DLBench: An Experimental Evaluation of Deep Learning Frameworks
Bibliographic record
Abstract
Recently, deep learning has become one of the most disruptive trends in the technology world. Deep learning techniques are increasingly achieving significant results in different domains such as speech recognition, image recognition and natural language processing. In general, there are various reasons behind the increasing popularity of deep learning techniques. These reasons include increasing data availability, the increasing availability of powerful hardware and computing resources in addition to the increasing availability of deep learning frameworks. In practice, the increasing popularity of deep learning frameworks calls for benchmarking studies that can effectively evaluate the performance characteristics of these systems. In this paper, we present an extensive experimental study of six popular deep learning frameworks, namely TensorFlow, MXNet, PyTorch, Theano, Chainer, and Keras. Our experimental evaluation covers different aspects for its comparison including accuracy, speed and resource consumption. Our experiments have been conducted on both CPU and GPU environments and using different datasets. We report and analyze the performance characteristics of the studied frameworks. In addition, we report a set of insights and important lessons that we have learned from conducting our experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".