https://github.com/pcattori/dataflow

https://github.com/pcattori/dataflow

Last synced: 14 days ago
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• Host: GitHub
• URL: https://github.com/pcattori/dataflow
• Owner: pcattori
• Created: 2017-04-13T21:41:56.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2017-05-13T02:31:47.000Z (over 7 years ago)
• Last Synced: 2024-11-08T19:12:06.267Z (2 months ago)
• Language: Python
• Size: 2.93 KB
• Stars: 2
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# dataflow

Toy example of setting up a delayed-execution graph, or ["dataflow"](https://www.tensorflow.org/versions/r0.11/resources/faq) in [TensorFlow](https://www.tensorflow.org/) terminology.

Check out the [stackoverflow question that inspired this demo](http://stackoverflow.com/a/43380155/1490091).

# usage

## step 1

Use the `@dataflow` decorator to wrap a function with dataflow capabilities:

```python

@dataflow

def f(x, y):

    return x**2 % y

@dataflow

def g(a, b, c):

    return a * b + c

@dataflow

def h(i, j):

    return i / j

```

Calling these function will now store the function and arguments for execution at a later point.

In other words, no mathematical operations will happen until you call

`graph.execute()` in step 3.

## step 2

Compose many `@dataflow` functions into a graph:

```python

graph = f(g(1, 2, h(3, 4)), 5)

```

## step 3

Execute the function:

```python

answer = graph.execute()

```

## variables

You can also define variables that get bound to values at execution time:

```python

z = Variable()

seed = Variable()

graph = f(g(z, 2, h(4, z)), seed)

```

You can execute the same graph with different values bound to the variables:

```python

answer1 = graph.execute({z: 1., seed: 5})

answer2 = graph.execute({z: 3., seed: 13})

```