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US 2019/0318240 A1Published application
Training machine learning models in distributed computing systems
AssigneeKazuhm, Inc.
InventorsRounak Prasad KULKARNI, Armin KADIYAN, Tim O'NEAL
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Abstract
Certain aspects of the present disclosure provide methods and systems for training a machine learning model, such as a neural network or deep learning model, in a distributed computing system. In some embodiments, aspects of the machine learning model are trained within containers distributed amongst nodes in the distributed computing environment.
Claims20
A method for training a machine learning model in a distributed computing system:
- receiving a model training request;
- receiving a training data set;
- determining a processing node available in a distributed computing system;
- receiving static status information regarding the processing node;
- causing a first container to be installed at the processing node based on the static status information, the first container being configured with a model training application;
- causing a second container to be installed at the processing node based on the static status information, the second container being configured with the model training application;
- assigning a first layer of a model to be trained by the model training application in the first container;
- assigning a second layer of the model to be trained by the model training application in the second container;
- receiving parameter data from the model training application in the first container, the model training application in the second container, and the model training application in the third container;
- and
- calculating a model parameter based on the parameter data.
8 more elements
An apparatus for managing deployment of distributed computing resources, comprising:
- a memory comprising computer-executable instructions;
- and
- a processor in data communication with the memory and configured to execute the computer-executable instructions and cause the apparatus to perform a method for training a machine learning model in a distributed computing system, the method comprising:
- receiving a model training request;
- receiving a training data set;
- determining a processing node available in a distributed computing system;
- receiving static status information regarding the processing node;
- causing a first container to be installed at the processing node based on the static status information, the first container being configured with a model training application;
- causing a second container to be installed at the processing node based on the static status information, the second container being configured with the model training application;
- assigning a first layer of a model to be trained by the model training application in the first container;
- assigning a second layer of the model to be trained by the model training application in the second container;
- receiving parameter data from the model training application in the first container and the model training application in the second container;
- and
- calculating a model parameter based on the parameter data.
11 more elements
A non-transitory computer-readable medium comprising instructions for performing a method for training a machine learning model in a distributed computing system, the method comprising:
- receiving a model training request;
- receiving a training data set;
- determining a processing node available in a distributed computing system;
- receiving static status information regarding the processing node;
- causing a first container to be installed at the processing node based on the static status information, the first container being configured with a model training application;
- causing a second container to be installed at the processing node based on the static status information, the second container being configured with the model training application;
- assigning a first layer of a model to be trained by the model training application in the first container;
- assigning a second layer of the model to be trained by the model training application in the second container;
- receiving parameter data from the model training application in the first container, the model training application in the second container, and the model training application in the third container;
- and
- calculating a model parameter based on the parameter data.
8 more elements
Dependencies are inferred from the claim text.