Seamless: a cell-based interactive workflow framework¶

Seamless is a framework to set up workflows and computations that respond to changes in cells. Cells define the input data as well as the source code of the computations. All cells and computations can be created, edited and connected interactively.

The main application domains are data science, scientific computing, software prototyping, and interactive web services.

Workflows, computations and results are all represented as directed acyclic graphs that consist of cell checksums. This makes them strongly interoperable and reproducible.

Documentation: http://sjdv1982.github.io/seamless ¶

Try out Seamless¶

You can try out Seamless in your browser, without any installation, thanks to the Binder project. Click on the badge below:

Quick installation¶

First, install Docker and miniconda.

docker pull rpbs/seamless
conda create -n seamless -c rpbs -c conda-forge seamless-cli -y
conda activate seamless

For more details, see Installation

Basic example¶

First, start IPython (seamless-ipython) or Jupyter (seamless-jupyter => create a new Python Notebook).

1. Import Seamless in IPython or Jupyter¶

from seamless.highlevel import Context
ctx = Context()

2. Set up a simple Seamless context¶

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

ctx.a = 10              # ctx.a => Seamless cell
ctx.b = 20              # ctx.b => Seamless cell
ctx.add = add           # ctx.add => Seamless transformer
ctx.add.a = ctx.a
ctx.add.b = ctx.b
ctx.c = ctx.add.result  # ctx.c => Seamless cell
await ctx.computation() # in a .py file, use "ctx.compute()" instead
ctx.c.value

Out[1]: <Silk: 30 >

ctx.a += 5
await ctx.computation()
ctx.c.value

Out[2]: <Silk: 35 >

3. Define schemas and validation rules¶

ctx.add.example.a = 0.0  # declares that add.a must be a number
ctx.add.example.b = 0.0

def validate(self):
    assert self.a < self.b

ctx.add.add_validator(validate, name="validate")

await ctx.computation()
print(ctx.add.exception)
# Validation passes => exception is None

4. Create an API for a Seamless cell¶

def report(self):
    value = self.unsilk
    if value is None:
        print("Sorry, there is no result")
    else:
        print("The result is: {}".format(value))

ctx.c.example.report = report
await ctx.computation()
ctx.c.value.report()

Out[3]: The result is 35

5. Mount cells to the file system¶

ctx.a.celltype = "plain"
ctx.a.mount("a.json")
ctx.b.celltype = "plain"
ctx.b.mount("b.json")
ctx.c.celltype = "plain"
ctx.c.mount("c.json", mode="w")
ctx.add.code.mount("code.py")
await ctx.translation()

7. Control cells from Jupyter¶

from ipywidgets import IntSlider, IntText

a = IntSlider(min=-10,max=30)
b = IntSlider(min=-10,max=30)
c = ctx.c.output()
ctx.a.traitlet().link(a)
ctx.b.traitlet().link(b)
display(a)
display(b)
display(c)

Out[4]

Jupyter widgets (shown only on github.io, not on github.com)

8. Save the entire state of the context¶

# Graph and checksums, as JSON
ctx.save_graph("basic-example.seamless")
# Checksum-to-buffer cache, as ZIP file
ctx.save_zip("basic-example.zip")

9. In a new notebook / IPython console:¶

from seamless.highlevel import load_graph
ctx = load_graph(
    "basic-example.seamless",
    zip="basic-example.zip"
)
await ctx.computation()
ctx.c.value

Out[1]: <Silk: 35 >

ctx.add.code.value

Out[2]: 'def add(a, b):\n return a + b'

>>> curl http://localhost:5813/ctx/c
35

Documentation¶

http://sjdv1982.github.io/seamless