pyflow-viz 0.1b3

Lazy computation directed acyclic graph builder

Pyflow is a light weight library that lets the user construct a memory efficient directed acyclic computation graph (DAG) that evaluates lazily. It can cache intermediate results, and only computes the parts of the graph that has data dependency.

Install

pip install pyflow-viz

Getting started

Let’s construct a simple computation graph: (Note the similarity of API to that of Keras functional API!)

from pyflow import GraphBuilder

def adding(a, b):
        return a + b

G = GraphBuilder()
a1 = G.add(adding)(2, 2)  # you add methods with `add` instance method.
a2 = G.add(adding)(3, a1)
a3 = G.add(adding)(a1, a2)

At this point, no evaluation has occurred. Also, the outputs a1, a2, and a3 are DataNode objects (well, more precisely, weak references to the DataNode objects, but more on this later!) You can kick off the evaluation by invoking get method from any of the output objects:

print(a3.get())  # 11

You can also easily visualize the DAG using view method:

G.view()

A couple notes:

1. The API was inspired by that of the Keras functional API

2. For demo, we are using a simple method of adding two integers, but the input method can be any python function, including instance methods, with arbitrary inputs such as numpy array, pandas dataframe or Spark dataframe.

Multi-output methods

What if we have a python function with multiple outputs? Due to dynamic nature of python, it is impossible to determine the number of outputs before the function is actually ran. In such a case, you need to specify the number of outputs by n_out argument:

from pyflow import GraphBuilder

def adding(a, b):
        return a + b

def multi_output_method(a, b):
        return a+1, b+1

G = GraphBuilder()
a1 = G.add(adding)(2, 2)
a2, b2 = G.add(multi_output_method, n_out=2)(a1, 2)  # n_out argument!
a3 = G.add(adding)(a2, 3)
a4 = G.add(adding)(b2, a1)

G.view()

Visualizing data flow

The view function actually has the ability to summarize the DAG by only showing the user the OperationNodes, which it does by default. We can override this default setting by using the summary parameter of the function:

G.view(summary=False)

With the summary functionality turned off, the complete DAG visualization will includes DataNodes as well as the OperationNodes.

Styling your DAG

Pyflow lets the user customize the DAG visuals to a certain degree, with more to come in the future. Let’s take a look at some examples.

from pyflow import GraphBuilder

def query_dataframe_A():
return 1  # pretend this was a pandas or Spark dataframe!

def query_dataframe_B():
        return 2

def product_transform(inp):
        return inp*2

def join_transform(inp1, inp2):
        return inp1 + inp2

def split_transform(inp):
        return inp+1, inp+2

G = GraphBuilder()
df1 = G.add(query_dataframe_A)()
df2 = G.add(query_dataframe_B)()
new_df1 = G.add(product_transform)(df1)
new_df2 = G.add(product_transform)(df2)
dfa, dfb = G.add(split_transform, n_out=2)(new_df2)
joined_df = G.add(join_transform)(new_df1, dfa)

G.view()

But since at a conceptual level, queries are similarly progenitors of new data, perhaps we want to put them side by side on top, and position is controlled by rank parameter. Also, since these are probably coming from some data storage, we might want to style their nodes accordingly, with different color.

G = GraphBuilder()
df1 = G.add(query_dataframe_A, rank=0, shape='cylinder', color='lightblue')()
df2 = G.add(query_dataframe_B, rank=0, shape='cylinder', color='lightblue')()
new_df1 = G.add(product_transform)(df1)
new_df2 = G.add(product_transform)(df2)
dfa, dfb = G.add(split_transform, n_out=2)(new_df2)
joined_df = G.add(join_transform)(new_df1, dfa)

G.view()

There are much more we can do with styling. We will dedicate a separate section for styling guides.

Computation and memory efficiency of Pyflow

When you invoke get method, pyflow will only then evaluate, and it will evaluate only the parts of the graph that is needed to be evaluated. Also, as soon as an intermediate result has no dependency, it will automatically release the memory back to the operating system. Let’s take a tour of the computation process to better understand this mechanism by turning on verbose parameter.

from pyflow import GraphBuilder

def adding(a, b):
        return a + b

def multi_output_method(a, b):
        return a+1, b+1

G = GraphBuilder(verbose=True)
a1 = G.add(adding)(1, 2)
a2, a3 = G.add(return2, n_out=2)(a1, 3)
a4 = G.add(adding)(a1, 5)
a5 = G.add(adding)(a4, a3)

a5.get()

With verbose=True, along with the final output, pyflow will also produce the following standard output:

computing for data_12
adding_11 activated!
adding_8 activated!
adding_0 activated!
return2_4 activated!
computing for data_10
computing for data_3
running adding_0
adding_0 deactivated!
running adding_8
data_3 still needed at return2_4
adding_8 deactivated!
computing for data_7
running return2_4
data_3 released!
return2_4 deactivated!
running adding_11
data_10 released!
data_7 released!
adding_11 deactivated!

Let’s take the tour of this process by looking at the graph. Notice that in verbose mode, the graph will actually print out the uid’s of the nodes not just their aliases (more on setting alias later!)

As pyflow tries to compute data_12, it will first activate all the OperationNodes that is needed for the computation, in out case, those are adding_11, adding_8, adding_0, return2_4. It will then follow the lineage of the graph to work on intermediate results needed to proceed down the graph. Notice that as the computation proceeds, the OperationNodes that were activated are deactivated. When it gets to data_3, notice that it is needed at both adding_8 and return2_4. Thus, once it completes adding_8, it cannot yet release the memory from data_3: data_3 still needed at return2_4. But as soon as return2_4 is ran, it releases data_3 from memory, as it is not needed anymore: data_3 released!. The DataNodes with raw inputs such as integers are not released since there is no way for the graph to reconstruct them.

By the same token, if you were to run the graph from middle, say, at a4:

a4.get()

You will see:

computing for data_10
adding_8 activated!
adding_0 activated!
computing for data_3
running adding_0
adding_0 deactivated!
running adding_8
data_3 released!
adding_8 deactivated!

In this case, since return2_4 is not activated, the data_3 does not consider its presence in deciding release of memory.

Lastly, you have the option of either persisting all of the intermediate results, or persisting part of the intermediate results.

To persist all intermediate results, use persist parameter at GraphBuilder level:

from pyflow import GraphBuilder

G = GraphBuilder(persist=True)

a1 = G.add(adding)(1, 2)
a2, a3 = G.add(return2, n_out=2)(a1, 3)
a4 = G.add(adding)(a1, 5)
a5 = G.add(adding)(a4, a3)

a5.get()

With persist enabled, after running a5.get(), when you try to run a4.get(), the graph will not recompute anything because a4 node result will have been cached in memory. The persist is turned off by default, as it is assumed that the user of the pyflow will process large amounts of data.

To persist parts of the data, you can specify the persist parameter at add level:

from pyflow import GraphBuilder

G = GraphBuilder(persist=False)  # default value

a1 = G.add(adding)(1, 2)
a2, a3 = G.add(return2, n_out=2)(a1, 3)
a4 = G.add(adding, persist=True)(a1, 5)  # persist here
a5 = G.add(adding)(a4, a3)

a5.get()

Then, when you run a4.get() it will not rerun the computation as a4 result has been cached in memory although all other intermediate results will have been released.