Method, system and device for cross-domain machine learning component jupyter
A machine learning and cross-domain technology, applied in the field of computer information, can solve the problems that Jupyter does not support cross-domain calls of application services and limited functions, and achieve the effects of reducing computing overhead, low overall cost, and small computing overhead
Active Publication Date: 2021-08-06
上海微亿智造科技有限公司 +1
View PDF5 Cites 0 Cited by
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
[0003] As the ml toolset of Kubernetes, Kubeflow includes many machine learning platform components. Usually, Jupyter, one of the components in the Kubeflow open source framework, does not support cross-domain calls of other application services and cannot be embedded in other application services, resulting in functional limitations. limit
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View moreImage
Smart Image Click on the blue labels to locate them in the text.
Smart ImageViewing Examples
Examples
Experimental program
Comparison scheme
Effect test
Embodiment Construction
[0031] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0032] The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices entity.
[0033] Likewise, the flow charts shown in the drawings are only exemplary illustrations, and do not necessarily include all contents and operations / steps, nor must they be performed in the order described. For example, some...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More PUM
Login to View More Abstract
The invention discloses a cross-domain method, system and device for machine learning component Jupyter, comprising the following steps: constructing a Jupyter container image according to a DockerFile; performing secondary development on a Jupyter-web-app module; performing system environment configuration on a container ; Mirror deployment of the Jupyter‑web‑app module after secondary development. The method provided by the present invention aims at running the Jupyter-web-app image through the container on the basis of secondary development of the Jupyter-web-app module, thereby realizing cross-domain functions, and the containerized operation can be more Small computational overhead means lower overall cost. And the present invention simultaneously solves the problem that the kubeflow open source framework component jupyter does not support cross-domain calls of other application services, and cannot be embedded in other application services, and due to the secondary development through the jupyter-wep-app service, it can be used for other applications. Application service calls, that is, to achieve cross-domain interaction between the front and back ends.
Description
technical field [0001] The present invention relates to the field of computer information technology, in particular to a method, system and device for cross-domain machine learning component Jupyter. Background technique [0002] With the rapid development of machine learning and artificial intelligence, many open source machine learning platforms have emerged in the industry. Due to the natural close combination of machine learning and big data, distributed task scheduling based on Hadoop Yarn is still the mainstream in the industry; however, with the development of containerization, following Borg's open source contribution, Docker and Kubernetes' container orchestration combination also show It has a strong vitality. [0003] As the ml toolset of Kubernetes, Kubeflow includes many machine learning platform components. Usually, Jupyter, one of the components in the Kubeflow open source framework, does not support cross-domain calls of other application services and cannot...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More Application Information
Patent Timeline
Login to View More Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50
CPCG06F9/5077G06F9/5083
Inventor 高明明
Owner 上海微亿智造科技有限公司