Cloud based machine learning
AssigneeSnap Inc.
InventorsEric Buehl, Jordan Hurwitz, Sergey Tulyakov, Shubham Vij
Life of this patent
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Abstract
Disclosed are various embodiments for implementing computational tasks in a cloud environment in one or more operating system level virtualized containers. A parameter file can specify different parameters including hardware parameters, library parameters, user code parameters, and job parameters (e.g., sets of hyperparameters). The parameter file can be converted via a mapping and implemented in a cloud-based container platform.
Claims20
A method comprising:
- receiving, using one more processors of a machine, a request to initiate one or more containers to run on a container platform, the request identifying a machine learning scheme parameter file that specifies model data for a machine learning model, platform parameters, and job-specific configurations for the one or more containers;
- converting the machine learning scheme parameter file into a configuration file using a mapping template, the configuration file comprising the platform parameters and job-specific configurations native to the container platform;
- initiating the one or more containers on the container platform with the configuration file by translating the model data into platform parameters native to the container platform;
- and
- storing output data generated by the one or more containers.
A system comprising:
- one or more processors of the system;
- and
- a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
- receiving, using the one more processors, a request to initiate one or more containers to run on a container platform, the request identifying a machine learning scheme parameter file that specifies model data for a machine learning model, platform parameters, and job-specific configurations for the one or more containers;
- converting the machine learning scheme parameter file into a configuration file using a mapping template, the configuration file comprising the platform parameters and job-specific configurations native to the container platform;
- initiating the one or more containers on the container platform with the configuration file by translating the model data into platform parameters native to the container platform;
- and
- storing output data generated by the one or more containers.
A non-transitory machine-readable storage device embodying instructions that, when executed by a device, cause the device to perform operations comprising:
- receiving, using one more processors of a machine, a request to initiate one or more containers to run on a container platform, the request identifying a machine learning scheme parameter file that specifies model data for a machine learning model, platform parameters, and job-specific configurations for the one or more containers;
- converting the machine learning scheme parameter file into a configuration file using a mapping template, the configuration file comprising the platform parameters and job-specific configurations native to the container platform;
- initiating the one or more containers on the container platform with the configuration file by translating the model data into platform parameters native to the container platform;
- and
- storing output data generated by the one or more containers, wherein the output data includes metrics indicative of the execution of the containers according to the job-specific configurations.
Dependencies are inferred from the claim text.