Bibliographic record
Abstract
Data preprocessing consisting of tasks like sample resizing, cropping, and filtering, is a crucial step in machine learning (ML) workflows. Even though the preprocessing step is largely ignored by work that focuses on optimizing training algorithms, in practice for many workloads preprocessing and training are pipelined. Popular ML frameworks like PyTorch use data loaders to feed data into model training. If the pipeline between preprocessing and training is not done carefully, it can cause significant waiting times on the GPU side. To address this limitation, we introduce SpeedyLoader, a system that overlaps preprocessing and training by leveraging asynchronous data preprocessing and avoiding head-of-line blocking. SpeedyLoader incorporates dedicated data loading threads, which organize preprocessed samples into queues based on their predicted processing times. Concurrently, GPUs fetch samples from these queues, ensuring training is not impeded by preprocessing completion. Compared to the default PyTorch DataLoader, SpeedyLoader reduces training time by up to 30% and increases GPU usage by 4.3×, all while maintaining a consistent evaluation accuracy of 91%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".