def __init__(self,
logdir,
max_queue=10,
flush_secs=120,
filename_suffix=""):
"""Summary writer for TensorBoard, compatible with eager execution.
If necessary, multiple instances of `SummaryWriter` can be created, with
distinct `logdir`s and `name`s. Each `SummaryWriter` instance will retain
its independent `global_step` counter and data writing destination.
Example:
```python
writer = tfe.SummaryWriter("my_model")
# ... Code that sets up the model and data batches ...
for _ in xrange(train_iters):
loss = model.train_batch(batch)
writer.scalar("loss", loss)
writer.step()
```
Args:
logdir: Directory in which summary files will be written.
max_queue: Number of summary items to buffer before flushing to
filesystem. If 0, summaries will be flushed immediately.
flush_secs: Number of secondsbetween forced commits to disk.
filename_suffix: Suffix of the event protobuf files in which the summary
data are stored.
Raises:
ValueError: If this constructor is called not under eager execution.
"""
# TODO(apassos, ashankar): Make this class and the underlying
# contrib.summary_ops compatible with graph model and remove this check.
if not context.in_eager_mode():
raise ValueError(
"Use of SummaryWriter is currently supported only with eager "
"execution enabled. File an issue at "
"https://github.com/tensorflow/tensorflow/issues/new to express "
"interest in fixing this.")
# TODO(cais): Consider adding name keyword argument, which if None or empty,
# will register the global global_step that training_util.get_global_step()
# can find.
with context.device(self._CPU_DEVICE):
self._name = uuid.uuid4().hex
self._global_step = 0
self._global_step_tensor = variable_scope.get_variable(
"global_step/summary_writer/" + self._name,
shape=[], dtype=dtypes.int64,
initializer=init_ops.zeros_initializer())
self._global_step_dirty = False
self._resource = gen_summary_ops.summary_writer(shared_name=self._name)
gen_summary_ops.create_summary_file_writer(
self._resource, logdir, max_queue, flush_secs, filename_suffix)
# Delete the resource when this object is deleted
self._resource_deleter = resource_variable_ops.EagerResourceDeleter(
handle=self._resource, handle_device=self._CPU_DEVICE)
def _make_summary_writer(name, factory, **kwargs):
resource = gen_summary_ops.summary_writer(shared_name=name)
# TODO(apassos): Consider doing this instead.
# node = factory(resource, **kwargs)
# if not context.executing_eagerly():
# ops.get_default_session().run(node)
ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME,
factory(resource, **kwargs))
return SummaryWriter(resource)
def _make_summary_writer(name, factory, **kwargs):
resource = gen_summary_ops.summary_writer(shared_name=name)
# TODO(apassos): Consider doing this instead.
# node = factory(resource, **kwargs)
# if not context.in_eager_mode():
# ops.get_default_session().run(node)
ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME,
factory(resource, **kwargs))
return SummaryWriter(resource)
def __init__(self,
logdir,
max_queue=10,
flush_secs=120,
filename_suffix=""):
"""Summary writer for TensorBoard, compatible with eager execution.
If necessary, multiple instances of `SummaryWriter` can be created, with
distinct `logdir`s and `name`s. Each `SummaryWriter` instance will retain
its independent `global_step` counter and data writing destination.
Example:
```python
writer = tfe.SummaryWriter("my_model")
# ... Code that sets up the model and data batches ...
for _ in xrange(train_iters):
loss = model.train_batch(batch)
writer.scalar("loss", loss)
writer.step()
```
Args:
logdir: Directory in which summary files will be written.
max_queue: Number of summary items to buffer before flushing to
filesystem. If 0, summaries will be flushed immediately.
flush_secs: Number of secondsbetween forced commits to disk.
filename_suffix: Suffix of the event protobuf files in which the summary
data are stored.
Raises:
ValueError: If this constructor is called not under eager execution.
"""
# TODO(apassos, ashankar): Make this class and the underlying
# contrib.summary_ops compatible with graph model and remove this check.
if not context.in_eager_mode():
raise ValueError(
"Use of SummaryWriter is currently supported only with eager "
"execution enabled. File an issue at "
"https://github.com/tensorflow/tensorflow/issues/new to express "
"interest in fixing this.")
# TODO(cais): Consider adding name keyword argument, which if None or empty,
# will register the global global_step that training_util.get_global_step()
# can find.
with context.device(self._CPU_DEVICE):
self._name = uuid.uuid4().hex
self._global_step = 0
self._global_step_tensor = variable_scope.get_variable(
"global_step/summary_writer/" + self._name,
shape=[], dtype=dtypes.int64,
initializer=init_ops.zeros_initializer())
self._global_step_dirty = False
self._resource = gen_summary_ops.summary_writer(shared_name=self._name)
gen_summary_ops.create_summary_file_writer(
self._resource, logdir, max_queue, flush_secs, filename_suffix)
def create_summary_file_writer(logdir,
max_queue=None,
flush_secs=None,
filename_suffix=None):
"""Creates a summary file writer in the current context."""
if max_queue is None:
max_queue = constant_op.constant(10)
if flush_secs is None:
flush_secs = constant_op.constant(120)
if filename_suffix is None:
filename_suffix = constant_op.constant("")
resource = gen_summary_ops.summary_writer()
gen_summary_ops.create_summary_file_writer(resource, logdir, max_queue,
flush_secs, filename_suffix)
context.context().summary_writer_resource = resource
def create_summary_file_writer(logdir,
max_queue=None,
flush_secs=None,
filename_suffix=None):
"""Creates a summary file writer in the current context."""
if max_queue is None:
max_queue = constant_op.constant(10)
if flush_secs is None:
flush_secs = constant_op.constant(120)
if filename_suffix is None:
filename_suffix = constant_op.constant("")
resource = gen_summary_ops.summary_writer()
gen_summary_ops.create_summary_file_writer(resource, logdir, max_queue,
flush_secs, filename_suffix)
context.context().summary_writer_resource = resource
def create_summary_file_writer(logdir,
max_queue=None,
flush_secs=None,
filename_suffix=None,
name=None):
"""Creates a summary file writer in the current context."""
if max_queue is None:
max_queue = constant_op.constant(10)
if flush_secs is None:
flush_secs = constant_op.constant(120)
if filename_suffix is None:
filename_suffix = constant_op.constant("")
resource = gen_summary_ops.summary_writer(shared_name=name)
# TODO(apassos) ensure the initialization op runs when in graph mode; consider
# calling session.run here.
gen_summary_ops.create_summary_file_writer(resource, logdir, max_queue,
flush_secs, filename_suffix)
return SummaryWriter(resource)
def create_summary_file_writer(logdir,
max_queue=None,
flush_secs=None,
filename_suffix=None,
name=None):
"""Creates a summary file writer in the current context."""
if max_queue is None:
max_queue = constant_op.constant(10)
if flush_secs is None:
flush_secs = constant_op.constant(120)
if filename_suffix is None:
filename_suffix = constant_op.constant("")
resource = gen_summary_ops.summary_writer(shared_name=name)
# TODO(apassos) ensure the initialization op runs when in graph mode; consider
# calling session.run here.
gen_summary_ops.create_summary_file_writer(resource, logdir, max_queue,
flush_secs, filename_suffix)
return SummaryWriter(resource)
def create_summary_file_writer(logdir,
max_queue=None,
flush_millis=None,
filename_suffix=None,
name=None):
"""Creates a summary file writer in the current context.
Args:
logdir: a string, or None. If a string, creates a summary file writer
which writes to the directory named by the string. If None, returns
a mock object which acts like a summary writer but does nothing,
useful to use as a context manager.
max_queue: the largest number of summaries to keep in a queue; will
flush once the queue gets bigger than this.
flush_millis: the largest interval between flushes.
filename_suffix: optional suffix for the event file name.
name: name for the summary writer.
Returns:
Either a summary writer or an empty object which can be used as a
summary writer.
"""
if logdir is None:
return SummaryWriter(None)
with ops.device("cpu:0"):
if max_queue is None:
max_queue = constant_op.constant(10)
if flush_millis is None:
flush_millis = constant_op.constant(2 * 60 * 1000)
if filename_suffix is None:
filename_suffix = constant_op.constant("")
resource = gen_summary_ops.summary_writer(shared_name=name)
# TODO(apassos) ensure the initialization op runs when in graph mode;
# consider calling session.run here.
ops.add_to_collection(
_SUMMARY_WRITER_INIT_COLLECTION_NAME,
gen_summary_ops.create_summary_file_writer(resource, logdir,
max_queue, flush_millis,
filename_suffix))
return SummaryWriter(resource)
def create_summary_file_writer(logdir,
max_queue=None,
flush_millis=None,
filename_suffix=None,
name=None):
"""Creates a summary file writer in the current context.
Args:
logdir: a string, or None. If a string, creates a summary file writer
which writes to the directory named by the string. If None, returns
a mock object which acts like a summary writer but does nothing,
useful to use as a context manager.
max_queue: the largest number of summaries to keep in a queue; will
flush once the queue gets bigger than this.
flush_millis: the largest interval between flushes.
filename_suffix: optional suffix for the event file name.
name: name for the summary writer.
Returns:
Either a summary writer or an empty object which can be used as a
summary writer.
"""
if logdir is None:
return SummaryWriter(None)
with ops.device("cpu:0"):
if max_queue is None:
max_queue = constant_op.constant(10)
if flush_millis is None:
flush_millis = constant_op.constant(2 * 60 * 1000)
if filename_suffix is None:
filename_suffix = constant_op.constant("")
resource = gen_summary_ops.summary_writer(shared_name=name)
# TODO(apassos) ensure the initialization op runs when in graph mode;
# consider calling session.run here.
ops.add_to_collection(
_SUMMARY_WRITER_INIT_COLLECTION_NAME,
gen_summary_ops.create_summary_file_writer(
resource, logdir, max_queue, flush_millis, filename_suffix))
return SummaryWriter(resource)
