# Copyright 2021 The NetKet Authors - All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from numbers import Number
from netket.utils.optional_deps import import_optional_dependency
def tree_log(tree, root, data):
"""
Maps all elements in tree, recursively calling tree_log with a new root string,
and when it reaches leaves pushes (string, leave) tuples to data.
Args:
tree: a pytree where the leaf nodes contain data
root: the root of the tags used to log to tensorboard
data: a container modified in place
"""
if tree is None:
return
elif isinstance(tree, list):
for i, val in enumerate(tree):
tree_log(val, root + f"/{i}", data)
elif isinstance(tree, list) and hasattr(tree, "_fields"):
for key in tree._fields:
tree_log(getattr(tree, key), root + f"/{key}", data)
elif isinstance(tree, tuple):
for i, val in enumerate(tree):
tree_log(val, root + f"/{i}", data)
elif isinstance(tree, dict):
for key, value in tree.items():
tree_log(value, root + f"/{key}", data) # noqa: F722
elif hasattr(tree, "to_compound"):
tree_log(tree.to_compound()[1], root, data) # noqa: F722
elif hasattr(tree, "to_dict"):
tree_log(tree.to_dict(), root, data) # noqa: F722
elif isinstance(tree, complex):
tree_log(tree.real, root + "/re", data) # noqa: F722
tree_log(tree.imag, root + "/im", data) # noqa: F722
else:
data.append((root, tree))
class TensorBoardLog:
"""
Creates a tensorboard logger using tensorboardX's summarywriter.
Refer to its documentation for further details
https://tensorboardx.readthedocs.io/en/latest/tensorboard.html
TensorBoardX must be installed.
Args:
logdir (string): Save directory location. Default is
runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.
Use hierarchical folder structure to compare
between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc.
for each new experiment to compare across them.
comment (string): Comment logdir suffix appended to the default
``logdir``. If ``logdir`` is assigned, this argument has no effect.
purge_step (int):
When logging crashes at step :math:`T+X` and restarts at step :math:`T`,
any events whose global_step larger or equal to :math:`T` will be
purged and hidden from TensorBoard.
Note that crashed and resumed experiments should have the same ``logdir``.
max_queue (int): Size of the queue for pending events and
summaries before one of the 'add' calls forces a flush to disk.
Default is ten items.
flush_secs (int): How often, in seconds, to flush the
pending events and summaries to disk. Default is every two minutes.
filename_suffix (string): Suffix added to all event filenames in
the logdir directory. More details on filename construction in
tensorboard.summary.writer.event_file_writer.EventFileWriter.
write_to_disk (boolean):
If pass `False`, TensorBoardLog will not write to disk.
Examples:
Logging optimisation to tensorboard.
>>> import pytest; pytest.skip("skip automated test of this docstring")
>>>
>>> import netket as nk
>>> # create a summary writer with automatically generated folder name.
>>> writer = nk.logging.TensorBoardLog()
>>> # folder location: runs/May04_22-14-54_s-MacBook-Pro.local/
>>> # create a summary writer using the specified folder name.
>>> writer = nk.logging.TensorBoardLog("my_experiment")
>>> # folder location: my_experiment
>>> # create a summary writer with comment appended.
>>> writer = nk.logging.TensorBoardLog(comment="LR_0.1_BATCH_16")
>>> # folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/
"""
__module__ = "netket.logging"
def __init__(
self,
*args,
**kwargs,
):
self._init_args = args
"""Store the args for the lazily initialized SummaryWriter's constructor."""
self._init_kwargs = kwargs
"""Store the kwargs for the lazily initialized SummaryWriter's constructor."""
self._writer = None
"""Lazily initialized summarywriter constructor"""
self._old_step = 0
def _init_tensorboard(self):
tensorboardX = import_optional_dependency(
"tensorboardX", descr="TensorBoardLog"
)
self._writer = tensorboardX.SummaryWriter(*self._init_args, **self._init_kwargs)
[docs] def __call__(self, step, item, machine):
if self._writer is None:
self._init_tensorboard()
data = []
tree_log(item, "", data)
for key, val in data:
if isinstance(val, Number):
self._writer.add_scalar(key[1:], val, step)
self._writer.flush()
self._old_step = step
def _flush_log(self):
if self._writer is not None:
self._writer.flush()
def _flush_params(self, _):
return None
[docs] def flush(self, machine=None):
"""
Writes to file the content of this logger.
:param machine: optionally also writes the parameters of the machine.
"""
self._flush_log()
if machine is not None:
self._flush_params(machine)
