Deterministic Training Pipeline for Neural Network Reproducibility
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Solution Overview
Problem
Machine learning models, particularly neural networks, face challenges in deterministic training due to non-reproducible data access and storage issues, especially on hard disk drives, which affects training quality and stability, especially for large-scale models.
Innovation Solution
Implementing a deterministic training system that ensures training data is accessed in a fixed and reproducible order across various storage mediums, including hard disk drives, using a deterministic dataset pipeline that indexes and processes training examples consistently, allowing for selective skipping of problematic examples and resuming training from checkpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If training data is stored on hard disk drive, then storage capacity and durability are improved, but data access determinism deteriorates due to mechanical nature of rotating disks and moving heads
Solution Approach 1:
The patent applies preliminary action by pre-loading training data from hard disk drive into memory before training begins. This ensures that all training examples are available in fast-access memory, eliminating mechanical access delays during training while maintaining the ability to store large datasets on HDD. The system deterministically loads data in a predetermined order into memory, ensuring reproducible training sequences.
2Adaptability or versatility
If conventional non-deterministic training process is used, then training flexibility is improved, but training reproducibility deteriorates as different batches of training inputs may be fed in different orders
Solution Approach 1:
The patent applies dynamics by implementing a hybrid approach where the data loading sequence is dynamically controlled to be deterministic. A predetermined sequence is established that adapts to the specific training run, ensuring that the same sequence is consistently followed. This dynamic control mechanism allows the system to maintain flexibility in handling different training scenarios while ensuring reproducible data access patterns through systematic sequence management.
3Speed
If data is accessed without predetermined indexing, then data access speed may be improved through parallel access, but training determinism deteriorates due to non-reproducible retrieval order
Solution Approach 1:
The patent introduces an intermediary component - a deterministic data loading system with predetermined indexing - that mediates between the training algorithm and the data storage. This intermediary maintains indexes that map training examples to their predetermined load order, ensuring that data is retrieved in a consistent sequence. The intermediary layer translates parallel access capabilities into deterministic sequential loading, reconciling speed and determinism.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model using a deterministic data pipeline. One of the methods may include receiving a first request to generate a deterministic training dataset: transforming raw training examples obtained from the raw data source into pre-processed training examples; assigning a unique index to each pre-processed training example; and caching the pre-processed training examples into the cache directory specified in the received first request; receiving a second request to use the deterministic training dataset to train a machine learning model, the second request specifying a start index; and in response to receiving the second request: reading, from the cache directory, the pre-processed training examples that have indices beginning from the start index; and providing the read training examples in an order of the assigned indices for use in training the machine learning model.