WO2022007434A1 - Visualization method and related device - Google Patents
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Definitions
- the present application relates to the field of computer technology, and in particular, to a visualization method and related equipment.
- deep learning models have been widely used in the field of image and video processing.
- deep learning models are obtained through training.
- the training process of deep learning models involves complex and esoteric calculations, and needs to be implemented through multiple iterations.
- the training time is long, and developers can only evaluate the performance of the deep learning model after training.
- Embodiments of the present application provide a visualization method and related equipment, which are used to visualize training data of a deep learning model.
- an embodiment of the present application provides a visualization method, which is applied to a user equipment, and the method includes:
- the visualization information is displayed.
- an embodiment of the present application provides a visualization apparatus, which is applied to a user equipment, and the apparatus includes:
- a sending unit configured to send a visualization request to the cloud server, where the visualization request is used to request visualization of the target training data of the target deep learning model
- a receiving unit configured to receive a visualization response sent by the cloud server, where the visualization response carries the visualization information of the target training data
- a display unit for displaying the visual information.
- an embodiment of the present application provides a visualization method, which is applied to a cloud server, and the method includes:
- a visualization response is sent to the user equipment, and the visualization response carries the visualization information.
- an embodiment of the present application provides a visualization device, which is applied to a cloud server, and the device includes:
- a receiving unit configured to receive a visualization request from the user equipment, where the visualization request is used to request visualization of the target training data of the target deep learning model;
- a sending unit configured to send a visualization response to the user equipment, where the visualization response carries the visualization information.
- the present application provides a computer device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured by
- the above-mentioned processor is executed, and the above-mentioned program includes instructions for executing the steps in the method described in the first aspect or the third aspect of the embodiments of the present application.
- an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the computer program as described in the first embodiment of the present application.
- an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute as implemented in the present application.
- the computer program product may be a software installation package.
- the user equipment first sends a visualization request to the cloud server, then receives the visualization response sent by the cloud server, the visualization response carries the visualization information of the target training data, and finally displays the visualization information.
- the visualization information of the data can intuitively understand the training status of the deep learning model, which helps to judge the feasibility of the current training strategy in time, and provides a basis for decision-making such as early stopping.
- FIG. 1 is a schematic diagram of the architecture of a visualization system provided by an embodiment of the present application.
- FIG. 2 is a schematic flowchart of a visualization method provided by an embodiment of the present application.
- FIG. 3 is a schematic structural diagram of a computer device provided by an embodiment of the present application.
- FIG. 4 is a schematic structural diagram of a visualization device provided by an embodiment of the present application.
- FIG. 5 is a schematic structural diagram of another visualization apparatus provided by an embodiment of the present application.
- FIG. 1 is a schematic diagram of the architecture of a visualization system provided by an embodiment of the application, where the visualization system includes a supercomputing cluster, a cloud server, a cloud storage, and user equipment.
- the visualization system can realize the visualization of multi-dimensional and multi-class intermediate training data based on javascript and svg technology, and supports fast and efficient visualization rendering of a large amount of intermediate training data.
- the visualization system is based on the kubernetes container orchestration system, which provides stable visualization system deployment at the production level and provides dynamic and controllable service capabilities.
- the kubernetes container is used to manage containerized applications on multiple hosts in the cloud service.
- the cloud server provides visualization services, and the visualization services are used to perform data operations such as preprocessing on the acquired intermediate training data, and visualize and render the intermediate training data after data operations to obtain visualization information.
- the cloud server may further provide at least one of the following services: a service gateway, a training management service, a data storage service, and a permission service.
- the service gateway can be used as the entrance of at least one of the following services: the entrance of training management service, data storage service, data visualization service and authority service.
- the service gateway can be an application.
- the service gateway may have a current limiting function.
- the training management service can provide at least one of the following services: monitoring whether the training tasks of the deep learning model are completed, recording, indexing and searching for abnormal training tasks, comparing the training process of multiple training tasks of the same deep learning model, and Share the training results of deep learning models.
- the data storage service can provide at least one of the following services: data persistence, using data threads to store intermediate training data generated by deep learning model training, and using cache to store intermediate training data generated by deep learning model training.
- the rights management service is used to ensure the security of the visualization system, and the visualization system can only be used after passing the rights management service.
- shared training is also authenticated through the rights management service, which supports unified authentication account login and/or ordinary There are two types of login methods to register.
- the cloud server can interact with the developer toolkit through HyperText Transfer Protocol (HTTP), Google Remote Procedure Call (GRPC) protocol or other protocols.
- HTTP HyperText Transfer Protocol
- GRPC Google Remote Procedure Call
- the cloud server can directly store the training data in the cloud storage.
- the supercomputing cluster provides development tools such as python and other programming language toolkits by running the developer toolkit for users to use with tensorflow, pytorch, caffe frameworks, etc., and/or open a new thread as a dedicated thread for data uploading, and
- the intermediate training data generated by the training of the deep learning model is uploaded to the cloud server through the HTTP communication protocol and the GRPC protocol on the dedicated thread for data uploading.
- the uploaded intermediate training data types can include vectors, scalars, pictures, videos, audios, etc. Data is stored in the developer kit's data cache.
- opening a new thread as a dedicated thread for data uploading can ensure the computing speed of the deep learning model.
- cloud storage can optionally be distributed storage, which is used for unified management of intermediate training data generated by deep learning models, providing massive storage functions for intermediate training data, and as the amount of intermediate training data increases, it can dynamically
- the capacity can be expanded to meet the storage requirements of intermediate training data and provide follow-up guarantee for the growth of visualization business.
- the cloud storage may also be data block-level cloud storage, file-level cloud storage, object-level cloud storage and/or other forms of cloud storage.
- the user equipment may communicate with the cloud server through preset application software, or communicate with the cloud server through a preset network address.
- the user equipment may include various handheld devices with communication functions, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to the wireless modem, and various forms of user equipment (User Equipment, UE), Mobile station (Mobile Station, MS), terminal device (terminal device) and so on.
- UE User Equipment
- MS Mobile Station
- terminal device terminal device
- FIG. 2 is a schematic flowchart of a visualization method provided by an embodiment of the present application, which is applied to the above-mentioned user equipment and cloud server, and specifically includes the following steps:
- Step 201 The user equipment sends a visualization request to the cloud server, where the visualization request is used to request visualization of the target training data of the target deep learning model.
- Step 202 The cloud server receives a visualization request from the user equipment, where the visualization request is used to request visualization of the target training data of the target deep learning model.
- the visualization request carries the training task identifier of the target deep learning model.
- the cloud server can obtain the relevant training data of the training task based on the training task identifier of the target deep learning model.
- the stored training data related to the training tasks of each deep learning model may be associated with the training task identifier of the deep learning model.
- training data may be stored according to training tasks, and relevant training data of different training tasks are stored in different locations.
- training data may be stored according to deep learning models or data types.
- the data type can be a two-dimensional heat map, a three-dimensional histogram, a feature map, a call sequence diagram, a scalar line graph, a directed acyclic graph, and so on.
- the training tasks are all located on the supercomputing cluster, and the training of the deep learning model can be accelerated by using the central processing unit of the supercomputing cluster.
- the training data may be stored in association with the task identifier of the training task to which the training data belongs, and so on, which is not limited in this embodiment of the present disclosure.
- the visualization request carries the training task identification of the target deep learning model and the index information of the target training data, and the index information is used to find the target training data to determine the storage location of the target training data.
- the index information may be implemented in multiple ways.
- the index information may include any one or more of data creation time, data type, and data label.
- the index information includes data type and data label.
- the same data type can have multiple data labels, and the data labels can be customized.
- the data labels can be exact values, loss values, and so on.
- the visualization request may be a request to sample all the data in the intermediate data set corresponding to the index information or randomly sample the data in the intermediate data set according to the training task identifier and the index information.
- the target deep learning model includes at least one deep neural network, and the target training data may be intermediate data generated by one of the deep neural networks in the target deep learning model, or may be intermediate data generated by multiple deep neural networks in the target deep learning model. .
- the visualization request may be a request to visualize the intermediate data generated by the target deep learning model in the first period of time, wherein the duration of the first period of time may be other values such as 3min, 5min, 9min, and 15min, and the termination time of the first moment may be is the current time, it can be earlier than the current time, or it can be later than the current time.
- the target training data is stored in cloud storage.
- the target training data includes at least one of the following: model performance change trend information, model loss trend information, model parameter distribution information, model processing intermediate results, model structure information, current progress information of model training, Comparing information for different trainings of the same model, and time series of scheduling time information for various processes.
- the category of information is a scalar line chart.
- the target training data is the model performance change trend information
- the target training data is the model loss trend information
- the target training data is the current progress information of model training
- the target training data is the comparison information of different trainings of the same model
- the category of visualization information is a three-dimensional histogram, and it can be determined whether the model parameter distribution is abnormal through the three-dimensional histogram. If the model parameter distribution is abnormal, the model training is stopped.
- the category of the visual information is the feature map, and through the feature map, it can be determined whether the features extracted by the model are correct, and if the extracted features are wrong, rebuild the model.
- the category of the visualization information is a directed acyclic graph
- the directed acyclic graph can determine whether the structure of the model is correct, and if the structure of the model is wrong, rebuild the The structure of the model.
- the architecture of the deep learning model supported by the directed acyclic graph can be a standard architecture such as Open Neural Network Exchange (ONNX), or other types of architectures, in which ONNX does not need to perform intermediate training data.
- ONNX Open Neural Network Exchange
- the types of visualization information are the calling sequence diagrams of various processes, and the time-consuming situation of each operator can be determined through the calling sequence diagram.
- the time consumption is greater than the preset time, the operators whose time consumption is greater than the preset time are optimized.
- the category of the visualization information is a two-dimensional heat map. Through the heat map, it can be determined whether the features extracted by the model are correct. Build the model.
- the feature map and the heat map are different representations of the target training data for the model to process the intermediate results.
- the types of target training data include at least one of the following: scalar, vector, picture, video, and audio.
- Step 203 The cloud server obtains target training data corresponding to the visualization request.
- the acquiring target training data corresponding to the visualization request includes:
- the target training data stored in the cloud storage is acquired based on the storage location information of the target training data.
- a list of intermediate training data is obtained based on the training task identification and index information, and the intermediate training data is obtained in cloud storage based on the list of intermediate training data.
- the visualization request carries the identification information of the target training data of the target deep learning model, and the identification information is used by the cloud server to obtain the target training data from the cloud storage; wherein, the identification information is preset, and the identification Information is unique in the cloud storage.
- the training tasks of the target deep learning model include training task A and training task B
- the training data generated by training task A includes training data A1 and training data A2
- the training data generated by training task B includes training data B1 and training data B2
- training data B1 and training data B2 are all stored in the cloud platform
- the identification information of training data A1 is 1, the identification information of training data A2 is 2
- the identification information of training data B1 is 3
- the identification information of the training data B2 is 4, if the identification information is 1, the target training data obtained by the cloud server from the cloud storage is the training data A1; if the identification information is 2, the target training data obtained by the cloud server from the cloud storage
- the data is training data A2; if the identification information is 3, the target training data obtained by the cloud server from the cloud storage is training data B1; if the identification information is 4, the target training data obtained by the cloud server from the cloud storage is training data B2.
- Step 204 The cloud server preprocesses the target training data to obtain visualization information.
- Step 205 The cloud server sends a visualization response to the user equipment, where the visualization response carries the visualization information.
- Step 206 The user equipment receives a visualization response sent by the cloud server, where the visualization response carries the visualization information of the target training data.
- Step 207 The user equipment displays the visualized information.
- the visualization information includes at least one of the following categories: directed acyclic graphs, three-dimensional histograms, call sequence diagrams of various processes, feature maps, two-dimensional heat maps, and scalar line graphs.
- the visualization information includes the following information: topology information of at least a part of the target deep learning model, wherein at least a part of the target deep learning model includes: multiple modules and/or multiple operators of the target deep learning model; resource occupation information of each operator in at least one operator included in the target deep learning model.
- the topology information of multiple modules includes at least one of the following: identification information of the multiple modules, dependencies between the multiple modules, data size of each module in the multiple modules, Information of at least one operator included in each of the modules.
- the identification information of the module is unique and can be preset.
- the information of the operators includes at least one of the following: identification information of the operators, dependencies between operators, and data size of the operators.
- the dependency relationship can be a sequential relationship or a parallel relationship.
- the resource occupation information is determined by the cloud server based on at least one of the data type of the operator, the input data information of the operator, and the output data information of the operator.
- the input of the operator can be a picture, video, audio, scalar, vector, etc., and different inputs correspond to different dimensions.
- a picture is equivalent to a two-dimensional matrix
- an audio is equivalent to a one-dimensional matrix.
- the data type of the operator can be a double-precision type or a single-precision type.
- the operator can be at least one of the following: convolution, batch normalization (BatchNorm, BN), full link, pooling, matrix multiplication and division, dropout (DropOut), activation, etc.
- the input data information may be the size of the input data
- the output data information may be the size of the output data
- the visualization information includes the performance change trend information of the target deep model, the loss trend information of the target deep model, the training progress information of the target deep learning model, and the difference in the target deep learning model. At least one of the training comparison information.
- the visualization information includes features extracted by the target deep learning model.
- the visualization information includes the parameter distribution of each operator in the plurality of operators.
- the visualization information includes the running time of each operator in at least one process of the multiple operators.
- the process may be at least one of an interpreted language process, a local process, and an AI chip process.
- the interpreted language process, the local process, and the AI chip process alternately execute the calling sequence diagram according to time.
- the user equipment first sends a visualization request to the cloud server, then receives the visualization response sent by the cloud server, the visualization response carries the visualization information of the target training data, and finally displays the visualization information.
- the visual information of the data can intuitively understand the training status of the deep learning model, which helps to improve the feasibility of timely judgment of the current training strategy, and provides a basis for decision-making such as early stopping.
- the displaying the visualized information includes:
- a module in the target deep learning model is used as the minimum display unit, wherein the module includes at least one operator; and/or
- the operator in the target deep learning model is used as the minimum display unit.
- the visualization information when the category of the visualization information is a directed acyclic graph, the visualization information can be displayed in the collapsed display mode, and the visualization information can also be displayed in the expanded display mode. , which displays visual information by expanding the display mode.
- the expanded display mode may be an operator in the expanded display module after the module receives an operation instruction.
- the expanded display mode may be to directly display the parameter distribution of each operator in each module.
- the expanded display mode may be to directly display the running time of each operator in each module in at least one process.
- the target deep learning model includes 2 modules (module A and module B), and module A includes 3 operators (A1, A2, and A3), the model B includes 2 operators (B1 and B2). If the visualized information is displayed in the folded display mode, module A and module B are displayed; if the visualized information is displayed in the expanded display mode, after the module A receives the operation instruction, the operators A1, A2 and A3 in the module A are displayed, and the module B receives the operation After the instruction, the operators B1 and B2 in module B are displayed.
- the target deep learning model includes 2 modules (module A and module B), module A includes 3 operators (A1, A2, and A3), and model B includes Including two operators (B1 and B2), module A corresponds to 3D histogram 1, and module B corresponds to 3D histogram B, then 3D histogram 1 includes the parameter distribution of A1, the parameter distribution of A2 and the parameter distribution of A3
- 3D histogram 1 includes the parameter distribution of A1, the parameter distribution of A2 and the parameter distribution of A3
- the three-dimensional histogram 2 includes the parameter distribution of B1 and the parameter distribution of B2.
- the target deep learning model includes 2 modules (module A and module B) and 2 processes (process C1 and C2), and module A includes 2 operators ( A1 and A2), model B includes 2 operators (B1 and B2), module A corresponds to call sequence diagram 1, and module B corresponds to call sequence diagram B, then the call sequence diagram 1 includes the running time of A1 in process C1 , the running duration of A1 in process C2, including the running duration of A2 in process C1 and the running duration of A2 in process C2; the calling sequence diagram 2 includes the running duration of B1 in process C1 and the running duration of B1 in process C2 The running duration includes the running duration of B2 in process C1 and the running duration of B2 in process C2.
- a display mode of visualized information may be preset, which may be to display the visualized information in a folded display mode first, and then display the visualized information in an expanded display mode after a first duration, which may be preset.
- the type of visualization information is a scalar line graph
- the expandable display mode may be to directly display the features extracted by the target deep learning model.
- the user equipment displays visual information in different ways, which is beneficial to improve the application scope of the user equipment.
- the method before the sending the visualization request to the cloud server, the method further includes:
- the intermediate training data of the target deep learning model is sent to the cloud server through a data upload thread, and the intermediate training data is used by the cloud server to obtain the index information and training task identifier of the target training data, and based on the
- the training task identifier stores the index information in a database, and stores the target training data in cloud storage based on the index information.
- the data uploading thread is a newly opened thread.
- the training task identification is unique, and the training task identification may be determined after the intermediate training data is generated, or may be determined at the beginning of training.
- the intermediate training data includes target training data, training task identifiers, and index information.
- the intermediate training data of the target deep learning model is sent to the cloud server through a data upload thread, and the intermediate data is used for the cloud server to obtain the identification information of the target training data, and to store the identification information in a database.
- the target training data is stored in cloud storage based on the identification information.
- the identification information is unique.
- the method before sending the intermediate training data of the target deep learning model to the cloud server through a data upload thread, the method further includes:
- the training task identifier and the index information of the target training data determine the training task identifier and the index information of the target training data, and carry the training task information and the index information of the target training data in the intermediate training data;
- the identification information of the target training data is determined, and the identification information is carried on the intermediate training data.
- the identification information may be stored in the first buffer area of the database, and the target training data may be stored in the second buffer area of the database based on the identification information.
- the target data is stored in the cloud storage by the cloud server, which is beneficial for the cloud server to quickly obtain the target training data after receiving the visualization request.
- the preprocessing of the target training data to obtain visualization information includes:
- the preprocessing of the picture or video includes one or more of the following processes: insensitive area removal processing, image precision enhancement processing, image noise reduction processing and image processing Binarization processing.
- the text preprocessing includes one or more of the following processes: document segmentation, text segmentation, and removal of stop words (including punctuation, numbers, monads, and other Meaningless words) processing, text feature extraction, word frequency statistics processing and text vectorization processing.
- stop words including punctuation, numbers, monads, and other Meaningless words
- the visual rendering is to assemble the preprocessed data into a hypertext markup language (Hypertext Markup Language, HTML).
- HTML Hypertext Markup Language
- the type of visualization information obtained by preprocessing is non-directed acyclic graph.
- the target training data is preprocessed to obtain preprocessed data, and then the preprocessed data is rendered to obtain visual information, which is beneficial to the target deep learning model and analysis based on the visual information.
- the preprocessing of the target training data to obtain visualization information includes:
- the target training data is parsed through a binary tree to obtain the visualization information.
- the type of the target deep learning model that processes the target training data through the binary tree is parrots.
- the type of visualization information obtained by parsing the target training data through a binary tree is a directed acyclic graph.
- the visualization information is obtained by analyzing the target training data, which is beneficial to analyze and analyze the target deep learning model based on the visualization information.
- FIG. 3 is a schematic structural diagram of a computer device provided by an embodiment of the present application.
- the computer device includes a processor, a memory, a communication interface, and one or more programs, wherein one or more of the above A plurality of programs are stored in the above-mentioned memory, and are configured to be executed by the above-mentioned processor.
- the computer device is user equipment
- the above program includes instructions for performing the following steps:
- the visualization information is displayed.
- the visualization information includes at least one of the following categories: directed acyclic graph, three-dimensional histogram, call sequence diagram of various processes, feature map, two-dimensional heat map, and scalar line graph; and/or
- the target training data includes at least one of the following: model performance change trend information, model loss trend information, model parameter distribution information, model processing intermediate results, model structure information, current progress information of model training, and information on different trainings for the same model. Comparison information, scheduling time information of various processes.
- the visualization information includes at least one of the following information:
- topology information of at least a part of the target deep learning model wherein at least a part of the target deep learning model includes: multiple modules and/or multiple operators of the target deep learning model;
- the topology information of the multiple modules includes at least one of the following:
- the identification information of the multiple modules the dependencies between the multiple modules, the data size of each module in the multiple modules, and the information of at least one operator included in each of the modules.
- the resource occupation information is determined by the cloud server based on at least one of the data type of the operator, the input data information of the operator, and the output data information of the operator.
- the visualization request carries a training task identifier of the target deep learning model and index information of the target training data, where the index information includes: data type and data label.
- the above-mentioned program includes instructions for executing the following steps:
- the display of visual data in different modes is supported, wherein, in some embodiments, the visual information may be displayed in a folding display mode or a presentation display mode, or other display modes may also be defined. Embodiments are not limited thereto.
- a module in the target deep learning model is used as the smallest display unit, wherein the module includes at least one operator.
- the modules here may be divided in a default division manner, or may also be set by a user, which is not limited in this embodiment of the present disclosure.
- the operator in the target deep learning model is used as the minimum display unit. At this point, the information of all operators in the model can be displayed.
- it can also be displayed in a mixed display mode, that is, some modules are folded and displayed, and other modules are displayed in an expanded manner, which can optionally be displayed based on user settings, which is not covered by the embodiments of the present disclosure. Do limit.
- the above program before sending the visualization request to the cloud server, the above program includes an instruction for performing the following steps:
- the intermediate training data of the target deep learning model is sent to the cloud server through a data upload thread, and the intermediate training data is used by the cloud server to obtain the index information and training task identifier of the target training data, and based on the
- the training task identifier stores the index information in a database, and stores the target training data in cloud storage based on the index information.
- the computer device is a cloud server
- the above program includes instructions for performing the following steps:
- a visualization response is sent to the user equipment, and the visualization response carries the visualization information.
- the visualization information includes at least one of the following categories: directed acyclic graph, three-dimensional histogram, call sequence diagram of various processes, feature map, two-dimensional heat map, and scalar line graph; and/or
- the target training data includes at least one of the following: model performance change trend information, model loss trend information, model parameter distribution information, model processing intermediate results, model structure information, current progress information of model training, and information on different trainings for the same model. Comparison information, scheduling time information of various processes.
- the visualization information includes at least one of the following information:
- topology information of at least a part of the target deep learning model wherein at least a part of the target deep learning model includes: multiple modules and/or multiple operators of the target deep learning model;
- the topology information of the multiple modules includes at least one of the following:
- the identification information of the multiple modules the dependencies between the multiple modules, the data size of each module in the multiple modules, and the information of at least one operator included in each of the modules.
- the above program includes instructions for executing the following steps:
- the visualization request carries a training task identifier of the target deep learning model and index information of the target training data, where the index information includes: data type and data label;
- the above-mentioned program includes an instruction for executing the following steps: searching an index database based on the training task identifier and index information carried in the visualization request, so as to obtain the target training data. storage location information; based on the storage location information of the target training data, obtain the target training data stored in the cloud storage.
- the above program includes instructions for executing the following steps:
- the above program includes instructions for executing the following steps:
- the target training data is parsed through a binary tree to obtain the visualization information.
- FIG. 4 is a visualization apparatus provided by an embodiment of the present application, applied to user equipment, and the apparatus includes:
- a sending unit 401 configured to send a visualization request to a cloud server, where the visualization request is used to request visualization of target training data of a target deep learning model;
- a receiving unit 402 configured to receive a visualization response sent by the cloud server, where the visualization response carries the visualization information of the target training data;
- the display unit 403 is configured to display the visualized information.
- the visualization information includes at least one of the following categories: directed acyclic graph, three-dimensional histogram, call sequence diagram of various processes, feature map, two-dimensional heat map, and scalar polyline Figures; and/or
- the target training data includes at least one of the following: model performance change trend information, model loss trend information, model parameter distribution information, model processing intermediate results, model structure information, current progress information of model training, and information on different trainings for the same model. Comparison information, scheduling time information of various processes.
- the visualized information includes at least one of the following information:
- topology information of at least a part of the target deep learning model wherein at least a part of the target deep learning model includes: multiple modules and/or multiple operators of the target deep learning model;
- the topology information of the multiple modules includes at least one of the following:
- the identification information of the multiple modules the dependencies between the multiple modules, the data size of each module in the multiple modules, and the information of at least one operator included in each of the modules.
- the resource occupation information is at least one of the cloud server based on the data type of the operator, the input data information of the operator, and the output data information of the operator definite.
- the visualization request carries a training task identifier of the target deep learning model and index information of the target training data, where the index information includes: data type and data label.
- the above-mentioned display unit 403 is specifically used for:
- a module in the target deep learning model is used as the minimum display unit, wherein the module includes at least one operator; and/or
- the operator in the target deep learning model is used as the minimum display unit.
- the above-mentioned sending unit 401 is specifically further configured to send the intermediate training data of the target deep learning model to the cloud server through a data upload thread before sending the visualization request to the cloud server, and the The intermediate training data is used for the cloud server to obtain the index information and training task identifier of the target training data;
- the apparatus further includes: a data storage unit 404, configured to store the index information in a database based on the training task identifier, and store the target training data in cloud storage based on the index information.
- a data storage unit 404 configured to store the index information in a database based on the training task identifier, and store the target training data in cloud storage based on the index information.
- the sending unit 401 and the receiving unit 402 can be implemented through a communication interface
- the display unit 403 can be implemented through a display screen
- the data storage unit 404 can be implemented through a processor screen.
- FIG. 5 is a visualization device provided by an embodiment of the present application, applied to a cloud server, and the device includes:
- a receiving unit 501 configured to receive a visualization request from a user equipment, where the visualization request is used to request visualization of target training data of a target deep learning model;
- an obtaining unit 502 configured to obtain target training data corresponding to the visualization request
- Determining unit 503, configured to preprocess the target training data to obtain visualization information
- the sending unit 504 is configured to send a visualization response to the user equipment, where the visualization response carries the visualization information.
- the visualization information includes at least one of the following categories: directed acyclic graph, three-dimensional histogram, call sequence diagram of various processes, feature map, two-dimensional heat map, and scalar polyline Figures; and/or
- the target training data includes at least one of the following: model performance change trend information, model loss trend information, model parameter distribution information, model processing intermediate results, model structure information, current progress information of model training, and information on different trainings for the same model. Comparison information, scheduling time information of various processes.
- the visualized information includes at least one of the following information:
- topology information of at least a part of the target deep learning model wherein at least a part of the target deep learning model includes: multiple modules and/or multiple operators of the target deep learning model;
- the topology information of the multiple modules includes at least one of the following:
- the identification information of the multiple modules the dependencies between the multiple modules, the data size of each module in the multiple modules, and the information of at least one operator included in each of the modules.
- the above program includes instructions for executing the following steps:
- the visualization request carries a training task identifier of the target deep learning model and index information of the target training data, where the index information includes: data type and data label;
- the above acquiring unit 502 is specifically used for:
- the target training data stored in the cloud storage is acquired based on the storage location information of the target training data.
- the above determining unit 503 is specifically configured to:
- the above determining unit 503 is specifically configured to:
- the target training data is parsed through a binary tree to obtain the visualization information.
- the receiving unit 501 and the sending unit 504 may be implemented by a communication interface
- the acquiring unit 502 and the determining unit 503 may be implemented by a processor.
- Embodiments of the present application further provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the electronic Some or all of the steps described by the device or cloud server.
- Embodiments of the present application further provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause the computer to execute the electronic method as described above. Some or all of the steps described by the device or cloud server.
- the computer program product may be a software installation package.
- the steps of the method or algorithm described in the embodiments of the present application may be implemented in a hardware manner, or may be implemented in a manner in which a processor executes software instructions.
- Software instructions can be composed of corresponding software modules, and software modules can be stored in random access memory (Random Access Memory, RAM), flash memory, read only memory (Read Only Memory, ROM), erasable programmable read only memory ( Erasable Programmable ROM, EPROM), Electrically Erasable Programmable Read-Only Memory (Electrically EPROM, EEPROM), registers, hard disk, removable hard disk, CD-ROM, or any other form of storage medium known in the art.
- An exemplary storage medium is coupled to the processor, such that the processor can read information from, and write information to, the storage medium.
- the storage medium can also be an integral part of the processor.
- the processor and storage medium may reside in an ASIC.
- the ASIC may reside in access network equipment, target network equipment or core network equipment.
- the processor and the storage medium may also exist in the access network device, the target network device or the core network device as discrete components.
- the functions described in the embodiments of the present application may be implemented in whole or in part by software, hardware, firmware, or any combination thereof.
- software it can be implemented in whole or in part in the form of a computer program product.
- the computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated.
- the computer may be a general purpose computer, special purpose computer, computer network, or other programmable device.
- the computer instructions may be stored in or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server or data center Transmission to another website site, computer, server or data center via wired (eg coaxial cable, optical fiber, Digital Subscriber Line, DSL) or wireless (eg infrared, wireless, microwave, etc.) means.
- the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes an integration of one or more available media.
- the available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, Digital Video Disc (DVD)), or semiconductor media (eg, Solid State Disk (SSD)) )Wait.
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Abstract
Description
Claims (20)
- 一种可视化方法,其特征在于,应用于用户设备,所述方法包括:A visualization method, characterized in that, applied to a user equipment, the method comprising:向云服务器发送可视化请求,所述可视化请求用于请求对目标深度学习模型的目标训练数据进行可视化;sending a visualization request to the cloud server, where the visualization request is used to request visualization of the target training data of the target deep learning model;接收所述云服务器发送的可视化响应,所述可视化响应携带有所述目标训练数据的可视化信息;receiving a visualization response sent by the cloud server, where the visualization response carries the visualization information of the target training data;显示所述可视化信息。The visualization information is displayed.
- 根据权利要求1所述的方法,其特征在于,所述可视化信息包括以下类别中的至少一种:有向无环图、三维直方图、多种进程的调用时序图、特征图、二维热力图以及标量折线图;和/或The method according to claim 1, wherein the visualization information includes at least one of the following categories: directed acyclic graph, three-dimensional histogram, calling sequence diagram of various processes, feature map, two-dimensional thermal graphs and scalar line graphs; and/or所述目标训练数据包括以下至少一种:模型性能变化趋势信息、模型损失趋势信息、模型参数分布信息、模型处理中间结果、模型结构信息、模型训练的当前进度信息、针对同一模型的不同训练的比较信息、多种进程的调度时间信息。The target training data includes at least one of the following: model performance change trend information, model loss trend information, model parameter distribution information, model processing intermediate results, model structure information, current progress information of model training, and information on different trainings for the same model. Comparison information, scheduling time information of various processes.
- 根据权利要求1或2所述的方法,所述可视化信息包括以下至少一种信息:The method according to claim 1 or 2, the visualization information includes at least one of the following information:所述目标深度学习模型的至少一部分的拓扑信息,其中,所述目标深度学习模型的至少一部分包括:所述目标深度学习模型的多个模块和/或多个算子;topology information of at least a part of the target deep learning model, wherein at least a part of the target deep learning model includes: multiple modules and/or multiple operators of the target deep learning model;所述目标深度学习模型所包括的至少一个算子中每个算子的资源占用信息。Resource occupation information of each operator in the at least one operator included in the target deep learning model.
- 根据权利要求3所述的方法,其特征在于,所述多个模块的拓扑信息包括以下至少一种:The method according to claim 3, wherein the topology information of the multiple modules includes at least one of the following:所述多个模块的标识信息、所述多个模块之间的依赖关系、所述多个模块中每个模块的数据量大小、每个所述模块包括的至少一个算子的信息。The identification information of the multiple modules, the dependencies between the multiple modules, the data size of each module in the multiple modules, and the information of at least one operator included in each of the modules.
- 根据权利要求3或4所述的方法,其特征在于,所述资源占用信息是所述云服务器基于所述算子的数据类型、所述算子的输入数据信息、所述算子的输出数据信息中的至少一种确定的。The method according to claim 3 or 4, wherein the resource occupation information is based on the data type of the operator, the input data information of the operator, and the output data of the operator by the cloud server. At least one of the information is determined.
- 根据权利要求1至5中任一项所述的方法,其特征在于,所述可视化请求携带所述目标深度学习模型的训练任务标识和所述目标训练数据的索引信息,其中,所述索引信息包括:数据类型和数据标签。The method according to any one of claims 1 to 5, wherein the visualization request carries a training task identifier of the target deep learning model and index information of the target training data, wherein the index information Includes: data types and data labels.
- 根据权利要求1-6中任一项所述的方法,其特征在于,所述显示所述可视化信息,包括:The method according to any one of claims 1-6, wherein the displaying the visualized information comprises:以折叠显示模式或展示显示模式显示所述可视化信息,其中,displaying the visual information in a collapsed display mode or a revealing display mode, wherein,在所述折叠显示模式中,以所述目标深度学习模型中的模块为最小显示单元,其中,所述模块包括至少一个算子;和/或In the folding display mode, a module in the target deep learning model is used as the minimum display unit, wherein the module includes at least one operator; and/or在所述展开显示模式中,以所述目标深度学习模型中的算子为最小显示单元。In the expanded display mode, the operator in the target deep learning model is used as the minimum display unit.
- 根据权利要求1-7任一项所述的方法,其特征在于,所述向云服务器发送可视化请求之前,所述方法还包括:The method according to any one of claims 1-7, wherein before the sending the visualization request to the cloud server, the method further comprises:通过数据上传线程向所述云服务器发送所述目标深度学习模型的中间训练数据,所述中间训练数据用于所述云服务器得到所述目标训练数据的索引信息和训练任务标识,以及基于所述训练任务标识将所述索引信息存储在索引数据库中,基于所述索引信息将所述目标训练数据存储到云存储中。The intermediate training data of the target deep learning model is sent to the cloud server through a data upload thread, and the intermediate training data is used by the cloud server to obtain the index information and training task identifier of the target training data, and based on the The training task identifier stores the index information in an index database, and stores the target training data in cloud storage based on the index information.
- 一种可视化方法,其特征在于,应用于云服务器,所述方法包括:A visualization method, characterized in that, applied to a cloud server, the method comprising:接收来自用户设备的可视化请求,所述可视化请求用于请求对目标深度学习模型的目标训练数据进行可视化;receiving a visualization request from the user equipment, where the visualization request is used to request visualization of the target training data of the target deep learning model;获取所述可视化请求对应的目标训练数据;obtaining target training data corresponding to the visualization request;对所述目标训练数据进行预处理,得到可视化信息;Preprocessing the target training data to obtain visualization information;向所述用户设备发送可视化响应,所述可视化响应携带所述可视化信息。A visualization response is sent to the user equipment, and the visualization response carries the visualization information.
- 根据权利要求9所述的方法,其特征在于,所述可视化信息包括以下类别中的至少一种:有向无环图、三维直方图、多种进程的调用时序图、特征图、二维热力图以及标量折线图;和/或The method according to claim 9, wherein the visualization information includes at least one of the following categories: directed acyclic graph, three-dimensional histogram, calling sequence diagram of various processes, feature map, two-dimensional thermal graphs and scalar line graphs; and/or所述目标训练数据包括以下至少一种:模型性能变化趋势信息、模型损失趋势信息、模型参数分布信息、模型处理中间结果、模型结构信息、模型训练的当前进度信息、针对同一模型的不同训练的比较信息、多种进程的调度时间信息。The target training data includes at least one of the following: model performance change trend information, model loss trend information, model parameter distribution information, model processing intermediate results, model structure information, current progress information of model training, and information on different trainings for the same model. Comparison information, scheduling time information of various processes.
- 根据权利要求9或10所述的方法,所述可视化信息包括以下至少一种信息:The method according to claim 9 or 10, the visualization information includes at least one of the following information:所述目标深度学习模型的至少一部分的拓扑信息,其中,所述目标深度学习模型的至少一部分包括:所述目标深度学习模型的多个模块和/或多个算子;topology information of at least a part of the target deep learning model, wherein at least a part of the target deep learning model includes: multiple modules and/or multiple operators of the target deep learning model;所述目标深度学习模型所包括的至少一个算子中每个算子的资源占用信息。Resource occupation information of each operator in the at least one operator included in the target deep learning model.
- 根据权利要求11所述的方法,其特征在于,所述多个模块的拓扑信息包括以下至 少一种:The method according to claim 11, wherein the topology information of the plurality of modules comprises at least one of the following:所述多个模块的标识信息、所述多个模块之间的依赖关系、所述多个模块中每个模块的数据量大小、每个所述模块包括的至少一个算子的信息。The identification information of the multiple modules, the dependencies between the multiple modules, the data size of each module in the multiple modules, and the information of at least one operator included in each of the modules.
- 根据权利要求9至12中任一项所述的方法,其特征在于,所述对所述目标训练数据进行预处理,得到可视化信息,包括:The method according to any one of claims 9 to 12, wherein the preprocessing of the target training data to obtain visualization information includes:基于所述目标深度学习模型中至少一个算子的数据类型、所述算子的输入数据信息、所述算子的输出数据信息中的至少一种,确定所述至少一个算子中每个算子的资源占用信息。Determine each operator in the at least one operator based on at least one of the data type of the at least one operator in the target deep learning model, the input data information of the operator, and the output data information of the operator Child resource occupancy information.
- 根据权利要求9至13中任一项所述的方法,其特征在于,所述可视化请求携带所述目标深度学习模型的训练任务标识、所述目标训练数据的索引信息,所述索引信息包括:数据类型和数据标签;The method according to any one of claims 9 to 13, wherein the visualization request carries a training task identifier of the target deep learning model and index information of the target training data, and the index information includes: data types and data labels;所述获取所述可视化请求对应的目标训练数据,包括:The acquiring target training data corresponding to the visualization request includes:基于所述可视化请求中携带的训练任务标识和索引信息查找索引数据库,以得到所述目标训练数据的存储位置信息;Search the index database based on the training task identifier and index information carried in the visualization request to obtain the storage location information of the target training data;基于所述目标训练数据的存储位置信息,获取云存储中存储的所述目标训练数据。The target training data stored in the cloud storage is acquired based on the storage location information of the target training data.
- 根据权利要求9至14中任一项所述的方法,其特征在于,所述对所述目标训练数据进行预处理,得到可视化信息,包括:The method according to any one of claims 9 to 14, wherein the preprocessing of the target training data to obtain visualization information includes:对所述目标训练数据进行至少一项预处理,得到预处理数据;Perform at least one preprocessing on the target training data to obtain preprocessing data;对所述预处理数据进行渲染处理,得到所述可视化信息。Perform rendering processing on the preprocessed data to obtain the visualization information.
- 根据权利要求9至15中任一项所述的方法,其特征在于,所述对所述目标训练数据进行预处理,得到可视化信息,包括:The method according to any one of claims 9 to 15, wherein the preprocessing of the target training data to obtain visualization information includes:在所述目标深度学习模型的训练任务在目标框架下执行的情况下,通过二叉树对所述目标训练数据进行解析处理,得到所述可视化信息。When the training task of the target deep learning model is executed under the target framework, the target training data is parsed through a binary tree to obtain the visualization information.
- 一种可视化装置,其特征在于,应用于用户设备,所述装置包括:A visualization device, characterized in that, applied to user equipment, the device comprising:发送单元,用于向云服务器发送可视化请求,所述可视化请求用于请求对目标深度学习模型的目标训练数据进行可视化;a sending unit, configured to send a visualization request to the cloud server, where the visualization request is used to request visualization of the target training data of the target deep learning model;接收单元,用于接收所述云服务器发送的可视化响应,所述可视化响应携带有所述目标训练数据的可视化信息;a receiving unit, configured to receive a visualization response sent by the cloud server, where the visualization response carries the visualization information of the target training data;显示单元,用于显示所述可视化信息。a display unit for displaying the visual information.
- 一种可视化装置,其特征在于,应用于云服务器,所述装置包括:A visualization device, characterized in that, applied to a cloud server, the device comprising:接收单元,用于接收来自用户设备的可视化请求,所述可视化请求用于请求对目标深度学习模型的目标训练数据进行可视化;a receiving unit, configured to receive a visualization request from the user equipment, where the visualization request is used to request visualization of the target training data of the target deep learning model;获取单元,用于获取所述可视化请求对应的目标训练数据;an obtaining unit for obtaining target training data corresponding to the visualization request;确定单元,用于对所述目标训练数据进行预处理,得到可视化信息;a determining unit for preprocessing the target training data to obtain visualization information;发送单元,用于向所述用户设备发送可视化响应,所述可视化响应携带所述可视化信息。A sending unit, configured to send a visualization response to the user equipment, where the visualization response carries the visualization information.
- 一种计算机设备,其特征在于,所述用户设备包括处理器、存储器、通信接口,以及一个或多个程序,所述一个或多个程序被存储在所述存储器中,并且被配置由所述处理器执行,所述程序包括用于执行如权利要求1-8或9-16任一项所述的方法中的步骤的指令。A computer device, characterized in that the user equipment includes a processor, a memory, a communication interface, and one or more programs, the one or more programs being stored in the memory and configured by the The processor executes the program comprising instructions for performing the steps in the method of any of claims 1-8 or 9-16.
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质存储有计算机程序,其中,所述计算机程序被处理执行如权利要求1-8或9-16任意一项所述的方法。A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein the computer program is processed to execute the method according to any one of claims 1-8 or 9-16 .
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---|---|---|---|---|
CN111797294A (en) * | 2020-07-09 | 2020-10-20 | 上海商汤智能科技有限公司 | Visualization method and related equipment |
CN112199345A (en) * | 2020-10-28 | 2021-01-08 | 上海大学 | Deep learning training real-time visualization method and device |
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CN113326113B (en) * | 2021-05-25 | 2022-11-22 | 北京市商汤科技开发有限公司 | Task processing method and device, electronic equipment and storage medium |
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CN114357253A (en) * | 2021-12-27 | 2022-04-15 | 上海商汤科技开发有限公司 | Data visualization method and device, electronic equipment and storage medium |
WO2024112075A1 (en) * | 2022-11-23 | 2024-05-30 | 삼성전자주식회사 | Electronic device for controlling function of process corresponding to one or more messenger data sets, and method thereof |
CN116360759A (en) * | 2023-03-10 | 2023-06-30 | 青软创新科技集团股份有限公司 | Visual system and method of artificial intelligence algorithm |
CN117592517A (en) * | 2023-11-02 | 2024-02-23 | 新疆新华水电投资股份有限公司 | Model training method and device |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2012012194A2 (en) * | 2010-07-21 | 2012-01-26 | Microsoft Corporation | Smart defaults for data visualizations |
CN104615735A (en) * | 2015-02-11 | 2015-05-13 | 中科九度(北京)空间信息技术有限责任公司 | Space-time information visualizing method based on geographic information space system |
CN109241141A (en) * | 2018-09-04 | 2019-01-18 | 北京百度网讯科技有限公司 | The training data treating method and apparatus of deep learning |
CN111797294A (en) * | 2020-07-09 | 2020-10-20 | 上海商汤智能科技有限公司 | Visualization method and related equipment |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR101719278B1 (en) * | 2015-04-14 | 2017-04-04 | (주)한국플랫폼서비스기술 | Deep learnig framework and image recognition method for content-based visual image recognition |
CN111079892A (en) * | 2019-10-30 | 2020-04-28 | 华为技术有限公司 | Deep learning model training method, device and system |
CN111190805A (en) * | 2019-12-29 | 2020-05-22 | 广东浪潮大数据研究有限公司 | Training log visualization system, method and equipment of deep learning training platform |
-
2020
- 2020-07-09 CN CN202010656553.8A patent/CN111797294A/en active Pending
-
2021
- 2021-03-23 KR KR1020217039065A patent/KR20220011134A/en unknown
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- 2021-03-23 JP JP2021570729A patent/JP2022543180A/en not_active Withdrawn
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2012012194A2 (en) * | 2010-07-21 | 2012-01-26 | Microsoft Corporation | Smart defaults for data visualizations |
CN104615735A (en) * | 2015-02-11 | 2015-05-13 | 中科九度(北京)空间信息技术有限责任公司 | Space-time information visualizing method based on geographic information space system |
CN109241141A (en) * | 2018-09-04 | 2019-01-18 | 北京百度网讯科技有限公司 | The training data treating method and apparatus of deep learning |
CN111797294A (en) * | 2020-07-09 | 2020-10-20 | 上海商汤智能科技有限公司 | Visualization method and related equipment |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114707654A (en) * | 2022-06-06 | 2022-07-05 | 浙江大学 | Algorithm training reasoning performance visualization method and device based on artificial intelligence framework |
CN114707654B (en) * | 2022-06-06 | 2022-08-23 | 浙江大学 | Algorithm training reasoning performance visualization method and device based on artificial intelligence framework |
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KR20220011134A (en) | 2022-01-27 |
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