Getting started

A quick setup tutorial for the pipeline python library.



Pipeline is a python library that provides a simple way to construct computational graphs for AI/ML. The library is suitable for both development and production environments supporting inference and training/finetuning. This library is also a direct interface to which provides a compute engine to run pipelines at scale and on enterprise GPUs.

The syntax used for defining AI/ML pipelines shares some similarities in syntax to sessions in Tensorflow v1, and Flows found in Prefect. In future releases we will be moving away from this syntax to a C based graph compiler which interprets python directly (and other languages) allowing users of the API to compose graphs in a more native way to the chosen language.

Installation instructions

Linux, Mac (intel)

pip install -U pipeline-ai

Mac (arm/M1)

Due to the ARM architecture of the M1 core it is necessary to take additional steps to install Pipeline, mostly due to the transformers library. We recoomend running inside of a conda environment as shown below.

  1. Make sure Rosetta2 is disabled.
  2. From terminal run:
xcode-select --install
  1. Install Miniforge, instructions here: or follow the below:
    1. Download the Miniforge install script here:
    2. Make the shell executable and run
    sudo chmod 775
  2. Create a conda based virtual env and activate:
conda create --name pipeline-env python=3.9
conda activate pipeline-env
  1. Install tensorflow
conda install -c apple tensorflow-deps
python -m pip install -U pip
python -m pip install -U tensorflow-macos
python -m pip install -U tensorflow-metal
  1. Install transformers
conda install -c huggingface transformers -y
  1. Install pipeline
python -m pip install -U pipeline-ai