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[tune] Remove unused TF loggers (#7090)
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Eric Liang
@@ -618,21 +618,6 @@ You can pass in your own logging mechanisms to output logs in custom formats as
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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>`__.
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TBXLogger (TensorboardX)
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~~~~~~~~~~~~~~~~~~~~~~~~
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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.
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.. code-block:: python
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from ray.tune.logger import TBXLogger
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tune.run(
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MyTrainableClass,
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name="experiment_name",
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loggers=[TBXLogger]
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)
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MLFlow
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~~~~~~
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@@ -3,7 +3,6 @@ import json
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import logging
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import os
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import yaml
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import distutils.version
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import numbers
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import numpy as np
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@@ -132,155 +131,6 @@ class JsonLogger(Logger):
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cloudpickle.dump(self.config, f)
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def tf2_compat_logger(config, logdir, trial=None):
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"""Chooses TensorBoard logger depending on imported TF version."""
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global tf
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if "RLLIB_TEST_NO_TF_IMPORT" in os.environ:
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logger.warning("Not importing TensorFlow for test purposes")
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tf = None
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raise RuntimeError("Not importing TensorFlow for test purposes")
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else:
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import tensorflow as tf
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use_tf2_api = (distutils.version.LooseVersion(tf.__version__) >=
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distutils.version.LooseVersion("1.15.0"))
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if use_tf2_api:
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# This is temporarily for RLlib because it disables v2 behavior...
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from tensorflow.python import tf2
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if not tf2.enabled():
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tf = tf.compat.v1
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return TFLogger(config, logdir, trial)
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tf = tf.compat.v2 # setting this for TF2.0
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return TF2Logger(config, logdir, trial)
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else:
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return TFLogger(config, logdir, trial)
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class TF2Logger(Logger):
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"""TensorBoard Logger for TF version >= 2.0.0.
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Automatically flattens nested dicts to show on TensorBoard:
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{"a": {"b": 1, "c": 2}} -> {"a/b": 1, "a/c": 2}
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If you need to do more advanced logging, it is recommended
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to use a Summary Writer in the Trainable yourself.
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"""
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def _init(self):
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global tf
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if tf is None:
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import tensorflow as tf
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tf = tf.compat.v2 # setting this for TF2.0
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self._file_writer = None
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self._hp_logged = False
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def on_result(self, result):
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if self._file_writer is None:
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from tensorflow.python.eager import context
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from tensorboard.plugins.hparams import api as hp
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self._context = context
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self._file_writer = tf.summary.create_file_writer(self.logdir)
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with tf.device("/CPU:0"):
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with tf.summary.record_if(True), self._file_writer.as_default():
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step = result.get(
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TIMESTEPS_TOTAL) or result[TRAINING_ITERATION]
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tmp = result.copy()
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if not self._hp_logged:
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if self.trial and self.trial.evaluated_params:
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try:
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hp.hparams(
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self.trial.evaluated_params,
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trial_id=self.trial.trial_id)
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except Exception as exc:
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logger.error("HParams failed with %s", exc)
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self._hp_logged = True
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for k in [
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"config", "pid", "timestamp", TIME_TOTAL_S,
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TRAINING_ITERATION
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]:
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if k in tmp:
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del tmp[k] # not useful to log these
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flat_result = flatten_dict(tmp, delimiter="/")
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path = ["ray", "tune"]
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for attr, value in flat_result.items():
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if type(value) in VALID_SUMMARY_TYPES:
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tf.summary.scalar(
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"/".join(path + [attr]), value, step=step)
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self._file_writer.flush()
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def flush(self):
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if self._file_writer is not None:
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self._file_writer.flush()
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def close(self):
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if self._file_writer is not None:
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self._file_writer.close()
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def to_tf_values(result, path):
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from tensorboardX.summary import make_histogram
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flat_result = flatten_dict(result, delimiter="/")
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values = []
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for attr, value in flat_result.items():
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if type(value) in VALID_SUMMARY_TYPES:
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values.append(
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tf.Summary.Value(
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tag="/".join(path + [attr]), simple_value=value))
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elif type(value) is list and len(value) > 0:
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values.append(
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tf.Summary.Value(
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tag="/".join(path + [attr]),
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histo=make_histogram(values=np.array(value), bins=10)))
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return values
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class TFLogger(Logger):
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"""TensorBoard Logger for TF version < 2.0.0.
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Automatically flattens nested dicts to show on TensorBoard:
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{"a": {"b": 1, "c": 2}} -> {"a/b": 1, "a/c": 2}
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If you need to do more advanced logging, it is recommended
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to use a Summary Writer in the Trainable yourself.
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"""
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def _init(self):
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global tf
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if tf is None:
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import tensorflow as tf
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tf = tf.compat.v1 # setting this for regular TF logger
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logger.debug("Initializing TFLogger instead of TF2Logger.")
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self._file_writer = tf.summary.FileWriter(self.logdir)
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def on_result(self, result):
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tmp = result.copy()
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for k in [
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"config", "pid", "timestamp", TIME_TOTAL_S, TRAINING_ITERATION
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]:
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if k in tmp:
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del tmp[k] # not useful to tf log these
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values = to_tf_values(tmp, ["ray", "tune"])
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train_stats = tf.Summary(value=values)
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t = result.get(TIMESTEPS_TOTAL) or result[TRAINING_ITERATION]
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self._file_writer.add_summary(train_stats, t)
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iteration_value = to_tf_values({
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TRAINING_ITERATION: result[TRAINING_ITERATION]
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}, ["ray", "tune"])
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iteration_stats = tf.Summary(value=iteration_value)
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self._file_writer.add_summary(iteration_stats, t)
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self._file_writer.flush()
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def flush(self):
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self._file_writer.flush()
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def close(self):
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self._file_writer.close()
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class CSVLogger(Logger):
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"""Logs results to progress.csv under the trial directory.
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@@ -3,7 +3,7 @@ import unittest
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import tempfile
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import shutil
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from ray.tune.logger import tf2_compat_logger, JsonLogger, CSVLogger, TBXLogger
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from ray.tune.logger import JsonLogger, CSVLogger, TBXLogger
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Trial = namedtuple("MockTrial", ["evaluated_params", "trial_id"])
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@@ -25,15 +25,6 @@ class LoggerSuite(unittest.TestCase):
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def tearDown(self):
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shutil.rmtree(self.test_dir, ignore_errors=True)
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def testTensorBoardLogger(self):
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config = {"a": 2, "b": 5}
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t = Trial(evaluated_params=config, trial_id=5342)
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logger = tf2_compat_logger(
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config=config, logdir=self.test_dir, trial=t)
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logger.on_result(result(2, 4))
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logger.on_result(result(2, 4))
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logger.close()
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def testCSV(self):
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config = {"a": 2, "b": 5}
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t = Trial(evaluated_params=config, trial_id="csv")
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