CN111612158A - Model deployment method, device, equipment and storage medium - Google Patents
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Abstract
The invention provides a model deployment method, a device, equipment and a storage medium, wherein the method comprises the following steps: if a training request is received, training based on sample data carried by the training request to obtain a training model; deploying the training model to a specified directory; and pushing the identification of the training model and the specified catalog to a model server so that the model server loads the training model according to the identification of the training model and the specified catalog, thereby realizing automatic deployment of the training model without restarting the model server and not influencing the use of the model server in the process of deploying the training model. By adopting the technical scheme of the invention, the model deployment efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a model deployment method, a model deployment device, model deployment equipment and a storage medium.
Background
At present, machine learning models such as a Bert model and the like are in a mutually separated state in a training stage and an application stage, namely, a required model is trained by using a large amount of data operation in the training stage, an executable program is manually established for the trained model in the application stage, and the trained model can be loaded by restarting a model server. For example, a Bert training code is adopted to load a collected training set to run model training once, then a sorted test set is used for model verification, after the model verification is completed, an executable program is manually established, and the model server is restarted to load the trained model, so that the model is newly added and updated.
However, when a new model is added and updated, the model server cannot be used because an executable program needs to be manually established and the model server needs to be restarted, thereby reducing the model deployment efficiency.
Disclosure of Invention
In view of this, the present invention provides a model deployment method, apparatus, device and storage medium, so as to solve the problem of low model deployment efficiency in the prior art.
Based on the above object, the present invention provides a model deployment method, comprising:
if a training request is received, training based on sample data carried by the training request to obtain a training model;
deploying the training model to a specified directory;
and pushing the identification of the training model and the specified catalogue to a model server so that the model server loads the training model according to the identification of the training model and the specified catalogue.
Further, in the model deployment method, the training based on the sample data carried by the training request to obtain a training model includes:
loading the training set based on the identification of the training set in the sample data and a first storage catalog;
training the training set to obtain a pre-training model;
loading the test set based on the identification of the test set in the sample data and a second storage directory;
inputting the test set into the pre-training model to obtain a test result;
and if the test result meets the preset model on-line condition, taking the pre-training model as the training model.
Further, in the model deployment method described above, the specified directory is set in the model server.
Further, the model deployment method further includes:
determining intention information corresponding to the sample data;
and determining a training algorithm corresponding to the intention information based on the incidence relation between the preset intention and the training algorithm so as to train by using the training algorithm.
The present invention also provides a model deployment apparatus, comprising:
the training module is used for training based on sample data carried by a training request to obtain a training model if the training request is received;
the deployment module is used for deploying the training model to a specified directory;
and the pushing module is used for pushing the identification of the training model and the specified catalogue to a model server so as to enable the model server to load the training model according to the identification of the training model and the specified catalogue.
Further, in the model deployment apparatus described above, the training module is specifically configured to:
loading the training set based on the identification of the training set in the sample data and a first storage catalog;
training the training set to obtain a pre-training model;
loading the test set based on the identification of the test set in the sample data and a second storage directory;
inputting the test set into the pre-training model to obtain a test result;
and if the test result meets the preset model on-line condition, taking the pre-training model as the training model.
Further, in the model deployment apparatus described above, the specified directory is provided in the model server.
Further, in the model deployment apparatus described above, the training module is further configured to:
determining intention information corresponding to the sample data;
and determining a training algorithm corresponding to the intention information based on the incidence relation between the preset intention and the training algorithm so as to train by using the training algorithm.
The invention also provides a model deployment device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described in any one of the above when executing the program.
The present invention also provides a storage medium storing computer instructions for causing a computer to perform the method of any one of the above.
From the above description, it can be seen that the model deployment method, apparatus, device and storage medium provided by the present invention, if a training request is received, training is performed based on sample data carried by the training request, after a training model is obtained, the training model is deployed to an assigned directory, and an identifier of the training model and the assigned directory are pushed to a model server, so that the model server loads the training model according to the identifier of the training model and the assigned directory, thereby implementing automatic deployment of the training model without restarting the model server, and in the process of deploying the training model, the use of the model server is not affected. By adopting the technical scheme of the invention, the model deployment efficiency can be improved.
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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 some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a model deployment method embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an embodiment of a model deployment apparatus of the present invention;
fig. 3 is a schematic structural diagram of an embodiment of a model deployment apparatus according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present invention should have the ordinary meanings as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
Fig. 1 is a flowchart of an embodiment of a model deployment method of the present invention, and as shown in fig. 1, the model deployment method of this embodiment may specifically include the following steps:
100. if a training request is received, training is carried out based on sample data carried by the training request to obtain a training model;
in a specific implementation process, a user can import the sorted corpora and the corresponding intention labels and the like as sample data into an online training platform and input a training request, and at the moment, if the training request is received, training can be performed based on the sample data carried by the training request to obtain a training model.
Specifically, the sample data may be divided into a training set and a test set, where the training set is preferably 80% of the sample data, and the test set is preferably 20% of the sample data. When the training set and the test set are divided, a random division method can be adopted for division. In this embodiment, when data partitioning is completed, the identifier of the training set and the first storage directory may be recorded, and the identifier of the test set and the second storage directory may be recorded. And respectively sending the identification of the training set, the identification of the first storage catalogue and the test set and the second storage catalogue to an online training platform, so that the training set can be loaded and trained on the basis of the identification of the training set and the first storage catalogue in sample data to obtain a pre-training model. After the pre-training model is obtained, a newly added model can be detected, at the moment, the test set can be loaded based on the identification of the test set in the sample data and the second storage directory, and the test set is input into the pre-training model to obtain a test result; and judging whether the test result meets the model online condition, and if the test result meets the preset model online condition, taking the pre-training model as a training model. And if the test result does not meet the online condition of the model, continuing training.
For example, the test result may include an accuracy and/or a recall rate of the pre-trained model, and accordingly, an accuracy of the online condition on the model is greater than a first preset threshold, and/or the recall rate is greater than a second preset threshold.
In this embodiment, regarding the test result, there are two possibilities that indicate how many samples of which the predicted samples are positive are true positive sample predictions, one is to predict the positive class as a positive class (TP), and the other is to predict the negative class as a positive class (FP), so that the accuracy P is TP/(TP + FP). For sample data, there are two possibilities to indicate how many positive examples in the sample are predicted correctly, one is to predict the original positive class as a positive class (TP), and the other is to predict the original positive class as a negative class (FN), so that the recall ratio R is TP/(TP + FN).
In the embodiment, only sample data such as manually collected intention classification corpora, intention labels and the like are needed, the training set and the test set can be automatically loaded through the online training platform, model training and evaluation are completed, and the efficiency of training the model is improved.
101. Deploying the training model to a specified directory;
after the training model is obtained, the training model may be deployed to a specified directory, preferably located in a model server.
102. And pushing the identification and the specified catalogue of the training model to a model server so that the model server loads the training model according to the identification and the specified catalogue of the training model.
In a specific implementation, a unique identifier is generated when each training model is obtained. In this embodiment, after the deployment of the training model is completed, the identifier and the designated directory of the training model may be automatically pushed to the model server through an HTTP interface or the like, so that the model server may be dynamically loaded to the corresponding training model without being restarted.
According to the model deployment method, if the training request is received, training is carried out based on sample data carried by the training request, after the training model is obtained, the training model is deployed to the specified directory, and the identification of the training model and the specified directory are pushed to the model server, so that the model server loads the training model according to the identification of the training model and the specified directory, the training model is automatically deployed under the condition that the model server is not restarted, and the use of the model server is not influenced in the process of deploying the training model. By adopting the technical scheme of the invention, the model deployment efficiency can be improved.
In practical application, in order to adapt to various training methods, a calling interface of various training methods can be set, and a user can select a required training method when inputting sample data. However, some users do not know the training method well and cannot accurately select the required training method, so the present invention further provides the following technical solutions to solve the above problems.
Data features can be extracted from part of sample data, so that intention information corresponding to the sample data is determined according to the extracted data features, and a training algorithm corresponding to the intention information is determined based on the incidence relation between preset intention and the training algorithm, so that training is performed by using the determined training algorithm. Therefore, the required training method can be still accurately selected for training under the condition that the user does not know the training method well, and meanwhile, the phenomenon that the user cannot train when forgetting to select the training algorithm can be avoided.
It should be noted that the method of the embodiment of the present invention may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In the case of such a distributed scenario, one device of the multiple devices may only perform one or more steps of the method according to the embodiment of the present invention, and the multiple devices interact with each other to complete the method.
Fig. 2 is a schematic structural diagram of an embodiment of a model deployment apparatus of the present invention, and as shown in fig. 2, the model deployment apparatus of this embodiment includes a training module 20, a deployment module 21, and a pushing module 22.
The training module 20 is configured to, if a training request is received, perform training based on sample data carried in the training request to obtain a training model;
specifically, loading a training set based on the identification of the training set in the sample data and a first storage directory; training the training set to obtain a pre-training model; loading the test set based on the identification of the test set in the sample data and the second storage directory; inputting the test set into a pre-training model to obtain a test result; and if the test result meets the preset model on-line condition, taking the pre-training model as a training model.
A deployment module 21, configured to deploy the training model to a specified directory;
wherein the specified directory is preferably provided in the model server.
A pushing module 22, configured to push the identifier of the training model and the specified directory to a model server, so that the model server loads the training model according to the identifier of the training model and the specified directory.
According to the model deployment device, if the training request is received, training is carried out based on sample data carried by the training request, after the training model is obtained, the training model is deployed to the specified directory, and the identification of the training model and the specified directory are pushed to the model server, so that the model server loads the training model according to the identification of the training model and the specified directory, the training model is automatically deployed under the condition that the model server is not restarted, and the use of the model server is not influenced in the process of deploying the training model. By adopting the technical scheme of the invention, the model deployment efficiency can be improved.
Further, in the above embodiment, the training module 20 is further configured to:
determining intention information corresponding to the sample data;
and determining a training algorithm corresponding to the intention information based on the incidence relation between the preset intention and the training algorithm so as to train by using the training algorithm.
The apparatus of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Fig. 3 is a schematic structural diagram of an embodiment of a model deployment device of the present invention, and as shown in fig. 3, the passing device of this embodiment may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The invention also provides a training system which comprises the model server and the model deployment equipment of the embodiment.
The present invention also provides a storage medium storing computer instructions for causing the computer to execute the control method of the distributed terminal of the above-described embodiment.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
In addition, well known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures for simplicity of illustration and discussion, and so as not to obscure the invention. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A method of model deployment, comprising:
if a training request is received, training based on sample data carried by the training request to obtain a training model;
deploying the training model to a specified directory;
and pushing the identification of the training model and the specified catalogue to a model server so that the model server loads the training model according to the identification of the training model and the specified catalogue.
2. The model deployment method of claim 1, wherein the training based on the sample data carried by the training request to obtain a training model comprises:
loading the training set based on the identification of the training set in the sample data and a first storage catalog;
training the training set to obtain a pre-training model;
loading the test set based on the identification of the test set in the sample data and a second storage directory;
inputting the test set into the pre-training model to obtain a test result;
and if the test result meets the preset model on-line condition, taking the pre-training model as the training model.
3. The model deployment method of claim 1 wherein the specified directory is provided in the model server.
4. The model deployment method of claim 1, further comprising:
determining intention information corresponding to the sample data;
and determining a training algorithm corresponding to the intention information based on the incidence relation between the preset intention and the training algorithm so as to train by using the training algorithm.
5. A model deployment apparatus, comprising:
the training module is used for training based on sample data carried by a training request to obtain a training model if the training request is received;
the deployment module is used for deploying the training model to a specified directory;
and the pushing module is used for pushing the identification of the training model and the specified catalogue to a model server so as to enable the model server to load the training model according to the identification of the training model and the specified catalogue.
6. The model deployment device of claim 5, wherein the training module is specifically configured to:
loading the training set based on the identification of the training set in the sample data and a first storage catalog;
training the training set to obtain a pre-training model;
loading the test set based on the identification of the test set in the sample data and a second storage directory;
inputting the test set into the pre-training model to obtain a test result;
and if the test result meets the preset model on-line condition, taking the pre-training model as the training model.
7. The model deployment apparatus of claim 5 wherein the specified directory is provided in the model server.
8. The model deployment device of claim 5, wherein the training module is further configured to:
determining intention information corresponding to the sample data;
and determining a training algorithm corresponding to the intention information based on the incidence relation between the preset intention and the training algorithm so as to train by using the training algorithm.
9. A model deployment apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 4 when executing the program.
10. A storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 4.
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