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Shashank Suhas
seminar-breakout
Commits
48375921
Commit
48375921
authored
Nov 18, 2017
by
Yuxin Wu
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docs/tutorial/index.rst
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Tutorials
Tutorials
---------------------
---------------------
A High Level Glance
Introduction
=============
=======
=============
.. image:: https://user-images.githubusercontent.com/1381301/29187907-2caaa740-7dc6-11e7-8220-e20ca52c3ca6.png
.. include:: intro.rst
* ``DataFlow`` is a library to load data efficiently in Python.
Apart from DataFlow, native TF operators can be used for data loading as well.
They will eventually be wrapped under the same ``InputSource`` interface and go through prefetching.
* You can use any TF-based symbolic function library to define a model, including
a small set of functions within tensorpack. ``ModelDesc`` is an interface to connect the graph with the
``InputSource`` interface.
* tensorpack trainers manage the training loops for you.
They also include data parallel logic for multi-GPU or distributed training.
At the same time, you have the power of customization through callbacks.
* Callbacks are like ``tf.train.SessionRunHook``, or plugins. During training,
everything you want to do other than the main iterations can be defined through callbacks and easily reused.
* All the components, though work perfectly together, are highly decorrelated: you can:
* Use DataFlow alone as a data loading library, without tensorfow at all.
* Use tensorpack to build the graph with multi-GPU or distributed support,
then train it with your own loops.
* Build the graph on your own, and train it with tensorpack callbacks.
User Tutorials
User Tutorials
========================
========================
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docs/tutorial/intro.rst
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What is tensorpack?
~~~~~~~~~~~~~~~~~~~
Tensorpack is a **training interface** based on TensorFlow, which means:
you'll use mostly tensorpack high-level APIs to do training, rather than TensorFlow low-level APIs.
Why tensorpack?
~~~~~~~~~~~~~~~~~~~
TensorFlow is powerful, but at the same time too complicated for a lot of people, especially when **speed** is a concern.
Users can often write slow code with low-level APIs or other high-level APIs.
Even a lot of official TensorFlow examples are written for simplicity rather than efficiency,
which as a result makes people think TensorFlow is slow.
Tensorpack uses TensorFlow efficiently, and hides these details under its APIs.
You no longer need to learn about
multi-GPU model replication, variables synchronization, queues, tf.data -- anything that's unrelated to the model itself.
You still need to learn to write models with TF, but everything else is taken care of by tensorpack, in the efficient way.
A High Level Glance
~~~~~~~~~~~~~~~~~~~
.. image:: https://user-images.githubusercontent.com/1381301/29187907-2caaa740-7dc6-11e7-8220-e20ca52c3ca6.png
* ``DataFlow`` is a library to load data efficiently in Python.
Apart from DataFlow, native TF operators can be used for data loading as well.
They will eventually be wrapped under the same ``InputSource`` interface and go through prefetching.
* You can use any TF-based symbolic function library to define a model, including
a small set of functions within tensorpack. ``ModelDesc`` is an interface to connect the model with the
``InputSource`` interface.
* tensorpack trainers manage the training loops for you.
They also include data parallel logic for multi-GPU or distributed training.
At the same time, you have the power of customization through callbacks.
* Callbacks are like ``tf.train.SessionRunHook``, or plugins. During training,
everything you want to do other than the main iterations can be defined through callbacks and easily reused.
* All the components, though work perfectly together, are highly decorrelated: you can:
* Use DataFlow alone as a data loading library, without tensorfow at all.
* Use tensorpack to build the graph with multi-GPU or distributed support,
then train it with your own loops.
* Build the graph on your own, and train it with tensorpack callbacks.
docs/tutorial/symbolic.md
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48375921
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@@ -9,6 +9,7 @@ simplify the code.
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@@ -9,6 +9,7 @@ simplify the code.
Note that these layers were written because there were no other alternatives back at that time.
Note that these layers were written because there were no other alternatives back at that time.
In the future we may shift the implementation to
`tf.layers`
because they will be better maintained.
In the future we may shift the implementation to
`tf.layers`
because they will be better maintained.
You can start using
`tf.layers`
today as long as it fits your need.
### argscope and LinearWrap
### argscope and LinearWrap
`argscope`
gives you a context with default arguments.
`argscope`
gives you a context with default arguments.
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