kubeflow

Issue when trying to pass data between Kubeflow components using files

若如初见. 提交于 2021-02-11 18:25:43
问题 I made two components using python functions and I am trying to pass data between them using files, but I am unable to do so. I want to calculate the sum and then send the answer to the other component using a file. Below is the partial code (The code works without the file passing). Please assist. # Define your components code as standalone python functions:====================== def add(a: float, b: float, f: comp.OutputTextFile(float)) -> NamedTuple( 'AddOutput', [ ('out', comp

Issue when trying to pass data between Kubeflow components using files

怎甘沉沦 提交于 2021-02-11 18:24:46
问题 I made two components using python functions and I am trying to pass data between them using files, but I am unable to do so. I want to calculate the sum and then send the answer to the other component using a file. Below is the partial code (The code works without the file passing). Please assist. # Define your components code as standalone python functions:====================== def add(a: float, b: float, f: comp.OutputTextFile(float)) -> NamedTuple( 'AddOutput', [ ('out', comp

Trying to distribute data processing across a cluster and then aggregate it in master

蓝咒 提交于 2021-02-11 14:50:56
问题 Right now I have a Python Application which runs 50 threads to process data. It takes an xlsx file and will process a list of values, and will output a simple csv. I said to myself, since this is a simple Python App with 50 threads, How can I create a cluster to distribute data-processing even more? FOR EXAMPLE: Have each Worker node process a subset given to it by the master. Well that sounds easy, just take the master app slice up the dataset generated and then push it to the workers with

Kubeflow pipeline doesnt create any pod; unknown status

懵懂的女人 提交于 2021-02-11 14:40:56
问题 I started working with kubeflow and created a first, little pipeline. Unfortunately it doesn't work, so when I try to create a run with my pipeline nothing happens. Neither it creates a Kubernetes pod nor does the status of the run change (it keeps saying "Unknown status"). I also cant see the belonging graph or run output. The code of my pipeline looks like this: import kfp from kfp import components from kfp import dsl from kfp import onprem import sys def train_op( epochs, validations,

Kubeflow pipeline doesnt create any pod; unknown status

跟風遠走 提交于 2021-02-11 14:35:35
问题 I started working with kubeflow and created a first, little pipeline. Unfortunately it doesn't work, so when I try to create a run with my pipeline nothing happens. Neither it creates a Kubernetes pod nor does the status of the run change (it keeps saying "Unknown status"). I also cant see the belonging graph or run output. The code of my pipeline looks like this: import kfp from kfp import components from kfp import dsl from kfp import onprem import sys def train_op( epochs, validations,

使用Argo CD实现Kubeflow的GitOps

孤街浪徒 提交于 2021-01-12 01:46:04
使用Argo CD实现Kubeflow的GitOps 原文: https://www.kubeflow.org/docs/use-cases/gitops-for-kubeflow/ 本指南将描述如何使用使用Argo CD实现Kubeflow-面向Kubernetes集群的机器学习框架的GitOps。 1、什么是GitOps? GitOps 持续交付方法论,以 Git为中心,作为单一的源向声明式框架和基础设施交付应用。 Git 仓库定义了应用的状态,通过声明式参数来进行定义。 GitOps工具( Argo CD)将解决 git repo 定义和实际系统的差异性。 系统作为保障的结果,GitOps 强制实施一种运维模型,使所有的变更都是可观测和校验的,通过 git commits 来完成。该声明参数流水线使开发者不必编写脚本来构建和部署自己的应用。 调试简化为开发者的一个变更日志集合,通过Git commits history可以查看。如果实际系统偏离了原苍鹭指定的状态,GitOps 方法将提供工具来修正实际系统到期望的状态。 最终,一旦新的提交被发现,回滚也变为简单地同步上一次的好的git commit。所有这些好处将会减少开发者的工作量,而以前不得不花费大量的时间来进行部署系统的管理。 另外一种动态维护Kubernetes应用状态的的方法是Operator

dsl.ContainerOp with python

江枫思渺然 提交于 2021-01-05 10:38:55
问题 What are the options to download .py files into the execution environment? In this example: class Preprocess(dsl.ContainerOp): def __init__(self, name, bucket, cutoff_year): super(Preprocess, self).__init__( name=name, # image needs to be a compile-time string image='gcr.io/<project>/<image-name>/cpu:v1', command=['python3', 'run_preprocess.py'], arguments=[ '--bucket', bucket, '--cutoff_year', cutoff_year, '--kfp' ], file_outputs={'blob-path': '/blob_path.txt'} ) run_preprocess.py file is

How to get the id of the run from within a component?

人走茶凉 提交于 2020-12-31 06:51:30
问题 I'm doing some experimentation with Kubeflow Pipelines and I'm interested in retrieving the run id to save along with some metadata about the pipeline execution. Is there any way I can do so from a component like a ContainerOp ? 回答1: I tried to do this using the Python's DSL but seems that isn't possible right now. The only option that I found is to use the method that they used in this sample code. You basically declare a string containing {{workflow.uid}} . It will be replaced with the

字节跳动火山引擎加入 Linux 云原生计算基金会(CNCF)

北城以北 提交于 2020-11-21 00:36:42
2020 年 11 月 18 日,在北美举办的 KubeCon + CloudNativeCon 上,云原生计算基金会(CNCF)宣布字节跳动旗下的数字服务与智能科技品牌 火山引擎正式成为 CNCF 白金会员 。未来,火山引擎将携数十万级容器集群规模应用实践,全面融入全球云原生技术生态,为云原生的落地应用以及开源生态建设,做出持续贡献。 CNCF,全称 Cloud Native Computing Foundation,是 Linux 基金会旗下的非盈利组织。自 2015 年 7 月成立以来,该基金会始终致力于通过建立社区、管理开源项目等方式推广技术、推进云原生的可持续发展,并以此聚集了一大批云原生技术专家。发展至今,CNCF 已经拥有近 50 家会员企业,旗下活动 KubeCon + CloudNativeCon 更是成了云原生领域的全球顶级峰会。 在本次峰会上,CNCF 执行总裁 Priyanka Sharma 表示:CNCF 对火山引擎加入基金会表示诚挚欢迎,字节跳动以业界领先的超大容器集群规模支撑着今日头条、抖音、西瓜视频等产品线,作为字节跳动旗下的企业服务品牌, 火山引擎加入云原生社区可以为企业应用云原生提供丰富经验 。 火山引擎副总经理兼云原生业务负责人张鑫表示,字节跳动拥有多款平台产品,其对海量多元化信息的承载能力,以及背后采用的智能算法和数据分析技术

机器学习开发的灵药:Docker容器

◇◆丶佛笑我妖孽 提交于 2020-11-17 03:45:54
来源:机器之心 本文 约4300字 ,建议阅读 10 分钟 转向容器化机器学习开发是应对众多挑战的一种途径。 大多数人都喜欢在笔记本电脑上做原型开发。当想与人协作时,通常会将代码推送到 GitHub 并邀请协作者。当想运行实验并需要更多的计算能力时,会在云中租用 CPU 和 GPU 实例,将代码和依赖项复制到实例中,然后运行实验。如果您对这个过程很熟悉,那么您可能会奇怪:为什么一定要用 Docker 容器呢? 运营团队中优秀的 IT 专家们可以确保您的代码持续可靠地运行,并能够根据客户需求进行扩展。那么对于运营团队而言,容器不就成了一种罕见的工具吗?您能够高枕无忧,无需担心部署问题,是因为有一群基础设施专家负责在 Kubernetes 上部署并管理您的应用程序吗? 在本文中,AWS会尝试说明为什么您应该考虑使用 Docker 容器进行机器学习开发。在本文的前半部分,将讨论在使用复杂的开源机器学习软件时遇到的主要难题,以及采用容器将如何缓和这些问题。然后,将介绍如何设置基于 Docker 容器的开发环境,并演示如何使用该环境来协作和扩展集群上的工作负载。 机器学习开发环境:基本需求 首先了解一下机器学习开发环境所需的四个基本要素: 计算:训练模型离不开高性能 CPU 和 GPU; 存储:用于存储大型训练数据集和您在训练过程中生成的元数据; 框架和库:提供用于训练的 API 和执行环境