Greenlight: Highlighting TensorFlow APIs Energy Footprint
Bibliographic record
Abstract
Deep learning (DL) models are being widely deployed in real-world applications, but their usage remains computationally intensive and energy-hungry. While prior work has examined model-level energy usage, the energy footprint of the DL frameworks, such as TensorFlow and PyTorch, used to train and build these models, has not been thoroughly studied. We present Greenlight, a large-scale dataset containing fine-grained energy profiling information of 1284 TensorFlow API calls. We developed a command line tool called CodeGreen to curate such a dataset. CodeGreen is based on our previously proposed framework FECoM, which employs static analysis and code instrumentation to isolate invocations of Tensor-Flow operations and measure their energy consumption precisely. By executing API calls on representative workloads and measuring the consumed energy, we construct detailed energy profiles for the APIS. Several factors, such as input data size and the type of operation, significantly impact energy footprints. Greenlight provides a ground-truth dataset capturing energy consumption along with relevant factors such as input parameter size to take the first step towards optimization of energy-intensive TensorFlow code. The Greenlight dataset opens up new research directions such as predicting API energy consumption, automated optimization, modeling efficiency trade-offs, and empirical studies into energy-aware DL system design.
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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 | 0.000 |
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".