liteflow 0.2.0

Lightweight task flow framework for Python

LiteFlow

A lightweight task flow framework

Getting started

Installation

pip install liteflow

Basic Usage

Flow initializes a flow with a thread pool executor. Tasks are added to the flow and can be run by their name.

from concurrent.futures import ThreadPoolExecutor
from liteflow import Flow, TaskOutput, NextTask, Context, StreamChunk
from liteflow.executor import PoolExecutor

executor = PoolExecutor(ThreadPoolExecutor(max_workers=4))

# thread pool executor is optional, defaults to 4 workers
flow = Flow(executor=executor)

# Simple task that returns a result
@flow.task("greet")
def my_task(context: Context) -> TaskOutput:
    return TaskOutput(output="Hello World!")

result = flow.run("greet")  # Returns {"greet": "Hello World!"}
print(result)

Using Ray for Distributed Execution

LiteFlow supports distributed execution using Ray. This allows you to scale your workflows across multiple cores or even multiple machines.

Installation with Ray Support

Ray is an optional dependency. To use Ray with LiteFlow, install it with:

pip install liteflow[ray]

Example Usage

from liteflow import Flow, TaskOutput, NextTask, RayExecutor

# Initialize Ray executor
executor = RayExecutor()  # Connects to local Ray instance

# For connecting to an existing Ray cluster:
# executor = RayExecutor(address="auto")

# Create flow with Ray executor
flow = Flow(executor=executor)

@flow.task("distributed_task")
def distributed_task(context):
    # This task will be executed as a Ray task
    return TaskOutput(output="Executed in Ray!")

result = flow.run("distributed_task")
print(result)  # Returns {"distributed_task": "Executed in Ray!"}

# Don't forget to shut down Ray when done
executor.shutdown()

Task Chaining

# Tasks can trigger other tasks
@flow.task("task1")
def task1(context: Context) -> TaskOutput:
    return TaskOutput(output="result1", next_tasks=[NextTask("task2")])

@flow.task("task2")
def task2(context: Context) -> TaskOutput:
    # Access results from previous tasks
    t1_result = context.get("task1")  # waits for task1 to complete
    print(t1_result)
    return TaskOutput(output="result2")

result = flow.run("task1")  # Returns {"task2": "result2"}
print(result)

Parallel Execution

import time

@flow.task("starter")
def starter(context: Context) -> TaskOutput:
    # Launch multiple tasks in parallel by simply adding them to the next_tasks list
    return TaskOutput(output="started", next_tasks=[NextTask("slow_task1"), NextTask("slow_task2")])

@flow.task("slow_task1")
def slow_task1(context: Context) -> TaskOutput:
    time.sleep(1)
    return TaskOutput(output="result1")

@flow.task("slow_task2")
def slow_task2(context: Context) -> TaskOutput:
    time.sleep(1)
    return TaskOutput(output="result2")

# Both slow_tasks execute in parallel, taking ~1 second total
result = flow.run("starter")
print(flow.context.get("starter"))
print(result)

Streaming Results

@flow.task("streaming_task")
def streaming_task(context: Context) -> TaskOutput:
    # Stream intermediate results
    stream = context.get_stream()
    for i in range(3):
        # (task_id, chunk_value)
        stream.put(StreamChunk("streaming_task", f"interim_{i}"))
    return TaskOutput(output="final")

# Get results as they arrive
for stream_chunk in flow.stream("streaming_task"):
    print(f"{stream_chunk.task_id}: {stream_chunk.value}")

# Prints:
# streaming_task: interim_0
# streaming_task: interim_1
# streaming_task: interim_2
# streaming_task: final

Dynamic Workflows

@flow.task("conditional_task")
def conditional_task(context: Context) -> TaskOutput:
    count = context.get("count", 0)

    if count >= 3:
        return TaskOutput(output="done")

    context.set("count", count + 1)
    return TaskOutput(
        output=f"iteration_{count}", next_tasks=[NextTask("conditional_task")]
    )


# Task will loop 3 times before finishing
flow.add_task("finish", lambda ctx: TaskOutput("completed", None))
result = flow.run("conditional_task")
print(result)

# Prints:
# {'conditional_task': 'done'}

Input Parameters

@flow.task("greet")
def parameterized_task(context: Context) -> TaskOutput:
    name = context.get("user_name")
    return TaskOutput(output=f"Hello {name}!")

result = flow.run("greet", inputs={"user_name": "Alice"})
print(result)
# Returns {"greet": "Hello Alice!"}

Push next task with inputs

def task1(ctx):
    return TaskOutput("result1", [NextTask("task2", inputs={"input1": "value1"})])

def task2(ctx, inputs):
    assert inputs == {"input1": "value1"}
    return TaskOutput("result2")

flow.add_task("task1", task1)
flow.add_task("task2", task2)
result = flow.run("task1")
print(result)
# Returns {"task2": "result2"}

Dynamic Routing

@flow.task("router")
def router(context: Context) -> TaskOutput:
    task_type = context.get("type")
    routes = {
        "process": [NextTask("process_task")],
        "analyze": [NextTask("analyze_task")],
        "report": [NextTask("report_task")]
    }
    return TaskOutput(output=f"routing to {task_type}", next_tasks=routes.get(task_type, []))

@flow.task("process_task")
def process_task(context: Context) -> TaskOutput:
    return TaskOutput(output="processed data")

result = flow.run("router", inputs={"type": "process"})
print(result)
# Returns {"process_task": "processed data"}

State Management

context = Context()
context.from_dict({"task1": "result1"})

flow = Flow(executor=executor, context=context)
flow.add_task("task2", lambda ctx: TaskOutput("result2"))
flow.run("task2")

print(flow.context.get("task1"))  # Should print "result1"
print(flow.context.get("task2"))  # Should print "result2"

# Serialize the context to a dictionary
print(flow.get_context().to_dict())
# Returns {"task1": "result1", "task2": "result2"}

Map Reduce

@flow.task("task1")
def task1(ctx):
    ctx.set("collector", [])

    return TaskOutput("result1", [
        NextTask("task2", spawn_another=True),
        NextTask("task2", spawn_another=True),
        NextTask("task2", spawn_another=True)
    ])

@flow.task("task2")
def task2(ctx):
    collector = ctx.get("collector")
    collector.append("result2")
    ctx.set("collector", collector)

    return TaskOutput("", [NextTask("task3")])

@flow.task("task3")
def task3(ctx):
    collector = ctx.get("collector")
    return TaskOutput(collector)

result = flow.run("task1")
print(result)
assert result == {"task3": ["result2", "result2", "result2"]}