Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

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|Build Status| |Documentation Status| |Package Status|

Deploy `tensorflow <https://www.tensorflow.org>`__ graphs for *fast*

evaluation and export to *tensorflow-less* environments running

`numpy <http://www.numpy.org>`__.

**Now with tensorflow 1.0 support.**

Evaluation usage

''''''''''''''''

.. code:: python

import tfdeploy as td

import numpy as np

model = td.Model("/path/to/model.pkl")

inp, outp = model.get("input", "output")

batch = np.random.rand(10000, 784)

result = outp.eval({inp: batch})

Installation and dependencies

'''''''''''''''''''''''''''''

Via `pip <https://pypi.python.org/pypi/tfdeploy>`__

.. code:: bash

pip install tfdeploy

or by simply copying the file into your project.

Numpy ≥ 1.10 should be installed on your system.

`Scipy <http://www.scipy.org/>`__ is optional. See

`optimization <#optimization>`__ for more info on optional packages.

By design, tensorflow is required when creating a model. All versions ≥

1.0.1 are supported.

Content

~~~~~~~

- `Why? <#why>`__

- `How? <#how>`__

- `Convert your graph <#convert-your-graph>`__

- `Load the model and evaluate <#load-the-model-and-evaluate>`__

- `Write your own operation <#write-your-own-operation>`__

- `Ensembles <#ensembles>`__

- `Optimization <#optimization>`__

- `Performance <#performance>`__

- `Contributing <#contributing>`__

- `Development <#development>`__

- `Authors <#authors>`__

- `License <#license>`__

Why?

----

Working with tensorflow is awesome. Model definition and training is

simple yet powerful, and the range of built-in features is just

striking.

However, when it comes down to model deployment and evaluation, things

get a bit more cumbersome than they should be. You either export your

graph to a new file *and* `save your trained

variables <https://www.tensorflow.org/versions/master/how_tos/variables/index.html#saving-variables>`__

in a separate file, or you make use of tensorflow's `serving

system <https://www.tensorflow.org/versions/master/tutorials/tfserve/index.html>`__.

Wouldn't it be great if you could just export your model to a simple

numpy-based callable? Of course it would. And this is exactly what

tfdeploy does for you.

To boil it down, tfdeploy

- is lightweight. A single file with < 150 lines of core code. Just

copy it to your project.

- `faster <#performance>`__ than using tensorflow's ``Tensor.eval``.

- **does not need tensorflow** during evaluation.

- only depends on numpy.

- can load one or more models from a single file.

- does not support GPUs (maybe

`gnumpy <http://www.cs.toronto.edu/~tijmen/gnumpy.html>`__ is worth a

try here).

How?

----

The central class is ``tfdeploy.Model``. The following two examples

demonstrate how a model can be created from a tensorflow graph, saved to

and loaded from disk, and eventually evaluated.

Convert your graph

''''''''''''''''''

.. code:: python

import tensorflow as tf

import tfdeploy as td

# setup tfdeploy (only when creating models)

td.setup(tf)

# build your graph

sess = tf.Session()

# use names for input and output layers

x = tf.placeholder("float", shape=[None, 784], name="input")

W = tf.Variable(tf.truncated_normal([784, 100], stddev=0.05))

b = tf.Variable(tf.zeros([100]))

y = tf.nn.softmax(tf.matmul(x, W) + b, name="output")

sess.run(tf.global_variables_initializer())

# ... training ...

# create a tfdeploy model and save it to disk

model = td.Model()

model.add(y, sess) # y and all its ops and related tensors are added recursively

model.save("model.pkl")

Load the model and evaluate

'''''''''''''''''''''''''''

.. code:: python

import numpy as np

import tfdeploy as td

model = td.Model("model.pkl")

# shorthand to x and y

x, y = model.get("input", "output")

# evaluate

batch = np.random.rand(10000, 784)

result = y.eval({x: batch})

Write your own ``Operation``

''''''''''''''''''''''''''''

tfdeploy supports most of the ``Operation``'s `implemented in

tensorflow <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html>`__.

However, if you miss one (in that case, submit a PR or an issue ;) ) or

if you're using custom ops, you might want to extend tfdeploy by

defining a new class op that inherits from ``tfdeploy.Operation``:

.. code:: python

import tensorflow as tf

import tfdeploy as td

import numpy as np

# setup tfdeploy (only when creating models)

td.setup(tf)

# ... write you model here ...

# let's assume your final tensor "y" relies on an op of type "InvertedSoftmax"

# before creating the td.Model, you should add that op to tfdeploy

class InvertedSoftmax(td.Operation):

@staticmethod

def func(a):

e = np.exp(-a)

# ops should return a tuple

return np.divide(e, np.sum(e, axis=-1, keepdims=True)),

# this is equivalent to

# @td.Operation.factory

# def InvertedSoftmax(a):

# e = np.exp(-a)

# return np.divide(e, np.sum(e, axis=-1, keepdims=True)),

# now we're good to go

model = td.Model()

model.add(y, sess)

model.save("model.pkl")

When writing new ops, three things are important:

- Try to avoid loops, prefer numpy vectorization.

- Return a tuple.

- Don't change incoming tensors/arrays in-place, always work on and

return copies.

Ensembles

---------

tfdeploy provides a helper class to evaluate an ensemble of models:

``Ensemble``. It can load multiple models, evaluate them and combine

their output values using different methods.

.. code:: python

# create the ensemble

ensemble = td.Ensemble(["model1.pkl", "model2.pkl", ...], method=td.METHOD_MEAN)

# get input and output tensors (which actually are TensorEnsemble instances)

input, output = ensemble.get("input", "output")

# evaluate the ensemble just like a normal model

batch = ...

value = output.eval({input: batch})

The return value of ``get()`` is a ``TensorEnsemble`` istance. It is

basically a wrapper around multiple tensors and should be used as keys

in the ``feed_dict`` of the ``eval()`` call.

You can choose between ``METHOD_MEAN`` (the default), ``METHOD_MAX`` and

``METHOD_MIN``. If you want to use a custom ensembling method, use

``METHOD_CUSTOM`` and overwrite the static ``func_custom()`` method of

the ``TensorEnsemble`` instance.

Optimization

------------

Most ops are written using pure numpy. However, multiple implementations

of the same op are allowed that may use additional third-party Python

packages providing even faster functionality for some situations.

For example, numpy does not provide a vectorized *lgamma* function.

Thus, the standard ``tfdeploy.Lgamma`` op uses ``math.lgamma`` that was

previously vectorized using ``numpy.vectorize``. For these situations,

additional implementations of the same op are possible (the *lgamma*

example is quite academic, but this definitely makes sense for more

sophisticated ops like pooling). We can simply tell the op to use its

scipy implementation instead:

.. code:: python

td.Lgamma.use_impl(td.IMPL_SCIPY)

Currently, allowed implementation types are numpy (``IMPL_NUMPY``, the

default) and scipy (``IMPL_SCIPY``).

Adding additional implementations

'''''''''''''''''''''''''''''''''

Additional implementations can be added by setting the ``impl``

attribute of the op factory or by using the ``add_impl`` decorator of

existing operations. The first registered implementation will be the

default one.

.. code:: python

# create the default lgamma op with numpy implementation

lgamma_vec = np.vectorize(math.lgamma)

@td.Operation.factory

# equivalent to

# @td.Operation.factory(impl=td.IMPL_NUMPY)

def Lgamma(a):

return lgamma_vec(a),

# add a scipy-based implementation

@Lgamma.add_impl(td.IMPL_SCIPY)

def Lgamma(a):

return sp.special.gammaln(a),

Auto-optimization

'''''''''''''''''

If scipy is available on your system, it is reasonable to use all ops in

their scipy implementation (if it exists, of course). This should be

configured before you create any model from tensorflow objects using the

second argument of the ``setup`` function:

.. code:: python

td.setup(tf, td.IMPL_SCIPY)

Ops that do not implement ``IMPL_SCIPY`` stick with the numpy version

(``IMPL_NUMPY``).

Performance

-----------

tfdeploy is lightweight (1 file, < 150 lines of core code) and fast.

Internal evaluation calls have only very few overhead and tensor

operations use numpy vectorization. The actual performance depends on

the ops in your graph. While most of the tensorflow ops have a numpy

equivalent or can be constructed from numpy functions, a few ops require

additional Python-based loops (e.g. ``BatchMatMul``). But in many cases

it's potentially faster than using tensorflow's ``Tensor.eval``.

This is a comparison for a basic graph where all ops are vectorized

(basically ``Add``, ``MatMul`` and ``Softmax``):

.. code:: bash

> ipython -i tests/perf/simple.py

In [1]: %timeit -n 100 test_tf()

100 loops, best of 3: 109 ms per loop

In [2]: %timeit -n 100 test_td()

100 loops, best of 3: 60.5 ms per loop

Contributing

------------

If you want to contribute with new ops and features, I'm happy to

receive pull requests. Just make sure to add a new test case to

``tests/core.py`` or ``tests/ops.py`` and run them via:

.. code:: bash

> python -m unittest tests

Test grid

'''''''''

In general, tests should be run for different environments:

+----------------------+-------------+

| Variation | Values |

+======================+=============+

| tensorflow version | ``1.0.1`` |

+----------------------+-------------+

| python version | 2, 3 |

+----------------------+-------------+

| ``TD_TEST_SCIPY`` | 0, 1 |

+----------------------+-------------+

| ``TD_TEST_GPU`` | 0, 1 |

+----------------------+-------------+

Docker

''''''

For testing purposes, it is convenient to use docker. Fortunately, the

official `tensorflow

images <https://hub.docker.com/r/tensorflow/tensorflow/>`__ contain all

we need:

.. code:: bash

git clone https://github.com/riga/tfdeploy.git

cd tfdeploy

docker run --rm -v `pwd`:/root/tfdeploy -w /root/tfdeploy -e "TD_TEST_SCIPY=1" tensorflow/tensorflow:1.0.1 python -m unittest tests

Development

-----------

- Source hosted at `GitHub <https://github.com/riga/tfdeploy>`__

- Report issues, questions, feature requests on `GitHub

Issues <https://github.com/riga/tfdeploy/issues>`__

Authors

-------

- `Marcel R. <https://github.com/riga>`__

- `Benjamin F. <https://github.com/bfis>`__

License

-------

The MIT License (MIT)

Copyright (c) 2017 Marcel R.

Permission is hereby granted, free of charge, to any person obtaining a

copy of this software and associated documentation files (the

"Software"), to deal in the Software without restriction, including

without limitation the rights to use, copy, modify, merge, publish,

distribute, sublicense, and/or sell copies of the Software, and to

permit persons to whom the Software is furnished to do so, subject to

the following conditions:

The above copyright notice and this permission notice shall be included

in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS

OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF

MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY

CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,

TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE

SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

.. |Build Status| image:: https://travis-ci.org/riga/tfdeploy.svg?branch=master

:target: https://travis-ci.org/riga/tfdeploy

.. |Documentation Status| image:: https://readthedocs.org/projects/tfdeploy/badge/?version=latest

:target: http://tfdeploy.readthedocs.org/en/latest/?badge=latest

.. |Package Status| image:: https://badge.fury.io/py/tfdeploy.svg

:target: https://badge.fury.io/py/tfdeploy

Deploy `tensorflow <https://www.tensorflow.org>`__ graphs for *fast*

evaluation and export to *tensorflow-less* environments running

`numpy <http://www.numpy.org>`__.

**Now with tensorflow 1.0 support.**

Evaluation usage

''''''''''''''''

.. code:: python

import tfdeploy as td

import numpy as np

model = td.Model("/path/to/model.pkl")

inp, outp = model.get("input", "output")

batch = np.random.rand(10000, 784)

result = outp.eval({inp: batch})

Installation and dependencies

'''''''''''''''''''''''''''''

Via `pip <https://pypi.python.org/pypi/tfdeploy>`__

.. code:: bash

pip install tfdeploy

or by simply copying the file into your project.

Numpy ≥ 1.10 should be installed on your system.

`Scipy <http://www.scipy.org/>`__ is optional. See

`optimization <#optimization>`__ for more info on optional packages.

By design, tensorflow is required when creating a model. All versions ≥

1.0.1 are supported.

Content

~~~~~~~

- `Why? <#why>`__

- `How? <#how>`__

- `Convert your graph <#convert-your-graph>`__

- `Load the model and evaluate <#load-the-model-and-evaluate>`__

- `Write your own operation <#write-your-own-operation>`__

- `Ensembles <#ensembles>`__

- `Optimization <#optimization>`__

- `Performance <#performance>`__

- `Contributing <#contributing>`__

- `Development <#development>`__

- `Authors <#authors>`__

- `License <#license>`__

Why?

----

Working with tensorflow is awesome. Model definition and training is

simple yet powerful, and the range of built-in features is just

striking.

However, when it comes down to model deployment and evaluation, things

get a bit more cumbersome than they should be. You either export your

graph to a new file *and* `save your trained

variables <https://www.tensorflow.org/versions/master/how_tos/variables/index.html#saving-variables>`__

in a separate file, or you make use of tensorflow's `serving

system <https://www.tensorflow.org/versions/master/tutorials/tfserve/index.html>`__.

Wouldn't it be great if you could just export your model to a simple

numpy-based callable? Of course it would. And this is exactly what

tfdeploy does for you.

To boil it down, tfdeploy

- is lightweight. A single file with < 150 lines of core code. Just

copy it to your project.

- `faster <#performance>`__ than using tensorflow's ``Tensor.eval``.

- **does not need tensorflow** during evaluation.

- only depends on numpy.

- can load one or more models from a single file.

- does not support GPUs (maybe

`gnumpy <http://www.cs.toronto.edu/~tijmen/gnumpy.html>`__ is worth a

try here).

How?

----

The central class is ``tfdeploy.Model``. The following two examples

demonstrate how a model can be created from a tensorflow graph, saved to

and loaded from disk, and eventually evaluated.

Convert your graph

''''''''''''''''''

.. code:: python

import tensorflow as tf

import tfdeploy as td

# setup tfdeploy (only when creating models)

td.setup(tf)

# build your graph

sess = tf.Session()

# use names for input and output layers

x = tf.placeholder("float", shape=[None, 784], name="input")

W = tf.Variable(tf.truncated_normal([784, 100], stddev=0.05))

b = tf.Variable(tf.zeros([100]))

y = tf.nn.softmax(tf.matmul(x, W) + b, name="output")

sess.run(tf.global_variables_initializer())

# ... training ...

# create a tfdeploy model and save it to disk

model = td.Model()

model.add(y, sess) # y and all its ops and related tensors are added recursively

model.save("model.pkl")

Load the model and evaluate

'''''''''''''''''''''''''''

.. code:: python

import numpy as np

import tfdeploy as td

model = td.Model("model.pkl")

# shorthand to x and y

x, y = model.get("input", "output")

# evaluate

batch = np.random.rand(10000, 784)

result = y.eval({x: batch})

Write your own ``Operation``

''''''''''''''''''''''''''''

tfdeploy supports most of the ``Operation``'s `implemented in

tensorflow <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html>`__.

However, if you miss one (in that case, submit a PR or an issue ;) ) or

if you're using custom ops, you might want to extend tfdeploy by

defining a new class op that inherits from ``tfdeploy.Operation``:

.. code:: python

import tensorflow as tf

import tfdeploy as td

import numpy as np

# setup tfdeploy (only when creating models)

td.setup(tf)

# ... write you model here ...

# let's assume your final tensor "y" relies on an op of type "InvertedSoftmax"

# before creating the td.Model, you should add that op to tfdeploy

class InvertedSoftmax(td.Operation):

@staticmethod

def func(a):

e = np.exp(-a)

# ops should return a tuple

return np.divide(e, np.sum(e, axis=-1, keepdims=True)),

# this is equivalent to

# @td.Operation.factory

# def InvertedSoftmax(a):

# e = np.exp(-a)

# return np.divide(e, np.sum(e, axis=-1, keepdims=True)),

# now we're good to go

model = td.Model()

model.add(y, sess)

model.save("model.pkl")

When writing new ops, three things are important:

- Try to avoid loops, prefer numpy vectorization.

- Return a tuple.

- Don't change incoming tensors/arrays in-place, always work on and

return copies.

Ensembles

---------

tfdeploy provides a helper class to evaluate an ensemble of models:

``Ensemble``. It can load multiple models, evaluate them and combine

their output values using different methods.

.. code:: python

# create the ensemble

ensemble = td.Ensemble(["model1.pkl", "model2.pkl", ...], method=td.METHOD_MEAN)

# get input and output tensors (which actually are TensorEnsemble instances)

input, output = ensemble.get("input", "output")

# evaluate the ensemble just like a normal model

batch = ...

value = output.eval({input: batch})

The return value of ``get()`` is a ``TensorEnsemble`` istance. It is

basically a wrapper around multiple tensors and should be used as keys

in the ``feed_dict`` of the ``eval()`` call.

You can choose between ``METHOD_MEAN`` (the default), ``METHOD_MAX`` and

``METHOD_MIN``. If you want to use a custom ensembling method, use

``METHOD_CUSTOM`` and overwrite the static ``func_custom()`` method of

the ``TensorEnsemble`` instance.

Optimization

------------

Most ops are written using pure numpy. However, multiple implementations

of the same op are allowed that may use additional third-party Python

packages providing even faster functionality for some situations.

For example, numpy does not provide a vectorized *lgamma* function.

Thus, the standard ``tfdeploy.Lgamma`` op uses ``math.lgamma`` that was

previously vectorized using ``numpy.vectorize``. For these situations,

additional implementations of the same op are possible (the *lgamma*

example is quite academic, but this definitely makes sense for more

sophisticated ops like pooling). We can simply tell the op to use its

scipy implementation instead:

.. code:: python

td.Lgamma.use_impl(td.IMPL_SCIPY)

Currently, allowed implementation types are numpy (``IMPL_NUMPY``, the

default) and scipy (``IMPL_SCIPY``).

Adding additional implementations

'''''''''''''''''''''''''''''''''

Additional implementations can be added by setting the ``impl``

attribute of the op factory or by using the ``add_impl`` decorator of

existing operations. The first registered implementation will be the

default one.

.. code:: python

# create the default lgamma op with numpy implementation

lgamma_vec = np.vectorize(math.lgamma)

@td.Operation.factory

# equivalent to

# @td.Operation.factory(impl=td.IMPL_NUMPY)

def Lgamma(a):

return lgamma_vec(a),

# add a scipy-based implementation

@Lgamma.add_impl(td.IMPL_SCIPY)

def Lgamma(a):

return sp.special.gammaln(a),

Auto-optimization

'''''''''''''''''

If scipy is available on your system, it is reasonable to use all ops in

their scipy implementation (if it exists, of course). This should be

configured before you create any model from tensorflow objects using the

second argument of the ``setup`` function:

.. code:: python

td.setup(tf, td.IMPL_SCIPY)

Ops that do not implement ``IMPL_SCIPY`` stick with the numpy version

(``IMPL_NUMPY``).

Performance

-----------

tfdeploy is lightweight (1 file, < 150 lines of core code) and fast.

Internal evaluation calls have only very few overhead and tensor

operations use numpy vectorization. The actual performance depends on

the ops in your graph. While most of the tensorflow ops have a numpy

equivalent or can be constructed from numpy functions, a few ops require

additional Python-based loops (e.g. ``BatchMatMul``). But in many cases

it's potentially faster than using tensorflow's ``Tensor.eval``.

This is a comparison for a basic graph where all ops are vectorized

(basically ``Add``, ``MatMul`` and ``Softmax``):

.. code:: bash

> ipython -i tests/perf/simple.py

In [1]: %timeit -n 100 test_tf()

100 loops, best of 3: 109 ms per loop

In [2]: %timeit -n 100 test_td()

100 loops, best of 3: 60.5 ms per loop

Contributing

------------

If you want to contribute with new ops and features, I'm happy to

receive pull requests. Just make sure to add a new test case to

``tests/core.py`` or ``tests/ops.py`` and run them via:

.. code:: bash

> python -m unittest tests

Test grid

'''''''''

In general, tests should be run for different environments:

+----------------------+-------------+

| Variation | Values |

+======================+=============+

| tensorflow version | ``1.0.1`` |

+----------------------+-------------+

| python version | 2, 3 |

+----------------------+-------------+

| ``TD_TEST_SCIPY`` | 0, 1 |

+----------------------+-------------+

| ``TD_TEST_GPU`` | 0, 1 |

+----------------------+-------------+

Docker

''''''

For testing purposes, it is convenient to use docker. Fortunately, the

official `tensorflow

images <https://hub.docker.com/r/tensorflow/tensorflow/>`__ contain all

we need:

.. code:: bash

git clone https://github.com/riga/tfdeploy.git

cd tfdeploy

docker run --rm -v `pwd`:/root/tfdeploy -w /root/tfdeploy -e "TD_TEST_SCIPY=1" tensorflow/tensorflow:1.0.1 python -m unittest tests

Development

-----------

- Source hosted at `GitHub <https://github.com/riga/tfdeploy>`__

- Report issues, questions, feature requests on `GitHub

Issues <https://github.com/riga/tfdeploy/issues>`__

Authors

-------

- `Marcel R. <https://github.com/riga>`__

- `Benjamin F. <https://github.com/bfis>`__

License

-------

The MIT License (MIT)

Copyright (c) 2017 Marcel R.

Permission is hereby granted, free of charge, to any person obtaining a

copy of this software and associated documentation files (the

"Software"), to deal in the Software without restriction, including

without limitation the rights to use, copy, modify, merge, publish,

distribute, sublicense, and/or sell copies of the Software, and to

permit persons to whom the Software is furnished to do so, subject to

the following conditions:

The above copyright notice and this permission notice shall be included

in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS

OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF

MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY

CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,

TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE

SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

.. |Build Status| image:: https://travis-ci.org/riga/tfdeploy.svg?branch=master

:target: https://travis-ci.org/riga/tfdeploy

.. |Documentation Status| image:: https://readthedocs.org/projects/tfdeploy/badge/?version=latest

:target: http://tfdeploy.readthedocs.org/en/latest/?badge=latest

.. |Package Status| image:: https://badge.fury.io/py/tfdeploy.svg

:target: https://badge.fury.io/py/tfdeploy

## Release History

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File Name & Checksum SHA256 Checksum Help | Version | File Type | Upload Date |
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