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[tune] Remove unused TF loggers (#7090)
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@ericl
ericl committed Feb 9, 2020
1 parent 98a07fe commit 48e2adb
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Showing 3 changed files with 1 addition and 175 deletions.
15 changes: 0 additions & 15 deletions doc/source/tune-usage.rst
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Expand Up @@ -618,21 +618,6 @@ You can pass in your own logging mechanisms to output logs in custom formats as
These loggers will be called along with the default Tune loggers. All loggers must inherit the `Logger interface <tune-package-ref.html#ray.tune.logger.Logger>`__. Tune enables default loggers for Tensorboard, CSV, and JSON formats. You can also check out `logger.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/logger.py>`__ for implementation details. An example can be found in `logging_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__.

TBXLogger (TensorboardX)
~~~~~~~~~~~~~~~~~~~~~~~~

Tune provides a logger using `TensorBoardX <https://github.com/lanpa/tensorboardX>`_. You can install tensorboardX via ``pip install tensorboardX`` or ``pip install 'ray[tune]'``. This logger automatically outputs loggers similar to using a default TensorFlow logging format. By default, it will log any scalar value provided via the result dictionary along with HParams information.

.. code-block:: python
from ray.tune.logger import TBXLogger
tune.run(
MyTrainableClass,
name="experiment_name",
loggers=[TBXLogger]
)
MLFlow
~~~~~~

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150 changes: 0 additions & 150 deletions python/ray/tune/logger.py
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Expand Up @@ -3,7 +3,6 @@
import logging
import os
import yaml
import distutils.version
import numbers

import numpy as np
Expand Down Expand Up @@ -132,155 +131,6 @@ def update_config(self, config):
cloudpickle.dump(self.config, f)


def tf2_compat_logger(config, logdir, trial=None):
"""Chooses TensorBoard logger depending on imported TF version."""
global tf
if "RLLIB_TEST_NO_TF_IMPORT" in os.environ:
logger.warning("Not importing TensorFlow for test purposes")
tf = None
raise RuntimeError("Not importing TensorFlow for test purposes")
else:
import tensorflow as tf
use_tf2_api = (distutils.version.LooseVersion(tf.__version__) >=
distutils.version.LooseVersion("1.15.0"))
if use_tf2_api:
# This is temporarily for RLlib because it disables v2 behavior...
from tensorflow.python import tf2
if not tf2.enabled():
tf = tf.compat.v1
return TFLogger(config, logdir, trial)
tf = tf.compat.v2 # setting this for TF2.0
return TF2Logger(config, logdir, trial)
else:
return TFLogger(config, logdir, trial)


class TF2Logger(Logger):
"""TensorBoard Logger for TF version >= 2.0.0.
Automatically flattens nested dicts to show on TensorBoard:
{"a": {"b": 1, "c": 2}} -> {"a/b": 1, "a/c": 2}
If you need to do more advanced logging, it is recommended
to use a Summary Writer in the Trainable yourself.
"""

def _init(self):
global tf
if tf is None:
import tensorflow as tf
tf = tf.compat.v2 # setting this for TF2.0
self._file_writer = None
self._hp_logged = False

def on_result(self, result):
if self._file_writer is None:
from tensorflow.python.eager import context
from tensorboard.plugins.hparams import api as hp
self._context = context
self._file_writer = tf.summary.create_file_writer(self.logdir)
with tf.device("/CPU:0"):
with tf.summary.record_if(True), self._file_writer.as_default():
step = result.get(
TIMESTEPS_TOTAL) or result[TRAINING_ITERATION]

tmp = result.copy()
if not self._hp_logged:
if self.trial and self.trial.evaluated_params:
try:
hp.hparams(
self.trial.evaluated_params,
trial_id=self.trial.trial_id)
except Exception as exc:
logger.error("HParams failed with %s", exc)
self._hp_logged = True

for k in [
"config", "pid", "timestamp", TIME_TOTAL_S,
TRAINING_ITERATION
]:
if k in tmp:
del tmp[k] # not useful to log these

flat_result = flatten_dict(tmp, delimiter="/")
path = ["ray", "tune"]
for attr, value in flat_result.items():
if type(value) in VALID_SUMMARY_TYPES:
tf.summary.scalar(
"/".join(path + [attr]), value, step=step)
self._file_writer.flush()

def flush(self):
if self._file_writer is not None:
self._file_writer.flush()

def close(self):
if self._file_writer is not None:
self._file_writer.close()


def to_tf_values(result, path):
from tensorboardX.summary import make_histogram
flat_result = flatten_dict(result, delimiter="/")
values = []
for attr, value in flat_result.items():
if type(value) in VALID_SUMMARY_TYPES:
values.append(
tf.Summary.Value(
tag="/".join(path + [attr]), simple_value=value))
elif type(value) is list and len(value) > 0:
values.append(
tf.Summary.Value(
tag="/".join(path + [attr]),
histo=make_histogram(values=np.array(value), bins=10)))
return values


class TFLogger(Logger):
"""TensorBoard Logger for TF version < 2.0.0.
Automatically flattens nested dicts to show on TensorBoard:
{"a": {"b": 1, "c": 2}} -> {"a/b": 1, "a/c": 2}
If you need to do more advanced logging, it is recommended
to use a Summary Writer in the Trainable yourself.
"""

def _init(self):
global tf
if tf is None:
import tensorflow as tf
tf = tf.compat.v1 # setting this for regular TF logger
logger.debug("Initializing TFLogger instead of TF2Logger.")
self._file_writer = tf.summary.FileWriter(self.logdir)

def on_result(self, result):
tmp = result.copy()
for k in [
"config", "pid", "timestamp", TIME_TOTAL_S, TRAINING_ITERATION
]:
if k in tmp:
del tmp[k] # not useful to tf log these
values = to_tf_values(tmp, ["ray", "tune"])
train_stats = tf.Summary(value=values)
t = result.get(TIMESTEPS_TOTAL) or result[TRAINING_ITERATION]
self._file_writer.add_summary(train_stats, t)
iteration_value = to_tf_values({
TRAINING_ITERATION: result[TRAINING_ITERATION]
}, ["ray", "tune"])
iteration_stats = tf.Summary(value=iteration_value)
self._file_writer.add_summary(iteration_stats, t)
self._file_writer.flush()

def flush(self):
self._file_writer.flush()

def close(self):
self._file_writer.close()


class CSVLogger(Logger):
"""Logs results to progress.csv under the trial directory.
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11 changes: 1 addition & 10 deletions python/ray/tune/tests/test_logger.py
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Expand Up @@ -3,7 +3,7 @@
import tempfile
import shutil

from ray.tune.logger import tf2_compat_logger, JsonLogger, CSVLogger, TBXLogger
from ray.tune.logger import JsonLogger, CSVLogger, TBXLogger

Trial = namedtuple("MockTrial", ["evaluated_params", "trial_id"])

Expand All @@ -25,15 +25,6 @@ def setUp(self):
def tearDown(self):
shutil.rmtree(self.test_dir, ignore_errors=True)

def testTensorBoardLogger(self):
config = {"a": 2, "b": 5}
t = Trial(evaluated_params=config, trial_id=5342)
logger = tf2_compat_logger(
config=config, logdir=self.test_dir, trial=t)
logger.on_result(result(2, 4))
logger.on_result(result(2, 4))
logger.close()

def testCSV(self):
config = {"a": 2, "b": 5}
t = Trial(evaluated_params=config, trial_id="csv")
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