TFX Kubeflow pipelines

Kubeflow pipelines built on top of Tensorflow TFX library

General info

This repository contains machine learning pipelines based on Tensorflow TFX library. Every pipeline is designed to be published on a Kubernetes/Kubeflow cluster on premise.

Each folder contains needed code and data for the Kubeflow Pipeline, plus a README that includes:

• pipeline general information
• specific data handling about pipeline on premise
• interactive notebooks instructions
• build and launch procedure

Further pipelines are welcome via pull request.

Pipelines:

• iris - Complete pipeline for a simple (Keras) model on IRIS dataset.
• cifar-10 - Complete pipeline for a CNN model on CIFAR-10 dataset [NEEDS UPDATE].
• inat-2019 - Complete pipeline for a MobilenetV2 model on iNaturalist 2019 dataset [NEEDS UPDATE].

TFX Custom image

Pipelines are actually using custom TFX images containing NVIDIA drivers for GPU usage from tfx-nvidia-gpu

Prerequisites

Here some prerequisites needed to deploy this repo.

Platform versions

• Kubeflow version >=1.0
• Tensorflow >=2.1.0
• Tensorflow TFX ==0.21.1

Kubernetes cluster

A PersistentVolumeClaim called tfx-pvc is needed so the cluster should have one ready before dropping the pipelines.

Here an example of a 100Gb claim with a local-path storageClass onboard.

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: tfx-pvc
  namespace: kubeflow
spec:
  accessModes:
    - ReadWriteOnce
  storageClassName: local-path
  resources:
    requests:
      storage: 100Gi

Utils files deployment

Cloning this repository into the root of the tfx PersistentVolume is needed before starting any pipeline.

Local development and building

Some python libraries are needed. Install them with:

pip install -r requirements.txt

requirements.txt file is on root of this repo.

Useful links

• Kubeflow
• Tensorflow
• Tensorflow TFX