Rats Processors
Project description
rats-processors
A package to create and compose pipelines in a high level API, where processors (classes or unbound methods) are mapped into pipeline nodes, node ports are inferred from the processors signature, and edges are created by connecting node ports inputs and outputs. Pipelines defined this way are immutable objects that can be reused and composed into larger pipelines, facilitating reusability.
Example
In your python project or Jupyter notebook, you can compose a pipeline as follows:
from typing import NamedTuple
from pathlib import Path
from sklearn.base import BaseEstimator
import pandas as pd
from rats.processors import task, pipeline, Pipeline, PipelineContainer
class DataOut(NamedTuple):
data: pd.DataFrame
class ModelOut(NamedTuple):
model: BaseEstimator
class MyContainer(PipelineContainer):
@task
def load_data(self, fname: Path) -> DataOut:
return DataOut(data=pd.read_csv(fname))
@task
def train_model(self, data: pd.DataFrame) -> ModelOut:
return {"model": "trained"}
@pipeline
def my_pipeline(self) -> Pipeline:
load_data = self.load_data()
train_model = self.get(train_model)
return self.combine(
pipelines=[load_data, train_model],
dependencies=(train_model.inputs.data << load_data.outputs.data),
)
The above example helps with modularization and bringing exploratory code from notebooks to more permanent code.
The example above illustrates already several important concepts:
rats.processors.PipelineContainer: we wire up code modularly, i.e., one container organizes and connects tasks and pipelines.rats.processors.ux.Pipeline: a data structure that represents a computation graph, or a direct acyclic graph (DAG) of operations.rats.processors.task: a decorator to define a computational task, which we refer as processor and register it into the container. The return value of this method israts.processors.ux.Pipeline, a (single-node) pipeline.rats.processors.pipeline: a decorator to register arats.processors.ux.Pipeline, which can be a combination of other pipelines, or any method that returns a pipeline.
Note that to create a pipeline, you first create tasks (processors) and then combine them into
larger pipelines, e.g. MyContainer.load_data and MyContainer.train_model are processors
wrapped by the task decorator, and MyContainer.my_pipeline is a pipeline wrapped by the
pipeline decorator.
To run the above pipeline, you can do the following:
from rats.apps import autoid, NotebookApp
app = NotebookApp()
app.add(MyContainer()) # add a container to the notebook app
p = app.get(autoid(MyContainer.my_pipeline)) # get a pipeline by its id
app.draw(p)
app.run(p, inputs={"fname": "data.csv"})
Concepts
| Concepts | Description |
|---|---|
| Pipelines | DAG organizing computation tasks |
| Orchestrated in run environments | |
| Figure display | |
| Tasks | Entry point for computation process |
| Accepts dynamic inputs/outputs | |
| Combined | Compose tasks & pipelines to draw more complex DAGs. |
| Dependency assignment |
Features
| Features | |||||
|---|---|---|---|---|---|
| Modular | Steps become independent; Plug & play |
 |
Distributed | Uses required resources (spark or GPUs) |  |
| Graph-based | Can operate on the DAG; Enables meta-pipelines |
 |
Reusable | Every pipeline is shareable allowing collaborations |  |
Goals
- Flexibility: multiple data sources; multiple ML frameworks (pytorch, sklearn, ...), etc.
- Scalability: both data and compute.
- Usability: everyone should be able to author components and share them.
- Reproducibility: Tracking and recreating results.
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