Reverse Dependencies of kfp

The following projects have a declared dependency on kfp:

• ai-pipeline-params — Functions to set and use the AI Pipeline Params
• auto-ml-openai-sdk — Async requests
• capctl — Command line tool for CAP
• cauldron-ml — A CLI tool for building and deploying Kubeflow Pipelines in GCP Vertex AI
• codeflare-torchx — TorchX SDK and Components
• cogflow — COG modules
• data-prep-lab-kfp — Data Preparation Laboratory Library. KFP support
• data-prep-toolkit-flows — Data Preparation Toolkit Library for creation and execution of ttansformers flows
• data-prep-toolkit-kfp — Data Preparation Kit Library. KFP support
• data-prep-toolkit-kfp-v1 — Data Preparation Kit Library. KFP support
• data-prep-toolkit-kfp-v2 — Data Preparation Kit Library. KFP support
• datatonic-pipeline-components — KFP components developed at Datatonic
• digitalhub-runtime-kfp — KFP runtime for DHCore
• disdat-kfp — no summary
• dkube — Dkube SDK
• dkube-cicd-controller — CICD controller to automate component building, pipeline deployment to DKube.
• dlpipeline-api — A pip-installable pipeline service provider
• e2enetworks — E2E Networks Plugins
• easy-kubeflow — sdk help users for a better use of kubeflow
• easykubeflow — High level of kubeflow SDK
• elyra — Elyra provides AI Centric extensions to JupyterLab
• elyra-server — The elyra-server package provides common core libraries and functions that are required by Elyra's individual extensions. Note: Installing this package alone will not enable the use of Elyra. Please install the 'elyra' package instead. e.g. pip install elyra[all]
• fondant — Fondant - Large-scale data processing made easy and reusable
• gdmix-workflow — no summary
• google-cloud-aiplatform — Vertex AI API client library
• google-cloud-automlops — Build MLOps Pipelines in Minutes.
• google-cloud-pipeline-components — This SDK enables a set of First Party (Google owned) pipeline components that allow users to take their experience from Vertex AI SDK and other Google Cloud services and create a corresponding pipeline using KFP or Managed Pipelines.
• hoodat-vertex-components — Re-usable kfp components for hoodat
• hypermodel — Hyper Model provides functionality to support MLOps
• ibm-orchestration-pipelines — Python utilities for IBM Orchestration Pipelines
• ibm-watson-pipelines — Python utilities for IBM Watson Pipelines
• katonic — A modern, enterprise-ready MLOps Python SDK
• kedro-kubeflow — Kedro plugin with Kubeflow Pipelines support
• kedro-vertexai — Kedro plugin with GCP Vertex AI support
• kfn — Kubeflow notebook component builder
• kfp-command-line-tools — no summary
• kfp-decorators — no summary
• kfp-deployer — Deploy the KFP ML Pipeline from CLI.
• kfp-local — kfp local client runs pipelines on local host or in docker container
• kfp-notebook — Elyra provides AI Centric extensions to JupyterLab
• kfp-tekton — Tekton Compiler for Kubeflow Pipelines
• kfp-toolbox — The toolbox for kfp (Kubeflow Pipelines SDK)
• kfpdist — Use Kubeflow Pipeline to run distributed training jobs
• kfputils — Kubeflow pipeline utils
• kfpx — Extends the kfp package
• kfpxtend — High level of kubeflow SDK
• kfx — Extensions for kubeflow pipeline sdk.
• kiwi-booster — Python utility functions and classes for KiwiBot AI&Robotics team
• kubeflow-kale — Convert JupyterNotebooks to Kubeflow Pipelines deployments
• kubextract — cli framework generator for developing ML on kubeflow
• kung-fu-pipelines — Kubeflow pipelines made easy.
• m4-utils — Biblioteca com funções de uso comum em projetos de aprendizado de máquina e ciencia de dados.
• magnus-extensions — Extensions to Magnus core
• MAMMOth-commons — Component interfaces of the MAMMOth fairness toolkit.
• metaai-compiler — kfp component compiler with metaai
• ml-liv — no summary
• ml-orchestrator — kubeflow extension
• ml-pipeline-gen — A tool for generating end-to-end pipelines on GCP.
• ml6-kfp-components — A compilation of ML6 shared KFP components.
• mlcube-kubeflow — no summary
• mlrun-pipelines-kfp-v1-8 — MLRun Pipelines package for providing KFP 1.8 compatibility
• mlrun-pipelines-kfp-v1-8-experiment — MLRun Pipelines package for providing KFP 1.8 compatibility
• mlrun-pipelines-kfp-v2 — MLRun Pipelines package for providing KFP 2.* compatibility
• mlrun-pipelines-kfp-v2-experiment — MLRun Pipelines package for providing KFP 2.* compatibility
• mms-pip — A custom MMS Analytics module for Python3 by the Touchpoint Analytics & Data Discovery
• mrx-link-core — A simple AI/ML modeling tool, Link™ ensures a smoother flow and a better experience throughout the model development cycle.
• noteline-kf — Noteline Kubeflow Pipeline step for executing Notebook
• odh-elyra — Elyra provides AI Centric extensions to JupyterLab
• rexify — Streamlined Recommender System workflows with TensorFlow and Kubeflow
• sameproject — Notebooks to Pipelines, reproducible data science, oh my.
• simple-kfp-task — Generate a simple Kubeflow Pipeline task
• soopervisor — no summary
• tabular-orchestrated — no summary
• tfx — TensorFlow Extended (TFX) is a TensorFlow-based general-purpose machine learning platform implemented at Google.
• tfx-addons — TFX Addons libraries
• torchx — TorchX SDK and Components
• torchx-applovin — TorchX SDK and Components
• torchx-nightly — TorchX SDK and Components
• unipipe — project_description
• vertex-deployer — Check, compile, upload, run, and schedule Kubeflow Pipelines on GCP Vertex AI in a standardized manner.
• vertexai — Please run pip install vertexai to use the Vertex SDK.
• wanna-ml — CLI tool for managing ML projects on Vertex AI
• wanna-ml-test — CLI tool for managing ML projects on Vertex AI
• waseda-tfx — TensorFlow Extended (TFX) is a TensorFlow-based general-purpose machine learning platform implemented at Google.
• wfp — A proxy application for kubeflow pipeline
• workflow-upa — A pip-installable UPA Workflow Definitions package
• zenml — ZenML: Write production-ready ML code.
• zenml-nightly — ZenML: Write production-ready ML code.