tensorboard_logger 0.1.0
Log TensorBoard events without Tensorflow
License: MIT license

tensorboard_logger
==================


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Log TensorBoard events without TensorFlow
-----------------------------------------

`TensorBoard <https://www.tensorflow.org/how_tos/summaries_and_tensorboard/>`_
is a visualization tool (not this project, it's a part of
`TensorFlow <https://www.tensorflow.org>`_ framework)
that makes it easy to check training progress, compare between
different runs, and has lots of other cool features.

.. image:: tensorboard_example.png

``tensorboard_logger`` library allows to write TensorBoard events without TensorFlow::

    from tensorboard_logger import configure, log_value

    configure("runs/run-1234")

    for step in range(1000):
        v1, v2 = do_stuff()
        log_value('v1', v1, step)
        log_value('v2', v2, step)


Note: if you are already using TensorFlow in your project,
you probably don't need this library.


Installation
------------

TensorFlow is required only for viewing logged events: please check installation guide
on the `official site <https://www.tensorflow.org/get_started/os_setup.html#download-and-setup>`_
(you probably want a CPU-only version).

``tensorboard_logger`` can be installed with pip::

    pip install tensorboard_logger


Usage
-----

You can either use default logger with ``tensorboard_logger.configure``
and ``tensorboard_logger.log_value`` functions, or use ``tensorboard_logger.Logger`` class.

This library can be used to log numerical values of some variables in TensorBoard format, so you can
use TensorBoard to visualize how they changed, and compare same variables between different runs.
Log file is written into a directory, so you need a separate directory for each run
(you can place other logs or output files you use in the same directory).
Directories from different runs you wish to compare should have the same parent
(there can be other files or directories with the same parent, TensorBoard will figure out
which directories contain logs).

Apart from variable names and their values, another important thing is the **step**: this must
be an integer that represents some increasing step - it can be a step in training or some
other number. The values are ordered by step in TensorBoard, although you can view them
ordered by time or relative step too.

A simple usage example::

    from tensorboard_logger import configure, log_value

    configure("runs/run-1234", flush_secs=5)

    for step in range(1000):
        v1, v2 = do_stuff()
        log_value('v1', v1, step)
        log_value('v2', v2, step)


You can start TensorBoard right away::

    tensorboard --logdir runs

And go check the metrics to TensorBoard UI at http://localhost:6006
(note that it binds to 0.0.0.0 by default).
Metrics are refreshed on switch to browser tab, and there is also a refresh button
at the top right.

Runtime overhead rather large, about 0.1 - 0.2 ms for a single value logged
(so about 5,000 - 10,000 operations per second).


API
---

``tensorboard_logger.configure(logdir, flush_secs=2)``

Configure logging: a file will be written to ``logdir``, and flushed every ``flush_secs``.
**NOTE:** right now file is flushed after each event written.

``tensorboard_logger.log_value(name, value, step=None)``

Log new ``value`` for given ``name`` on given ``step``.
``value`` should be a real number (it will be converted to float),
and ``name`` should be a string (it will be converted to a valid
TensorFlow summary name). ``step`` should be an non-negative integer,
and is used for visualization: you can log several different
variables on one step, but should not log different values
of the same variable on the same step (this is not checked).
You can also omit step entirely.

``tensorboard_logger.Logger``

A class for writing logs in a directory.
Use it if default logger used in two above functions is not enough for you
(e.g. you want to log into several different directories, or don't like
global variables).
Constructor has the same signature as ``tensorboard_logger.configure``,
and it has a single ``log_value`` method with the same signature as
``tensorboard_logger.log_value``.


Development
-----------

Compiling python protobuf files::

    protoc --python_out . tensorboard_logger/tf_protobuf/summary.proto
    protoc --python_out . tensorboard_logger/tf_protobuf/event.proto


License
-------

`MIT license`_

.. _MIT license: LICENSE

----

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	:alt: define hyperiongray


History
=======

0.1.0 (2018-02-08)
------------------

* Ability to log histograms and images (#18)


0.0.4 (2017-07-31)
------------------

* Fix working with TensorFlow installed (#11)


0.0.3 (2016-11-17)
------------------

* Fix PyPI packaging


0.0.2 (2016-11-03)
------------------

* TensorFlow is no longer required (#1)
* Travis build is fixed (#2)


0.0.1 (2016-11-01)
------------------

* First release on PyPI.