Distributed Training Orchestration With Accuracy-Guided Resource Scheduling
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Solution Overview
Problem
Existing deep learning model training on distributed computing platforms is inefficient due to the need for manual system scaling, resource management, and parallelization, which hinders user productivity and increases time to achieve sufficient accuracy.
Innovation Solution
A method for orchestrating deep learning model training on a distributed computing platform that automates the process of generating workloads, allocating resources, and scheduling concurrent executions, while providing real-time accuracy feedback to dynamically adjust resources based on user input or automated thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual system scaling and resource management is used for deep learning model training, then resource allocation can be controlled, but user productivity decreases and time to achieve sufficient accuracy increases
Solution Approach 1:
The system enables self-service through automated workload generation, resource allocation, and scheduling. The orchestrator automatically manages the training process without requiring manual system scaling or resource management interventions, allowing users to focus solely on model development while the system handles infrastructure complexities autonomously
Solution Approach 2:
The system implements feedback mechanisms by monitoring training progress and accuracy in real-time, then dynamically adjusting resource allocation and workload scheduling based on performance metrics. This closed-loop control optimizes the training process and reduces time to achieve sufficient accuracy by adapting to actual model performance
2Productivity
If automated workload generation and resource allocation is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The system segments the complex training orchestration into distinct functional modules: workload generation component, resource allocation component, scheduling component, and monitoring component. Each module handles specific aspects of the training process independently, making the overall complex system more manageable and maintainable while enabling automated productivity improvements
Solution Approach 2:
The orchestrator acts as an intermediary layer between users and the underlying computing infrastructure. It abstracts the complexity of resource management and scheduling from users, providing simplified interfaces for workload submission while handling the intricate details of resource allocation, parallelization, and coordination behind the scenes
3Use of energy by moving object
If concurrent execution of multiple workloads is scheduled, then resource utilization optimizes, but coordination and management becomes more complex
Solution Approach 1:
The system performs preliminary actions by pre-defining workload dependencies, resource requirements, and execution priorities before concurrent workload submission. The orchestrator maintains a queue of pending workloads with predetermined characteristics, enabling efficient batch scheduling that optimizes resource utilization while managing coordination complexity through advance planning
Solution Approach 2:
The scheduling system implements dynamics by continuously adapting workload assignments based on real-time resource availability, training progress, and performance metrics. It dynamically adjusts the concurrent execution strategy to optimize resource utilization while managing complexity through flexible, adaptive decision-making rather than rigid predetermined schedules
Data Source
AI summary
A method includes: partitioning a dataset into data groups; assigning the data groups to a set of workers; generating a first set of workloads including a first workload for training a first model configuration according to the set of data groups; allocating subclusters of resources of the set of workers to the first set of workloads for a first epoch; scheduling concurrent execution of the first set of workloads at the set of workers for the first epoch; calculating a first accuracy value for the first model configuration for the first epoch; in response to the first accuracy value failing to exceed a threshold accuracy value, generating a second set of workloads excluding the first workload; allocating subclusters of resources to the second set of workloads for a second epoch; and scheduling concurrent execution of the second set of workloads at the set of workers for the second epoch.