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Try premade models for free that have been made using this library:

Table of Contents


Pipeline is a python library that provides a simple way to construct computational flows for AI/ML models. The library is suitable for both development and production environments supporting inference and training/finetuning. This library is also a direct interface to Mystic which provides a compute engine to run pipelines at scale and on enterprise GPUs. This SDK can also be used with Pipeline Core on a private hosted cluster.

The syntax used for defining AI/ML pipelines shares some similarities in syntax to sessions in Tensorflow v1, and Flows found in Prefect.

Installation and quickstart

To install pipeline run:

pip install pipeline-ai

To create a new pipeline navigate to the directory you want to create the pipeline in and run:

pipeline container init -n quickstart

This will create two files in the directory:

  • pipeline.yaml - The configuration file for the container to run the pipeline.
  • - The python file to populate with your pipeline.


Below are some popular models that have been premade by the community on Mystic. You can find more models in the explore section of Mystic, and the source code for these models is also referenced in the table.

Model Category Description Source
meta/llama2-7B LLM A 7B parameter LLM created by Meta (vllm accelerated) source
meta/llama2-13B LLM A 13B parameter LLM created by Meta (vllm accelerated) source
meta/llama2-70B LLM A 70B parameter LLM created by Meta (vllm accelerated) source
runwayml/stable-diffusion-1.5 Vision Text -> Image source
stabilityai/stable-diffusion-xl-refiner-1.0 Vision SDXL Text -> Image source
matthew/e5_large-v2 LLM Text embedding source
matthew/musicgen_large Audio Music generation source
matthew/blip Vision Image captioning source

Example and tutorials

Tutorial Description
Entity objects Use entity objects to persist values and store things
Cold start optimisations Premade functions to do heavy tasks seperately
Input/output types Defining what goes in and out of your pipes
Files Inputing or outputing files from your runs
Pipeline building Building pipelines - how it works
Runs Running a pipeline remotely - how it works

Below is some sample python that demonstrates various features and how to use the Pipeline SDK to create a simple pipeline that can be run locally or on Mystic.

from pathlib import Path
from typing import List

import torch
from diffusers import StableDiffusionPipeline

from pipeline import Pipeline, Variable, pipe, entity
from import compute_requirements
from pipeline.objects import File
from pipeline.objects.graph import InputField, InputSchema

class ModelKwargs(InputSchema): # TUTORIAL: Input/output types
    height: int | None = InputField(default=512, ge=64, le=1024)
    width: int | None = InputField(default=512, ge=64, le=1024)
    num_inference_steps: int | None = InputField(default=50)
    num_images_per_prompt: int | None = InputField(default=1, ge=1, le=4)
    guidance_scale: int | None = InputField(default=7.5)

@entity # TUTORIAL: Entity objects
class StableDiffusionModel:
    @pipe(on_startup=True, run_once=True) # TUTORIAL: Cold start optimisations
    def load(self):
        model_id = "runwayml/stable-diffusion-v1-5"
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.pipe = StableDiffusionPipeline.from_pretrained(
        self.pipe =

    def predict(self, prompt: str, kwargs: ModelKwargs) -> List[File]: # TUTORIAL: Input/output types
        defaults = kwargs.to_dict()
        images = self.pipe(prompt, **defaults).images

        output_images = []
        for i, image in enumerate(images):
            path = Path(f"/tmp/sd/image-{i}.jpg")
            path.parent.mkdir(parents=True, exist_ok=True)
            output_images.append(File(path=path, allow_out_of_context_creation=True)) # TUTORIAL: Files

        return output_images

with Pipeline() as builder: # TUTORIAL: Pipeline building
    prompt = Variable(str)
    kwargs = Variable(ModelKwargs)
    model = StableDiffusionModel()
    output = model.predict(prompt, kwargs)

my_pl = builder.get_pipeline()


This project is made with poetry, so firstly setup poetry on your machine.

Once that is done, please run


With this you should be good to go. This sets up dependencies, pre-commit hooks and pre-push hooks.

You can manually run pre commit hooks with

pre-commit run --all-files

To run tests manually please run


For developing v4, i.e. containerized pipelines, you need to override the installed pipeline-ai python package on the container. This can be done by bind mounting your target pipeline directory, e.g. using raw docker


Pipeline is licensed under Apache Software License Version 2.0.