CN111240703A - Cluster system adaptation detection method and device for AI platform deployment - Google Patents
Cluster system adaptation detection method and device for AI platform deployment Download PDFInfo
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Abstract
The invention provides a cluster system adaptation detection method and a device for AI platform deployment, wherein the method comprises the following steps: s1, setting a local test machine, establishing connection between the local test machine and a server cluster, and setting a test script in the local test machine; s2, configuring a test script to obtain the IP address of each server in the server cluster, and sequentially obtaining the actual information of each server according to the IP address; s3, configuring a test script to obtain standard information of each server in the server cluster; and S4, comparing the standard information with the actual information of each server, and judging whether each server in the cluster meets the adaptation requirement of AI platform installation. The invention provides a method for completing the detection of the whole user cluster and the AI installation package dependent environment before the deployment of the cluster system AI platform, thereby improving the cluster detection efficiency when the AI platform is installed and deployed, facilitating the quick installation of the AI platform and reducing the failure rate according to the feedback result.
Description
Technical Field
The invention belongs to the technical field of cluster detection, and particularly relates to a cluster system adaptation detection method and device for AI platform deployment.
Background
With the development of technology, the installation of each website platform requires the deployment of a cluster server at present. Even though the client confirms the cluster information, the problem that basic information of a management node and a computing node is inconsistent due to factors such as the scale of the cluster equipment, errors of operation and maintenance implementing personnel and the like still occurs, so that in order to complete the rapid installation of the AI platform, the detection of the whole cluster of the user and the dependence environment of the AI installation package needs to be completed before the system is installed and deployed.
In the existing scheme, after a cluster system is deployed, a corresponding command is run in each server in a cluster to obtain return information, and whether each device meets the installation and deployment requirements of an AI platform is checked, but when more devices exist in the cluster system, operation and maintenance personnel may miss information of each device when checking the information of each device, which causes failed deployment of the AI platform during installation.
Therefore, it is very necessary to provide a method and an apparatus for detecting cluster system adaptation for AI platform deployment to overcome the above-mentioned drawbacks in the prior art.
Disclosure of Invention
Aiming at the defects that in the prior art, each server needs to obtain respective information, and operation and maintenance personnel have large workload, so that omission possibility exists, the invention provides a cluster system adaptation detection method and device for AI platform deployment, so as to solve the technical problems.
In a first aspect, the present invention provides a cluster system adaptation detection method for AI platform deployment, including the following steps:
s1, setting a local test machine, establishing connection between the local test machine and a cluster system, and setting a test script in the local test machine;
s2, configuring a test script to obtain the IP address of each server and the actual information of each server in the cluster system;
s3, configuring a test script to obtain standard information of each server in the cluster system;
and S4, comparing the standard information and the actual information of each server by the configuration test script, and judging whether each server in the cluster system meets the adaptation requirement of AI platform installation. The test script is written based on a python statement.
Further, the step S1 specifically includes the following steps:
s11, setting a local test machine, and establishing connection between the local test machine and a cluster system;
s12, installing a window operating system in the local testing machine, and installing python, a python dependent package and a testing script under the window operating system. The execution detection is carried out under Windows, and the implementation can be realized by installing the python and the related dependency package in Windows without logging in each server of the cluster system.
Further, the step S2 specifically includes the following steps:
s21, configuring a test script to acquire the IP address of each server in the cluster system through an environment detection tool of python;
s22, configuring a test script to automatically acquire cluster user names and passwords, sequentially logging in each server in the cluster system according to the cluster user names and the passwords, and verifying whether the cluster user names and the passwords are consistent;
s23, when the cluster user name and the cluster password are consistent, actual information of each server in the cluster system is obtained through an environment detection tool by the configuration test script;
and S24, configuring the test script to generate a first list with the IP address as an index according to the returned actual information of each server.
Further, the actual information of the server includes a system version number, a GPU type and card number, a CPU core number, a partition name and size, a memory, and whether installation is minimized.
Further, the step S3 specifically includes the following steps:
s31, configuring a test script to acquire standard information of each server in the cluster system;
and S32, configuring a test script to generate a second list with the IP address as an index for the standard information of each server in the cluster system.
Further, the step S4 specifically includes the following steps:
s41, configuring a test script to compare actual information of a corresponding server in the first list and the second list with standard information;
s42, when the information of the corresponding items of the servers in the cluster system is compared and consistent, judging that the adaptation requirement of AI platform installation is met in the cluster system;
s43, when the cluster system has the server with inconsistent information comparison of the corresponding items, judging that the cluster system does not meet the adaptation requirement of AI platform installation, and marking inconsistent data information of the server.
In a second aspect, the present invention provides a device for detecting cluster system adaptation for AI platform deployment, which includes
The local test machine setting module is used for setting a local test machine, establishing connection between the local test machine and the cluster system, and setting a test script in the local test machine;
the cluster system actual information acquisition module is used for configuring a test script to acquire the IP address of each server and the actual information of each server in the cluster system;
the cluster system standard information acquisition module is used for configuring a test script to acquire the standard information of each server in the cluster system;
and the AI platform adaptation judgment module is used for comparing the standard information and the actual information of each server by the configuration test script and judging whether each server in the cluster system meets the adaptation requirement of AI platform installation.
Further, the local tester setting module comprises:
the connection establishing unit is used for setting a local testing machine and establishing the connection between the local testing machine and the cluster system;
and the operating system and software installation unit is used for installing the window operating system in the local testing machine and installing python, the python dependent package and the test script under the window operating system.
Further, the cluster system actual information acquisition module includes:
the IP address acquisition unit is used for configuring a test script to acquire the IP address of each server in the cluster system through a python environment detection tool;
the cluster user name and password verification unit is used for configuring a test script to automatically acquire a cluster user name and a password, sequentially logging in each server in the cluster system according to the cluster user name and the password, and verifying whether the cluster user name and the password are consistent;
the system comprises an actual information acquisition unit, a cluster system management unit and a test script configuration unit, wherein the actual information acquisition unit is used for acquiring actual information of each server in the cluster system through an environment detection tool when a cluster user name and a cluster password are consistent;
and the first list generating unit is used for configuring the actual information of each returned server to generate a first list with the IP address as an index by the test script.
Further, the cluster system standard information acquisition module comprises:
the standard information acquisition unit is used for configuring a test script to acquire standard information of each server in the cluster system;
the second list generating unit is used for configuring a test script to generate a second list with the IP address as an index for the standard information of each server in the cluster system;
the AI platform adaptation judgment module comprises:
the information comparison unit is used for configuring a test script to compare the actual information of the corresponding server in the first list and the second list with the standard information;
the AI platform adaptation judging unit is used for judging that the adaptation requirement of the AI platform installation is met in the cluster system when the information of the corresponding items of the servers in the cluster system is compared and consistent;
and the inconsistent information marking unit is used for judging that the cluster system does not meet the adaptation requirement of the AI platform installation when the cluster system has a server with inconsistent information comparison of the corresponding items, and marking inconsistent data information of the server.
The beneficial effect of the invention is that,
according to the cluster system adaptation detection method and device for AI platform deployment, provided by the invention, the detection of the whole cluster of the user and the dependence environment of the AI installation package is completed before the system is installed and deployed, so that the cluster detection efficiency during the installation and deployment of the AI platform is improved, the rapid installation of the AI platform is conveniently completed, and the failure rate can be reduced according to the feedback result.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a first schematic flow chart of the method of the present invention;
FIG. 2 is a second schematic flow chart of the method of the present invention;
FIG. 3 is a schematic diagram of the system of the present invention;
in the figure, 1-local tester setup module; 1.1-a connection establishing unit; 1.2-operating system and software installation unit; 2-local tester setting module; 2.1-IP address acquisition unit; 2.2-cluster username password authentication unit; 2.3-actual information acquisition unit; 2.4-a first list generation unit; 3-a cluster system standard information acquisition module; 3.1-standard information acquisition unit; 3.2-a second list generating unit; 4-AI platform adaptation judgment module; 4.1-an information comparison unit; 4.2-AI platform adaptation decision element; 4.3-inconsistent information labeling Unit.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
as shown in fig. 1, the present invention provides a cluster system adaptation detection method for AI platform deployment, which includes the following steps:
s1, setting a local test machine, establishing connection between the local test machine and a cluster system, and setting a test script in the local test machine;
s2, configuring a test script to obtain the IP address of each server and the actual information of each server in the cluster system;
s3, configuring a test script to obtain standard information of each server in the cluster system;
and S4, comparing the standard information and the actual information of each server by the configuration test script, and judging whether each server in the cluster system meets the adaptation requirement of AI platform installation.
Example 2:
as shown in fig. 2, the present invention provides a cluster system adaptation detection method for AI platform deployment, which includes the following steps:
s1, setting a local test machine, establishing connection between the local test machine and a cluster system, and setting a test script in the local test machine; the method comprises the following specific steps:
s11, setting a local test machine, and establishing connection between the local test machine and a cluster system;
s12, installing a window operating system in the local testing machine, and installing python, a python dependent package and a testing script under the window operating system;
s2, configuring a test script to obtain the IP address of each server and the actual information of each server in the cluster system; the method comprises the following specific steps:
s21, configuring a test script to acquire the IP address of each server in the cluster system through an environment detection tool of python;
s22, configuring a test script to automatically acquire cluster user names and passwords, sequentially logging in each server in the cluster system according to the cluster user names and the passwords, and verifying whether the cluster user names and the passwords are consistent;
s23, when the cluster user name and the cluster password are consistent, actual information of each server in the cluster system is obtained through an environment detection tool by the configuration test script;
s24, configuring a test script to generate a first list with the IP address as an index according to the returned actual information of each server;
s3, configuring a test script to obtain standard information of each server in the cluster system; the method comprises the following specific steps:
s31, configuring a test script to acquire standard information of each server in the cluster system;
s32, configuring a test script to generate a second list with the IP address as an index for the standard information of each server in the cluster system;
s4, comparing the standard information and the actual information of each server by the configuration test script, and judging whether each server in the cluster system meets the adaptation requirement of AI platform installation; the method comprises the following specific steps:
s41, configuring a test script to compare actual information of a corresponding server in the first list and the second list with standard information;
s42, when the information of the corresponding items of the servers in the cluster system is compared and consistent, judging that the adaptation requirement of AI platform installation is met in the cluster system;
s43, when the cluster system has the server with inconsistent information comparison of the corresponding items, judging that the cluster system does not meet the adaptation requirement of AI platform installation, and marking inconsistent data information of the server.
In the above embodiment 2, the command prompt window CMD of the local windows is opened, and the environment detection tool is entered into the directory through the cd command, and then the python checkenv. The ip2 command; if the cluster system has more servers, placing the ip address in an ip.txt file of the same directory, and executing python checkenv.
After the command is executed, IP information flow is obtained according to different reference forms (a plurality of IPs in order; "division or configuration files") for division, and is divided into a plurality of independent IPs which are stored in an IP table;
traversing and saving an IP list of the IP, and acquiring information of each server in the cluster, wherein the information comprises a password, a system version number, a GPU type, a card number, a CPU core number, a memory, a partition name, a partition size and whether installation is minimized;
judging the information acquired by each server and a defined standard value so as to judge whether the adaptation requirement of AI platform installation is met; and after all the information is detected, returning the specific information of each device and the final adaptation result.
Example 3:
as shown in fig. 3, the present invention provides a cluster system adaptation detection apparatus for AI platform deployment, including:
the local test machine setting module 1 is used for setting a local test machine, establishing connection between the local test machine and the cluster system, and setting a test script in the local test machine; the local test machine setting module 1 includes:
a connection establishing unit 1.1, configured to set a local test machine, and establish a connection between the local test machine and the cluster system;
the operating system and software installation unit 1.2 is used for installing a window operating system in a local test machine and installing python, a python dependence package and a test script under the window operating system;
the local test machine setting module 2 is used for configuring a test script to acquire the IP address of each server and the actual information of each server in the cluster system; the cluster system actual information acquisition module 2 includes:
an IP address obtaining unit 2.1, configured to configure a test script to obtain IP addresses of servers in the cluster system through a python environment detection tool;
the cluster user name and password verification unit 2.2 is used for configuring a test script to automatically acquire a cluster user name and a password, sequentially logging in each server in the cluster system according to the cluster user name and the password, and verifying whether the cluster user name and the password are consistent;
the actual information acquisition unit 2.3 is used for acquiring actual information of each server in the cluster system through an environment detection tool when the cluster user names and the cluster passwords are consistent by the configuration test script;
a first list generating unit 2.4, configured to configure the test script to generate a first list using the IP address as an index for the returned actual information of each server;
the cluster system standard information acquisition module 3 is used for configuring a test script to acquire the standard information of each server in the cluster system; the cluster system standard information acquisition module 3 includes:
a standard information obtaining unit 3.1, configured to configure a test script to obtain standard information of each server in the cluster system;
a second list generating unit 3.2, configured to configure the test script to generate a second list with the IP address as an index for the standard information of each server in the cluster system;
the AI platform adaptation judgment module 4 is used for comparing the standard information and the actual information of each server by the configuration test script and judging whether each server in the cluster system meets the adaptation requirement of the AI platform installation; the AI platform adaptation determination module 4 includes:
the information comparison unit 4.1 is used for configuring a test script to compare the actual information of the corresponding server in the first list and the second list with the standard information;
the AI platform adaptation judging unit 4.2 is used for judging that the adaptation requirement of the AI platform installation is met in the cluster system when the information of the corresponding items of the servers in the cluster system is compared and consistent;
and the inconsistent information marking unit 4.3 is used for judging that the cluster system does not meet the adaptation requirement of AI platform installation when the cluster system has a server with inconsistent information comparison of the corresponding items, and marking inconsistent data information of the server.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A cluster system adaptation detection method for AI platform deployment is characterized by comprising the following steps:
s1, setting a local test machine, establishing connection between the local test machine and a cluster system, and setting a test script in the local test machine;
s2, configuring a test script to obtain the IP address of each server and the actual information of each server in the cluster system;
s3, configuring a test script to obtain standard information of each server in the cluster system;
and S4, comparing the standard information and the actual information of each server by the configuration test script, and judging whether each server in the cluster system meets the adaptation requirement of AI platform installation.
2. The cluster system adaptation detection method for AI platform deployment of claim 1, wherein step S1 specifically includes the following steps:
s11, setting a local test machine, and establishing connection between the local test machine and a cluster system;
s12, installing a window operating system in the local testing machine, and installing python, a python dependent package and a testing script under the window operating system.
3. The cluster system adaptation detection method for AI platform deployment according to claim 2, wherein step S2 specifically includes the following steps:
s21, configuring a test script to acquire the IP address of each server in the cluster system through an environment detection tool of python;
s22, configuring a test script to automatically acquire cluster user names and passwords, sequentially logging in each server in the cluster system according to the cluster user names and the passwords, and verifying whether the cluster user names and the passwords are consistent;
s23, when the cluster user name and the cluster password are consistent, actual information of each server in the cluster system is obtained through an environment detection tool by the configuration test script;
and S24, configuring the test script to generate a first list with the IP address as an index according to the returned actual information of each server.
4. The method of claim 3, wherein the actual information of the server includes a system version number, a GPU type and a card number, a CPU core number, a partition name and size, a memory, and whether installation is minimized.
5. The cluster system adaptation detection method for AI platform deployment of claim 3, wherein step S3 specifically includes the following steps:
s31, configuring a test script to acquire standard information of each server in the cluster system;
and S32, configuring a test script to generate a second list with the IP address as an index for the standard information of each server in the cluster system.
6. The cluster system adaptation detection method for AI platform deployment of claim 5, wherein step S4 specifically includes the following steps:
s41, configuring a test script to compare actual information of a corresponding server in the first list and the second list with standard information;
s42, when the information of the corresponding items of the servers in the cluster system is compared and consistent, judging that the adaptation requirement of AI platform installation is met in the cluster system;
s43, when the cluster system has the server with inconsistent information comparison of the corresponding items, judging that the cluster system does not meet the adaptation requirement of AI platform installation, and marking inconsistent data information of the server.
7. A cluster system adaptation detection device for AI platform deployment, characterized by comprising
The local test machine setting module (1) is used for setting a local test machine, establishing connection between the local test machine and the cluster system, and setting a test script in the local test machine;
the local test machine setting module (2) is used for configuring a test script to acquire the IP address of each server and the actual information of each server in the cluster system;
the cluster system standard information acquisition module (3) is used for configuring a test script to acquire the standard information of each server in the cluster system;
and the AI platform adaptation judgment module (4) is used for comparing the standard information and the actual information of each server by the configuration test script and judging whether each server in the cluster system meets the adaptation requirement of the AI platform installation.
8. The cluster system adaptation detection device for AI platform deployment of claim 7, wherein the local tester setup module (1) comprises:
the connection establishing unit (1.1) is used for setting a local test machine and establishing the connection between the local test machine and the cluster system;
and the operating system and software installation unit (1.2) is used for installing the window operating system in the local testing machine and installing python, the python dependent package and the test script under the window operating system.
9. The cluster system adaptation detection device for AI platform deployment of claim 8, wherein the cluster system actual information acquisition module (2) comprises:
the IP address acquisition unit (2.1) is used for configuring a test script to acquire the IP address of each server in the cluster system through a python environment detection tool;
the cluster user name and password verification unit (2.2) is used for configuring a test script to automatically acquire a cluster user name and a password, sequentially logging in each server in the cluster system according to the cluster user name and the password, and verifying whether the cluster user name and the password are consistent;
the actual information acquisition unit (2.3) is used for acquiring the actual information of each server in the cluster system through an environment detection tool when the cluster user names and the cluster passwords are consistent by the configuration test script;
and a first list generating unit (2.4) for configuring the actual information of each server returned by the test script to generate a first list with the IP address as an index.
10. The cluster system adaptation detection device for AI platform deployment of claim 8, wherein the cluster system standard information acquisition module (3) comprises:
the standard information acquisition unit (3.1) is used for configuring a test script to acquire the standard information of each server in the cluster system;
a second list generating unit (3.2) for configuring a test script to generate a second list with the IP address as an index for the standard information of each server in the cluster system;
the AI platform adaptation judgment module (4) comprises:
the information comparison unit (4.1) is used for configuring a test script to compare the actual information of the corresponding server in the first list and the second list with the standard information;
the AI platform adaptation judging unit (4.2) is used for judging that the adaptation requirement of the AI platform installation is met in the cluster system when the information of the corresponding items of the servers in the cluster system is compared and consistent;
and the inconsistent information marking unit (4.3) is used for judging that the cluster system does not meet the adaptation requirement of AI platform installation when the cluster system has a server with inconsistent information comparison of corresponding items, and marking inconsistent data information of the server.
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| CN112367415A (en) * | 2021-01-14 | 2021-02-12 | 腾讯科技（深圳）有限公司 | Generation method and device of attribute information, electronic equipment and computer readable medium |
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| CN112367415A (en) * | 2021-01-14 | 2021-02-12 | 腾讯科技（深圳）有限公司 | Generation method and device of attribute information, electronic equipment and computer readable medium |
| CN112367415B (en) * | 2021-01-14 | 2021-04-23 | 腾讯科技（深圳）有限公司 | Generation method and device of attribute information, electronic equipment and computer readable medium |
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Application publication date: 20200605 |