biopredict: Handle number of parallel jobs correctly

bob/learn/tensorflow/utils/reproducible.py

+'''Helps training reproducible networks.
+'''
 import os
+import random as rn
 import numpy as np
 import tensorflow as tf
-import random as rn
-# from tensorflow.contrib import keras
-
-# reproducible networks
-# The below is necessary in Python 3.2.3 onwards to
-# have reproducible behavior for certain hash-based operations.
-# See these references for further details:
-# https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSEED
-# https://github.com/fchollet/keras/issues/2280#issuecomment-306959926
-os.environ['PYTHONHASHSEED'] = '0'
-
-# The below is necessary for starting Numpy generated random numbers
-# in a well-defined initial state.
-np.random.seed(42)
-
-# The below is necessary for starting core Python generated random numbers
-# in a well-defined state.
-rn.seed(12345)
-
-# Force TensorFlow to use single thread.
-# Multiple threads are a potential source of
-# non-reproducible results.
-# For further details, see:
-# https://stackoverflow.com/questions/42022950/which-seeds-have-to-be-set-where-to-realize-100-reproducibility-of-training-res
-session_config = tf.ConfigProto(intra_op_parallelism_threads=1,
-                                inter_op_parallelism_threads=1,
-                                log_device_placement=True)
-
-# The below tf.set_random_seed() will make random number generation
-# in the TensorFlow backend have a well-defined initial state.
-# For further details, see:
-# https://www.tensorflow.org/api_docs/python/tf/set_random_seed
-tf_random_seed = 1234
-tf.set_random_seed(tf_random_seed)
-# sess = tf.Session(graph=tf.get_default_graph(), config=session_config)
-# keras.backend.set_session(sess)
-
-run_config = tf.estimator.RunConfig()
-run_config = run_config.replace(session_config=session_config)
-run_config = run_config.replace(tf_random_seed=tf_random_seed)
+
+
+def set_seed(seed=0, python_hash_seed=0, log_device_placement=False):
+    """Sets the seeds in python, numpy, and tensorflow in order to help
+    training reproducible networks.
+
+    Parameters
+    ----------
+    seed : :obj:`int`, optional
+        The seed to set.
+    python_hash_seed : :obj:`int`, optional
+        https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSEED
+    log_device_placement : :obj:`bool`, optional
+        Optionally, log device placement of tensorflow variables.
+
+    Returns
+    -------
+    :any:`tf.ConfigProto`
+        Session config.
+    :any:`tf.estimator.RunConfig`
+        A run config to help training estimators.
+
+    Notes
+    -----
+        This functions return a list and its length might change. Please use
+        indices to select one of returned values. For example
+        ``sess_config, run_config = set_seed()[:2]``.
+    """
+    # reproducible networks
+    # The below is necessary in Python 3.2.3 onwards to
+    # have reproducible behavior for certain hash-based operations.
+    # See these references for further details:
+    # https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSEED
+    # https://github.com/fchollet/keras/issues/2280#issuecomment-306959926
+    os.environ['PYTHONHASHSEED'] = '{}'.format(python_hash_seed)
+
+    # The below is necessary for starting Numpy generated random numbers
+    # in a well-defined initial state.
+    np.random.seed(seed)
+
+    # The below is necessary for starting core Python generated random numbers
+    # in a well-defined state.
+    rn.seed(seed)
+
+    # Force TensorFlow to use single thread.
+    # Multiple threads are a potential source of
+    # non-reproducible results.
+    # For further details, see:
+    # https://stackoverflow.com/questions/42022950/which-seeds-have-to-be-set-where-to-realize-100-reproducibility-of-training-res
+    session_config = tf.ConfigProto(intra_op_parallelism_threads=1,
+                                    inter_op_parallelism_threads=1,
+                                    log_device_placement=log_device_placement)
+
+    # The below tf.set_random_seed() will make random number generation
+    # in the TensorFlow backend have a well-defined initial state.
+    # For further details, see:
+    # https://www.tensorflow.org/api_docs/python/tf/set_random_seed
+    tf.set_random_seed(seed)
+    # sess = tf.Session(graph=tf.get_default_graph(), config=session_config)
+    # keras.backend.set_session(sess)
+
+    run_config = tf.estimator.RunConfig()
+    run_config = run_config.replace(session_config=session_config)
+    run_config = run_config.replace(tf_random_seed=seed)
+
+    return [session_config, run_config, None, None, None]