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AI Workflows Infrastructure for Intel® Architecture

Description

On this page you will find details and instructions on how to set up an environment that supports Intel's AI Pipelines container build and test infrastructure.

Dependency Requirements

Only Linux systems are currently supported. Please make sure the following are installed in your package manager of choice:

• make
• docker.io

A full installation of docker engine with docker CLI is required. The recommended docker engine version is 19.03.0+.

• docker-compose

The Docker Compose CLI can be installed both manually and via package manager.

$ DOCKER_CONFIG=${DOCKER_CONFIG:-$HOME/.docker}
$ mkdir -p $DOCKER_CONFIG/cli-plugins
$ curl -SL https://github.com/docker/compose/releases/download/v2.7.0/docker-compose-linux-x86_64 -o $DOCKER_CONFIG/cli-plugins/docker-compose
$ chmod +x $DOCKER_CONFIG/cli-plugins/docker-compose

$ docker compose version
Docker Compose version v2.7.0

Build and Run Workflows

Each pipeline will contain specific requirements and instructions for how to provide its specific dependencies and what customization options are possible. Generally, pipelines are run with the following format:

git submodule update --init --recursive

This will pull the dependent repo containing the scripts to run the end2end pipeline's inference and/or training.

<KEY>=<VALUE> ... <KEY>=<VALUE> make <PIPELINE_NAME>

Where KEY and VALUE pairs are environment variables that can be used to customize both the pipeline's script options and the resulting container. For more information about the valid KEY and VALUE pairs, see the README.md file in the folder for each workflow container:

AI Workflow	Framework/Tool	Mode
Chronos Time Series Forecasting	Chronos and PyTorch*	Training
Document-Level Sentiment Analysis	PyTorch*	Training
Friesian Recommendation System	Spark with TensorFlow	Training | Inference
Habana® Gaudi® Processor Training and Inference using OpenVINO™ Toolkit for U-Net 2D Model	OpenVINO™	Training and Inference
Privacy Preservation	Spark with TensorFlow and PyTorch*	Training and Inference
NLP workflow for AWS Sagemaker	TensorFlow and Jupyter	Inference
NLP workflow for Azure ML	PyTorch* and Jupyter	Training | Inference
Protein Structure Prediction	PyTorch*	Inference
Quantization Aware Training and Inference	OpenVINO™	Quantization Aware Training(QAT)
Ray Recommendation System	Ray with PyTorch*	Training | Inference
RecSys Challenge Analytics With Python	Hadoop and Spark	Training
Video Streamer	TensorFlow	Inference
Vision Based Transfer Learning	TensorFlow	Training | Inference
Wafer Insights	SKLearn	Inference

Cleanup

Each pipeline can remove all resources allocated by executing make clean.