""
Parses the output of the provisioning for changed and returns a bool.
:param output: A string containing the output of the ansible run.
:return: bool
"""
# Remove blank lines to make regex matches easier
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ansible/molecule | molecule/command/idempotence.py | Idempotence._non_idempotent_tasks | def _non_idempotent_tasks(self, output):
"""
Parses the output to identify the non idempotent tasks.
:param (str) output: A string containing the output of the ansible run.
:return: A list containing the names of the non idempotent tasks.
"""
# Remove blank lines to make... | python | def _non_idempotent_tasks(self, output):
"""
Parses the output to identify the non idempotent tasks.
:param (str) output: A string containing the output of the ansible run.
:return: A list containing the names of the non idempotent tasks.
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ansible/molecule | molecule/command/init/scenario.py | scenario | def scenario(ctx, dependency_name, driver_name, lint_name, provisioner_name,
role_name, scenario_name, verifier_name): # pragma: no cover
""" Initialize a new scenario for use with Molecule. """
command_args = {
'dependency_name': dependency_name,
'driver_name': driver_name,
... | python | def scenario(ctx, dependency_name, driver_name, lint_name, provisioner_name,
role_name, scenario_name, verifier_name): # pragma: no cover
""" Initialize a new scenario for use with Molecule. """
command_args = {
'dependency_name': dependency_name,
'driver_name': driver_name,
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ansible/molecule | molecule/command/init/scenario.py | Scenario.execute | def execute(self):
"""
Execute the actions necessary to perform a `molecule init scenario` and
returns None.
:return: None
"""
scenario_name = self._command_args['scenario_name']
role_name = os.getcwd().split(os.sep)[-1]
role_directory = util.abs_path(os.... | python | def execute(self):
"""
Execute the actions necessary to perform a `molecule init scenario` and
returns None.
:return: None
"""
scenario_name = self._command_args['scenario_name']
role_name = os.getcwd().split(os.sep)[-1]
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ansible/molecule | molecule/driver/docker.py | Docker.sanity_checks | def sanity_checks(self):
"""Implement Docker driver sanity checks."""
if self._config.state.sanity_checked:
return
log.info("Sanity checks: '{}'".format(self._name))
HAS_DOCKER_PY = None
try:
from ansible.module_utils.docker_common import HAS_DOCKER_PY
... | python | def sanity_checks(self):
"""Implement Docker driver sanity checks."""
if self._config.state.sanity_checked:
return
log.info("Sanity checks: '{}'".format(self._name))
HAS_DOCKER_PY = None
try:
from ansible.module_utils.docker_common import HAS_DOCKER_PY
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ansible/molecule | molecule/verifier/lint/flake8.py | Flake8.bake | def bake(self):
"""
Bake a `flake8` command so it's ready to execute and returns None.
:return: None
"""
self._flake8_command = sh.flake8.bake(
self.options,
self._tests,
_env=self.env,
_out=LOG.out,
_err=LOG.error) | python | def bake(self):
"""
Bake a `flake8` command so it's ready to execute and returns None.
:return: None
"""
self._flake8_command = sh.flake8.bake(
self.options,
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_err=LOG.error) | [
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ansible/molecule | molecule/dependency/ansible_galaxy.py | AnsibleGalaxy._setup | def _setup(self):
"""
Prepare the system for using ``ansible-galaxy`` and returns None.
:return: None
"""
role_directory = os.path.join(self._config.scenario.directory,
self.options['roles-path'])
if not os.path.isdir(role_directory)... | python | def _setup(self):
"""
Prepare the system for using ``ansible-galaxy`` and returns None.
:return: None
"""
role_directory = os.path.join(self._config.scenario.directory,
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ansible/molecule | molecule/command/converge.py | converge | def converge(ctx, scenario_name, ansible_args): # pragma: no cover
"""
Use the provisioner to configure instances (dependency, create, prepare
converge).
"""
args = ctx.obj.get('args')
subcommand = base._get_subcommand(__name__)
command_args = {
'subcommand': subcommand,
}
... | python | def converge(ctx, scenario_name, ansible_args): # pragma: no cover
"""
Use the provisioner to configure instances (dependency, create, prepare
converge).
"""
args = ctx.obj.get('args')
subcommand = base._get_subcommand(__name__)
command_args = {
'subcommand': subcommand,
}
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ansible/molecule | molecule/command/converge.py | Converge.execute | def execute(self):
"""
Execute the actions necessary to perform a `molecule converge` and
returns None.
:return: None
"""
self.print_info()
self._config.provisioner.converge()
self._config.state.change_state('converged', True) | python | def execute(self):
"""
Execute the actions necessary to perform a `molecule converge` and
returns None.
:return: None
"""
self.print_info()
self._config.provisioner.converge()
self._config.state.change_state('converged', True) | [
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ansible/molecule | molecule/scenarios.py | Scenarios.all | def all(self):
"""
Return a list containing all scenario objects.
:return: list
"""
if self._scenario_name:
scenarios = self._filter_for_scenario()
self._verify()
return scenarios
scenarios = [c.scenario for c in self._configs]
... | python | def all(self):
"""
Return a list containing all scenario objects.
:return: list
"""
if self._scenario_name:
scenarios = self._filter_for_scenario()
self._verify()
return scenarios
scenarios = [c.scenario for c in self._configs]
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ansible/molecule | molecule/scenarios.py | Scenarios._verify | def _verify(self):
"""
Verify the specified scenario was found and returns None.
:return: None
"""
scenario_names = [c.scenario.name for c in self._configs]
if self._scenario_name not in scenario_names:
msg = ("Scenario '{}' not found. "
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"""
Verify the specified scenario was found and returns None.
:return: None
"""
scenario_names = [c.scenario.name for c in self._configs]
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ansible/molecule | molecule/scenarios.py | Scenarios._filter_for_scenario | def _filter_for_scenario(self):
"""
Find the scenario matching the provided scenario name and returns a
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:return: list
"""
return [
c.scenario for c in self._configs
if c.scenario.name == self._scenario_name
] | python | def _filter_for_scenario(self):
"""
Find the scenario matching the provided scenario name and returns a
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:return: list
"""
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ansible/molecule | molecule/scenarios.py | Scenarios._get_matrix | def _get_matrix(self):
"""
Build a matrix of scenarios with sequence to include and returns a
dict.
{
scenario_1: {
'subcommand': [
'action-1',
'action-2',
],
},
scenario_2: {
... | python | def _get_matrix(self):
"""
Build a matrix of scenarios with sequence to include and returns a
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{
scenario_1: {
'subcommand': [
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ansible/molecule | molecule/logger.py | get_logger | def get_logger(name=None):
"""
Build a logger with the given name and returns the logger.
:param name: The name for the logger. This is usually the module
name, ``__name__``.
:return: logger object
"""
logging.setLoggerClass(CustomLogger)
logger = logging.getLogger(name)
... | python | def get_logger(name=None):
"""
Build a logger with the given name and returns the logger.
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:return: logger object
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] | 766dc35b0b0ce498cd5e3a62b40f828742d0d08c | https://github.com/ansible/molecule/blob/766dc35b0b0ce498cd5e3a62b40f828742d0d08c/molecule/logger.py#L86-L107 | train |
ansible/molecule | molecule/command/lint.py | lint | def lint(ctx, scenario_name): # pragma: no cover
""" Lint the role. """
args = ctx.obj.get('args')
subcommand = base._get_subcommand(__name__)
command_args = {
'subcommand': subcommand,
}
base.execute_cmdline_scenarios(scenario_name, args, command_args) | python | def lint(ctx, scenario_name): # pragma: no cover
""" Lint the role. """
args = ctx.obj.get('args')
subcommand = base._get_subcommand(__name__)
command_args = {
'subcommand': subcommand,
}
base.execute_cmdline_scenarios(scenario_name, args, command_args) | [
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ansible/molecule | molecule/command/lint.py | Lint.execute | def execute(self):
"""
Execute the actions necessary to perform a `molecule lint` and
returns None.
:return: None
"""
self.print_info()
linters = [
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"""
Execute the actions necessary to perform a `molecule lint` and
returns None.
:return: None
"""
self.print_info()
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ansible/molecule | molecule/provisioner/ansible.py | Ansible.inventory | def inventory(self):
"""
Create an inventory structure and returns a dict.
.. code-block:: yaml
ungrouped:
vars:
foo: bar
hosts:
instance-1:
instance-2:
children:
$child_group_n... | python | def inventory(self):
"""
Create an inventory structure and returns a dict.
.. code-block:: yaml
ungrouped:
vars:
foo: bar
hosts:
instance-1:
instance-2:
children:
$child_group_n... | [
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ansible/molecule | molecule/provisioner/ansible.py | Ansible.cleanup | def cleanup(self):
"""
Executes `ansible-playbook` against the cleanup playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.cleanup)
pb.execute() | python | def cleanup(self):
"""
Executes `ansible-playbook` against the cleanup playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.cleanup)
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ansible/molecule | molecule/provisioner/ansible.py | Ansible.converge | def converge(self, playbook=None, **kwargs):
"""
Executes ``ansible-playbook`` against the converge playbook unless
specified otherwise and returns a string.
:param playbook: An optional string containing an absolute path to a
playbook.
:param kwargs: An optional keywor... | python | def converge(self, playbook=None, **kwargs):
"""
Executes ``ansible-playbook`` against the converge playbook unless
specified otherwise and returns a string.
:param playbook: An optional string containing an absolute path to a
playbook.
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ansible/molecule | molecule/provisioner/ansible.py | Ansible.destroy | def destroy(self):
"""
Executes ``ansible-playbook`` against the destroy playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.destroy)
pb.execute() | python | def destroy(self):
"""
Executes ``ansible-playbook`` against the destroy playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.destroy)
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ansible/molecule | molecule/provisioner/ansible.py | Ansible.side_effect | def side_effect(self):
"""
Executes ``ansible-playbook`` against the side_effect playbook and
returns None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.side_effect)
pb.execute() | python | def side_effect(self):
"""
Executes ``ansible-playbook`` against the side_effect playbook and
returns None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.side_effect)
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ansible/molecule | molecule/provisioner/ansible.py | Ansible.create | def create(self):
"""
Executes ``ansible-playbook`` against the create playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.create)
pb.execute() | python | def create(self):
"""
Executes ``ansible-playbook`` against the create playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.create)
pb.execute() | [
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ansible/molecule | molecule/provisioner/ansible.py | Ansible.prepare | def prepare(self):
"""
Executes ``ansible-playbook`` against the prepare playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.prepare)
pb.execute() | python | def prepare(self):
"""
Executes ``ansible-playbook`` against the prepare playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.prepare)
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ansible/molecule | molecule/provisioner/ansible.py | Ansible.syntax | def syntax(self):
"""
Executes ``ansible-playbook`` against the converge playbook with the
``-syntax-check`` flag and returns None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.converge)
pb.add_cli_arg('syntax-check', True)
pb.execute(... | python | def syntax(self):
"""
Executes ``ansible-playbook`` against the converge playbook with the
``-syntax-check`` flag and returns None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.converge)
pb.add_cli_arg('syntax-check', True)
pb.execute(... | [
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ansible/molecule | molecule/provisioner/ansible.py | Ansible.verify | def verify(self):
"""
Executes ``ansible-playbook`` against the verify playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.verify)
pb.execute() | python | def verify(self):
"""
Executes ``ansible-playbook`` against the verify playbook and returns
None.
:return: None
"""
pb = self._get_ansible_playbook(self.playbooks.verify)
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