[pls merge] docs and release (#814)

* docs and release

* Update package_info.py

* Update layers.rst

docs/modules/distributed.rst

@@ -3,164 +3,21 @@ API - Distributed Training
 
 (Alpha release - usage might change later)
 
-Helper sessions and methods to run a distributed training.
-Check this `minst example <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mnist_distributed.py>`_.
+Helper API to run a distributed training.
+Check these `examples <https://github.com/tensorlayer/tensorlayer/tree/master/examples/distributed_training>`_.
 
 .. automodule:: tensorlayer.distributed
 
 .. autosummary::
 
-   TaskSpecDef
-   TaskSpec
-   DistributedSession
-   StopAtTimeHook
-   LoadCheckpoint
-
+   Trainer
 
 Distributed training
 --------------------
 
-
-TaskSpecDef
+Trainer
 ^^^^^^^^^^^
 
-.. autofunction:: TaskSpecDef
-
-Create TaskSpecDef from environment variables
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autofunction:: TaskSpec
-
-Distributed session object
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autofunction:: DistributedSession
-
-Data sharding
-^^^^^^^^^^^^^^^^^^^^^^
-
-In some cases we want to shard the data among all the training servers and
-not use all the data in all servers. TensorFlow >=1.4 provides some helper classes
-to work with data that support data sharding: `Datasets <https://www.tensorflow.org/programmers_guide/datasets>`_
-
-It is important in sharding that the shuffle or any non deterministic operation
-is done after creating the shards:
-
-.. code-block:: python
-
-  from tensorflow.contrib.data import TextLineDataset
-  from tensorflow.contrib.data import Dataset
-
-  task_spec = TaskSpec()
-  task_spec.create_server()
-  files_dataset = Dataset.list_files(files_pattern)
-  dataset = TextLineDataset(files_dataset)
-  dataset = dataset.map(your_python_map_function, num_threads=4)
-  if task_spec is not None:
-        dataset = dataset.shard(task_spec.num_workers, task_spec.shard_index)
-  dataset = dataset.shuffle(buffer_size)
-  dataset = dataset.batch(batch_size)
-  dataset = dataset.repeat(num_epochs)
-  iterator = dataset.make_one_shot_iterator()
-  next_element = iterator.get_next()
-  with tf.device(task_spec.device_fn()):
-        tensors = create_graph(next_element)
-  with tl.DistributedSession(task_spec=task_spec,
-                             checkpoint_dir='/tmp/ckpt') as session:
-        while not session.should_stop():
-            session.run(tensors)
-
-
-Logging
-^^^^^^^^^^^^^^^^^^^^^^
-
-We can use task_spec to log only in the master server:
-
-.. code-block:: python
-
-  while not session.should_stop():
-        should_log = task_spec.is_master() and your_conditions
-        if should_log:
-            results = session.run(tensors_with_log_info)
-            logging.info(...)
-        else:
-            results = session.run(tensors)
-
-Continuous evaluation
-^^^^^^^^^^^^^^^^^^^^^^
-
-You can use one of the workers to run an evaluation for the saved checkpoints:
-
-.. code-block:: python
-
-  import tensorflow as tf
-  from tensorflow.python.training import session_run_hook
-  from tensorflow.python.training.monitored_session import SingularMonitoredSession
-
-  class Evaluator(session_run_hook.SessionRunHook):
-        def __init__(self, checkpoints_path, output_path):
-            self.checkpoints_path = checkpoints_path
-            self.summary_writer = tf.summary.FileWriter(output_path)
-            self.lastest_checkpoint = ''
-
-        def after_create_session(self, session, coord):
-            checkpoint = tf.train.latest_checkpoint(self.checkpoints_path)
-            # wait until a new check point is available
-            while self.lastest_checkpoint == checkpoint:
-                time.sleep(30)
-                checkpoint = tf.train.latest_checkpoint(self.checkpoints_path)
-            self.saver.restore(session, checkpoint)
-            self.lastest_checkpoint = checkpoint
-
-        def end(self, session):
-            super(Evaluator, self).end(session)
-            # save summaries
-            step = int(self.lastest_checkpoint.split('-')[-1])
-            self.summary_writer.add_summary(self.summary, step)
-
-        def _create_graph():
-            # your code to create the graph with the dataset
-
-        def run_evaluation():
-            with tf.Graph().as_default():
-                summary_tensors = create_graph()
-                self.saver = tf.train.Saver(var_list=tf_variables.trainable_variables())
-                hooks = self.create_hooks()
-                hooks.append(self)
-                if self.max_time_secs and self.max_time_secs > 0:
-                    hooks.append(StopAtTimeHook(self.max_time_secs))
-                # this evaluation runs indefinitely, until the process is killed
-                while True:
-                    with SingularMonitoredSession(hooks=[self]) as session:
-                        try:
-                            while not sess.should_stop():
-                                self.summary = session.run(summary_tensors)
-                        except OutOfRangeError:
-                            pass
-                        # end of evaluation
-
-  task_spec = TaskSpec().user_last_worker_as_evaluator()
-  if task_spec.is_evaluator():
-        Evaluator().run_evaluation()
-  else:
-        task_spec.create_server()
-        # run normal training
-
-
-
-Session hooks
-----------------------
-
-TensorFlow provides some `Session Hooks <https://www.tensorflow.org/api_guides/python/train#Training_Hooks>`_
-to do some operations in the sessions. We added more to help with common operations.
-
-
-Stop after maximum time
-^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autofunction:: StopAtTimeHook
+.. autofunction:: Trainer
 
-Initialize network with checkpoint
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. autofunction:: LoadCheckpoint

docs/modules/layers.rst

@@ -804,6 +804,9 @@ BinaryConv2d
 
 Ternary Dense Layer
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+TernaryDenseLayer
+"""""""""""""""""""""
 .. autoclass:: TernaryDenseLayer
 
 Ternary Convolutions
@@ -849,10 +852,6 @@ QuanConv2dWithBN
 """""""""""""""""""""
 .. autoclass:: QuanConv2dWithBN
 
-DoReFa
-^^^^^^^^^^^^^^
-
-see Convolutional and Dense APIs.
 
 .. -----------------------------------------------------------
 ..                  Recurrent Layers

tensorlayer/package_info.py

@@ -3,7 +3,7 @@
 """Deep learning and Reinforcement learning library for Researchers and Engineers"""
 
 # Use the following formatting: (major, minor, patch, prerelease)
-VERSION = (1, 10, 0, "rc0")
+VERSION = (1, 10, 0, "")
 __shortversion__ = '.'.join(map(str, VERSION[:3]))
 __version__ = '.'.join(map(str, VERSION[:3])) + "".join(VERSION[3:])