Dynamic Worker Scaling for ML Training Convergence
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Solution Overview
Problem
Existing distributed machine learning frameworks, such as CoCoA, face challenges in scaling out the number of workers efficiently due to decreased convergence rates and increased communication overhead, leading to suboptimal resource utilization and longer training times.
Innovation Solution
A dynamic framework that adjusts the number of workers based on the temporal evolution of the duality-gap, scaling in from a larger number of workers to a smaller one when a deterioration in convergence rate is detected, using a scale-in policy that compares short-term and long-term slopes to determine the optimal moment for adjustment, thereby optimizing resource usage and reducing training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of workers is increased to speed up training, then productivity is improved, but convergence rate deteriorates and communication overhead increases
Solution Approach 1:
The patent implements dynamic scaling of worker numbers based on real-time monitoring of convergence metrics. The system transitions from static worker allocation to dynamic adjustment, scaling in when convergence deterioration is detected and scaling out when convergence improves, thereby adapting the parallelization degree to the current training state
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring convergence metrics (such as duality-gap evolution) and using this information to adjust the number of workers. The feedback loop compares short-term convergence trends against long-term trends to detect deterioration and trigger scaling-in actions
2Duration of action of moving object
If the number of workers is increased to reduce training time, then duration of action is reduced, but resource utilization becomes suboptimal
Solution Approach 1:
The system dynamically adjusts resource allocation (number of workers) throughout the training process rather than maintaining a fixed allocation. This allows optimal resource utilization at each training stage, using more workers when convergence is healthy and fewer workers when convergence deteriorates, thereby improving overall resource efficiency
Solution Approach 2:
The patent changes the parameter of worker count based on training progression and convergence behavior. By modifying this key parameter dynamically, the system adapts resource utilization to match the actual training needs at different stages, avoiding both over-provisioning and under-provisioning
3Device complexity
If parallel training is maintained with fixed worker count, then device complexity is reduced, but convergence rate deteriorates over time
Solution Approach 1:
The training framework performs self-diagnosis by monitoring its own convergence metrics and automatically adjusts the number of workers in response to detected deterioration. This self-service capability allows the system to maintain optimal convergence rates without requiring external intervention or complex manual tuning
Data Source
AI summary
Parallel training of a machine learning model on a computerized system may be provided. Computing tasks can be assigned to multiple workers of a system. A method may include accessing training data. A parallel training of the machine learning model can be started based on the accessed training data, so as for the training to be distributed through a first number K of workers, K>1. Responsive to detecting a change in a temporal evolution of a quantity indicative of a convergence rate of the parallel training (e.g., where said change reflects a deterioration of the convergence rate), the parallel training of the machine learning model is scaled-in, so as for the parallel training to be subsequently distributed through a second number K′ of workers, where K>K′≥1. Related computerized systems and computer program products may be provided.