# US 12,223,402 B2 · Cloud based machine learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,223,402 B2 - Document ID: US12223402B2 - Status: Granted - Kind: B2 - Application number: 17/741,614 - Filed: 2022-05-11 - Published: 2022-12-22 - Granted: 2025-02-11 - Earliest claimed: 2018-09-28 - Assignees: Snap Inc. - Applicants: Snap Inc. - Inventors: Eric Buehl; Jordan Hurwitz; Sergey Tulyakov; Shubham Vij - Web page: https://patentgrep.com/patent/US12223402B2 ## CPC - G06N 20/00: Machine learning - G06F 9/44505: {Configuring for program initiating, e.g. using registry, configuration files} - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## 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. ## Claims ### Claim 1 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. ### Claim 2 (depends on 1) The method of claim 1, wherein converting further comprises: using the mapping template to convert the machine learning scheme parameter file into the configuration file by mapping the platform parameters of the machine learning scheme parameter file into commands native to the container platform. ### Claim 3 (depends on 1) The method of claim 1, wherein the machine learning scheme parameter file includes a detailed mapping of computational resources to specific machine learning tasks within the one or more containers, and wherein the initiating includes allocating the computational resources to the containers in accordance with the detailed mapping. ### Claim 4 (depends on 1) The method of claim 1, wherein the machine learning scheme parameter file specifies a container image for the machine learning model, wherein the container platform is configured to manage the one or more containers. ### Claim 5 (depends on 4) The method of claim 4, wherein the initiation is dynamically configured based on the platform parameters and job-specific configurations derived from the machine learning scheme parameter file. ### Claim 6 (depends on 4) The method of claim 4, wherein the model data comprises a network address of code that is executable in the one or more containers using the container image, the network address of the code located on a network server. ### Claim 7 (depends on 4) The method of claim 4, wherein the model data comprises machine learning model configuration data that specifies a set of configuration parameters of the machine learning model, wherein the machine learning model configuration data specifies a plurality of container jobs. ### Claim 8 (depends on 7) The method of claim 7, wherein each container job comprises a differently configured set of configuration parameters of the machine learning model. ### Claim 9 (depends on 7) The method of claim 7, wherein the initiating the one or more containers comprises initiating a plurality of containers using the same machine learning container image. ### Claim 10 (depends on 4) The method of claim 4, wherein the model data comprises hardware resource parameter data specifying one or more processor units and memory units, wherein the hardware resource parameter data specifies hardware resources for each container job. ### Claim 11 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. ### Claim 12 (depends on 11) The system of claim 11, wherein converting further comprises: using the mapping template to convert the machine learning scheme parameter file into the configuration file. ### Claim 13 (depends on 11) The system of claim 11, wherein converting further comprises: mapping the platform parameters of the machine learning scheme parameter file into commands native to the container platform. ### Claim 14 (depends on 11) The system of claim 11, wherein the machine learning scheme parameter file specifies a container image for the machine learning model, wherein the container platform is configured to manage the one or more containers. ### Claim 15 (depends on 14) The system of claim 14, wherein the initiation is dynamically configured based on the platform parameters and job-specific configurations derived from the machine learning scheme parameter file. ### Claim 16 (depends on 14) The system of claim 14, wherein the model data comprises a network address of code that is executable in the one or more containers using the container image, the network address of the code located on a network server. ### Claim 17 (depends on 14) The system of claim 14, wherein the model data comprises machine learning model configuration data that specifies a set of configuration parameters of the machine learning model, wherein the machine learning model configuration data specifies a plurality of container jobs. ### Claim 18 (depends on 17) The system of claim 17, wherein each container job comprises a differently configured set of configuration parameters of the machine learning model. ### Claim 19 (depends on 17) The system of claim 17, wherein the initiating the one or more containers comprises initiating a plurality of containers using the same machine learning container image. ### Claim 20 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.