Source code for tensorpack.tfutils.sesscreate

# -*- coding: utf-8 -*-
# File:

from ..compat import tfv1 as tf
from ..utils import logger
from .common import get_default_sess_config

__all__ = ['NewSessionCreator', 'ReuseSessionCreator', 'SessionCreatorAdapter']

A SessionCreator should:
    create the session
    initialize all variables
    return a session that is ready to use
    not finalize the graph

_WRN1 = """User-provided custom session config may not work due to TF bugs. If you saw logs like
tensorflow/core/common_runtime/gpu/] Found device 0 with properties:
before this line, then your GPU has been initialized and custom GPU options may not take effect. """

_WRN2 = """To workaround this issue, you can do one of the following:
1. Avoid initializing the GPU too early. Find code that initializes the GPU and skip it.
   Typically examples are: creating a session; check GPU availability; check GPU number.
2. Manually set your GPU options earlier. You can create a session with custom
   GPU options at the beginning of your program, as described in

[docs]class NewSessionCreator(tf.train.SessionCreator):
[docs] def __init__(self, target='', config=None): """ Args: target, config: same as :meth:`Session.__init__()`. config: a :class:`tf.ConfigProto` instance, defaults to :func:`tfutils.get_default_sess_config()` """ = target if config is None: # distributed trainer doesn't support user-provided config # we set this attribute so that they can check self.user_provided_config = False config = get_default_sess_config() else: self.user_provided_config = True logger.warn(_WRN1) logger.warn(_WRN2) self.config = config
[docs] def create_session(self): sess = tf.Session(, config=self.config) def blocking_op(x): """ Whether an op is possibly blocking. """ if x.op_def is not None and not x.op_def.is_stateful: return False if "Dequeue" in x.type or "Enqueue" in x.type: return True if "Unstage" in x.type: return True if x.type in ["ZMQPull"]: return True return False def run(op): try: from tensorflow.contrib.graph_editor import get_backward_walk_ops # deprecated except ImportError: from tensorflow.python.ops.op_selector import get_backward_walk_ops deps = get_backward_walk_ops(op, control_inputs=True) for dep_op in deps: if blocking_op(dep_op): logger.warn( "Initializer '{}' depends on a blocking op '{}'. " "This initializer is likely to hang!".format(, run(tf.global_variables_initializer()) run(tf.local_variables_initializer()) run(tf.tables_initializer()) return sess
[docs]class ReuseSessionCreator(tf.train.SessionCreator): """ Returns an existing session. """
[docs] def __init__(self, sess): """ Args: sess (tf.Session): the session to reuse """ self.sess = sess
[docs] def create_session(self): return self.sess
[docs]class SessionCreatorAdapter(tf.train.SessionCreator): """ Apply a function on the output of a SessionCreator. Can be used to create a debug session. Note: Since TF 1.6, debug session may not work properly with Monitored session. This is a tensorflow bug. To use tfdbg, use the :class:`TFLocalCLIDebugHook` callback instead. """
[docs] def __init__(self, session_creator, func): """ Args: session_creator (tf.train.SessionCreator): a session creator func (tf.Session -> tf.Session): takes a session created by ``session_creator``, and return a new session to be returned by ``self.create_session`` """ self._creator = session_creator self._func = func
[docs] def create_session(self): sess = self._creator.create_session() return self._func(sess)