Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers (2007.01547v6)

Abstract: Choosing the optimizer is considered to be among the most crucial design decisions in deep learning, and it is not an easy one. The growing literature now lists hundreds of optimization methods. In the absence of clear theoretical guidance and conclusive empirical evidence, the decision is often made based on anecdotes. In this work, we aim to replace these anecdotes, if not with a conclusive ranking, then at least with evidence-backed heuristics. To do so, we perform an extensive, standardized benchmark of fifteen particularly popular deep learning optimizers while giving a concise overview of the wide range of possible choices. Analyzing more than $50,000$ individual runs, we contribute the following three points: (i) Optimizer performance varies greatly across tasks. (ii) We observe that evaluating multiple optimizers with default parameters works approximately as well as tuning the hyperparameters of a single, fixed optimizer. (iii) While we cannot discern an optimization method clearly dominating across all tested tasks, we identify a significantly reduced subset of specific optimizers and parameter choices that generally lead to competitive results in our experiments: Adam remains a strong contender, with newer methods failing to significantly and consistently outperform it. Our open-sourced results are available as challenging and well-tuned baselines for more meaningful evaluations of novel optimization methods without requiring any further computational efforts.

• The paper provides an extensive empirical evaluation of 15 deep learning optimizers over 50,000 runs, showing that no single method excels across all tasks.
• The paper finds that using default parameters across multiple optimizers can perform comparably to extensively tuned methods, suggesting a cost-efficient strategy.
• The study establishes a baseline for future research by identifying adaptive methods like Adam as robust contenders and calling for innovation in optimizer design.

Evaluating Deep Learning Optimizers: A Comprehensive Benchmark

In contemporary deep learning practice, the choice of the optimizer is a pivotal decision that greatly influences the efficiency and outcomes of training processes. Despite the vast landscape of available optimization methods, encompassing the likes of stochastic gradient descent (SGD) and its numerous adaptations, there remains an evident lack of comprehensive empirical guidance. The paper "Descending through a Crowded Valley: Benchmarking Deep Learning Optimizers" by Schmidt et al. provides an in-depth empirical evaluation of fifteen widely recognized deep learning optimizers across a suite of diverse optimization tasks. This paper seeks to provide an empirical backbone to the selection and evaluation process of optimizers, which has traditionally been guided by anecdote rather than robust evidence.

Key Contributions

The research offers several crucial insights:

1. Diverse Performance Across Tasks: The paper eloquently demonstrates that no single optimizer consistently outperforms others across all the considered tasks. The performance landscape is notably task-dependent, underscoring the complexity inherent in the choice of an optimizer.
2. Effectiveness of Default Parameters: One of the noteworthy observations is that evaluating a variety of optimizers with default parameters can yield results similar in effectiveness to those obtained through the meticulous tuning of a single optimizer. This finding suggests that practitioners might benefit from trialing multiple optimization methods as a cost-efficient alternative to extensive hyperparameter tuning.
3. Comprehensive Benchmarking: The authors conducted over 50,000 individual runs, offering an unprecedented dataset that can serve as a baseline for future research. This extensive benchmarking effort covers diverse tasks, ensuring the results' relevance across different problem domains in deep learning.
4. Shift Towards Fewer, Well-Chosen Methods: The analysis suggests a reduced subset of specific optimizers and parameter choices that generally produce competitive results. Among these, adaptive methods like Adam continue to be robust contenders, with some newer approaches failing to achieve statistically significant improvements.

Methodology

The evaluation framework involved eight representative deep learning tasks drawn from varied domains, assessing each using four different hyperparameter tuning budgets and learning rate schedules. These configurations simulated realistic scenarios that a practitioner might encounter, thereby enhancing the paper's practical applicability. The experimental setup included both simpler models and more complex architectures, ensuring a comprehensive investigation into the efficacy of the optimizers across different contexts.

Implications and Future Directions

For practitioners, the paper's results serve as a pragmatic guideline for selecting optimizers, tilting the practice towards a simpler strategy of trying multiple methods with default settings. This insight has potential implications for resource allocation in deep learning projects, particularly in minimizing computational overhead associated with hyperparameter tuning.

The paper's findings simultaneously highlight the stagnation in optimizer performance improvement, prompting a call for more innovative research directions. Instead of minor adaptations of existing methods, future work could benefit from focusing on optimizers tailored to specific problem characteristics or investigating the automatic adaptation of hyperparameters during training.

Conclusion

This paper lays a critical empirical foundation upon which future deep learning optimizer research can build. While offering methodological insights, it also provides substantial empirical data that contributes to understanding the optimizer landscape better. Moving forward, leveraging these insights to develop optimizers that are not only efficient but also adaptable and context-sensitive will be paramount in advancing machine learning efficiencies. In essence, this work is a clarion call for the deep learning community to refine their optimizer selection processes based on evidence, thus paving the way towards more robust and efficient training paradigms.