CN112434284B - Machine learning training platform implementation based on sandbox environment - Google Patents
Machine learning training platform implementation based on sandbox environment Download PDFInfo
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Abstract
The invention provides a machine learning training platform implementation based on a sandbox environment. The system comprises a mirror image management module, a resource scheduling module, a data platform module, a training management service module and a sandbox management service module, wherein the sandbox management service module is used for managing a user sandbox training environment and isolating the user sandbox training environment from the data platform module, the training platform is connected with the user mirror image platform, the user mirror image platform comprises a user training code and a sandbox API module, and the sandbox API module can judge whether the user sandbox training environment is located or not and is suitable for different methods according to different environments. The invention has the beneficial effects that: the network is isolated in the whole interaction process, the data is encrypted in the whole process, and the safety and the effectiveness of the data are guaranteed to the maximum extent.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a sandbox environment-based machine learning training platform implementation method.
Background
The existing machine learning platform such as the hundred-degree PaddlePaddle, Photon ML of Linkdin, Xlering of 360, x-deplering of Alibara, Angel of Tencent and the like mainly solves the problems of resource scheduling and resource isolation, but does not consider the safety aspect on data access.
When accessing own data set or built-in data set of the platform, a user directly uses the API corresponding to the data source to access data (a file system, a network disk, Amazon object storage S3, an Arrecourse object storage OSS and the like).
The direct use of the data source API approach has several problems:
1. the user must have or can easily obtain all the data set data for training, and the rights and interests of the data set provider cannot be guaranteed
2. The user needs to do corresponding code integration work aiming at different data source APIs
3. The data access process is not safe enough, the network is not isolated, the transmission process is not encrypted, and other persons who take the user code or the mirror image (the user shares the user with other persons, or obtains the data illegally, and the like) can also access the data.
Disclosure of Invention
In order to solve the technical problems, the invention discloses a machine learning training platform implementation based on a sandbox environment, and the technical scheme of the invention is implemented as follows:
a machine learning training platform implementation based on a sandbox environment is characterized by comprising a mirror image management module, a resource scheduling module, a data platform module, a training management service module and a sandbox management service module; wherein
The sandbox management service module is used for managing a user sandbox training environment and isolating the user sandbox training environment from the data platform module;
the training platform is connected with the user mirror image platform, and the user mirror image platform comprises user training codes and a sandbox API module;
the method for machine learning in a non-sandbox environment comprises the following steps:
s1: calling a sandbox API module to request data through a user training code module;
s2: the sandbox API module judges that the sandbox API module is currently in a non-sandbox environment and requests the sandbox management service module to acquire example data;
s3: the sandbox management service module requests example data from the data platform;
s4: after the user training codes are debugged, the user can pack the training codes into a mirror image and upload the mirror image to the mirror image management module
The method for machine learning of the sandbox environment comprises the following steps:
s1: a user calls a training management service module to train, and a mirror image for training, a data set and resource information are provided;
s2: the training management service module calls the sandbox management service module to create a new sandbox task;
s3: the training management service module calls a service to start a training task;
s4: the resource scheduling module acquires a mirror image for user training from the mirror image management module;
s5: the resource scheduling module distributes corresponding amount and specification of computing resources according to the computing resources required by the user training, and uses the user mirror image for training;
s6: the user training code starts to execute and call a sandbox API module to access data;
s7: the sandbox API module judges that the sandbox API module is currently in a sandbox training environment, and requests the sandbox management service module to acquire a sandbox Agent;
s8: the sandbox management service module encrypts the secret key according to the requested sandbox API module ID, injects the encrypted secret key into the sandbox Agent and returns the encrypted sandbox Agent;
s9: the subsequent user training codes say that the request for accessing the data is available, and the sandbox API module calls the sandbox Agent interface to request the sandbox management service module;
s10: all data communication between the sandbox Agent and the sandbox management service module is encrypted by using a secret key in the sandbox Agent;
s11: when receiving a request of a sandbox Agent, the sandbox management service module firstly verifies the correctness and the validity of the secret key, and if the secret key is correct and valid, decrypts the request data and then carries out the next processing;
s12: and the sandbox management service processes the decrypted request, requests the corresponding back-end service, encrypts a returned result by using the corresponding secret key and sends the encrypted result to the corresponding sandbox Agent.
Preferably, the sandbox API module determines that in a non-sandboxed environment, the sandbox API module will convert the data request to a request for sample data rather than the entire data set.
Preferably, the sandbox management service module processes the decrypted user request, transmits the processed user request to the back-end service, encrypts a result fed back by the back-end service, and transmits the encrypted result to the sandbox Agent.
Preferably, in S5 in the machine learning method for a sandbox environment, a network isolation is provided between the computing resource and the background service, and only communication through the sandbox management service module is allowed.
Preferably, the communication data includes pre-training model reads, hyper-parameter reads, data set reads, training visualization data save, and training structure save.
Preferably, in the machine learning method of the sandbox environment, the sandbox API module obtains the sandbox Agent including the key from the sandbox management service module.
Preferably, the sandbox management service module communicates only with the sandbox Agent using the key, and all data communications are encrypted in the whole process.
By implementing the technical scheme of the invention, the technical problems that the rights and interests of a data set provider cannot be guaranteed, corresponding code integration work needs to be carried out aiming at different data source APIs (application program interfaces), and the data access process is not safe enough in the prior art can be solved; by implementing the technical scheme of the invention, the sandbox API module judges and adopts the means of network isolation and data encryption, so that the technical effect of ensuring the safety and the effectiveness of data to the maximum extent can be realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only one embodiment of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
In which like parts are designated by like reference numerals. It should be noted that the terms "front," "back," "left," "right," "upper" and "lower" used in the following description refer to directions in the drawings, and the terms "bottom" and "top," "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular component.
FIG. 1 is a schematic diagram of the steps of a non-sandboxed environment machine learning method;
fig. 2 is a schematic diagram of the steps of a sandbox environment machine learning method.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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
In embodiment 1, as shown in fig. 1, a sandbox environment-based machine learning training platform implementation is characterized by comprising a mirror image management module, a resource scheduling module, a data platform module, a training management service module and a sandbox management service module; wherein
The sandbox management service module is used for managing a user sandbox training environment and isolating the user sandbox training environment from the data platform module;
the training platform is connected with the user mirror image platform, and the user mirror image platform comprises user training codes and a sandbox API module;
the method for machine learning in a non-sandbox environment comprises the following steps:
s1: calling a sandbox API module to request data through a user training code module;
s2: the sandbox API module judges that the sandbox API module is currently in a non-sandbox environment and requests the sandbox management service module to acquire example data;
s3: the sandbox management service module requests example data from the data platform;
s4: after the user training codes are debugged, the user can pack the training codes into a mirror image and upload the mirror image to the mirror image management module
The method for machine learning of the sandbox environment comprises the following steps:
s1: a user calls a training management service module to train, and a mirror image for training, a data set and resource information are provided;
s2: the training management service module calls the sandbox management service module to create a new sandbox task;
s3: the training management service module calls a service to start a training task;
s4: the resource scheduling module acquires a mirror image for user training from the mirror image management module;
s5: the resource scheduling module distributes corresponding amount and specification of computing resources according to the computing resources required by the user training, and uses the user mirror image for training;
s6: the user training code starts to execute and call a sandbox API module to access data;
s7: the sandbox API module judges that the sandbox API module is currently in a sandbox training environment, and requests the sandbox management service module to acquire a sandbox Agent;
s8: the sandbox management service module encrypts the secret key according to the requested sandbox API module ID, injects the encrypted secret key into the sandbox Agent and returns the encrypted sandbox Agent;
s9: the subsequent user training codes say that the request for accessing the data is available, and the sandbox API module calls the sandbox Agent interface to request the sandbox management service module;
s10: all data communication between the sandbox Agent and the sandbox management service module is encrypted by using a secret key in the sandbox Agent;
s11: when receiving a request of a sandbox Agent, the sandbox management service module firstly verifies the correctness and the validity of the secret key, and if the secret key is correct and valid, decrypts the request data and then carries out the next processing;
s12: and the sandbox management service processes the decrypted request, requests the corresponding back-end service, encrypts a returned result by using the corresponding secret key and sends the encrypted result to the corresponding sandbox Agent.
In this embodiment, in a non-sandbox environment, as shown in fig. 1, the machine learning training platform implementation includes two parts, a user mirror image and a training platform, where the user mirror image includes a user training code and a sandbox API module, and the training platform includes a sandbox management service module, a data platform module and a mirror management service module. When the user debugs the training codes, the user accesses the data set on the data platform in a mode of integrating the sandbox API module. Firstly, data is requested from a sandbox API module through a user training code, the sandbox API module can request sample data under a non-sandbox environment, then a sandbox service management module can process the request from the sandbox API module and send the corresponding data request to a data platform module, and finally the sandbox service management module encrypts a result processed by the data platform module by using a secret key and sends the result to the corresponding sandbox Agent. The method ensures that the user can only obtain the sample data of the data set but not the data of the whole data set in the non-sandbox environment, and prevents the data in the data set from being stolen, thereby ensuring the rights and interests of the data set provider.
In the sandbox training environment, as shown in fig. 2, the sandbox API module obtains a sandbox Agent containing a key from the sandbox management service module. Under the sandbox environment, the system comprises a training management service module, a sandbox management service module, a mirror image management service module, a resource scheduling module, a data platform module and a mirror image management service module, wherein the mirror image management service module comprises a user training code, a sandbox API module and a sandbox Agent module, and the data platform module comprises a hyper-parameter service, a data set service, a model management service and a visualization service. Firstly, a user carries out corresponding machine learning training through a training management service module, the training management service module calls a sandbox management service module to create a new sandbox task, then the training management service module starts the training task through a resource scheduling service module, the resource scheduling service module obtains a user training mirror image and corresponding resources from a mirror image management service module, after a user training code starts to be executed, the sandbox API module requests the sandbox management service module to obtain a sandbox Agent, and then all requests for accessing data of the user are requested to the sandbox management service module through the sandbox API module calling a sandbox Agent interface. The training nodes and the back-end service are isolated by adopting a network, only the communication is allowed through the sandbox management service module, the subsequent sandbox management service module only communicates with the sandbox Agent, and all data communication is encrypted in the whole process.
In a preferred embodiment, the sandbox API module may determine that if it is not a sandbox environment, the sandbox API module may convert the data request into a request for sample data rather than the entire data set.
In this embodiment, the sandbox API module analyzes and determines whether the data set is in the sandbox environment, and when the sandbox API module analyzes that the data set is in the non-sandbox environment, the data request sent by the sandbox API module is directed to the sample data rather than the entire data set. When the sandbox API module analyzes that it is in the sandbox environment, the sandbox API module issues a request to the entire data set.
In a preferred embodiment, the sandbox management service module processes the decrypted user request, transmits the processed user request to the back-end service, encrypts a result fed back by the back-end service, and sends the encrypted result to the sandbox Agent.
In the embodiment, under the environment of a non-sandbox, the sandbox management service module is responsible for information and data transmission, decrypts a user request and transmits the decrypted user request to the corresponding back-end service, encrypts a result returned by the back-end service and transmits the encrypted result to the corresponding sandbox Agent, and the security and confidentiality of data are always kept.
In a preferred implementation, in S5 of the machine learning method in a sandbox environment, a network isolation is provided between the computing resource and the background service, allowing only communication through the sandbox management service module.
In this embodiment, S5 in the machine learning method in a sandbox environment is that the resource scheduling service module calculates resources required by the user training according to the user training requirement, then allocates a corresponding amount and specification of calculation resources to the resources, and performs training using the user image. The computing resources and the background service can only communicate through the sandbox management service, so that the safety of data is guaranteed.
In a preferred embodiment, the communication data includes data from pre-training model reads, hyper-parameter reads, data set reads, training visualization data stores, and training structure stores.
In the present embodiment, S6, S9, S10, S11, and S12 in the machine learning method in a sandbox environment all relate to data communication, and the data communication involved includes, but is not limited to, data such as pre-training model reading, hyper-parameter reading, data set reading and writing, training visualization data storage, and training result storage.
In a preferred embodiment, in the machine learning method of the sandbox environment, the sandbox API module obtains the sandbox Agent including the key from the sandbox management service module.
In this embodiment, in the machine learning method in a sandbox environment, the sandbox API module may first request the sandbox management service module to obtain the sandbox Agent, and the sandbox management service module may generate a unique key according to the ID of the sandbox API module that has sent the request, encrypt the generated unique key, and inject the encrypted key into the sandbox Agent.
In a preferred embodiment, the sandbox management service module communicates only with the sandbox Agent using the key, and all data communications are encrypted in the whole process.
In the embodiment, in the machine learning method under the sandbox environment, after the corresponding sandbox Agent with the key is obtained according to the user request, the sandbox management service module only communicates with the sandbox Agent through the unique key, and the communication information is encrypted in the whole process. Through the mode, the user can only train all data of the data set in the sandbox environment, the network is isolated in the whole interaction process, the data is encrypted in the whole process, and the safety and the effectiveness of the data are guaranteed to the maximum extent.
It should be understood that the above-described embodiments are merely exemplary of the present invention, and are not intended to limit the present invention, and that any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (7)
1. A machine learning training platform implementation based on a sandbox environment is characterized by comprising a mirror image management module, a resource scheduling module, a data platform module, a training management service module and a sandbox management service module; wherein
The sandbox management service module is used for managing a user sandbox training environment and isolating the user sandbox training environment from the data platform module;
the training platform is connected with a user mirror image platform, and the user mirror image platform comprises user training codes and a sandbox API module;
the method for machine learning in a non-sandbox environment comprises the following steps:
s1: calling a sandbox API module to request data through a user training code module;
s2: the sandbox API module judges that the sandbox API module is currently in a non-sandbox environment and requests the sandbox management service module to acquire example data;
s3: the sandbox management service module requests example data from the data platform;
s4: after the user training codes are debugged, the user can pack the training codes into a mirror image and upload the mirror image to the mirror image management module
The method for machine learning of the sandbox environment comprises the following steps:
s1: a user calls a training management service module to train, and a mirror image for training, a data set and resource information are provided;
s2: the training management service module calls the sandbox management service module to create a new sandbox task;
s3: the training management service module calls a service to start a training task;
s4: the resource scheduling module acquires a mirror image for user training from the mirror image management module;
s5: the resource scheduling module distributes corresponding amount and specification of computing resources according to the computing resources required by the user training, and uses the user mirror image for training;
s6: the user training code starts to execute, and a sandbox API module is called to access data;
s7: the sandbox API module judges that the sandbox API module is currently in a sandbox training environment, and requests the sandbox management service module to acquire a sandbox Agent;
s8: the sandbox management service module encrypts the secret key according to the requested sandbox API module ID, injects the encrypted secret key into the sandbox Agent and returns the encrypted sandbox Agent;
s9: the subsequent user training codes say that the request for accessing the data is available, and the sandbox API module calls the sandbox Agent interface to request the sandbox management service module;
s10: all data communication between the sandbox Agent and the sandbox management service module is encrypted by using a secret key in the sandbox Agent;
s11: when receiving a request of a sandbox Agent, the sandbox management service module firstly verifies the correctness and the validity of the secret key, and if the secret key is correct and valid, decrypts the request data and then carries out the next processing;
s12: and the sandbox management service processes the decrypted request, requests the corresponding back-end service, encrypts a returned result by using the corresponding secret key and sends the encrypted result to the corresponding sandbox Agent.
2. The sandbox environment based machine learning training platform implementation of claim 1, wherein: the sandbox API module determines that in a non-sandboxed environment, the sandbox API module may convert the data request to a request for sample data rather than the entire data set.
3. The sandbox environment based machine learning training platform implementation of claim 1, wherein: and the sandbox management service module processes the decrypted user request, transmits the user request to the back-end service, encrypts a result fed back by the back-end service and transmits the result to the sandbox Agent.
4. The sandbox environment based machine learning training platform implementation of claim 1, wherein: in S5 of the machine learning method for a sandbox environment, network isolation is provided between the computing resources and the background services, allowing only communication through the sandbox management service module.
5. The sandbox environment based machine learning training platform implementation of claim 1, wherein: the communication data comprises pre-training model reading, hyper-parameter reading, data set reading, training visual data storage and training structure storage.
6. The sandbox environment based machine learning training platform implementation of claim 1, wherein: in the machine learning method of the sandbox environment, the sandbox API module obtains the sandbox Agent including the key from the sandbox management service module.
7. The sandbox environment based machine learning training platform implementation of claim 6, wherein: the sandbox management service module is only communicated with the sandbox Agent by using a secret key, and all data communication is encrypted in the whole process.
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