Parallel Container Model Training for Accuracy and Speed
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Solution Overview
Problem
Training multiple machine learning models with different configurations is resource-intensive, consuming processing power, memory, and time, which hinders efficient model creation and accuracy.
Innovation Solution
Implementing a system where a master job in one container performs pre-processing and orchestrates the parallel training of child jobs in separate containers, such as GPU-based containers, to streamline and accelerate model creation by aggregating or selecting the final model based on metrics like the f1 score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple models are trained with different configurations, then model accuracy is improved, but processing resources and time are excessively consumed
Solution Approach 1:
The training system is segmented into a master job that coordinates training and multiple child jobs that execute training in parallel. Each child job trains a separate model configuration independently, allowing simultaneous execution of multiple training processes without resource conflicts, thereby improving accuracy while reducing total training time
Solution Approach 2:
The system transitions from sequential training in a single process to parallel training across multiple containerized processes. By adding the dimension of parallel execution through containers, the system can train multiple models simultaneously, resolving the contradiction between comprehensive model training and time consumption
2Measurement precision
If multiple models are trained with different configurations, then model accuracy is improved, but processing power and memory resources are excessively consumed
Solution Approach 1:
The system divides training workloads into separate child jobs, each handling specific model configurations. This segmentation allows processing power to be distributed across multiple independent training processes rather than concentrated in a single resource-intensive process, enabling comprehensive model training while managing processing power demands
Solution Approach 2:
The master job acts as an intermediary that manages resource allocation and coordination between multiple child jobs. It monitors training progress, handles data distribution, and aggregates results, thereby optimizing processing power utilization across the parallel training ensemble while maintaining the ability to train multiple accurate models
Data Source
AI summary
Embodiments relate to systems and methods that create a final model by parallel training of models executed within separate containers. A master job present within one container, performs pre-processing (e.g., noise reduction; duplicate removal) of incoming data. The master job orchestrates the training of individual models by child jobs that are executed in parallel within respective separate containers. After checking the status of completion of the child jobs (e.g., via HTTP or by reading local progress files) the master job references the trained models in order to determine a final model. This final model determination may comprise aggregating the trained models, or selecting one model based upon a metric (such as a f1 score). Parallel training of models by child jobs executed within separate containers, streamlines and accelerates model creation. Particular embodiments may be suited to train a model that identifies unique entities from incoming data including names and addresses.