KubeFATE
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Manage federated learning workload using cloud native technologies.
Issue: - Guest (10,000,000 ID SM3)  - Host (10,000,000 ID SM3)  Env: - Fate 1.7.2 - VM: 64C / 64G / 1TB * 3 Solution: - add more...
**What deployment mode you are use? ** docker-compose **What KubeFATE and FATE version you are using? ** v1.7.1.1 **MUST** Please state the KubeFATE and FATE version you found the issue...
Fixes ISSUE#xxx Changes: 1. Provide correct error msg if chart's value template example not found
## Changes - Add images select for docker-compose and chart By modifying these configurations, different images can be used. ```bash computing=Eggroll federation=Eggroll storage=Eggroll enabled_nn=false algorithm=Basic device=IPCL ``` - Change image...
Signed-off-by: Chen Jing Fixes ISSUE#xxx Changes: 1. 2. 3.
**Summary** Create document on how to deploy and use kubefate in k8s mode when users only has admin roles in certain namespaces **User Story** As a k8s user with only...
**What deployment mode you are use? ** 2. Kuberentes. **What KubeFATE and FATE version you are using? ** fate: 1.8.0 kubefate: 1.8.0 fate-serving: 2.1.5 **What OS you are using for...
**Describe the bug** 为了表述清楚,暂用中文。 在使用kube-fate via docker-compose 时候,发现建模速度很慢。单机版不存在这个问题,主要是无法定位问题 例如下述的任务,从提交到启动花费了近半分钟。对于横向lr,单机版只需要3分钟,而docker-compose 部署的,需要7分钟  不清楚是kube-fate 的问题还是fateflow 的问题,下述是fateflow 的log (请忽略时区问题) 在board中看到是42分32秒提交的任务,但在flow的log中42分56秒才收到请求  看上去网络也不存在问题,通过请求version接口,响应也很快  我在(#3565)[https://github.com/FederatedAI/FATE/issues/3565] 里,好像也存在这个问题?观察第一张图的submit time 和start time **Desktop (please complete the following...
**User Story** As a operator, I would like to deploy a Hive cluster as the FATE storage provider because since FATE v1.7.0, Hive is supported to be storage engine. **Details**...