# d3viz: Interactive visualization of Theano compute graphs¶

## Requirements¶

d3viz requires the pydot package, which can be installed with pip:

pip install pydot

## Overview¶

d3viz extends Theano’s printing module to interactively visualize compute graphs. Instead of creating a static picture, it creates an HTML file, which can be opened with any web-browser. d3viz allows

• to zoom to different regions and to move graphs via drag and drop,
• to position nodes both manually and automatically,
• to retrieve additional information about nodes and edges such as their data type or definition in the source code,
• to edit node labels,
• to visualizing profiling information, and
• to explore nested graphs such as OpFromGraph nodes.
In [1]:
import theano as th
import theano.tensor as T
import numpy as np


As an example, consider the following multilayer perceptron with one hidden layer and a softmax output layer.

In [2]:
ninputs = 1000
nfeatures = 100
noutputs = 10
nhiddens = 50

rng = np.random.RandomState(0)
x = T.dmatrix('x')
wh = th.shared(rng.normal(0, 1, (nfeatures, nhiddens)), borrow=True)
bh = th.shared(np.zeros(nhiddens), borrow=True)
h = T.nnet.sigmoid(T.dot(x, wh) + bh)

wy = th.shared(rng.normal(0, 1, (nhiddens, noutputs)))
by = th.shared(np.zeros(noutputs), borrow=True)
y = T.nnet.softmax(T.dot(h, wy) + by)

predict = th.function([x], y)


The function predict outputs the probability of 10 classes. You can visualize it with pydotprint as follows:

In [3]:
from theano.printing import pydotprint
import os

if not os.path.exists('examples'):
os.makedirs('examples')
pydotprint(predict, 'examples/mlp.png')

The output file is available at examples/mlp.png

In [4]:
from IPython.display import Image
Image('examples/mlp.png', width='80%')

Out[4]:

To visualize it interactively, import the d3viz function from the d3viz module, which can be called as before:

In [5]:
import theano.d3viz as d3v
d3v.d3viz(predict, 'examples/mlp.html')


When you open the output file mlp.html in your web-browser, you will see an interactive visualization of the compute graph. You can move the whole graph or single nodes via drag and drop, and zoom via the mouse wheel. When you move the mouse cursor over a node, a window will pop up that displays detailed information about the node, such as its data type or definition in the source code. When you left-click on a node and select Edit, you can change the predefined node label. If you are dealing with a complex graph with many nodes, the default node layout may not be perfect. In this case, you can press the Release node button in the top-left corner to automatically arrange nodes. To reset nodes to their default position, press the Reset nodes button.

## Profiling¶

Theano allows function profiling via the profile=True flag. After at least one function call, the compute time of each node can be printed in text form with debugprint. However, analyzing complex graphs in this way can be cumbersome.

d3viz can visualize the same timing information graphically, and hence help to spot bottlenecks in the compute graph more easily! To begin with, we will redefine the predict function, this time by using profile=True flag. Afterwards, we capture the runtime on random data:

In [6]:
predict_profiled = th.function([x], y, profile=True)

x_val = rng.normal(0, 1, (ninputs, nfeatures))
y_val = predict_profiled(x_val)

In [7]:
d3v.d3viz(predict_profiled, 'examples/mlp2.html')


When you open the HTML file in your browser, you will find an additional Toggle profile colors button in the menu bar. By clicking on it, nodes will be colored by their compute time, where red corresponds to a high compute time. You can read out the exact timing information of a node by moving the cursor over it.

## Different output formats¶

Internally, d3viz represents a compute graph in the Graphviz DOT language, using the pydot package, and defines a front-end based on the d3.js library to visualize it. However, any other Graphviz front-end can be used, which allows to export graphs to different formats.

In [8]:
formatter = d3v.formatting.PyDotFormatter()
pydot_graph = formatter(predict_profiled)

pydot_graph.write_png('examples/mlp2.png');
pydot_graph.write_pdf('examples/mlp2.pdf');

In [9]:
Image('./examples/mlp2.png')

Out[9]:

Here, we used the PyDotFormatter class to convert the compute graph into a pydot graph, and created a PNG and PDF file. You can find all output formats supported by Graphviz here.

## OpFromGraph nodes¶

An OpFromGraph node defines a new operation, which can be called with different inputs at different places in the compute graph. Each OpFromGraph node defines a nested graph, which will be visualized accordingly by d3viz.

In [10]:
x, y, z = T.scalars('xyz')
e = T.nnet.sigmoid((x + y + z)**2)
op = th.OpFromGraph([x, y, z], [e])

e2 = op(x, y, z) + op(z, y, x)
f = th.function([x, y, z], e2)

In [11]:
d3v.d3viz(f, 'examples/ofg.html')


In this example, an operation with three inputs is defined, which is used to build a function that calls this operations twice, each time with different input arguments.

In the d3viz visualization, you will find two OpFromGraph nodes, which correspond to the two OpFromGraph calls. When you double click on one of them, the nested graph appears with the correct mapping of its input arguments. You can move it around by drag and drop in the shaded area, and close it again by double-click.

An OpFromGraph operation can be composed of further OpFromGraph operations, which will be visualized as nested graphs as you can see in the following example.

In [12]:
x, y, z = T.scalars('xyz')
e = x * y
op = th.OpFromGraph([x, y], [e])
e2 = op(x, y) + z
op2 = th.OpFromGraph([x, y, z], [e2])
e3 = op2(x, y, z) + z
f = th.function([x, y, z], [e3])

In [13]:
d3v.d3viz(f, 'examples/ofg2.html')


## Feedback¶

If you have any problems or great ideas on how to improve d3viz, please let me know!