Cloud Container System for ML Hyperparameter Optimization
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Solution Overview
Problem
Setting up cloud computing tasks for hyperparameter optimization of machine learning models is time-consuming and complex, requiring intricate interactions between hardware, libraries, and user code, which can be prohibitively expensive in a cloud environment.
Innovation Solution
A cloud container system is implemented to streamline machine learning tasks by receiving a parameter file that specifies computational tasks, hardware resources, code libraries, user code, and job configurations, converting this information into a native deployment file for a virtualization environment like Kubernetes, and managing container execution environments to provision resources and run jobs efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud computing tasks are set up manually with intricate hardware, library, and code interactions, then computational power and functionality are improved, but setup time and operational complexity increase significantly
Solution Approach 1:
The patent introduces a cloud container system as an intermediary layer between the user and the complex cloud infrastructure. This system automatically manages the intricate interactions between hardware, libraries, and code by containerizing the machine learning environment, thereby providing full computational functionality while eliminating manual setup complexity for users
Solution Approach 2:
The cloud container system implements self-service by automatically provisioning and configuring all necessary computational resources, dependencies, and environments. The system autonomously handles hardware allocation, library installation, and code execution without requiring users to manually manage these complex interactions, thus maintaining high computational power while reducing operational burden
2Adaptability or versatility
If cloud computing tasks are set up manually with detailed configurations, then task functionality is improved, but setup time becomes prohibitively long
Solution Approach 1:
The patent applies preliminary action by pre-configuring and containerizing all necessary machine learning environments, libraries, and dependencies before user deployment. The cloud container system prepares standardized, pre-tested computational environments that can be instantly deployed, thereby maintaining full task functionality while reducing setup time from hours or days to minutes or seconds
Solution Approach 2:
The system enables rapid deployment by changing the parameter of environment configuration from manual, step-by-step setup to automated, pre-packaged container images. Users can specify high-level parameters (such as model type and dataset) while the system automatically handles all lower-level configuration parameters, thus preserving task functionality while dramatically reducing setup time
3Manufacturing precision
If manual setup procedures are used for cloud machine learning tasks, then configuration precision is improved, but operational ease deteriorates
Solution Approach 1:
The cloud container system acts as an intermediary that encapsulates complex configuration precision requirements within standardized container images. Users interact with simplified, high-level interfaces while the intermediary automatically handles precise configuration details, thereby maintaining configuration precision while significantly improving operational ease
Solution Approach 2:
The patent uses copying by distributing pre-configured, validated container images that contain exact, precision-configured environments. Instead of requiring users to manually recreate precise configurations, the system copies proven, optimized container images to the cloud infrastructure, thereby preserving configuration precision while making operations trivial for users
4Reliability
If intricate hardware and code interactions are managed manually, then computational task accuracy is improved, but computational cost increases prohibitively
Solution Approach 1:
The cloud container system implements self-service by automatically optimizing resource allocation and management. The system autonomously manages hardware interactions, library dependencies, and code execution environments to ensure task accuracy while minimizing resource waste and computational costs through efficient, automated infrastructure management
Solution Approach 2:
The system changes the operational parameter from manual, inefficient resource management to automated, optimization-driven resource allocation. The containerized environment enables precise control over computational parameters (such as GPU utilization, memory allocation, and parallel processing) to maintain task accuracy while reducing overall computational costs through systematic optimization
Data Source
AI summary
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.