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 __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)