Machine Learning Code Tuning Service with Predictive Scheduling
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Solution Overview
Problem
The process of tuning machine learning models for optimal performance in specific execution environments is time-consuming and resource-intensive, often requiring substantial expertise and taking days or weeks, even with state-of-the-art automated frameworks.
Innovation Solution
A network-accessible machine learning code tuning service conducts distributed tuning experiments across various hardware and software environments, storing results in a database and using a performance prediction model to quickly provide tuned code, reducing the need for clients to perform extensive tuning themselves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tuning frameworks are used to optimize machine learning model performance, then model execution efficiency is improved, but tuning time and resource consumption increase to days or weeks
Solution Approach 1:
The system pre-generates and stores tuning results for multiple machine learning operators across different execution environments in a database before actual deployment. When a model needs tuning, the system queries this pre-populated database rather than performing new tuning experiments, dramatically reducing the time from days/weeks to minutes while maintaining optimized performance.
2Speed
If extensive parameter tuning is performed to achieve optimal performance, then model execution speed is improved, but computational resources and expertise requirements increase substantially
Solution Approach 1:
Instead of performing complex tuning experiments directly on the target system, the system creates copies of tuning results from the database and applies them to the deployment environment. This copying approach eliminates the need for users to have deep expertise or allocate substantial computational resources for tuning, while still achieving optimal execution speed.
Solution Approach 2:
The system introduces a database as an intermediary layer between the tuning process and the deployment environment. This database stores pre-computed tuning parameters and acts as a mediator that translates complex tuning requirements into ready-to-apply configurations, reducing both the complexity and resource requirements for the actual deployment system.
3Adaptability or versatility
If comprehensive tuning experiments are conducted across multiple execution environments, then adaptability to different hardware is improved, but time and resource consumption increase
Solution Approach 1:
The system performs comprehensive tuning experiments across multiple execution environments in advance and stores the results in a database. This preliminary action allows the system to maintain high adaptability to different hardware environments while achieving fast deployment, as the adaptability work is completed beforehand rather than during actual model deployment.
Data Source
AI summary
Tuning result records indicating tuned schedules for machine learning tuning tasks are stored at a data store accessed from a tuning service. A given schedule indicates at least an order in which sub-operations of a tuning task are to be executed. In response to determining that the data store does not include a result record whose tuning task meets a similarity criterion to a tuning task determined from a tuning request, performance predictions for a set of candidate schedules are obtained using a performance prediction model, without running code which executes the candidate schedules. In a response to the tuning request, executable code corresponding to a preferred schedule identified from the candidate schedules using the performance predictions is included.