Network-accessible machine learning model training and hosting system
AssigneeAmazon Technologies, Inc.
InventorsThomas Albert Faulhaber, Jr., Stefano Stefani, Owen Thomas
Life of this patent
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Abstract
A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.
Claims19
A system comprising:
- a training model data store;
- and
- a first computing device to host a first virtual machine instance, the first computing device comprising computer-executable instructions that, when executed, cause the first computing device to:
- obtain, from a user device, a training request, wherein the training request comprises an indication of a storage location of a container image, an indicator of training data, and one or more first hyperparameter values;
- initialize a first machine learning (ML) training container in the first virtual machine instance, wherein the first ML training container is formed from the container image;
- initialize a second ML training container in the first virtual machine instance, wherein the second ML training container is formed from the container image;
- cause the first virtual machine instance to execute code stored within the ML training container according to the one or more first hyperparameter values, wherein execution of the code causes the first virtual machine instance to train a machine learning model using the training data and to generate model data that represents characteristics of the machine learning model;
- and
- cause the first virtual machine instance to execute second code stored within the second ML training container according to the one or more first hyperparameter values in parallel with the execution of the code stored within the first ML training container.
A computer-implemented method comprising:
- receiving, from a user device over a network, a training request, wherein the training request comprises an indication of a storage location of a container image and an indicator of training data;
- initializing a first machine learning (ML) training container in a first virtual machine instance hosted by a first computing device, wherein the first ML training container is formed from the container image;
- and
- initializing a second ML training container in the first virtual machine instance, wherein the second ML training is formed from the container image;
- causing the first virtual machine instance to execute code stored within the ML training container, wherein execution of the code causes the first virtual machine instance to train a machine learning model using the training data and to generate model data that represents characteristics of the machine learning model;
- and
- causing the first virtual machine instance to execute second code stored within the second ML training container according to one or more first hyperparameter values in parallel with the execution of the code stored within the ML training container.
One or more non-transitory, computer-readable storage media storing computer-executable instructions, which if performed by one or more processors, cause the one or more processors to at least:
- obtain, from a user device, an indication of a storage location of a container image and an indicator of training data;
- initialize a training container in a first virtual machine instance using the container image;
- initialize a second training container in the first virtual machine instance, wherein the second training container is formed from the container image;
- cause the first virtual machine instance to execute code stored within the training container, wherein execution of the code causes the first virtual machine instance to train a machine learning model using the training data and to generate model data that represents characteristics of the machine learning model;
- and
- cause the first virtual machine instance to execute second code stored within the second training container according to one or more first hyperparameter values in parallel with the execution of the code stored within the training container.
Dependencies are inferred from the claim text.