The light pipeline library pipely can execute any class or any sequence of classes in any order. Install with:

pip install pipely

1. Quick Start

To build a pipeline with executable classes, create a config .yaml file in the following format:

steps:
    [step_name_1]:
        exec: [relative path to a file]:[class to execute]
    [step_name_2]:
        exec: [relative path to a file]:[class to execute]
    [step_name_3]:
        exec: [relative path to a file]:[class to execute]
        depends_on:
        - [step_name_1]
        - [step_name_2]

• [steps] names should be unique;
• depends_on defines order and enables pipely to detect independent steps and execute them in parallel;
• the executable classes should have a __call__ method (see example below);

Then trigger the pipeline in cli:

python -m pipely from-pipeline <file.yaml> [dict.json]

• <file.yaml> (required) is the path to a yaml config file (any format): ../../file.yaml, or path/to/file.yaml, or file.yaml.
• [dict.json] (optional argument) is the path to a shared dictionary json file if value exchange between classes is needed (more in Section 1.2.)

1.1. Example

Let's create a test.yaml config file:

steps:
    a1_print:
        exec: src/file1.py:firstA
    a2_print:
        exec: src/file1.py:secondA
    final_print:
        exec: src/file2.py:printDone
        depends_on:
        - a1_print
        - a2_print

depends_on parameter sets the following order for pipely:

1. firstly execute a1_print and a2_print in parallel
2. and then execute final_print

Let's look at executable classes. To use pipely, the executable classes should have a __call__ method, as shown below (check example/src/):

#example/src/file1.py

class firstA:
    def run(self):
        a = 32
        print(a)

    def __call__(self): #call method
        self.run()

class secondA:
    def run(self):
        a = 12
        print(a)

    def __call__(self): #call method
        self.run()

#example/src/file2.py

class printDone:
    def run(self):
        print("Done.")

    def __call__(self): #call method
        self.run()

To start pipely, type in cli:

python -m pipely from-pipeline test.yaml

1.2. Example w/ a shared dictionary

Let's create a testContext.yaml config file:

steps:
    a_first:
        exec: src/file1_shared.py:firstA
    a_second:
        exec: src/file1_shared.py:secondA
    a_sum:
        exec: src/file2_shared.py:aSum
        depends_on:
        - a_first
        - a_second
    a_sum_print:
        exec: src/file3_shared.py:aSumPrint
        depends_on:
        - a_sum

depends_on parameter sets the following order for pipely:

1. executes a_first and a_second in parallel
2. then executes a_sum, which sums up both a's in the previous steps
3. finally executes a_sum_print, which prints the final result previously calculated in a_sum

Let's look at executale classes to understand how values are transferred between them (check example/src folder):

#example/src/file1_shared.py

class firstA:
    def run(self):
        a = 32
        self.result = a

    def __call__(self, context): #include context
        self.run()
        context["a1"] = self.result #to save into shared dictionary

class secondA:
    def run(self):
        a = 12
        self.result = a

    def __call__(self, context): #include context
        self.run()
        context["a2"] = self.result #to save into shared dictionary

Now we can use previously saved values a1 and a2 in another class, as shown below:

#example/src/file2_shared.py

class aSum:
    def run(self, context): #include context
        a1 = context["a1"] #to extract from shared dictionary
        a2 = context["a2"] #to extract from shared dictionary
        self.result = a1 + a2

    def __call__(self, context): #include context
        self.run(context) #to run the function
        context["aSum"] = self.result #and save into shared dictionary

Then trigger the pipeline in cli with an optional second argument --context-path, which the path to a shared dictionary example_context.json:

python -m pipely from-pipeline testContext.yaml --context-path example_context.json

2. Imperative way

Pipely can also trigger a specific class from a specific .py file.

python -m pipely from-class <path/to/file.py>:<TestClass>

Below is an example of a command that triggers a printDone class from src/file4.py file.

python3 -m pipely from-class src/file4.py:printDone

If your class needs to operate on a shared dictionary, the command from-class could use an optional second argument --context-path. This argument awaits a path to a json representing the shared dictionary.

python -m pipely from-class src/file4.py:printShared --context-path example_context.json