Pipelines is a language and runtime for crafting massively parallel pipelines. Unlike other languages for defining data flow, the Pipeline language requires implementation of components to be defined seperately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline, while providing access to thousands of active libraries for machine learning, data analysis and processing.
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9.7 10.0 pipelines VS RayRay is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.
SCOOP (Scalable COncurrent Operations in Python)
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Pipelines is a language and runtime for crafting massively parallel pipelines. Unlike other languages for defining data flow, the Pipeline language requires implementation of components to be defined separately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline, while providing access to thousands of active libraries for machine learning, data analysis and processing. Skip to Getting Started to install the Pipeline compiler.
As an introductory example, a simple pipeline for Fizz Buzz on even numbers could be written as follows -
from fizzbuzz import numbers from fizzbuzz import even from fizzbuzz import fizzbuzz from fizzbuzz import printer numbers /> even |> fizzbuzz where (number=*, fizz="Fizz", buzz="Buzz") |> printer
Meanwhile, the implementation of the components would be written in Python -
def numbers(): for number in range(1, 100): yield number def even(number): return number % 2 == 0 def fizzbuzz(number, fizz, buzz): if number % 15 == 0: return fizz + buzz elif number % 3 == 0: return fizz elif number % 5 == 0: return buzz else: return number def printer(number): print(number)
Running the Pipeline document would safely execute each component of the pipeline in parallel and output the expected result.
Components are scripted in Python and linked into a pipeline using imports. The syntax for an import has 3 parts - (1) the path to the module, (2) the name of the function, and (3) the alias for the component. Here's an example -
from parser import parse_fasta as parse
That's really all there is to imports. Once a component is imported it can be referenced anywhere in the document with the alias.
Every pipeline is operated on a stream of data. The stream of data is created by a Python generator. The following is an example of a generator that generates a stream of numbers from 0 to 1000.
def numbers(): for number in range(0, 1000): yield number
Here's a generator that reads entries from a file
def customers(): for line in open("customers.csv", 'r'): yield line
The first component in a pipeline is always the generator. The generator is run in parallel with all other components and each element of data is passed through the other components.
from utils import customers as customers # a generator function in the utils module from utils import parse_row as parser from utils import get_recommendations as recommender from utils import print_recommendations as printer customers |> parser |> recommender |> printer
Pipes are what connect components together to form a pipeline. As of now, there are 2 types of pipes in the Pipeline language - (1) transformer pipes, and (2) filter pipes. Transformer pipes are used when input is to be passed through a component. For example, a function can be defined to determine the potential of a particle and a function can be defined to print the potential.
particles |> get_potential |> printer
The above pipeline code would pass data from the stream generated by
get_potential and then the output of
printer. Filter pipes work similarly except they use the following component to filter data. For example, a function can be defined to determine if a person is over 50 and then print their names to a file.
population /> over_50 |> printer
This would use the function referenced by
over_50 to filter out data from the stream generated by
population and then pass output to
where keyword lets you pass in multiple parameters to a component as opposed to just what the output from the previous component was. For example, a function can be defined to print to a file the names of all applicants under a certain age.
applicants |> printer where (person=*, age_limit=21)
This could be done using a filter as well.
applicants /> age_limit where (person=*, age=21) |> printer
In this case, the function for
age_limit could look something like this -
def age_limit(person, age): return person.age <= age
Note that this function still has just one return value - the boolean expression that is used to determine wether input to the component is passed on as output.
to keyword is for when you want the previous component has multiple return values and you want to specify which ones to pass on to the next component. As an example, if you had a function for calculating the electronegativity and electron affinity of an atom, you could use it in a pipeline as follows -
atoms |> calculator to (electronegativity, electron_affinity) |> printer where (line=electronegativity)
Here's an example using a filter.
atoms /> below where (atom=*, limit=2) to (is_below, electronegativity, electron_affinity) with is_below |> printer where (line=electronegativity)
Note the use of the
with keyword here. This is necessary for filters to specify which return value of the function is used to filter out elements in the stream.
All you need to get started is the Pipelines compiler. You can install it by downloading the executable from Releases.
If you have the Nimble package manager installed and
~/.nimble/binpermanantly added to your PATH environment variable (look this up > if you don't know how to do this), you can also install by running the following command.
nimble install pipelines
Pipelines' only dependency is the Python interpreter being installed on your system. At the moment, most versions 2.7 and earlier are supported and support for Python 3 is in the works. Once Pipelines is installed and added to your PATH, you can create a
.pipelinefile, run or compile anywhere on your system -
$ pipelines the .pipeline compiler (v:0.1.0)
usage: pipelines Show this pipelines Compile .pipeline file pipelines Compile all .pipeline files in folder pipelines run Run .pipeline file pipelines clean Remove all compiled .py files from folder
for more info, go to github.com/calebwin/pipelines
### Some next steps There are several things I'm hoping to implement in the future for this project. I'm hoping to implement some sort of `and` operator for piping data from the stream into multiple components in parallel with the output ending up in the stream in a nondeterministic order. Further down the line, I plan on porting the whole thing to C and putting in a complete error handling system <!--- - String imports - Control allocation of processes with Pool - Use Pipe instead of multiple Queue - Only have num_cpus running at one time --->