tfdbg.DebugDumpDir

class tfdbg.DebugDumpDir

See the guide: TensorFlow Debugger > Classes for debug-dump data and directories

Data set from a debug-dump directory on filesystem.

An instance of DebugDumpDir contains all DebugTensorDatum instances in a tfdbg dump root directory.

Properties

dumped_tensor_data

run_feed_keys_info

Get a str representation of the feed_dict used in the Session.run() call.

Returns:

If the information is available, a str obtained from repr(feed_dict). If the information is not available, None.

run_fetches_info

Get a str representation of the fetches used in the Session.run() call.

Returns:

If the information is available, a str obtained from repr(fetches). If the information is not available, None.

size

Total number of dumped tensors in the dump root directory.

Returns:

(int) total number of dumped tensors in the dump root directory.

t0

Absolute timestamp of the first dumped tensor.

Returns:

(int) absolute timestamp of the first dumped tensor, in microseconds.

Methods

__init__(dump_root, partition_graphs=None, validate=True)

DebugDumpDir constructor.

Args:

• dump_root: (str) path to the dump root directory.
• partition_graphs: A repeated field of GraphDefs representing the partition graphs executed by the TensorFlow runtime.
• validate: (bool) whether the dump files are to be validated against the partition graphs.

Raises:

• IOError: If dump_root does not exist as a directory.

debug_watch_keys(node_name)

Get all tensor watch keys of given node according to partition graphs.

Args:

• node_name: (str) name of the node.

Returns:

(list of str) all debug tensor watch keys. Returns an empty list if the node name does not correspond to any debug watch keys.

Raises:

LookupError: If debug watch information has not been loaded from partition graphs yet.

devices()

Get the list of devices.

Returns:

(list of str) names of the devices.

Raises:

• LookupError: If node inputs and control inputs have not been loaded from partition graphs yet.

find(predicate, first_n=0)

Find dumped tensor data by a certain predicate.

Args:

• predicate: A callable that takes two input arguments:

  python def predicate(debug_tensor_datum, tensor): # returns a bool

  where debug_tensor_datum is an instance of DebugTensorDatum, which carries the metadata, such as the Tensor's node name, output slot timestamp, debug op name, etc.; and tensor is the dumped tensor value as a numpy.ndarray. * first_n: (int) return only the first n DebugTensotDatum instances (in time order) for which the predicate returns True. To return all the DebugTensotDatum instances, let first_n be <= 0.

Returns:

A list of all DebugTensorDatum objects in this DebugDumpDir object for which predicate returns True, sorted in ascending order of the timestamp.

get_dump_sizes_bytes(node_name, output_slot, debug_op)

Get the sizes of the dump files for a debug-dumped tensor.

Unit of the file size: byte.

Args:

• node_name: (str) name of the node that the tensor is produced by.
• output_slot: (int) output slot index of tensor.
• debug_op: (str) name of the debug op.

Returns:

(list of int): list of dump file sizes in bytes.

Raises:

• ValueError: If the tensor watch key does not exist in the debug dump data.

get_rel_timestamps(node_name, output_slot, debug_op)

Get the relative timestamp from for a debug-dumped tensor.

Relative timestamp means (absolute timestamp - t0), where t0 is the absolute timestamp of the first dumped tensor in the dump root. The tensor may be dumped multiple times in the dump root directory, so a list of relative timestamps (numpy.ndarray) is returned.

Args:

• node_name: (str) name of the node that the tensor is produced by.
• output_slot: (int) output slot index of tensor.
• debug_op: (str) name of the debug op.

Returns:

(list of int) list of relative timestamps.

Raises:

• ValueError: If the tensor watch key does not exist in the debug dump data.

get_tensor_file_paths(node_name, output_slot, debug_op)

Get the file paths from a debug-dumped tensor.

Args:

• node_name: (str) name of the node that the tensor is produced by.
• output_slot: (int) output slot index of tensor.
• debug_op: (str) name of the debug op.

Returns:

List of file path(s) loaded. This is a list because each debugged tensor may be dumped multiple times.

Raises:

• ValueError: If the tensor does not exist in the debug-dump data.

get_tensors(node_name, output_slot, debug_op)

Get the tensor value from for a debug-dumped tensor.

The tensor may be dumped multiple times in the dump root directory, so a list of tensors (numpy.ndarray) is returned.

Args:

• node_name: (str) name of the node that the tensor is produced by.
• output_slot: (int) output slot index of tensor.
• debug_op: (str) name of the debug op.

Returns:

List of tensors (numpy.ndarray) loaded from the debug-dump file(s).

Raises:

• ValueError: If the tensor does not exist in the debug-dump data.

loaded_partition_graphs()

Test whether partition graphs have been loaded.

node_attributes(node_name)

Get the attributes of a node.

Args:

• node_name: Name of the node in question.

Returns:

Attributes of the node.

Raises:

• LookupError: If no partition graphs have been loaded.
• ValueError: If no node named node_name exists.

node_device(node_name)

Get the device of a node.

Args:

• node_name: (str) name of the node.

Returns:

(str) name of the device on which the node is placed.

Raises:

• LookupError: If node inputs and control inputs have not been loaded from partition graphs yet.
• ValueError: If the node does not exist in partition graphs.

node_exists(node_name)

Test if a node exists in the partition graphs.

Args:

• node_name: (str) name of the node to be checked.

Returns:

A boolean indicating whether the node exists.

Raises:

• LookupError: If no partition graphs have been loaded yet.

node_inputs(node_name, is_control=False)

Get the inputs of given node according to partition graphs.

Args:

• node_name: Name of the node.
• is_control: (bool) Whether control inputs, rather than non-control inputs, are to be returned.

Returns:

(list of str) inputs to the node, as a list of node names.

Raises:

• LookupError: If node inputs and control inputs have not been loaded from partition graphs yet.
• ValueError: If the node does not exist in partition graphs.

node_op_type(node_name)

Get the op type of given node.

Args:

• node_name: (str) name of the node.

Returns:

(str) op type of the node.

Raises:

• LookupError: If node op types have not been loaded from partition graphs yet.
• ValueError: If the node does not exist in partition graphs.

node_recipients(node_name, is_control=False)

Get recipient of the given node's output according to partition graphs.

Args:

• node_name: (str) name of the node.
• is_control: (bool) whether control outputs, rather than non-control outputs, are to be returned.

Returns:

(list of str) all inputs to the node, as a list of node names.

Raises:

• LookupError: If node inputs and control inputs have not been loaded from partition graphs yet.
• ValueError: If the node does not exist in partition graphs.

node_traceback(element_name)

Try to retrieve the Python traceback of node's construction.

Args:

• element_name: (str) Name of a graph element (node or tensor).

Returns:

(list) The traceback list object as returned by the extract_trace method of Python's traceback module.

Raises:

• LookupError: If Python graph is not available for traceback lookup.
• KeyError: If the node cannot be found in the Python graph loaded.

nodes()

Get a list of all nodes from the partition graphs.

Returns:

All nodes' names, as a list of str.

Raises:

• LookupError: If no partition graphs have been loaded.

partition_graphs()

Get the partition graphs.

Returns:

Partition graphs as repeated fields of GraphDef.

Raises:

• LookupError: If no partition graphs have been loaded.

set_python_graph(python_graph)

Provide Python Graph object to the wrapper.

Unlike the partition graphs, which are protobuf GraphDef objects, Graph is a Python object and carries additional information such as the traceback of the construction of the nodes in the graph.

Args:

• python_graph: (ops.Graph) The Python Graph object.

transitive_inputs(node_name, include_control=True)

Get the transitive inputs of given node according to partition graphs.

Args:

• node_name: Name of the node
• include_control: Include control inputs (True by default).

Returns:

(list of str) all transitive inputs to the node, as a list of node names.

Raises:

• LookupError: If node inputs and control inputs have not been loaded from partition graphs yet.
• ValueError: If the node does not exist in partition graphs.

watch_key_to_data(debug_watch_key)

Get all DebugTensorDatum instances corresponding to a debug watch key.

Args:

• debug_watch_key: (str) debug watch key.

Returns:

A list of DebugTensorDatum instances that correspond to the debug watch key. If the watch key does not exist, returns an empty list.

Raises:

• ValueError: If the debug watch key does not exist.

Defined in tensorflow/python/debug/lib/debug_data.py.