Change: 119768540

handling 64).
Change: 119759411

Change: 119755164

Change: 119712558

Change: 119706066

Change: 119703236

Change: 119698585

Adds option to pass callable initializer function to Variable constructor to allow colocation of variable initialization with the device the variable is on, instead of always being on the chief supervisor.

Also updates variable_scope.get_variable() and create_partitioned_variables() to take advantage of this when an initializer fn is passed in.
Change: 119697860

Change: 119695799

Change: 119694323

Change: 119691101

Extends functionality of Tensor::flat_inner_dims() and Tensor::flat_outer_dims() by adding a template param to specify the desired rank of the output. This is useful, e.g., in order to turn a tensor into a (rank-3) batch of matrices.
Change: 119685357

Change: 119671640

Change: 119666432

Change: 119657458

* Set supervisor fields for event writing to None for non-chiefs, so that non-chiefs do not accidentally write summaries.
* Added RuntimeErrors for public API endpoints to alert users when they try to use non-chief supervisors to write events.
Change: 119654303

Change: 119650236

Change: 119649248

Change: 119648305

Change: 119643238

Change: 119605636

Usage example: ./remote_test.sh --num-workers 3 --sync-replicas

Also changed:
    1) In local and remote tests, let different workers contact separate GRPC
    sessions.
    2) In local and remote tests, adding the capacity to specify the number of
    workers. Before it was hard-coded at 2.
    Usage example:
    ./remote_test.sh --num-workers 2 --sync-replicas
    3) Using device setter in mnist_replica.py
Change: 119599547

Change: 119591021

Change: 119589456

Goals:
- Have enough of each summary type that tag grouping is useful.
(Wound up recording e.g. mean and stddev and min/max for each variable)
- Use every summary type (adds images)
- Write to multiple directories so there are several "runs"
Change: 119585022

Update bower dependencies.
Also force urls to lowercase.
Change: 119584968

Change: 119572994

Moving favicon to datauri.
Change: 119569013

Change: 119565375

Change: 119565115

Clarify that OUT_OF_RANGE is raised only when reaching the end of
input for interable contents.

Change the few places where we incorrectly raised OUT_OF_RANGE to raise
ILLEGAL_ARGUMENT instead.

This will make code that catches the OUT_OF_RANGE exception more robust
as it won't get confused by spurious uses of the exception class.
Change: 119560848

on the caller. This allows us to use the macros for other purposes than
calling REGISTER_KERNEL, in particular in variadic template parameter lists.

Update REGISTER_KERNEL_BUILDER accordingly to add a semicolon, so that existing
code continues to compile.
Change: 119551677

Change: 119549296

correctly.
Change: 119549145

Change: 119544956

This is used only within Google.
Change: 119543426

Dimension.__str__ now spits out ? for unknown dimensions and an integer
otherwise.  Previously this logic was contained in TensorShape.__str__.

In addition, the exception produced TensorShape.merge_with now encodes all of
the two shapes, so something like

    Dimensions 2 and 9 are not compatible

becomes

    Shapes (?, 2) and (4, 9) are not compatible
Change: 119536897

Change: 119533248

Use eigen ThreadPool instead of tensorflow one if TENSORFLOW_USE_EIGEN_THREADPOOL is defined. This will allow to switch to the new non-blocking ThreadPool.
Change: 119512280

The RNN performance bug:
* When passing sequence_length to rnn(), calculations were being performed past
  max_sequence_length.

This bug had one major side effect:
* It slowed down the calculation past max_sequence_length (it *should*
return zeros for outputs and copy state through)

The calculations themselves were still correct:  The state was still
copied through and the output was still all zeros.  But that calculation
was performed via a vector-conditional select() instead of a single
scalar cond().  As a result a lot of extra copying was happening both
in fw and backprop.

Thanks to Nat Roth (natusroth@gmail) for unearthing this bug.

**************
Also:
- updates to benchmarks.py (allow more specific benchmarks, added
  support for --benchmarks=all).
- cleaned up RNN benchmarks code a bit.

New and updated benchmarks:

Calculation: Static Unroll with Halved Sequence Length vs. Half Static Unroll
batch    full_t          units   gpu     dt(half_seq_len)        dt(unroll_half)         dt(half_seq_len)/dt(unroll_half)
128      50              256     False   0.164351                0.155019                1.060204
128      50              256     True    0.033295                0.028203                1.180550

Calculation: Static Unroll with Dynamic Flow LSTM vs. Dynamic Unroll LSTM
batch    max_t   units   gpu     dt(static)      dt(dynamic)     dt(dynamic)/dt(static)
256      50      512     False   1.759111        1.692570        0.962173
256      50      512     True    0.178953        0.190454        1.064269
256      50      256     False   0.533132        0.567228        1.063955
256      50      256     True    0.078298        0.085024        1.085905
256      50      128     False   0.220362        0.215350        0.977255
256      50      128     True    0.053379        0.059129        1.107723
Change: 119495675