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README
Apache-2.0

Cube Studio

整体架构

image

cube studio是 腾讯音乐 开源的一站式云原生机器学习平台,目前主要包含

模块分组 功能 功能明细
基础能力 项目组管理 AI平台需要通过项目划分,支持配置相应项目组用户的权限,任务/服务的挂载,资源组,集群,服务代理,项目组内角色应用
基础能力 网络 支持非80端口,支持公网/域名,支持反向代理和内网穿透方式访问,支持https
基础能力 用户管理 角色管理/权限管理 管理平台用户的基本信息,组织架构,支持账号密码,rbac权限体系。 增加修改和删除,清理等操作的历史记录
基础能力 计量计费功能 1、支持平台资源限制的分配和查看;项目组资源限制,租户资源限制、任务资源限制,项目组下个人的资源限制,包括开发资源,训练资源、推理资源等。   额度限制限制在notebook,docker构建,pipeline,超参搜索,内部服务,推理服务中的生效。限制支持单任务,并行任务总和和历史任务总和等方法2、提供统一的开发、训练、推理服务资源监控,从租户、项目、任务角度分析模型资源分配及使用情况。 3、支持自定义计费模式,通过计量结果自定义获取计费值
基础能力 SSO单点登录 账号密码注册自动登录,支持对接公司账号体系AUTH_OID/AUTH_LDAP/AUTH_REMOTE_USER等登录注册方式,支持消息推送。 增加登录验证,强密码,远程用户,登录频率限制,密码密文传输等
基础能力 支持多种算力 提供多种规格的资源支持不同的使用场景,cpu/gpu等 支持T4/V100/A100等多种卡型,支持arm64芯片,支持vgpu等模式。支持国产gpu,支持海光gpu,海飞科dcu,华为npu,支持rdma调度,mellanox。支持gpu禁用模型,共享模式,独占模式
基础能力 多资源组/多集群 支持划分多资源组,支持ipvs的k8s网络模式,支持多k8s集群,支持containerd容器运行态
基础能力 边缘集群 支持边缘集群模式,支持边缘节点开发,训练,推理
基础能力 serverless集群模式 支持腾讯云serverless集群模式,(notebook,pipeline,推理服务模块支持)阿里云serverless集群模式(notebook,pipeline,推理服务模块支持)
基础能力 数据库存储 支持外部mysql作为元数据库支持外部postgres作为元数据库
基础能力 存储盘管理 支持web界面添加存储盘,支持项目组绑定,notebook pipeline 推理服务,直接在pod中挂载外部分布式存储。支持nfs,cfs,oss,nas,cos,glusterfs,cephfs,s3/minio
基础能力 国际化能力 mlops支持配置多语言配置,目前支持中英文
数据管理 数据地图 元数据库表管理,指标,维表
数据管理 数据计算 sqllab交互查询,支持mysql,postgresql等计算引擎
数据管理 ETL编排 数据ETL任务流编排,任务管理等对接公司数据中台相应计算/调度引擎
数据管理 数据集管理 允许用户随时上传样本集(图片、音频、文本等),支持sdk进行数据集对接,支持数据集一键探索功能
数据管理 数据标注 支持标注平台,图/文/音/多模态各类型标注能力,对接一站式机器学习平台,支持自动化标注(需购买aihub):支持目标识别,目标边界识别,目标遮罩识别,图片分类,图片描述,ocr,关键点检测。支持大模型自动化标注:文本分类,文本翻译,命名实体识别,阅读理解,问答,摘要提取。
开发环境 镜像功能 镜像仓库/镜像管理/在线构建镜像。同时提供平台所有镜像,包括模板镜像/服务镜像/notebook镜像/gpu基础环境的构建方法和构建后镜像,支持dockerfile在线构建 支持同一仓库多个秘钥配置
开发环境 notebook 支持基于开源的Jupyterlab/vscode,提供在线的交互式开发调试工具;提供多种可选环境ide和开发示例,支持资源类型选择 支持大数据版本,机器学习版本,深度学习版本 大数据版本支持用户信息和内网spark链接 支持ssh remote与notebook对接远程开发,方便快速将本地代码提交到平台的训练环境。ssh隧道代理,单端口开放 支持matlab,Rstudio等在线ide 支持gpu,cpu,内存,监控,支持git交互 支持自定义notebook镜像,便于封装公司自己的notebook 多环境notebook,支持R语言/julia语言/python2.7/python3.6/python3.7/python3.8/python3.9/python3.10环境和cube-studio专有环境 支持tensorboard任务可视化 notebook支持环境镜像保存 jupyter支持密码保护 notebook支持整卡占用,虚拟卡占用,gpu共享占用 支持notebook启动自动初始化环境
模型训练 拖拉拽任务流编排调试 提供拖拽式交互开发环境,支持开发者以拖拽的方式完成业务逻辑的PIPLINE; 支持单任务调试,训练支持多种资源规格(CPU、GPU等),支持卡型的选择,超时重试等。分布式任务模板支持单任务调试用户镜像而非模板镜像 分布式任务模板支持gpu型号透传,rdma高速通信,拉取秘钥透传 pipeline调试,支持定时调度,补录,并发限制,超时,实例依赖等,任务管理,workflow实例管理,资源监控,支持任务输入输出,任务流全局变量,文本/图片/echart结果可视化,支持workflow暂停和恢复。 支持单任务和pipeline运行中任务监听端口提供运行中服务监听能力 任务流支持任务推荐
模型训练 主流功能算子 基础算子:自定义镜像,逻辑节点,python 数据同步:数据集导入,datax,模型导入 数据处理工具:hadoop/spark作业提交,volcanojob/ray分布式数据处理,sparkjob 特征处理: -数据合并,包含union、join操作 -去除重复样本 -数据变换,包括boxcox转换、二值化、数据类型转换、dct变换、根据函数转换、ma移动平均、多项式展开 -非数值型变量处理,包括hash、根据统计量转换、one-hot -异常值检测 -获取变量的统计量 -去除值过于单一的变量 -删除缺失率过高的值 -删除缺失率过高的值 -填充缺失值 -数据离散化,等宽、等频、聚类离散化 -标准化、正则化、归一化,有最大绝对值归一化、最大最小归一化、z_score标准化 -索引处理,包含增加索引、索引转列、列索引重命名 -排序 -执行sql -hadamard乘积 -特征组合,用于衍生特征 -降维,包括pca降维和卡方降维 -特征重要性,通过随机森林、逻辑回归、xgboost等模型计算特征重要性,可计算特征的iv值、互信息值、方差等 -特征向量间的相关性计算 -数据拆分,包括列内拆分、列间拆分、行间拆分、svd奇异值分解 -采样,包括随机采样、分层采样、过采样、欠采样 传统机器学习:ray-sklearn分布式,xgb单机训练推理 传统机器学习算法:ar/arima时间序列算法/random-forest/random-forest-regression/lr/lightgbm/knn/kmean/gbdt/decision-tree/pca/lda/catboost/xgb/超参搜索 分布式深度学习框架:tf/pytorch/mxnet/horovod/paddlejob/mindspore分布式训练 分布式加速框架:mpi/colossalai/deepspeed/horovod/megatron 模型处理:模型评估,模型格式转换 模型服务化:模型注册,模型离线推理,模型部署 媒体分布式处理:分布式媒体下载,视频提取图片,视频提取图片
模型训练 算子自定义 支持算子自定义,通过web界面操作将自定义算法代码镜像,注册为可被他人复用的pipeline算子
模型训练 自动学习 面向非AI背景的用户提供自动学习服务,用户选择某一个场景之后,上传训练数据即可自动开始训练和模型部署,支持示例automl任务流导入导出
模型训练 自定义镜像 面向高级 AI 开发者,提供自定义训练作业(执行环境 + 代码)功能;
模型训练 自动调参 基于单机/分布式自动超参搜索
模型训练 TensorBoard作业 实时/离线观察模型训练过程中的参数和指标变化情况
模型管理 推理服务 内部服务 支持开发或运维工具快捷部署,提供mysql-web,postgresql web,mobgo web, redis web,neo4j,rstudio等开源工具
模型管理 推理服务 模型管理 模型管理用于对模型多版本管理,支持模型发布为推理服务
模型管理 推理服务 推理服务 支持ml/tf/pytorch/tentortrt/onnx常规模型的多版本的0代码发布。 支持gpu卡型选择,支持vgpu,独占,共享占用,支持cpu/mem/gpu等弹性伸缩,支持服务优先级,支持远程模型路径,支持流量分流,流量复制,sidecar配置,支持泛域名配置,支持配置文件挂载,启动目录/命令/环境变量/端口/指标/健康检查等 支持调试环境/测试环境/生产环境 支持域名/ip代理多种形式 支持服务负载指标监控 支持多版本服务滚动升级和回滚,支持单pod滚动发布 支持禁用k8s service负载均衡器 提供ml/tf/pytorch/tentortrt/onnx常规模型推理服务镜像 支持用户自定义模型推理镜像
监控 整体资源 所有集群,所有计算机器的使用情况,包括机器的所属集群,所属资源组,机器ip,cpu/gpu类型和卡型,当前cpu/内存/gpu的使用率 所有集群,所有计算pod的使用情况,包括pod所属集群,所属资源组,所属命名空间,调度ip,pod名称,启动用户,cpu,gpu,内存的申请使用率 整体资源页面,支持管理员批量删除
监控 监控体系 所有机器的gpu资源的使用情况, 所有机器的内存/cpu/网络io/磁盘io的负载情况, 所有pod的内存/cpu/gpu/网络io负载情况 所有推理服务的内存/cpu/gpu/qps/吞吐/vgpu负载情况 支持ib流量监控
模型应用市场 模型应用管理方案 提供cubestudio sdk,提供模型开发规范和使用规范
模型应用市场 模型应用管理方案 提供web端模型应用体验,支持同步/异步推理
模型应用市场 模型应用管理方案 提供开发多个python cuda版本的基础镜像
模型应用市场 预训练模型 提供视觉,听觉,nlp,多模态等400+预训练模型,提供预训练模型的模型加载和推理能力,可直接一键部署服务,并提供api
模型应用市场 模型市场 aihub应用对接cube-studio平台进行卡片式展示
模型应用市场 模型一键开发 提供一键转notebook开发,提供符合当前模型所需环境的jupyter
模型应用市场 模型一键微调 支持一键转pipeline微调链路,包括示例数据集下载,微调,模型注册,模型部署,支持微调后模型部署
模型应用市场 模型一键部署web 提供模型一键部署提供手机端和pc端web界面和api,和demo示例弹窗演示
模型应用市场 模型自动化标注 支持部署对接labelstudio自动化标注
模型应用市场 数据集sdk 支持通过python sdk搜索上传下载数据集,支持数据集的加解密/解压缩/数据集基础信息查看等
模型应用市场 notebook sdk 支持通过api,对接cube-studio创建notebook,并跳转到指定目录,用于其他算法平台在当前平台的调试和演示
模型应用市场 pipeline训练sdk 支持AI开发主流语言 Python,提供Python SDK支持用户通过SDK来进行pipeline任务流管理和训练任务启动以及任务流编排
模型应用市场 推理服务sdk 提供python sdk,对接cube tudio进行推理服务的发布,服务升级
大模型 大模型分布式多机多卡 支持分布式多机多卡训练,例如mpi/deepspeed/Colossal-AI
大模型 支持大模型推理 支持chatglm/chatglm2/lalma/llama2/通义千问部署
大模型 支持大模型微调 支持chatglm2/llama2/baichuan2 lora微调
大模型 智能对话 提供支持多场景对话,支持提示词构建,推理接口配置,llm问答,支持问询中模型切换,清理,历史上下文
大模型 私有知识库 私有知识库配置,私有知识库召回
大模型 私有知识库 支持召回列表模式
大模型 私有知识库 支持aigc模式
大模型 私有知识库 支持微信公众号服务号对接
大模型 私有知识库 支持企业微信群聊机器人对接
大模型 私有知识库 支持钉钉群聊机器人对接

帮助文档

https://github.com/tencentmusic/cube-studio/wiki

开源共建

学习、部署、体验、开源建设、商业合作 欢迎来撩。或添加微信luanpeng1234,备注<开源建设>

支持模板

提示:

  • 1、可自由定制任务插件,更适用当前业务需求
模块 模板 类型 文档地址
数据导入导出 datax 单机 job-template/job/datax/README.md
数据导入导出 数据集导入 单机 job-template/job/dataset/README.md
数据导入导出 模型导入 单机 job-template/job/model_download/README.md
数据预处理 data-process 单机 job-template/job/data-process/README.md
数据处理工具 hadoop 单机 job-template/job/hadoop/README.md
数据处理工具 spark 分布式 job-template/job/spark/README.md
数据处理工具 ray 分布式 job-template/job/ray/README.md
数据处理工具 volcanojob 分布式 job-template/job/volcano/README.md
特征处理 feature-process 单机 job-template/job/feature-process/README.md
机器学习框架 ray-sklearn 分布式 job-template/job/ray_sklearn/README.md
机器学习算法 random_forest 单机 job-template/job/random_forest/README.md
机器学习算法 lr 单机 job-template/job/lr/README.md
机器学习算法 lightgbm 单机 job-template/job/lightgbm/README.md
机器学习算法 knn 单机 job-template/job/knn/README.md
机器学习算法 kmeans 单机 job-template/job/kmeans/README.md
机器学习算法 nni 单机 job-template/job/hyperparam-search-nni/README.md
机器学习算法 xgb 单机 job-template/job/xgb/README.md
机器学习算法 gbdt 单机 job-template/job/gbdt/README.md
机器学习算法 decision-tree 单机 job-template/job/decision_tree/README.md
机器学习算法 bayesian 单机 job-template/job/bayesian/README.md
机器学习算法 adaboost 单机 job-template/job/adaboost/README.md
深度学习 tfjob 分布式 job-template/job/tf/README.md
深度学习 pytorchjob 分布式 job-template/job/pytorch/README.md
深度学习 paddle 分布式 job-template/job/paddle/README.md
深度学习 mxnet 分布式 job-template/job/mxnet/README.md
深度学习 mindspore 分布式 job-template/job/mindspore/README.md
深度学习 horovod 分布式 job-template/job/horovod/README.md
深度学习 mpi 分布式 job-template/job/mpi/README.md
深度学习 colossalai 分布式 job-template/job/colossalai/README.md
深度学习 deepspeed 分布式 job-template/job/deepspeed/README.md
深度学习 megatron 分布式 job-template/job/megatron/README.md
模型处理 model-evaluation 单机 job-template/job/model_evaluation/README.md
模型服务化 model-convert 单机 job-template/job/model_convert/README.md
模型服务化 model-register 单机 job-template/job/model_register/README.md
模型服务化 deploy-service 单机 job-template/job/deploy-service/README.md
模型服务化 model-offline-predict 分布式 job-template/job/model_offline_predict/README.md
多媒体类 media-download 分布式 job-template/job/video-audio/README.md
多媒体类 video-img 分布式 job-template/job/video-audio/README.md
多媒体类 video-audio 分布式 job-template/job/video-audio/README.md
大模型 llama2 单机多卡 job-template/job/llama2/README.md
大模型 chatglm2 单机多卡 job-template/job/chatglm2/README.md
大模型 baichuan2 单机多卡 job-template/job/baichuan2/README.md

公司

图片 1

平台简介

完整的平台包含

  • 1、机器的标准化
  • 2、分布式存储(单机可忽略)、k8s集群、监控体系(prometheus/efk/zipkin)
  • 3、基础能力(tf/pytorch/mxnet/valcano/ray等分布式,nni/katib超参搜索)
  • 4、平台web部分(oa/权限/项目组、在线构建镜像、在线开发、pipeline拖拉拽、超参搜索、推理服务管理等)
image

算力/存储/用户管理

算力:

  • 云原生统筹平台cpu/gpu等算力
  • 支持划分多资源组,支持多k8s集群,多地部署
  • 支持T4/V100/A100/昇腾/dcu/VGPU等异构GPU/NPU环境
  • 支持边缘集群模式,支持边缘节点上开发/训练/推理
  • 支持鲲鹏芯片arm64架构,RDMA

存储:

  • 自带分布式存储,支持多机分布式下文件处理
  • 支持外部存储挂载,支持项目组挂载绑定
  • 支持个人存储空间/组空间等多种形式
  • 平台内存储空间不需要迁移

用户权限:

  • 支持sso登录,对接公司账号体系
  • 支持项目组划分,支持配置相应项目组用户的权限
  • 管理平台用户的基本信息,组织架构,rbac权限体系

多集群管控

cube支持多集群调度,可同时管控多个训练或推理集群。在单个集群内,不仅能做到一个项目组内对在线开发、训练、推理的隔离,还可以做到一个k8s集群下多个项目组算力的隔离。另外在不同项目组下的算力间具有动态均衡的能力,能够在多项目间共享公共算力池和私有化算力池,做到成本最低化。

image

分布式存储

cube会自动为用户挂载用户的个人目录,同一个用户在平台任何地方启动的容器,其用户个人子目录均为/mnt/$username。可以将pvc/hostpath/memory/configmap等挂载成容器目录。同时可以在项目组中配置项目组的默认挂载,进而实现一个项目组共享同一个目录等功能。

image

在线开发

  • 系统多租户/多实例管理,在线交互开发调试,无需安装三方控件,只需浏览器就能完成开发。
  • 支持vscode,jupyter,Matlab,Rstudio等多种在线IDE类型
  • Jupyter支持cube-studio sdk,Julia,R,python,pyspark多内核版本,

image

  • 支持c++,java,conda等多种开发语言,以及tensorboard/git/gpu监控等多种插件
  • 支持ssh remote与notebook互通,本地进行代码开发
  • 在线镜像构建,通过Web Shell方式在浏览器中完成构建;并提供各种版本notebook,inference,gpu,python等基础镜像

image

标注平台:

  • 支持图/文/音/多模态/大模型多种类型标注功能,用户管理,工作任务分发
  • 对接aihub模型市场,支持自动化标注;对接数据集,支持标注数据导入;对接pipeline,支持标注结果自动化训练

image

拖拉拽pipeline编排

1、Ml全流程

数据导入,数据预处理,超惨搜索,模型训练,模型评估,模型压缩,模型注册,服务上线,ml算法全流程

2、灵活开放

支持单任务调试、分布式任务日志聚合查看,pipeline调试跟踪,任务运行资源监控,以及定时调度功能(包含补录,忽略,重试,依赖,并发限制,过期淘汰等功能)

image

分布式框架

1、训练框架支持分布式(协议和策略)
2、代码识别分布式角色(有状态)
3、控制器部署分布式训练集群(operator)
4、配置分布式训练集群的部署(CRD)

多层次多类型算子

以k8s为核心,
1、支持tf分布式训练、pytorch分布式训练、spark分布式数据处理、ray分布式超参搜索、mpi分布式训练、horovod分布式训练、nni分布式超参搜索、mxnet分布式训练、volcano分布式数据处理、kaldi分布式语音训练等,
2、 以及在此衍生出来的分布式的数据下载,hdfs拉取,cos上传下载,视频采帧,音频抽取,分布式的训练,例如推荐场景的din算法,ComiRec算法,MMoE算法,DeepFM算法,youtube dnn算法,ple模型,ESMM模型,双塔模型,音视频的wenet,containAI等算法的分布式训练。

image

功能模板化

  • 和非模板开发相比,使用模板建立应用成本会更低一些,无需开发平台。
  • 迁移更加容易,通过模板标准化后,后续应用迁移迭代只需迁移配置模板,简化复杂的配置操作。
  • 配置复用,通过简单的配置就可以复用这些能力,算法与工程分离避免重复开发。

为了避免重复开发,对pipeline中的task功能进行模板化开发。平台开发者或用户可自行开发模板镜像,将镜像注册到平台,这样其他用户就可以复用这些功能。平台自带模板在job-template目录下

image

流水线调试

  • Pipeline调试支持定时执行,支持,补录,并发限制,超时,实例依赖等。
  • Pipeling运行,支持变量在任务间输入输出,全局变量,流向控制,模板变量,数据时间等
  • Pipeling运行,支持任务结果可视化,图片、csv/json,echart源码可视化

image

nni超参搜索

界面化呈现训练各组数据,通过图形界面进行直观呈现。 减少以往开发调参过程的枯燥感,让整个调参过程更加生动具有趣味性,完全无需丰富经验就能实现更精准的参数控制调节。

# 上报当前迭代目标值
nni.report_intermediate_result(test_acc)
# 上报最终目标值
nni.report_final_result(test_acc)

# 接收超参数为输入参数
parser.add_argument('--batch_size', type=int)

image

推理服务

0代码发布推理服务从底层到上层,包含服务网格,serverless,pipeline,http框架,模型计算。

  • 服务网格阶段:主要工作是代理流量的中转和管控,例如分流,镜像,限流,黑白名单之类的。

  • serverless阶段:主要为服务的智能化运维,例如服务的激活,伸缩容,版本管理,蓝绿发布。

  • pipeline阶段:主要为请求在各数据处理/推理之间的流动。推理的前后置处理逻辑等。

  • http/grpc框架:主要为处理客户端的请求,准备推理样本,推理后作出响应。

  • 模型计算:模型在cpu/gpu上对输入样本做前向计算。

主要功能:

  • 支持模型管理注册,灰度发布,版本回退,模型指标可视化,以及在piepline中进行模型注册
  • 推理服务支持多集群,多资源组,异构gpu环境,平台资源统筹监控,VGPU,服务流量分流,复制,sidecar
  • 支持0代码的模型发布,gpu推理加速,支持训练推理混部,服务优先级,自定义指标弹性伸缩。

image

监控和推送

监控:cube-studio集成prometheus生态,可以监控包括主机,进程,服务流量,gpu等相关负载,并配套grafana进行可视化

推送:cube-studio开放推送接口,可自定义推送给企业oa系统

image

AIHub

  • 系统自带通用模型数量400+,覆盖绝大数行业场景,根据需求可以不断扩充。
  • 模型开源、按需定制,方便快速集成,满足用户业务增长及二次开发升级。
  • 模型标准化开发管理,大幅降低使用门槛,开发周期时长平均下降30%以上。

image

  • AIHub模型可一键部署为WEB端应用,手机端/PC端皆可,实时查看模型应用效果
  • 点击模型开发即可进入notebook进行模型代码的二次开发,实现一键开发
  • 点击训练即可加入自己的数据进行一键微调,使模型更贴合自身场景

image

GPT训练微调

  • cube-studio支持deepspeed/colossalai等分布式加速框架,可一键实现大模型多机多卡分布式训练
  • AIHub包含gpt/AIGC大模型,可一键转为微调pipeline,修改为自己的数据后,便可以微调并部署

image

GPT-RDMA

rdma插件部署后,k8s机器可用资源

capacity:
  cpu: '128'
  memory: 1056469320Ki
  nvidia.com/gpu: '8'
  rdma/hca: '500'

代码分布式训练中使用IB设备

export NCCL_IB_HCA=mlx5
export MLP_WORKER_GPU=$GPU_NUM
export MLP_WORKER_NUM=$WORLD_SIZE
export MLP_ROLE_INDEX=$RANK
export MLP_WORKER_0_HOST=$MASTER_ADDR
export MLP_WORKER_0_PORT=$MASTER_PORT

image

gpt私有知识库

  • 数据智能模块可配置专业领域智能对话,快速敏捷使用llm
  • 可为某个聊天场景配置私有知识库文件,支持主题分割,语义embedding,意图识别,概要提取,多路召回,排序,多种功能融合

image

gpt智能聊天

  • 可以将智能会话与AIHub相结合,例如下面AIGC模型与聊天会话
  • 可使用Autogpt方式串联所有aihub模型,进行图文音智能化处理
  • 智能会话与公共直接打通,可在微信公众号中进行图文音对话

image

数据中台对接

为了加速AI算法平台的使用,cube-studio支持对接公司原有数据中台,包括数据计算引擎sqllab,元数据管理,指标管理,维表管理,数据ETL,数据集管理

image

实时训练

tmeps支持tf框架实时训练,秒级上线,能应对embedding稀疏大模型推荐场景

image

三种方式部署

针对企业需求,根据不同场景对计算实时性的不同需求,可以提供三种建设模式

模式一:私有化部署——对数据安全要求高、预算充足、自己有开发能力
模式二:边缘集群部署——算力分散,多个子网环境的场景,或边缘设备场景
模式三:serverless集群——成本有限,按需申请算力的场景

边缘计算

通过边缘集群的形式,在中心节点部署平台,并将边缘节点加入调度,每个私有网用户,通过项目组,将notebook,pipeline,service部署在边缘节点

  • 1、避免数据到中心节点的带宽传输
  • 2、避免中心节点的算力成本,充分利用边缘节点算力
  • 3、避免边缘节点的运维成本

图片 1

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If Recipient institutes patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Program itself (excluding combinations of the Program with other software or hardware) infringes such Recipient's patent(s), then such Recipient's rights granted under Section 2(b) shall terminate as of the date such litigation is filed. All Recipient's rights under this Agreement shall terminate if it fails to comply with any of the material terms or conditions of this Agreement and does not cure such failure in a reasonable period of time after becoming aware of such noncompliance. If all Recipient's rights under this Agreement terminate, Recipient agrees to cease use and distribution of the Program as soon as reasonably practicable. However, Recipient's obligations under this Agreement and any licenses granted by Recipient relating to the Program shall continue and survive. Everyone is permitted to copy and distribute copies of this Agreement, but in order to avoid inconsistency the Agreement is copyrighted and may only be modified in the following manner. The Agreement Steward reserves the right to publish new versions (including revisions) of this Agreement from time to time. No one other than the Agreement Steward has the right to modify this Agreement. The Eclipse Foundation is the initial Agreement Steward. The Eclipse Foundation may assign the responsibility to serve as the Agreement Steward to a suitable separate entity. Each new version of the Agreement will be given a distinguishing version number. The Program (including Contributions) may always be Distributed subject to the version of the Agreement under which it was received. In addition, after a new version of the Agreement is published, Contributor may elect to Distribute the Program (including its Contributions) under the new version. Except as expressly stated in Sections 2(a) and 2(b) above, Recipient receives no rights or licenses to the intellectual property of any Contributor under this Agreement, whether expressly, by implication, estoppel or otherwise. All rights in the Program not expressly granted under this Agreement are reserved. Nothing in this Agreement is intended to be enforceable by any entity that is not a Contributor or Recipient. No third-party beneficiary rights are created under this Agreement. Exhibit A - Form of Secondary Licenses Notice "This Source Code may also be made available under the following Secondary Licenses when the conditions for such availability set forth in the Eclipse Public License, v. 2.0 are satisfied: {name license(s), version(s), and exceptions or additional permissions here}." Simply including a copy of this Agreement, including this Exhibit A is not sufficient to license the Source Code under Secondary Licenses. If it is not possible or desirable to put the notice in a particular file, then You may include the notice in a location (such as a LICENSE file in a relevant directory) where a recipient would be likely to look for such a notice. You may add additional accurate notices of copyright ownership.

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cube studio开源云原生一站式机器学习/深度学习AI平台,支持sso登录,多租户/多项目组,数据资产对接,notebook在线开发,拖拉拽任务流pipeline编排,多机多卡分布式算法训练,超参搜索,推理服务VGPU,多集群调度,边缘计算,serverless,标注平台,自动化标注,数据集管理,大模型一键微调,llmops,私有知识库,AI应用商店,支持模型一键开发/推理/微调,私有化部署 展开 收起
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