commit | c638401cbbe07a19c2b585e706fa8916acaaf2b3 | [log] [tgz] |
---|---|---|
author | Kent Yao <yaooqinn@hotmail.com> | Sun May 03 22:49:17 2020 +0800 |
committer | Liu Xun <liuxun@apache.org> | Mon May 04 13:38:34 2020 +0000 |
tree | d9ee47a97f63ea7162c251ac7188c0c086055071 | |
parent | 4db85d849203d8c548e4bbc54dadd9eecf7cb224 [diff] |
SUBMARINE-488. DCL Framework: SHOW ROLES syntax ### What is this PR for? Part of DCL framework, this PR adds the show roles syntax support ### What type of PR is it? Feature ### Todos * [ ] - Task ### What is the Jira issue? https://issues.apache.org/jira/browse/SUBMARINE-488 ### How should this be tested? add new unit tests ### Screenshots (if appropriate) ### Questions: * Does the licenses files need update? No * Is there breaking changes for older versions? No * Does this needs documentation? No Author: Kent Yao <yaooqinn@hotmail.com> Closes #274 from yaooqinn/SUBMARINE-488 and squashes the following commits: 0edd58e [Kent Yao] SUBMARINE-488. DCL Framework: SHOW ROLES syntax
Apache Submarine is a unified AI platform which allows engineers and data scientists to run Machine Learning and Deep Learning workload in distributed cluster.
Goals of Submarine:
Submarine Workbench is a WEB system. Algorithm engineers can perform complete lifecycle management of machine learning jobs in the Workbench.
Projects
Manage machine learning jobs through project.
Data
Data processing, data conversion, feature engineering, etc. in the workbench.
Job
Data processing, algorithm development, and model training in machine learning jobs as a job run.
Model
Algorithm selection, parameter adjustment, model training, model release, model Serving.
Workflow
Automate the complete life cycle of machine learning operations by scheduling workflows for data processing, model training, and model publishing.
Team
Support team development, code sharing, comments, code and model version management.
The submarine core is the execution engine of the system and has the following features:
ML Engine
Support for multiple machine learning framework access, such as tensorflow, pytorch, mxnet.
Data Engine
Docking the externally deployed Spark calculation engine for data processing.
SDK
Support Python, Scala, R language for algorithm development, The SDK is provided to help developers use submarine's internal data caching, data exchange, and task tracking to more efficiently improve the development and execution of machine learning tasks.
Submitter
Compatible with the underlying hybrid scheduling system of yarn and k8s for unified task scheduling and resource management, so that users are not aware.
You can use mini-submarine for a quick experience submairne.
This is a docker image built for submarine development and quick start test.
Read the Quick Start Guide
Read the Apache Submarine Community Guide
How to contribute Contributing Guide
The Apache Submarine project is licensed under the Apache 2.0 License. See the LICENSE file for details.